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Study of Factors Influencing Bacterial
Identification
by Raman Spectroscopy
Mya Myintzu Hlaing
A Thesis submitted for
Degree of Doctor of Philosophy
2015
Faculty of Science, Engineering and Technology
Swinburne University of Technology
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ABSTRACT
The formation of microbial biofilms causes serious problems in natural
environments, industrial systems and in medical situations. Therefore, for timely,
appropriate treatment and control measures, there is a need to develop an analytical
technique that can facilitate rapid, in situ microbiological identification directly from
biofilms. Raman spectroscopy has been promoted as a non-invasive optical technique
for differential bacterial identification at species and strain level as well as the study
of bacterial growth phases. To the best of our knowledge, the bacterial cells in these
Raman spectroscopic studies were mostly from planktonic suspension, cells
recovered from biofilm, cells within pseudo-mixed biofilms and cells from single-
species biofilms under controlled laboratory conditions. This thesis explores the
implications for extending Raman spectroscopy identification techniques to more
practical real-world settings. In particular, real-world biofilm samples will include
bacteria from different points in their life cycle, responding to the presence of other
organisms in the consortium and exposed to different physicochemical and
environmental conditions.
In view of the fact that individual cellular differences in macromolecular
composition contribute metabolic heterogeneity within a bacterial population, it is
necessary to attain specific bacterial identification and classification from different
time points of the growth cycle as well as throughout biofilm development. In this
study, Raman spectroscopy in combination with chemometric analysis was applied
for the identification of (and discrimination between) diverse bacterial species at
various growth time points. The results showed that bacterial cells from a particular
growth time point (as well as from random growth phase) can be well-discriminated
among the four species using principal component analysis (PCA). The results also
showed that the bacteria from different growth phases can be classified with the help
of a prediction model, based on principal component and linear discriminant analysis
(PC-LDA). These findings demonstrated that Raman spectroscopy with the
application of PC-LDA model rooted in chemotaxonomic analysis may provide
valuable applications in rapid sensing of microbial cells in environmental and clinical
studies. However, it is not yet representative of real-world situation for biofilm study.
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Therefore, Raman spectroscopy experiments were performed on intact bacterial
colony and biofilm for moving towards realistic settings by examining the cellular
changes of surface-attached bacterial cells. The results showed that the content of
carbohydrates, proteins and nucleic acids in the biofilm matrix increased
significantly along with the biofilm growth of the four bacterial species. The findings
strongly suggested the Raman spectroscopy has significant potential for studying
chemical variations during biofilm formation. However, poor classification results
were obtained for surface-attached biofilm cells using PC-LDA planktonic model. It
is generally believed that cells within biofilm experience a unique mode of growth
and behave differently from their planktonic counterparts. Thus, it was not surprising
that the planktonic cell model was ineffective in classifying results of cells from
biofilm, highlighting that a new model was required for surface-attached cells. A PC-
LDA biofilm model was calibrated using single spectra from biofilm cells of each
species and validated using pure E. coli biofilms grown on quartz substrate,
achieving a high accuracy in potential classification. The application of this biofilm
model provided 75% sensitivity in detecting the presence of E. coli and V. vulnificus
species in dual-species biofilms. This prediction accuracy is useful not only for
understanding species interactions but also for analysing biofilm formation with
species of interest in a more complex community.
Finally, the effects of different surface chemistries on Raman identifiable
macromolecular changes in surface-attached bacterial cells were examined. The
interaction of E. coli cells with plasma polymer thin films containing hydrocarbon,
amine and carboxyl groups provided differences in cell attachment phenotypes, cell
viability and subsequent biofilm formations. The identification of surface-attached
cells from polymer surfaces was challenged by weak spectral features resulted from
polymer background involvement. Correct identification outcomes were achieved
when the surface attached cells were removed from the polymer coated substrate and
smeared onto clear CaF2 slides. While these results were encouraging, the outcome
may be dependent on intra-species variability versus inter-species variability and the
identification may become more difficult as more species are added to the
identification database. Nevertheless, this study opens the pathway to extend further
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bacterial identification techniques in real-world setting by considering other
influencing factors such as environmental conditions.
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ACKNOWLEDGEMENTS
Foremost, I would like to express my deepest gratitude to Prof. Sally McArthur, my
supervisor, for all her valuable guidance, advice, patience and support throughout my
three and a half years of study. Thanks for her creation of encouraging and energetic
working atmosphere in the lab. I am grateful for having discussion times which were
source of my new inspirations. Her guidance helped me in all the time of research
and writing of this thesis. I could not have imagined having a better supervisor for
my PhD study.
I would like to express my appreciation to all the members of the advisory
committee, especially warm thanks are due to A/Prof Paul Stoddard for his valuable
supportive encouragements, enthusiasm, and immense knowledge. I really appreciate
his detailed insights into the theory and experiment processes during my study.
I also owe special thanks to Prof. Peter Cadusch for his guidance, knowledge and
support of data analysis. My special thanks also go to Dr. Michelle Dunn for her
kindness, advice and contribution for multivariate statistical analysis.
I am grateful to all of my colleagues from the McArthur group: Dr. Scott Wade, Dr.
Adoracion Pegalajar Jurado (Dori), Dr. Chiara Paviolo, Dr. Mirren Charnley, Ms.
Jennifer Hartley, Ms Martina Abrigo, Ms Hannah Askew, Mr. Nainesh Godhani and
Ms. Nilusha Perera for providing me with a friendly and cheerful environment in the
biomedical lab. We had such a good time together, and their friendship has been a
source of real joy for me in the last years. I would like to thank Dr. Thomas
Ameringer and Dr. De Ming Zhu for their technical support.
I would like to acknowledge Faculty of Science, Engineering and Technology
(FSET) for scholarship support and DMTC funding for scholarship top-up.
Finally, I am really grateful to my friend, Dr. Shigeaki Kinoshita, for enlightening
me the first glance of research area at Swinburne University of Technology.
Last but not the least, I would like to thank my family, particularly my parents and
my husband, for supporting me spiritually throughout my life and encouraging me to
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fight for my dreams. Without their support, my dream for this PhD study would not
have been come true.
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DECLARATION
This thesis contains no material that has been previously submitted or accepted for
the award of any other degree or diploma in any university or college of advanced
education. To the best of my knowledge and belief, the thesis contains no material
previously published or written by another person except where due reference is
made.
Mya Myintzu Hlaing
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CONTENTS
ABSTRACT .................................................................................................................. i
ACKNOWLEDGEMENTS ......................................................................................... v
DECLARATION ....................................................................................................... vii
CONTENTS ................................................................................................................ ix
LIST OF FIGURES ................................................................................................... xv
LIST OF TABLE .................................................................................................... xxiii
LIST OF ABBREVIATIONS .................................................................................. xxv
................................................................................................................ 1
LITERATURE REVIEW............................................................................................. 1
1.1 Introduction......................................................................................................... 1
1.2 Physiology of bacterial cells and biofilm ........................................................... 4
1.2.1 Prokaryotic bacterial cell ............................................................................... 4
1.2.2 Bacterial biofilm ............................................................................................ 9
1.2.2.1 Biofilm structure and composition ..................................................... 11
1.3 Bacterial identification...................................................................................... 14
1.3.1 Traditional culture-based methods ............................................................... 14
1.3.2 Molecular methods....................................................................................... 15
1.3.3 Spectroscopic methods................................................................................. 16
1.4 Raman spectroscopy for bacteria identification ............................................... 18
1.4.1 Theory of Raman spectroscopy ................................................................... 18
1.4.2 Application of Raman spectroscopy for bacterial identification ................. 20
1.4.2.1 Raman spectroscopy on bacterial biofilm .......................................... 23
1.4.3 Raman spectral data analysis ....................................................................... 24
1.4.3.1 Pre-processing of Raman spectra ....................................................... 25
1.4.3.1.1 Noise removal and Smoothing of Raman spectra ..................... 25
1.4.3.1.2 Fluorescence Background Subtraction from Raman Spectra .... 26
1.4.3.1.3 Normalisation and mean-centring of Raman spectra ................ 28
1.4.3.2 Chemometric methods for Raman spectrum data analysis................. 29
1.4.3.2.1 Principal component analysis (PCA) ......................................... 30
1.4.3.2.2 Linear Discriminant analysis (LDA) ......................................... 31
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1.4.3.2.3 Principal component logistic regression (PCLR) ...................... 31
1.5 Factors influencing bacterial chemistry ............................................................ 32
1.5.1 Bacterial characteristics ............................................................................... 33
1.5.2 Surface (substratum) characteristics in biofilm formation ........................... 35
1.5.2.1 Influence of surface hydrophobicity and roughness ........................... 35
1.5.2.2 Influence of surface charge ................................................................. 36
1.5.2.3 Influence of surface chemistry ........................................................... 37
1.5.3 Cell-cell interactions in biofilm formation ................................................... 39
1.6 Research motivation and thesis scope .............................................................. 41
............................................................................................................... 45
MATERIALS AND METHODS ............................................................................... 45
2.1 Materials ........................................................................................................... 45
2.1.1 Bacterial species and strains ........................................................................ 45
2.1.2 Bacterial culture media ................................................................................ 45
2.1.3 Substrates used for Raman experiments ...................................................... 46
2.1.4 Chemicals and reagents ................................................................................ 46
2.2 Methods ............................................................................................................ 47
2.2.1 Bacterial culture and growth conditions ...................................................... 47
2.2.2 Bacterial growth curve and phase measurement .......................................... 47
2.2.3 Sample preparation for Raman spectroscopy experiments .......................... 48
2.2.3.1 Planktonic sample preparation ............................................................ 48
2.2.3.2 Bacterial micro colony isolation ......................................................... 48
2.2.3.3 Biofilm cultivation .............................................................................. 49
2.2.4 Bacterial visualisation .................................................................................. 50
2.2.4.1 Bacterial viability test ......................................................................... 50
2.2.4.2 Two-dimensional cell counting and colour segmentation .................. 51
2.2.4.3 Fluorescence in situ hybridisation (FISH) .......................................... 52
2.2.4.3.1 Preparation of probe ................................................................... 53
2.2.4.3.2 Sample preparation for FISH ..................................................... 53
2.2.4.3.3 Pre-hybridization and hybridization .......................................... 54
2.2.4.4 Extracellular polymeric substance (EPS) staining .............................. 54
2.2.4.5 Visualisation of the hybridized E. coli cells and ConA stained EPS . 55
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2.2.4.6 Probe efficiency test ........................................................................... 55
2.2.5 Raman spectroscopy experimental set up .................................................... 58
2.2.5.1 Instrument set up, calibration and spectrum acquisition .................... 58
2.2.5.2 Raman signal pre-processing for statistical data analysis .................. 59
2.2.5.2.1 Cosmic ray removal ................................................................... 59
2.2.5.2.2 Background removal .................................................................. 60
2.2.5.2.3 Smoothing and intensity normalisation ..................................... 60
2.2.5.2.4 Mean-centring the data .............................................................. 60
2.2.6 Statistical data analysis ................................................................................ 61
2.2.6.1 Principal component analysis (PCA).................................................. 61
2.2.6.2 Principal component linear discriminant analysis (PC-LDA) ............ 61
2.2.6.3 Specific peak analysis (univariate analysis) ....................................... 62
.............................................................................................................. 63
OPTIMISATION OF RAMAN SPECTROSCOPY FOR BACTERIAL CELLS .... 63
3.1 Introduction....................................................................................................... 63
3.2 Experimental set up and spectrum acquisition ................................................. 63
3.2.1 Selection of substrate for Raman spectroscopy experiment ........................ 63
3.2.2 Raman spectra from reference samples ....................................................... 64
3.2.3 Raman spectra from bacterial cells .............................................................. 69
3.3 Attempts to achieve consistent fluorescence background subtraction ............. 71
3.3.1 Application of Raman software ................................................................... 71
3.3.2 Application of polynomial curve fitting ...................................................... 73
3.3.3 Weighted penalized least squares method in “R” language ......................... 75
3.4 Improved methods for fluorescence background subtraction from Raman
spectra ........................................................................................................................ 76
3.4.1 Experimental data ........................................................................................ 77
3.5 Raman signal pre-processing for statistical data analysis................................. 80
3.5.1 Intensity normalisation................................................................................. 80
3.5.2 Mean centring the data ................................................................................. 85
3.6 Sample preparation and storage for Raman spectroscopy ................................ 87
3.6.1 Materials and methods ................................................................................. 88
3.6.1.1 Bacterial strain, growth conditions and sample preparation .............. 88
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3.6.1.2 Raman spectroscopy measurements ................................................... 89
3.6.1.3 Raman data acquisition and processing .............................................. 89
3.6.2 Results and discussion ................................................................................. 90
3.6.2.1 Raman analysis of planktonic E. coli cells from fresh and stored
samples ............................................................................................... 90
3.6.2.2 Principal component analysis for Raman spectra of planktonic E. coli
cells from fresh and stored samples .................................................... 91
3.6.2.3 Raman spectroscopic analysis of planktonic E. coli cells from fresh
and frozen samples at different phases of the growth cycle ............... 95
3.6.3 Proposed protocol of sample preparation for bacteria identification ........... 98
3.7 Conclusions ....................................................................................................... 98
............................................................................................................. 101
RAMAN ANALYSIS OF PLANKTONIC BACTERIAL CELLS ......................... 101
4.1 Introduction ..................................................................................................... 101
4.2 Materials and methods .................................................................................... 103
4.3 Results and discussion .................................................................................... 103
4.3.1 Raman classification of planktonic cells at species level........................... 103
4.3.2 Raman classification of planktonic cells at the metabolic phase level ...... 112
4.3.3 Effect of growth phase on the differentiation of four bacterial species ..... 127
4.3.4 PC-LDA Classification model ................................................................... 133
4.3.5 PC-LDA Classification model for classification of metabolic phases in
individual species ................................................................................................... 140
4.4 Conclusion ...................................................................................................... 143
............................................................................................................. 144
RAMAN ANALYSIS OF BACTERIAL (MICRO) COLONIES AND BIOFILMS
ISOLATED ON SUBSTRATES ............................................................................. 145
5.1 Introduction ..................................................................................................... 145
5.2 Materials and methods .................................................................................... 146
5.3 Results and discussion .................................................................................... 147
5.3.1 Raman analysis of agar-grown bacterial (micro) colonies ......................... 147
5.3.2 Raman analysis of intact membrane-grown bacterial micro-colonies ....... 152
5.3.3 Raman analysis of bacterial cells in developing biofilms .......................... 160
5.3.3.1 Single-species surface-attached cells ................................................ 163
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5.3.4 PC-LDA models for classification of biofilm cells ................................... 178
5.3.4.1 Single-species surface-attached cells ............................................... 178
5.3.4.2 Raman- Fluorescence in situ hybridisation (FISH) analysis of bacterial
cells from dual-species biofilm ........................................................ 181
5.4 Conclusion ...................................................................................................... 188
............................................................................................................ 191
RAMAN ANALYSIS OF BACTERIA ON DIFFERENT SURFACE
CHEMISTRIES ....................................................................................................... 191
6.1 Introduction..................................................................................................... 191
6.2 Materials and methods .................................................................................... 191
6.3 Results and discussion .................................................................................... 194
6.3.1 Characterisation of the plasma polymer thin films .................................... 194
6.3.1.1 Surface wettability ............................................................................ 194
6.3.1.2 X-ray photoelectron spectroscopy .................................................... 195
6.3.1.3 Raman spectroscopy measurement................................................... 197
6.3.2 Bacterial adhesion to plasma-polymerised surfaces .................................. 198
6.3.3 Two-dimensional cell counting and quantifying cell viability .................. 202
6.3.4 Raman analysis of bacterial cells grown on polymer surfaces .................. 206
6.3.5 Raman analysis of bacterial cells from different polymer surfaces ........... 208
6.4 Conclusion ...................................................................................................... 220
............................................................................................................ 222
CONCLUSIONS ...................................................................................................... 223
REFERENCES ......................................................................................................... 229
APPENDIX .............................................................................................................. 259
LISTS OF PUBLICATIONS ................................................................................... 271
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LIST OF FIGURES
Figure 1.1 A schematic representation of a prokaryotic bacterial cell. ....................... 5
Figure 1.2 Overall structures of Gram-positive and Gram-negative bacteria cell
walls. ........................................................................................................... 7
Figure 1.3 Model of biofilm formation process. ....................................................... 11
Figure 1.4 Schematic of an energy diagram for Rayleigh and Raman scattering. .... 19
Figure 1.5 Model of a typical confocal Raman spectrometer system using a visible
laser, notch filter, spectrometer and the charge-coupled device (CCD)
detector. ..................................................................................................... 20
Figure 1.6 Summarised illustrations of the factors that can influence bacterial
adhesion in the initial stages of biofilm formation. .................................. 41
Figure 2.1 Application of the colour segmentation plugin implemented in ImageJ
software ..................................................................................................... 51
Figure 2.2 Specificity test of FISH rRNA probe efficiency with fixed planktonic
cells of E. coli and V. vulnificus species ................................................... 56
Figure 2.3 Two-dimensional confocal laser scanning microscope images of single-
species biofilms of E. coli ......................................................................... 57
Figure 2.4 Two-dimensional confocal laser scanning microscope images of single-
species biofilms of E. coli during biofilm growth .................................... 58
Figure 3.1 Raman spectra of different substrates: ..................................................... 64
Figure 3.2 Original Raman spectra from reference samples: .................................... 66
Figure 3.3 Typical Raman spectra of polysaccharide (dextran), bulk protein
(fibrinogen), a mixture of dextran and fibrinogen in 1:8 molar ratios and
D-tyrosine. ................................................................................................ 68
Figure 3.4 Typical averaged Raman spectrum from planktonic E. coli cells with
characteristics peak assignments. ............................................................. 69
Figure 3.5 Background corrections using the baseline subtraction tool from the
WiRE 3.4 Raman software ....................................................................... 73
Figure 3.6 Background corrections using the polynomial curve fitting tool from
MATLAB. ................................................................................................. 74
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Figure 3.7 Background baseline corrections using the weighted penalized least
squares algorithm, implemented in “R” language. ................................... 75
Figure 3.8 Simulated data set fitted with adaptive-weight penalised least squares .. 77
Figure 3.9 Experimental data fitted by five different methods ................................. 79
Figure 3.10 Typical original and background corrected results for the Raman
spectrum of single planktonic E. coli cells using the APLS method ........ 80
Figure 3.11 Raman spectra of before and after signal processing. ............................ 82
Figure 3.12 Application of different normalisation methods .................................... 84
Figure 3.13 Application of different normalisation methods together with mean-
centring. .................................................................................................... 86
Figure 3.14 Flow chart summarising the different sample preparation procedures for
planktonic E. coli cells .............................................................................. 88
Figure 3.15 Background subtracted and normalised average Raman spectra from
planktonic E. coli cells taken from (i) fresh sample; (ii) refrigerated
sample before cell washing steps and (iii) frozen sample. ........................ 91
Figure 3.16 Principal component analysis of Raman spectra for planktonic E. coli
cells taken from (i) fresh sample; (ii) refrigerated sample before cell
washing steps; (iii) frozen sample. ............................................................ 93
Figure 3.17 Intensity changes of DNA/RNA and protein/lipid structure-specific
peaks in the E. coli Raman spectra ........................................................... 94
Figure 3.18 Score plots for the first and second principal components of Raman
spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen
samples. ..................................................................................................... 95
Figure 3.19 Average value plots for the first principal component of the Raman
spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen
samples. ..................................................................................................... 96
Figure 3.20 Loading value plots for the first principal component of Raman spectra
for planktonic E. coli cells taken from (A) fresh samples and (B) frozen
samples. ..................................................................................................... 97
Figure 4.1 Averaged, intensity-normalised and background subtracted Raman
spectra from planktonic cells of the four bacterial species. .................... 104
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Figure 4.2 Scatter plot from principal component analysis (PCA) of four different
bacterial species. ..................................................................................... 105
Figure 4.3 Principal component analysis of four different bacterial species: ......... 106
Figure 4.4 Scatter plot of the first and second principal components (PC1 and PC2)
................................................................................................................ 108
Figure 4.5 Loading plots from the principal component analysis (PCA) ............... 109
Figure 4.6 Intensity changes of DNA/RNA and protein/lipid structure-specific peaks
................................................................................................................ 110
Figure 4.7 Representative growth curves and viable cell counts of four bacterial
species ..................................................................................................... 113
Figure 4.8 Background-subtracted and intensity normalised Raman spectra of E. coli
cells at different phases of the growth cycle ........................................... 115
Figure 4.9 Principal component analysis of E. coli cells at different phases of the
growth cycle ............................................................................................ 116
Figure 4.10 Background-subtracted and intensity normalised Raman spectra of V.
vulnificus cells at different phases of the growth cycle. ......................... 118
Figure 4.11 Principal component analysis of V. vulnificus cells at different phases of
the growth cycle: ..................................................................................... 119
Figure 4.12 Background-subtracted and intensity normalised Raman spectra of P.
aeruginosa cells at different phases of the growth cycle ........................ 121
Figure 4.13 Principal component analysis of P. aeruginosa cells at different phases
of the growth cycle ................................................................................. 122
Figure 4.14 Background-subtracted and intensity normalised Raman spectra of S.
aureus cells at different phases of the growth cycle ............................... 124
Figure 4.15 Principal component analysis of S. aureus cells at different phases of the
growth cycle: ........................................................................................... 126
Figure 4.16 Scatter plots of principal component analysis (PCA) comparing the
Raman spectra of four planktonic bacterial species ................................ 127
Figure 4.17 PCA of the effect of physiological differences due to growth phase on
the clustering of four different bacterial species ..................................... 128
Figure 4.18 Normalisation against selected spectral features ................................. 130
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Figure 4.19 Comparison of the ratio of DNA/RNA to protein in four bacterial
species ..................................................................................................... 131
Figure 4.20 Linear discriminant analysis (LDA) based on the retained principal
components (PCs) for bacterial species differentiation .......................... 133
Figure 4.21 Calibration of PC-LDA model using a leave-one-out cross-validation
(LOOCV) with training and testing data................................................. 134
Figure 4.22 Validation of the PC-LDA model on 10 new spectra from individual
species and from mixed culture .............................................................. 137
Figure 4.23 Confocal laser scanning microscopy images of mixture of E. coli and V.
vulnificus planktonic samples (x–y sections) .......................................... 139
Figure 5.1 Averaged, intensity-normalised and background subtracted Raman
spectra from planktonic and colony cells of E. coli species ................... 147
Figure 5.2 Principal component analysis of Raman spectra collected from E. coli
planktonic and colony cells. .................................................................... 149
Figure 5.3 Analysis of specific peaks from the Raman spectra of E. coli planktonic
and colony cells. ...................................................................................... 150
Figure 5.4 Classification and identification of spectra from colony cells of E. coli
grown on nutrient agar using the PC-LDA planktonic model. ............... 151
Figure 5.5 Bacterial micro-colonies isolated on a nitrocellulose membrane .......... 153
Figure 5.6 Recovery of Raman spectra from intact colony grown on membrane: . 154
Figure 5.7 (A) Classification and identification of spectra from colony cells of four
bacterial species isolated on nitrocellulose membrane, based on the
planktonic PC-LDA model. (B) Test Raman spectra from the four
bacterial species. ..................................................................................... 155
Figure 5.8 Classification and identification of spectra from cells in different regions
of micro-colonies of four bacterial species isolated on nitrocellulose
membranes with the application of the PC-LDA planktonic model. ...... 157
Figure 5.9 Investigation of population behaviours of E. coli cells from spectra of
different regions of colony cells.............................................................. 159
Figure 5.10 (A) Optical micrographs of E. coli ATCC 25922 biofilms at different
time points. .............................................................................................. 162
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Figure 5.11 Averaged, intensity-normalised and background subtracted Raman
spectra from biofilm cells of the four bacterial species .......................... 164
Figure 5.12 Averaged, intensity-normalised and background subtracted Raman
spectra of E. coli surface-attached cells during biofilm development. ... 165
Figure 5.13 Principal component analysis of Raman spectra collected from E. coli
surface-attached cells during biofilm development ................................ 166
Figure 5.14 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of E. coli surface-attached cells .... 167
Figure 5.15 Averaged, intensity-normalised and background subtracted Raman
spectra of V. vulnificus surface-attached cells during biofilm development
................................................................................................................ 168
Figure 5.16 Principal component analysis of Raman spectra collected from V.
vulnificus surface-attached cells during biofilm development ............... 169
Figure 5.17 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of V. vulnificus surface-attached cells
................................................................................................................ 171
Figure 5.18 Averaged, intensity-normalised and background subtracted Raman
spectra of P. aeruginosa surface-attached cells during biofilm
development ............................................................................................ 172
Figure 5.19 Principal component analysis of Raman spectra collected from P.
aeruginosa surface-attached cells ........................................................... 173
Figure 5.20 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of P. aeruginosa surface-attached
cells ......................................................................................................... 174
Figure 5.21 Averaged, intensity-normalised and background subtracted Raman
spectra of S. aureus surface-attached cells during biofilm development 175
Figure 5.22 Principal component analysis of Raman spectra collected from S. aureus
surface-attached cells during biofilm development ................................ 176
Figure 5.23 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of S. aureus surface-attached cells 177
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Figure 5.24 Classification and identification of spectra from surface-attached cells of
E. coli grown on a quartz substrate with the planktonic PC-LDA model.
................................................................................................................. 178
Figure 5.25 Linear discriminant analysis (LDA) based on the retained principal
components (PCs) for bacterial species differentiation during biofilm
growth ..................................................................................................... 180
Figure 5.26 Validation of PC-LDA biofilm model on 9 new spectra of E. coli cells
from a single-species biofilm .................................................................. 181
Figure 5.27 Application of the PC-LDA biofilm model to 12 spectra from dual-
species (E. coli and V. vulnificus) biofilm culture .................................. 183
Figure 5.28 Two-dimensional confocal laser scanning microscope images of dual-
species biofilms of E. coli and V. vulnificus ........................................... 185
Figure 5.29 Spatial organisation of 79 h old dual-species biofilms ........................ 186
Figure 6.1 XPS survey spectra of plasma polymerised ........................................... 196
Figure 6.2 Averaged, intensity-normalised and background subtracted Raman
spectra collected from the plasma polymerised thin films and the control
quartz slide. ............................................................................................. 197
Figure 6.3 Two-dimensional CSLM images of E. coli attached to the surfaces at
initial attachment. .................................................................................... 199
Figure 6.4 Two-dimensional CSLM images of E. coli attached to the surfaces after
24 h of incubation. .................................................................................. 200
Figure 6.5 Two-dimensional CSLM images of E. coli attached to the surfaces after
120 h of incubation. ................................................................................ 201
Figure 6.6 E. coli adhesion to different plasma-polymerised surfaces and quartz
substrate at 1 hour incubation time ......................................................... 202
Figure 6.7 Viability of E. coli cells from 120 h old biofilm grown on plasma
polymerised surfaces and quartz substrate .............................................. 204
Figure 6.8 Averaged, intensity-normalised and background subtracted Raman
spectra from ............................................................................................ 207
Figure 6.9 (A) Averaged intensity-normalised and background subtracted Raman
spectra of 24 h-old transferred cells from the surfaces (a: ppOD, b;
ppAAm, c; ppAAc and d; quartz) and (e) planktonic cells smeared on
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CaF2 substrate and (B) classification of surface-attached cells which were
transferred from the surfaces. ................................................................. 210
Figure 6.10 principal component analyses of Raman spectra from E. coli planktonic
cells and those from transferred E. coli surface-attached cells after 24 hour
incubation. ............................................................................................... 211
Figure 6.11 Principal component analyses of Raman spectra from E. coli planktonic
cells and those from E. coli surface-attached cells transferred to CaF2 after
24 hour incubation .................................................................................. 213
Figure 6.12 Scatter plots of the first and second principal components (PC1 and PC2)
comparing the Raman spectra of E. coli cells from the control quartz slide
with those from polymer surfaces ........................................................... 214
Figure 6.13 Intensity changes of DNA/RNA specific peaks in the Raman spectra of
E. coli surface-attached cells transferred from different surfaces, measured
relative to planktonic cells ...................................................................... 217
Figure 6.14 Intensity changes of protein/lipid specific peaks in the Raman spectra of
E. coli surface-attached cells transferred from different surfaces, relative
to planktonic cells ................................................................................... 219
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LIST OF TABLE
Table 1.1 Chemical composition of a prokaryotic bacterial cell ................................. 9
Table 1.2 Summary for bacterial identification methods mentioned in Section 1.4. 17
Table 2.1 Bacterial species and strains used in this study. ........................................ 45
Table 2.2 Oligonucleotides probe used in this study ................................................. 53
Table 3.1 Selected Raman frequencies and their peak assignments for the spectra.. 70
Table 4.1 Calibration of PC-LDA model based on the first 10, 16, 20 and 30
principal components (PCs) for a total of 144 spectra of four bacterial
species. .................................................................................................... 135
Table 4.2 Calibration accuracy results of PC-LDA model with the first 16 PCs on a
total of 144 spectra of four bacterial species. ......................................... 136
Table 4.3 Error rates for the calibration of PC-LDA model with the first 16 PCs on a
total of 144 spectra of four bacterial species. ......................................... 136
Table 4.4 Validation of PC-LDA model on new spectra from individual species and
from mixed culture. ................................................................................. 138
Table 4.5 Calibration of the PC-LDA model on a total of 36 spectra of individual
species in different growth phases. ......................................................... 141
Table 4.6 Classification accuracy results of PC-LDA model at metabolic phase level.
................................................................................................................ 141
Table 4.7 Classification results of 10 new spectra from individual species (spectra
from Table 4.4) using the PC-LDA model at metabolic phase level. ..... 142
Table 5.1 Classification of colony cells from four bacterial species ....................... 158
Table 5.2 Analysis of population behaviour of E. coli colony cells........................ 160
Table 5.3 Calibration accuracy results of the PC-LDA model with the first 16 PCs on
a total of 54 spectra of three bacterial species from their different biofilm
growth points. ......................................................................................... 180
Table 5.4 Application of PC-LDA model to 12 spectra from a dual-species biofilm.
................................................................................................................ 183
Table 6.1 Plasma polymerisation conditions for 1, 7-octadiene, allylamine and
acrylic acid .............................................................................................. 193
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Table 6.2 XPS Atomic composition and atomic ratios of plasma polymerised thin
films deposited on quartz substrates. ...................................................... 196
Table 6.3 Identification of E.coli biofilm cells from different polymer surfaces using
dual-species biofilm model. .................................................................... 208
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LIST OF ABBREVIATIONS
A600 Absorbance at 600 nm
AFM Atomic force microscopy
APLS Adaptive-weight penalized least squares
APoly Adaptively weighted polynomial
CCD charge-coupled device
CFU Colony forming unit
CLSM Confocal laser scanning microscopy
ConA Concanavalin A, Tetramethylrhodamine conjugate
DNA Deoxyribonucleic acid
DPA Diaminopimelic acid
E. coli Escherichia coli
EM Electron microscopy
EPS Extracellular polymeric substance
ESEM Environmental scanning electron microscopy
FISH Fluorescence in situ hybridisation
FT-IR Fourier transform infrared spectroscopy
h Hour
IModPoly Improved modified polynomial
LDA Linear discriminant analysis
LOOCV Leave-one-out cross-validation
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LPS Lipopolysaccharide
MALDI-TOF Matrix-assisted laser desorption ionization time of flight
MIC Microbiologically influenced corrosion
ModPoly Modified polynomial
mM Millimolarity
mW Milliwatt
NA Numerical aperture
OD Optical density
P. aeruginosa Pseudomonas aeruginosa
PBS Phosphate buffered saline
PC-LDA Principal component linear discriminant analysis
PCA Principal component analysis
PCR Polymerase chain reaction
ppAAc Plasma polymerised acrylic acid
ppAAm Plasma polymerised allylamine
ppOD Plasma polymerised 1,7-octadiene
rpm Revolutions per minute
RNA Ribonucleic acid
S. aureus Staphylococcus aureus
SEM Scanning electron microscopy
TEM Transmission electron microscopy
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V. vulnificus Vibrio vulnificus
v/v Volume per volume
w/v Weight per volume
WPLS Weighted penalized least squares
XPS X-ray photoelectron spectroscopy
Mya Myintzu Hlaing Literature Review/1
LITERATURE REVIEW
1.1 Introduction
The incidence of microbial biofilms in natural environments and in medical
situations has received considerable interest regarding the importance of microbial
aggregations, the understanding of the function of biofilm forming microbes and the
interpretation of cellular behaviour changes that occur within a biofilm (1, 2).
Biofilms are accountable for most chronic soft tissue and wound infections and are
the main cause of endocarditis, medical implant and cystic fibrosis-associated
infections (3). In industry, biofilms are also associated with food and drinking water
contamination (4), metal surface corrosion (5) and pollution of the environment (1).
In addition, highly complex processes including microbiologically influenced
corrosion (MIC) can occur from the presence of biofilms in aquatic environments.
The development of bacterial biofilm is one of the major progressive processes for
bacteria from the unicellular state to a multicellular community (6). Bacteria can be
grown in vitro as planktonic cultures, colonies on agar plates and biofilms in systems
(7). The term “biofilm” refers to an aggregation of microbial cells such as bacteria,
algae, fungi and protozoa enclosed in a matrix that is attached to a surface. The
biofilm matrix consists of a mixture of polymeric compounds, primarily
polysaccharides known as extracellular polymeric substance (EPS) (8, 9). EPS plays
a crucial role in initial bacterial adhesion and the development of complex
architectures in the later stages of bacterial biofilm formation (10). Once embedded
in the EPS architecture, pathogenic bacteria can be protected from antibiotics and
host immune responses (11). This can in turn lead to chronic and recurring infection.
In biofilm development, the bacteria undergo a transition from an individual,
planktonic way of life to a community-based existence in which they must interact
with various species within the enclosed EPS matrix environment. Cells within the
biofilm thus experience a unique mode of growth that allows the cell to survive in
hostile environments and behave differently from their planktonic counterparts (1,
12). The characteristics of different bacterial species found within a complex biofilm
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and the interactions between each of the cells can also influence the development of
a biofilm community (13). Apart from these bacterial cell characteristics, many
studies have suggested that surface properties and environmental response/signals
can also play a role as factors influencing biofilm development (14).
Early, rapid and reliable detection of pathogenic bacteria can be extremely beneficial
for the treatment of patients with severe infection (15). The ability to identify
different bacterial species in biofilm consortia can also improve the efficacy of
management and control measures (16). If we are to understand or correlate the
structure of the biofilm with infection or corrosion processes, we need methods to
characterize and identify bacterial communities within the intact biofilm. Many
methods (such as traditional culture-based methods, molecular methods and
spectroscopic methods) have been established for bacterial identification.
Conventional culture-based microbiological identification techniques are relatively
slow and time consuming as they are derived from analysis of the bacterial growth
characteristics using specific media and growth conditions (17). Moreover, many
bacteria from the natural environment are difficult to grow using standard isolation
media (18). Because of these challenges, analysis methods that enable a fast and
reliable identification, such as those based on molecular and chemotaxonomic
techniques, have become popular (19). However, molecular methods such as
polymerase chain reaction (PCR), sequencing, micro-arrays, southern blot and
nucleic acid in situ hybridisation, require more steps (such as DNA/RNA isolation)
and the limited availability of specific probes makes the process slow and costly
(20).
Conversely, chemotaxonomic methods based on differences and similarities in
chemical markers associated with macromolecules in bacterial cells have recently
been shown to enable rapid bacterial identification (21). Specifically, vibrational
spectroscopic techniques such as Fourier transform infrared spectroscopy (FT-IR)
and Raman spectroscopy can be used for the rapid identification of bacteria (22).
These methods are non-invasive, reagent-less and rapid, operating at single bacterial
cell level with minimal time and effort (23, 24). Applications of Raman spectroscopy
have many advantages in terms of requiring small sample volumes and involving
Mya Myintzu Hlaing Literature Review/3
minimal peak overlap from water molecules (25). Raman spectroscopy has thus
attracted interest among spectroscopic techniques for differential bacterial
identification at species and strain level, as well as for the study of bacterial growth
phases (26-29).
To the best of our knowledge, the bacterial cells in most previous Raman
spectroscopic studies were taken from planktonic suspension, cells recovered from
biofilm (30) and cells within pseudo-mixed biofilms (31). In fact, bacterial cells
developing as a biofilm exhibit a number of properties that are dissimilar from cells
grown in suspension, including changes in protein production and in gene expression
levels (32, 33). Many studies based on proteomic approaches have been reported to
investigate the interrelationships among planktonic cells, colonies and biofilms (7,
32). However, few, if any, studies based on a spectroscopic approach have examined
intact biofilm cells in comparison with their counterparts and/or in differential
identification. Therefore, it is of interest to investigate whether the Raman
spectroscopic approach can be used for characterisation of bacterial cells as they
appear in intact biofilms.
To test this hypothesis, Raman spectroscopy was used in combination with
chemometric analysis to identify different bacterial species at different phases of
metabolic growth. From these Raman spectra of planktonic cells, a model was
constructed for each bacterial species. The prediction model was calibrated and
validated on new batch of planktonic cells and biofilms cells. The next step in this
study was to obtain specific bacterial identification and understand their spatial
distribution within a mixed biofilm community. Thus, the constructed fingerprinting
system for single bacterial species was tested on a dual-species biofilm model
consisting of Escherichia coli and Vibrio vulnificus. Raman spectral profiles and
spectral changes related to the cellular response during biofilm growth on different
plasma polymer films were also examined to get a better understanding of the effects
of cell-surface interactions.
This literature Chapter will begin with the structure and biochemical composition of
prokaryotic bacteria and a review of biochemical changes occurring during biofilm
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formation, followed by an overview of different bacterial identification techniques.
The fundamental theory of Raman spectroscopy and its application to bacteria will
then be covered. Finally, Raman spectroscopy studies of the bacterial growth curve
and heterogeneity of bacterial micro-colonies and biofilm cells will be reviewed, in
the context of demonstrating the feasibility of rapid bacterial identification in
environmental and clinical samples.
1.2 Physiology of bacterial cells and biofilm
Bacterial physiology is the study of the structures and functions that allow bacteria to
survive in natural environments. This includes a range of topics from the
composition of bacterial cells to the biomolecules involved in chemical or physical
functions. The study of bacterial functional activity and growth within a population
can be considered as a major approach for understanding the life style of biofilm
communities. A brief description of bacterial cell physiology and biofilm formation
is summarised in this section. This review summarises the current status of the field
and provides the background for this research.
1.2.1 Prokaryotic bacterial cell
All living organisms are composed of the cell which is the most basic structural,
functional and biological unit. The set of organisms whose cells lack a membrane-
bound nucleus are called prokaryotes, while those with a nucleus are called
eukaryotes. Bacteria are prokaryotic cells which are mostly unicellular. Prokaryotic
bacterial cells have a simple intracellular structure and are smaller in size (~1-5 µm)
compared to eukaryotic cells. As shown in Figure 1.1, a prokaryotic bacterial cell has
three main architectural regions, namely the extracellular structures (fimbriae, pili, S-
layers, Glycocalyx, flagella), cell envelope (capsule, cell wall, plasma membrane)
and intracellular structures (cytoplasm containing DNA, plasmids, ribosomes and
other inclusions).
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Figure 1.1 A schematic representation of a prokaryotic bacterial cell.
(Adapted from http://www.clker.com/clipart-29949.html)
The most recognizable extracellular structures of bacterial cell are flagella. Different
species of bacteria have different numbers and arrangements of flagella. Flagella are
whip-like structures protruding from the bacterial cell wall and are responsible for
bacterial motility (i.e. movement). Flagella also seem to facilitate the attachment of a
bacterium to a surface (e.g. biofilm formation) or to other cells (2). Depending on
environmental conditions and bacterial metabolic growth phases, bacteria can exhibit
very different patterns of flagellum expression, motility and cellular morphology (34-
36).
Many bacterial cells have an outermost defined layer named the capsule which is
composed of polysaccharides (37). The capsule not only provides an extra source of
nutrients for bacteria but also protects them from environmental stress (such as pH,
temperature, osmotic pressure) and the host immune system. Moreover, the bacterial
capsule facilitates cell aggregation and attachment in biofilm formation. The inner
layer of the bacterial cell membrane is surrounded by a rigid cell wall which also
protects the cell from cell lysis due to mechanical damage. The cell wall is made up
of a substance called peptidoglycan or murein. The peptidoglycan layer is a crystal
lattice structure that is formed by linear chains of two alternating amino sugars (such
as N-acetylglucosamine and N-acetylmuramic acids). These alternating sugars are
Capsule
Cell wallPlasma membrane
Cytoplasm
Ribosome
Plasmid
FlagellumNucleotide (DNA)
Pili
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connected by peptide cross-links of L-alanine, D-alanine, D-glutamic acid and L-
lysine or diaminopimelic acid (DPA). The degree of cross-linking determines the
firmness of the cell wall and varies between different bacteria. The peptidoglycan
layer is thus responsible for the rigidity of the bacterial cell wall and determines the
cell shape (37).
Based on the cell wall structure as differentiated by Gram staining, bacteria can
generally be divided into two major groups, called Gram-positive and Gram-negative
bacteria (38). Structural differences in the cell wall of Gram-positive and Gram-
negative bacteria are shown in Fig. 1.2. The main differences between Gram-positive
and Gram-negative bacteria are the outer membrane and thickness of the cell wall.
The Gram-positive cell wall is simple and consists of a single thicker layer (20–
80 nm) of peptidoglycan with no outer membrane. Conversely, the Gram-negative
cell wall is a relatively complex multilayered structure. The Gram-negative bacteria
cell wall has only a thin layer of peptidoglycan (2–3 nm) surrounded by the outer cell
membrane. The outer membrane of Gram-negative bacteria is made up of
lipopolysaccharide (LPS), which contains polysaccharides and proteins (39). In
addition, the structure of the peptidoglycan layer is different between Gram-positive
and Gram-negative bacteria. As mentioned above, peptidoglycan is a polymer of
disaccharides cross-linked by short chains of amino acid (L-alanine, D-alanine, D-
glutamic acid and L-lysine or DPA). In Gram-negative bacteria, D-alanine of one
unit is directly linked to DPA of the next. However, in some gram-positive bacteria,
D-alanine of one unit is linked to lysine molecules of another unit via a peptide
consisting of 5 glycine molecules (pentapeptide) (38, 39). Another structural
difference in the bacterial cell wall is the presence of teichoic acid covalently linked
to the peptidoglycan layer in the Gram-positive cell wall. Teichoic acid is a
phosphodiester polymer of glycerol or ribitol joined by phosphate groups with side
chains of amino acids and sugars. Although the function of teichoic acid is not well
understood, it appears to stabilize the cell wall and make it stronger. These bacterial
structural characteristics are believed to play a role in biofilm formation (39).
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Figure 1.2 Overall structures of Gram-positive and Gram-negative bacteria cell
walls.
(Adapted from http://medimoon.com/wp-content/uploads/2013/04/gramstructure)
The inner layer after the cell wall is the cytoplasmic membrane (cell membrane)
which encloses the intracellular components (i.e. proteins, genetic material and other
metabolites) of prokaryotic cells. The bacterial cell membrane is composed of a
phospholipid bilayer with proteins (~40%) and glycoproteins (~60%). Inside the
bacterial cytoplasmic membrane, the chromosomal DNA, which aggregate to form
the nucleoid, can be found (38).
A short review of bacterial cell structure and the predominant chemical composition
is shown in Table 1.1 (39, 40). All bacterial cells are composed of water (as the
major constituent), macromolecules (proteins, nucleic acids, polysaccharides and
lipids), small molecules (amino acids, nucleotides, fatty acids, carbohydrate and
coenzymes, etc.) and inorganic ions (39). The polar properties of water enhance the
stability of large molecules and plays a crucial role in the formation of biological cell
structures (38). Among the macromolecules, proteins and nucleic acids such as
deoxy ribonucleic acid (DNA) and ribonucleic acid (RNA) are known as
informational macromolecules of the cell (38). The sequence of monomers in nucleic
acid carries genetic information whereas those in proteins carry structural and
functional information (39). Bacterial classifications are generally based on the
LipoteichoicTeichoic acid
Membrane protein
Cytoplasmic
membrane
Periplasmic space
Peptidoglycan
Outer
membrane layer
PolysaccharidesPorins
Protein
Lipids
Gram-positive Gram-negative
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chemical and molecular composition of their cell wall/membrane structure (such as
polysaccharides, phospholipid, lipid A), nucleic acids, proteins, amino acids and
peptide bonds. Moreover, the synthesis of these macromolecules (i.e. DNA/RNA and
protein) can vary throughout the metabolic growth cycle of bacterial cells (39).
Bacterial growth is the asexual reproduction (division) of one copy of the bacterial
cell into two daughter cells in a process called binary fission. The normal bacterial
growth cycle (curve) has four stages referred to lag phase, log (exponential) phase,
stationary phase and decline (death) phase in batch culture (41). Lag, log and
stationary phases are characterized by distinct biochemical reactions for the synthesis
of cellular components necessary for cell growth and division. In the lag phase,
although there is no growth, bacteria begin to prepare for reproduction. An increase
in overall bacterial enzyme production can be seen in this phase (42). During the log
phase, the number of bacterial cells becomes double with every unit of time by
binary fission reproduction. Bacterial metabolic activities such as protein and
DNA/RNA synthesis also increase and secretions of EPS begin (43). Because of
nutrient exhaustion and waste accumulation in continuous incubation, the bacteria
growth rate becomes the same as the death rate and cellular metabolic activity
decreases in the stationary phase (43). In the stationary phase, the biochemical
composition of cells is different from that in the log phase and a heterogenous cell
population can be seen (41). Finally, the decline phase is reached due to nutrient
depletion, more waste accumulation, depletion of cellular energy and pH changes
(42). Moreover, secretion of EPS also varies throughout the growth phases. Some
bacterial species have maximum EPS production in the exponential phase (44, 45),
while for others, EPS production is maximized in the stationary phase (46-48). In the
sense that bacterial species are defined by their unique DNA sequence, structural and
metabolic characteristics, differences in chemical composition should provide
reliable species identification. Moreover, studying the changes in biochemical
composition during bacterial growth could improve our understanding of microbial
physiology as well as the population behaviour of different bacterial species and
strains.
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For more than a century, bacteria have been identified by isolation in culture
followed by enzymatic reactions and morphological analyses. In recent years,
molecular and chemotaxonomic techniques have become popular for bacterial
identification (20). The challenges of traditional bacterial identification methods and
the advantages of molecular and chemotaxonomic techniques for bacterial
identification are discussed in the following sections (Section 1.4). The applications
of Raman spectroscopy on bacterial cells are further broadly reviewed (Section 1.5).
Table 1.1 Chemical composition of a prokaryotic bacterial cell (39, 40).
Location in the cell Macromolecule Primary subunit
Cell wall / membrane, pili,
flagella, ribosomes, as enzymes
Proteins amino acids
Membranes, storage depots Lipids fatty acids
Cell wall, capsule, inclusions Polysaccharides carbohydrates
Membranes Lipopolysaccharides Sugars and fatty
acids
Ribosomes RNA Nucleotides
nucleoid, plasmid DNA Nucleotides
Abbreviations: DNA, deoxyribonucleic acid; RNA, ribonucleic acid.
1.2.2 Bacterial biofilm
A biofilm is an aggregation of matrix-enclosed microorganisms irreversibly attached
to a surface (8, 9). Biofilms can consist of different types of microorganisms, such as
bacteria, fungi, algae and protozoa which are enclosed by a matrix of EPS. Inside this
self-produced EPS matrix, bacterial cells can be protected from the host immune
system, environmental stress and antimicrobial agents (49, 50). Biofilms may form
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on a wide range of surfaces, including living tissues, implanted medical devices,
industrial or potable water system piping, or natural aquatic systems (14).
Typical bacterial biofilm formation involves a series of distinct stages consisting of
reversible attachment, irreversible attachment, maturation and detachment. A
schematic representation of the biofilm formation process is shown in Fig 1.3. First,
biofilm formation starts with weak, reversible adhesion of planktonic (free-floating)
bacterial cells to a surface. This initial attachment is basically influenced by
electrostatic forces (e.g., repulsion, attraction) and other interactions between bacterial
cells and the surface. Extracellular organelles (such as fimbriae, flagella and pili) and
adhesion proteins help the bacteria to overcome the interfacial repulsive forces and
achieve stable attachment (14).
If the bacterial cells become irreversibly attached to a surface, they will then begin to
grow and form micro colonies. Once bacterial colonization has begun, the biofilm
matrix develops through self-production of EPS (49). These substances mediate
bacterial adhesion to surfaces by providing a cohesive, three-dimensional polymer
network where biofilm cells are immobilised. This biofilm matrix provides a
favourable living environment for the resident bacteria and protects them from the
host immune system, environmental stresses (such as pH, temperature, osmotic
pressure) and antimicrobial agents (49, 50).
Within the biofilm matrix, bacterial cells are able to communicate and interact with
each other through quorum sensing and signalling molecules which are required for
biofilm maturation (51, 52). In this step, the biofilm architecture becomes more
complex by additional recruitment and colonisation of planktonic bacteria. Increased
synthesis of EPS and the development of antibiotic resistance associated with
surface-attached bacteria can be seen in this maturation step (52). These biofilm
bacteria may also develop other properties such as increased resistance to UV light,
increased rates of genetic exchange, altered biodegradability and enhanced
production of secondary metabolites. All of these situations appear to create a
protective environment for biofilm bacteria and cause biofilms to be a persistent
clinical problem (2).
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The final stage of biofilm formation is known as the detachment or dispersal process.
In this process, bacteria have evolved ways to recognize environmental changes and
measure whether it is still beneficial to reside within biofilm or whether it is time to
resume a planktonic lifestyle (50). Throughout the biofilm process, microorganisms
thus undergo profound changes during their transition from planktonic organisms to
cells that are part of a complex, surface-attached community.
Figure 1.3 Model of biofilm formation process.
1.2.2.1 Biofilm structure and composition
Biofilms are highly hydrated (98% water) and tenaciously bound to the underlying
surface. The biofilm structure is heterogeneous with water channels that allow
diffusion of essential nutrients and oxygen to the microbial cells growing within the
biofilm (53). The biofilm is primarily composed of micro-colonies of microbial cells
and EPS matrix. The micro-colonies that make up the biofilm can contain single-
species populations or consortium communities of bacteria. The proportion of EPS in
biofilms is approximately 50-90% of the total organic matter and can be considered
as the primary matrix material for biofilm architecture (54).
EPS from different bacterial species may vary in chemical and physical properties,
but it is mainly composed of polysaccharides and extracellular DNA (eDNA. Some
polysaccharides of EPS matrix from Gram-negative bacteria are either neutral or
polyanionic whereas eDNAs are polyanionic components of EPS. The presence of
uronic acids (D-glucuronic, D-galactouronic and mannuronic) or ketal-linked
I. Reversible
adsorption of
bacteria
II. Irreversible
attachment of
bacteria
III. Growth
and division
of bacteria
IV. EPS
production and
biofilm
formation
V. Detachment
or dispersal of
bacterial cells
Biotic/ Abiotic surface
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pyruvates provide the anionic properties (53). This property allows an association
with divalent cations (such as calcium and magnesium) which subsequently crosslink
the polymer strands and strengthen the biofilm structure (54). In contrast, the
chemical composition of EPS from Gram-positive bacteria can be slightly different
due to their primarily cationic nature (55). EPS may associate with metal ions and
macro molecules of bacterial cells (such as proteins, nucleic acids and lipids). Apart
from EPS and microbial cells, the biofilm matrix can contain blood components,
other non-cellular materials such as mineral crystals, corrosion particles and silt
particles, depending on the environment where they form (56).
A range of methods have been proposed for the study of biofilm. For instance, some
methods are for quantification of biofilm matrix while others allows the evaluation of
live and dead cells in biofilm. Specifically, colorimetric methods to evaluate biofilm
matrix (i.e. crystal violet, dimethyl methylene blue) (57-59) or viable cells (i.e.
fluorescein-di-acetate and LIVE/DEAD BacLight) (60, 61) and molecular methods
to estimate the bacterial population (i.e. polymerase chain reaction, PCR and
fluorescence in situ hybridisation, FISH) (62-64) have been reported. Advanced
microscopic techniques such as electron microscopy (EM), confocal laser scanning
microscopy (CLSM) and atomic force microscopy (AFM) are employed to visualise
microbial biofilms.
Before the use of CLSM, electron microscopy (EM) was the method of choice for
biofilm characterization (65). In particular, transmission electron microscopy (TEM),
scanning electron microscopy (SEM) and environmental scanning electron
microscopy (ESEM) are used for qualitative assessment of the biofilm’s contribution
to surface deterioration (66). TEM has the capability to image the interior of biofilms
and intracellular features from cross-sections of ultra-thin sliced samples, while SEM
reveals the surface topography and composition of biofilms at a high magnification
(67). Although these EM techniques take advantage of the higher resolution allowed
by the use of an electron beam to resolve nanometre-scale details, the drawback of
using these methods is that sample preparation introduces artefacts and samples need
to be dehydrated for vacuum operations (68). The use of ESEM may however allow
direct visualisation of intact hydrated and non-conductive biofilm samples (68).
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Confocal laser scanning microscopy (CSLM) using a wide range of specific
fluorescent probes and nonspecific fluorescent compounds has been effectively
applied for the visualisation of biofilm structure and EPS components for the last
decade (69-71). CLSM is performed on an optical microscope equipped with a laser
beam. It is mainly used in biology and life sciences to scan thick biological samples
(e.g. a microbial biofilm) by acquiring images in the x, y and z axes. Biological
samples must be stained with a specific fluorescent dye so that the fluorescent light
emitted from the illuminated spot is collected into the objective and transformed by a
photodiode into an electrical signal that can be processed by a computer (72).
Basically, CLSM scans a sample sequentially point by point, line by line or multiple
points at once and assembles the pixel information in a high contrast and high
resolution three-dimensional image (73). Although CLSM is a powerful optical
microscopic technique, the main limitations are that only a few fluorescent stains can
be applied simultaneously, showing just a few components in the same image and a
restricted number of excitation wavelengths are available with common lasers
(referred to as laser lines) (74).
Atomic force microscopy (AFM) is a scanning probe microscopy technique which
uses a sharp probe or tip to scan the sample in close vicinity to its surface. AFM is
widely used for the characterization of bacterial cells and biofilms because it
provides high resolution down to the nanometre scale, allows non-destructive
analysis in air and in water and requires no special sample preparation (75, 76). The
main limitation of AFM techniques is that only the sample surface and the inner
portion close to the surface can be analysed. Moreover, the maximum scan size and
average time taken to obtain an AFM image are typically 70 µm and 10 min (77).
Thus, another limitation of this technique is the inability to obtain large area survey
scans before increasing the magnification.
The analytical techniques to study microbial biofilm described above are mostly used
to visualise the biofilm structure and its components. The information obtained from
application of these techniques is mainly morphological in nature with little
information on the identity or chemistry of the bacterial cells within the biofilm.
Since bacterial biofilms consist of a very complex environment of single or mixed
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bacterial species enclosed within EPS, there is a need to better identify the bacterial
species within biofilm so as to gain more understanding of the biofilm formation.
1.3 Bacterial identification
Bacterial taxonomy includes the classification (on the basic of similarities),
nomenclature (naming of these groups) and identification (verification that a
bacterium belongs to one of these groups) (78). Bacterial identification is of great
importance in clinical medicine, public health, environmental, food and drinking
water contamination studies. Early, rapid and reliable detection of pathogenic
bacteria can be extremely beneficial for the treatment of patients with severe
infection (15). The ability to identify different bacterial species in biofilm consortia
can also improve the efficacy of management and control measures (16). In addition,
it has been proven that rapid bacterial identification results in clinical and financial
benefits (79). Many methods have been established for bacterial identification and a
brief overview of some of them (such as traditional culture-based methods,
molecular methods and spectroscopic methods) are discussed below.
1.3.1 Traditional culture-based methods
Traditional bacterial identification methods rely on bacterial phenotypes such as their
morphology and ability to grow in selective media under a variety of conditions (80).
These phenotypic identification techniques include differential staining techniques
(Gram stain, acid-fast stain and capsule stain etc.), growth characterisation,
biochemical screening and serological confirmation (81). Traditional methods are
often slow as organisms take time to grow and up to 72 hours are required to obtain
confirmed results. Indeed, a complex series of tests need to be performed before the
identification is confirmed. The results of these tests are sometimes hard to interpret
for highly related species due to limitations in corresponding databases. Moreover,
many bacteria from the natural environment are difficult to grow using standard
isolation media (18). Although culture-based approaches are useful for understanding
the physiology of isolated organisms, they cannot offer comprehensive information
on the microbial communities in biofilm matrix.
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1.3.2 Molecular methods
For microorganisms that cannot be identified using standard culture-based methods,
the primary source of information for their identification is based on their
biomolecules such as nucleic acids, lipids and proteins. Molecular methods such as
polymerase chain reaction (PCR), sequencing, micro-arrays, southern blot and
nucleic acid in situ hybridisation have been introduced for identification since the
mid-1980s (82, 83).
Most of these methods are targeted at whole genomes or selected genes that allow
species-specific identification or demonstrate the presence of antibiotic resistance
genes (84). The major advantages of molecular methods are high sensitivity,
specificity and being faster than culture-based methods. Since only one copy of
bacterial DNA is generally required for PCR based methods, it becomes possible to
detect non-cultivable organisms or identify fastidious organisms at an earlier time
(85). However, PCR techniques often require DNA extraction steps and provide only
the presence of bacterial cells but not information on their spatial localisation in the
biofilm community (86). Fluorescence in situ hybridisation (FISH) methods with
rRNA-targeted oligonucleotide (probe) have become a powerful tool for studying the
presence and spatial distribution of bacterial cells. The limitation of these FISH
methods is that they are dependent on the availability of specific and suitably
discriminating probes (87). New technologies such as DNA and protein microarray
methods have been proposed and use high numbers of molecular probes to
discriminate between different strains and species in one chip (88, 89). The
microarray technology can be applied for rapid and high-throughput screening in
gene expression and gene identification. The limitations of this technology are
associated with conflicts in interpretation of the results depending on the efficiency
of nucleic acid labelling with fluorescent dyes, instability of isolated RNA and
challenges in building protein array chips (17).
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1.3.3 Spectroscopic methods
Spectroscopic techniques based on differences and similarities in chemical markers
associated with macromolecules in bacterial cells have recently been shown to enable
rapid bacterial identification. Matrix-assisted laser desorption ionization time of
flight (MALDI-TOF) mass spectrometry has become a prominent technique in
microbiological analysis due to its high speed (90). The limitations of the MALDI-
TOF mass spectrometry technique include the destructive nature of analysis and the
requirement for mixing with an ionising matrix, thus increasing sample preparation
and leaving the sample unfit for further analyses (91, 92).
Over the last few years, vibrational spectroscopic techniques such as Fourier
transform infrared spectroscopy (FT-IR) and Raman spectroscopy have demonstrated
a great potential for rapid identification of bacteria (21, 22). In fact, the applications
of FTIR and Raman spectroscopy have been reported for the study of prokaryotic
and eukaryotic cells (93-97). These vibrational spectroscopic techniques provide
significant benefits as non-invasive, reagent-less and rapid diagnostic tools at the
single cell level (24). In biomedical studies, vibrational spectroscopy shows the
advantage of providing information on both chemical composition and the structure
of biological molecules for proteins, nucleic acids, lipids and carbohydrates (98, 99).
In microbial studies, this approach is also mentioned as a 'whole-organism' finger
print, because different microorganisms have unique spectral characteristics (26, 100,
101). Moreover, these methods can provide information on bacteria at the strain level
with minimal time and effort (23).
Compared to FT-IR, Raman spectroscopy requires only small sample volumes
(comparable to the size of a single cell) with minimal peak overlap from water
molecules (25). The different bacterial identification methods discussed in this
section are summarised in Table 1.2. The work presented in this thesis (see details
mentioned in later Chapters) investigates the ability of Raman spectroscopy to
identify bacteria at any stage of their life cycle and in any environment. Therefore, an
overview of the applications of Raman spectroscopy in bacterial identification will
be discussed in the next section.
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Table 1.2 Summary for bacterial identification methods mentioned in Section 1.4.
Traditional methods
(culture-based)
Molecular methods Spectroscopic methods
-Differential staining
(Gram, acid-fast, capsule
stain etc.)
-Growth characteristics
(colony morphology,
selective media)
-Biochemical methods
-Serological methods
(agglutination test, ELISA,
Western blots etc.)
-Identification based on
biomolecules
(nucleic acids, lipids, proteins)
-Unique genome analysis of
individual species and strains
-PCR, sequencing, micro-
arrays, Southern blot, nucleic
acid in situ hybridization
-Based on differences and
similarities in chemical
markers associated with
macromolecules
- MALDI-TOF MS, FTIR,
RM,
Advantages:
-Pure isolates,
-Unique characteristics
-Accurate identification
Limitations:
-Take time to grow and
confirm results
-Highly related species
difficult to separate
-Limited corresponding
databases
-Many bacterial species are
difficult to grow
Advantages:
-Highly sensitive and specific
-Faster than culture-based
methods
-Detection of fastidious
bacteria and those which are
difficult to grow
-For presence and spatial
distribution of bacterial cells
(i.e. FISH)
-Discrimination of bacteria at
species/stains level (i.e. DNA,
protein microarray)
-High throughput screening in
gene expression
Limitations:
-DNA extraction steps
-Availability of probe
-Efficiency of nucleic acid
labelling
-Instability of isolated RNA
Advantages:
-simple, non-invasive, reagent-
less, rapid and reproducible
method
-‘whole-organism’ fingerprint
identification
-non-destructive study to
sample (i.e. FTIR, RM)
-Requirement of small sample
volume (i.e. RM)
-Minimal peak overlap from
water molecules (i.e. RM)
Limitations:
-Destructive nature due to
requirement for mixing with an
ionising matrix
(i.e.MALDI-TOF)
-Water peak interference in
FTIR
Abbreviations: ELISA, Enzyme-linked immunosorbent assay; PCR, Polymerase
Chain Reaction; FISH, Fluorescence in situ hybridisation; MALDI-TOF MS, Matrix
assisted laser desorption ionisation time-of flight mass spectrometry; FTIR, Fourier
transform infrared spectroscopy; RM, Raman spectroscopy.
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1.4 Raman spectroscopy for bacteria identification
1.4.1 Theory of Raman spectroscopy
Raman spectroscopy (named after Sir C. V. Raman) is a spectroscopic technique
based on inelastic scattering (Raman scattering) of monochromatic light, usually
from a laser in the visible, near infrared, or near ultraviolet range. When
monochromatic light interacts with a molecule, the photons which make up the light
can excite the molecule from the ground state to a virtual energy state. In most cases,
the molecule will relax back to its original state, emitting a photon of the same
energy and this is called Rayleigh scattering (Fig 1.4). However, in the spontaneous
Raman scattering process, the excited molecule from a virtual energy state returns to
a different rotational or vibrational state when it relaxes. In this process, the energy
difference between the original state and this new state results a shift in the emitted
photon's frequency from the excitation wavelength (Fig 1.4). In general, a small
fraction of the scattered photons (approximately 1 in 10 million) have a frequency
different from the incident photons. The shift in energy gives information about the
vibrational modes of the molecule (102, 103).
There are two potential ways of shifting energy in Raman scattering, known as
Stokes and anti-Stokes processes. In the Stokes shift, a molecule is excited to a final
vibrational state which is more energetic than the initial ground state. Then, the
scattered photon will be shifted to a lower frequency or energy (longer wavelength)
because of the energy loss between the two states. Conversely, when the emitted
photon has more energy and thus higher frequency, the energy difference is called an
anti-Stokes shift (Fig 1.4). Anti-Stokes signal is usually an order of magnitude
weaker than Stokes signal (at room temperature), hence in Raman spectroscopy, only
the more intense Stokes line is typically measured. Raman shifts are normally
described in wavenumbers, which are proportional to photon energy or frequency
and have units of inverse wavelength (cm-1) (103).
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Figure 1.4 Schematic of an energy diagram for Rayleigh and Raman scattering.
Raman spectra provide detailed information about the chemical composition,
bonding situation, symmetry, structures and physical parameters of materials and
compounds (104). The combination of Raman spectroscopy and confocal
microscopy provides a better spatial resolution to allow measurements from small
sample volumes, as well as from single cells (100, 105, 106). With the application of
the confocal technique, the excitation laser is focussed on a small area of the sample
through a microscope objective and the scattered Raman signal is limited to the focal
region by a confocal pinhole. The smaller the pinhole, the better is the axial (depth)
resolution, but on the other hand, so too is the signal intensity decreased (102).
As shown in Fig 1.5, a typical confocal Raman spectroscope system consists of the
following components: (1) a monochromatic light source (laser), (2) filtering steps to
remove weak emissions other than the main exciting line of the laser, (3) microscope
unit for directing the laser beam onto the sample and (4) spectrometer unit. The back
scattered radiation collected from the microscope is incident on the filter
(holographic ‘notch’ or dielectric ‘edge’ filter) and the light is then passed through a
diffraction grating for splitting the Raman scattered light into component
wavelengths, i.e. a spectrum. Finally, the Raman spectra are recorded with the
charge-coupled device (CCD) detector (103).
Ground state
Vibrational state
Virtual energy state
Rayleigh
scattering
Stokes anti-Stokes
Raman scattering
En
erg
y l
eve
l
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Figure 1.5 Model of a typical confocal Raman spectrometer system using a visible
laser, notch filter, spectrometer and the charge-coupled device (CCD) detector.
The application of confocal Raman microscopy, in combination with appropriate
chemometric processing, has been proposed for the characterization, discrimination
and identification of microbes at species level (26, 107). In addition, by
understanding the chemical and structural variations between cells, this approach
could be used to monitor phenotypic changes from environmental stress and cell
heterogeneity during the growth cycle (29). A detailed review of the application of
Raman spectroscopy on bacterial cells is discussed in the next section.
1.4.2 Application of Raman spectroscopy for bacterial identification
Raman spectroscopy was being applied and was co-occurring with laser
developments in biological studies since the early 1970s. The potential of Raman
spectroscopy for identification purposes in microbiology was initially introduced by
Dalterio et al. in late 1986 through the study of chromobacteria species from water
with a resonance Raman microprobe (108, 109). Since then, Raman spectroscopy has
become a method of great interest to scientists. Many research articles for Raman
spectroscopy applications on single bacterial cells, yeast cells and cell components of
single bacteria or spores have subsequently been reported. Some of those reports
which are mostly related to this study are discussed here.
The application of Raman spectroscopy was expanded for bacterial identification
after the study by Pupples et al. on single living cells, chromosomes and human
microscope
camera
sample
notch
filter
filters
pinholegrating
CCD
detector
Laser
source
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granulocytes by confocal Raman microspectroscopy (105, 110). Choo-Smith et al.
first reported a novel method to detect the Raman signal from micro-organisms
grown on solid growth media using confocal Raman micro spectroscopy (111, 112).
Applications of Raman spectroscopy have seen a steady increase in bacterial
identification and classification by introducing the different types of experimental
apparatus and analysis methods permitting microbial investigations at the single-cell
level (26, 104, 113-115).
Among a range of these reports, an interesting study of Huang et al. demonstrated
the combination of the Raman confocal microscope with multivariate methods that
could discriminate seven different bacterial species (26). In their experiment, they
generated a Raman spectral profile from a single microbial cell of each different
species, thereby using this profile to detect differences in the profiles according to
the physiology of the individual species. In their analysis method, the baseline
corrected and normalised Raman spectra were first analysed by the multivariate
technique of principal component analysis (PCA) to get the greatest differentiation
between dimensions of multivariate data. Then, discriminant functional analysis
(DFA) was used to discriminate between groups based on these retained principal
components. The data showed that the clustering of the three species, based on
species phenotypic differences, was robust despite differences in cellular physiology
during the growth. From their analysis of specific peaks selected from the first PC
loading plot, a decrease in DNA/RNA- related peak intensities and an increase in
protein-specific peak intensities was seen over time (i.e. from exponential to
stationary phase). This is most likely due to growth-phase dependent variations in
proteins and nucleic acids synthesis as a response to environmental stress induced by
the depletion of nutrients. This report opened the door for further work to establish
spectral databases to determine whether these findings hold true for a larger range of
organisms. Moreover, it is interesting to investigate the spectral changes associated
with cellular responses throughout the bacterial growth curve, including exponential,
stationary and decline phases, in order to confirm that the bacteria can still be
identified regardless of their growth phase.
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With the benefit of the high spatial resolution provided by confocal Raman
spectroscopy, phenotypic heterogeneity within microbial populations based on
culture conditions can also be investigated (104, 112, 116). In the study of Choo-
Smith et.al, they conducted a broad Raman spectroscopy study to compare
compositional heterogeneities of single bacterial cells in (micro) colonies cultured in
time series (112). They found that RNA and glycogen content were growth stage
dependent as higher contents were seen in the deeper layer of colonies (older cells).
In addition, they reported that cells from longer incubation times appeared to be
more heterogeneous in their biochemical composition. Their experiment also
demonstrated that non-destructive Raman spectroscopic techniques can be useful
tools for examining the nature of colony development and biofilm formation.
Very recently in 2014, species-level identification of clinically relevant
microorganisms directly from an agar culture by Raman spectroscopy was reported
by Espagnon et.al (117). Raman spectra were recorded directly from colonies
(macro-colony and micro-colony) of different bacterial species grown on TSA. Then,
they performed a classification analysis at the species level using linear discriminant
analysis for the Raman spectra collected from macro-colonies and micro-colonies.
The authors reported that correct identification rates were obtained from both the
macro-colony and micro-colony data (94.1% and 91.5% respectively). Moreover,
they mentioned that the spectral differences observed between micro-colonies and
macro-colonies were due to biological differences as a result of different growth
stages. The authors suggest that the micro-colony average spectrum shows some
characteristics of metabolic activity markers (i.e. higher nucleic acid content
observed in the exponential phase compared to the stationary phase). However, there
is uncertainty about the sample preparation for macro-colony and micro-colony. The
macro-colony in their experiment was 24 hour (h) old colony grown on TSA
inoculated from a stock culture. The micro-colony was 6 h old culture of
intermediate overnight culture primarily inoculated from a stock culture. It would be
more interesting to see the spectral changes of colony development based on time
series and the comparison of these changes with the planktonic growth curve data.
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The research of Espagnon et al. provides a useful parallel study with the research
presented in this thesis in investigating the phenotypic changes within microbial
populations in colonies. A Raman spectroscopy study of the chemical properties of
bacteria over their lifetime in a biofilm matrix provides a further extension of this
study. Therefore, the literature for Raman spectroscopy applications on biofilm cells
is reviewed in the following section.
1.4.2.1 Raman spectroscopy on bacterial biofilm
As discussed in Section 1.2.2, bacteria can form structured communities called
biofilms and spend most of their life in the biofilms. In a biofilm, bacterial cells are
encased in self-produced EPS matrix adherent on a living or non-living surface.
Bacterial cells from biofilms are clearly distinguished from the planktonic cells by
their unique characteristics. A number of biological studies have been devoted to
understanding the functions and structures of biofilms to assist in the development of
control measures. Rapid differential identification of bacterial species and strains is
also important to any field where biofilm forms. A literature review of Raman
spectroscopy studies on bacterial biofilm is briefly discussed in this section.
In 2004, Marcotte et al. first reported that Raman micro spectroscopy could be used
to investigate in situ spatial distribution of the biomass and chemical diffusion in
hydrated bacterial biofilms (118). Their research provided a further approach for
Raman spectroscopy applications to determine the diffusion of molecules, including
antibiotics, in bacterial biofilms (119). Furthermore, a comprehensive study on the
applicability of Raman microscopy for non-destructive chemical analysis of biofilm
and EPS matrix has been performed by Ivleva et al in 2009 (120). From their
experiment, characteristic frequency regions and specific marker bands for different
biofilm constituents were revealed based on Raman reference spectra of biofilm-
specific polysaccharides and proteins. Their experiment provides further motivation
to study the structure and function of biofilms by understanding the chemical
information of different substances within the biofilm matrix.
For the differential identification of bacteria in biofilm, Huang et al. published a
comparative single-cell analysis showing that bacterial cells recovered from biofilm
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have an identifiable Raman spectral profile in comparison with planktonic cells (30).
In 2010, Beier et al. reported that confocal Raman spectroscopy analysis, in
combination with a training (prediction) model based on chemometric methods,
enables discrimination between different Gram-positive bacteria grown in pseudo-
mixed biofilms (31). In their study, they first created a training model for two
bacterial species from dehydrated biofilms. The prediction model constructed from
principal component analysis and logistic regression was calibrated and validated
using pure biofilms of each species achieving 96% overall accuracy. Finally, they
applied this model to pseudo-mixed biofilms (stained/unstained cells of known
species) and 97% were correctly identified.
A few years later, the same research group reported the successful application of this
model to identify the species within hydrated biofilms and further application to
species identification in two-species grown biofilm (121). With the ability to detect
the presence of these two bacterial species in the mixed biofilm, the author
mentioned that their study is the first report for creating spatial maps within biofilm.
In their experiment, however, there was no reference method mentioned to provide a
confirmation for the accuracy of correct species identification in the mixed biofilm.
Therefore it would be interesting to apply Raman microscopy together with a
molecular technique, in particular, fluorescence in situ hybridization (FISH) for
simultaneous independent confirmation of differential identification within biofilm.
The literature review mentioned above is highly encouraging to extend confocal
Raman spectroscopy in combination with appropriate chemometric methods for
bacterial identification to more practical real-world settings.
1.4.3 Raman spectral data analysis
In general, Raman spectral data analysis has two main steps: (1) pre-processing of
Raman spectra and (2) chemometric methods for extracting and interpreting the
qualitative and quantitative information from the spectra.
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1.4.3.1 Pre-processing of Raman spectra
Many approaches using different algorithms and chemometric methods have been
reported for signalling processing of Raman spectra (28, 100). In spite of these
established approaches, the application of Raman spectroscopy in a routine clinical
laboratory is still hampered by the challenges in the analysis of Raman spectra (122).
This is because of a fact that Raman spectra contain Raman fingerprint information
together with other features like cosmic ray spikes, Gaussian noise, fluorescence
background and other effects dependent on experimental parameters (123). These
features have to be removed and calibrated before the analysis, in order to ensure that
the analysis is based on the Raman measurements and not on other effects. The pre-
processing procedures are categorised in three steps (smoothing for noise reduction,
background subtraction, normalisation and mean-centring) and they are briefly
reviewed below.
1.4.3.1.1 Noise removal and Smoothing of Raman spectra
Since Raman spectrometers are generally coupled with charge-coupled device (CCD)
detectors, cosmic rays produced by high energy particles hitting the CCD can often
be seen as visible spikes in Raman spectra. These spikes are normally narrow
bandwidth, positive unidirectional peaks and are in random positions. Cosmic spikes
can disturb or even destroy the meaningful chemical information from Raman
spectra. Several approaches using algorithms have been proposed in the literature for
the detection and removal of cosmic spikes. Some of these approaches can only
detect whether a spectrum contains spikes and cannot find their exact positions in the
spectrum and others can both detect and remove spikes (124). In this study, the
WiRE 3.4 Raman software integrated in the Renishaw inVia Raman spectroscopy
system has been applied for cosmic ray removal (see details in Chapter 2).
The low signal-to-noise ratio in Raman spectra can arise due to several reasons such
as: (1) low scattering intensities from a sample, (2) low signals in specific spectral
regions caused by detector falloff, (3) decline in the grating efficiency. Filtering or
smoothing methods are used to remove Gaussian distributed noise originating from
uncorrelated processes. In some cases, smoothing can help to remove enough noise
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and visualise the presence of peak features. However, over smoothing the spectra can
lose the peak information as well, so it must be applied with great care (125). Many
commercial packages are available for smoothing routines. Among them, the
Savitzky-Golay filter, which is a smoothing algorithm based in a least-squares
polynomial fitting, is probably the most versatile method and widely used in
analytical chemistry (123, 126, 127). In this study, the Savitzky-Golay filter (span =
7, polynomial degree = 2, curve fitting toolbox in MATLAB) was used to reduce the
noise of the spectra (see details in Chapter 2).
1.4.3.1.2 Fluorescence Background Subtraction from Raman Spectra
The most significant challenge for many applications of Raman spectroscopy is that
the spectra are often accompanied by noise superimposed on a broad background.
This background is generally dominated by intrinsic fluorescence from the sample
(128). Consequently, the fluorescence background has to be removed in order to
perform further quantitative analysis on the Raman spectra, including multivariate
analysis. A brief overview of methods applied in fluorescence background removal
from Raman spectra and in preparation for chemometric analysis of Raman data are
discussed in this section.
In order to remove fluorescence background from measured Raman signals,
approaches based on instrumental, experimental and computational methods have
been widely applied (129). Instrumental approaches to minimise the fluorescence
background, such as excitation wavelength shifting, time-gating and photo-
bleaching, require hardware modifications in the spectroscopic system (130-132).
The excitation wavelength shifting technique requires two closely spaced excitation
wavelengths to achieve two spectra and further processing to fit the Raman spectrum.
Although it requires some system modification, it has been reported to eliminate both
the fluorescence background and systematic noise from the spectra (133). There are a
number of reported attempts to develop a time-gating system to solve the problem of
low signal-to-noise ratio spectra, but there are difficulties in system modification to
achieve low peak power pulses with high gating efficiency at a safe threshold for
biological samples (134, 135). Photo-bleaching of samples has been proposed to
reduce the broad fluorescence background, but the relative heights of Raman peaks
Mya Myintzu Hlaing Literature Review/27
obtained from the sample are progressively altered as a consequence of the
irradiation and the removal of fluorescence background from the samples may be
inadequate (136, 137). Experimental approaches such as selection of substrates
(calcium fluoride or zinc selenide) and improved sample preparation have been
proposed in order to increase the data quality by minimizing the fluorescence
background (138). However, substantial background remains in the data due to the
interaction between the light source and the intrinsic fluorescence in many samples.
As a result of these challenges, computational methods have become the standard
way to correct for contributions from fluorescence in the background. These require
no system modifications and impose no limitations on sample preparation. Among
these mathematical techniques, first- and second-order derivatives, frequency-
domain filtering, polynomial fitting and wavelet transformation methods have been
proposed as useful tools for background removal in certain situations (129). The
accuracy of the first- and second-order derivative methods is reliant on peak
selection. Due to the difficulty of peak picking, particularly in the presence of
multiple overlapping peaks such as occur in complex biological samples, missing
some peaks could result in aspects of the Raman spectrum being placed in the
baseline, resulting in a poor baseline estimate. The first- and second-order derivative
methods can severely diminish and distort Raman spectral features unless there are
complex mathematical fitting algorithms to reproduce a traditional spectral form
(139, 140). Fast-Fourier transform filtering (FFT) is one of the frequency-domain
filtering techniques and also requires the separation of the frequency components of
the Raman spectrum from those of the background and noise (141). Polynomial
fitting has become the most popular fluorescence removal technique for a wide range
of applications. However, manual polynomial fitting relies on user intervention for
selection of locations where the curves are to be fitted in the data. Although
automatic polynomial fitting methods have been proposed to remove the need for
manual curve-fitting, their use can be limited in high noise circumstances (142-144).
Wavelet transform methods can also be used to automate the curve fitting, but
difficulties in the selection of suitable wavelet thresholds and the proper level of
resolution to represent the baseline may affect the background removal results (145).
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Recently, a background-correction algorithm for Raman spectra has been developed
using wavelet peak detection, wavelet derivative calculation for peak width
estimation and penalized least squares background fitting (146). This approach
adaptively separates the measured data samples into peak and non-peak
(background) values by setting the least-squares weights to one for background and
zero for peak regions. The application of these binary valued weights may cause
some sudden changes in gradient that appear questionable in the context of a Raman
background subtraction.
In this study, an enhanced automated algorithm for fluorescence removal based on a
combination of adaptive weighting factors with penalized least squares estimation
has been applied (147). A detailed discussion on the application of the algorithm has
been included in Chapter 3).
1.4.3.1.3 Normalisation and mean-centring of Raman spectra
Another challenge is that Raman spectra acquired in sequence or intermittently even
from the same sample can exhibit variations in intensity. These variations can affect
the classification and quantitative comparison of Raman spectra. This effect can be
eliminated by a spectral normalization step, which is an adjustment to the data set
that equalizes the magnitude of each sample (148). Normalization is basically
performed by dividing each intensity value of a Raman spectrum by a constant value.
Different normalization techniques are based on the choice of this constant value.
The most common techniques used in normalization are using the highest peak (149)
and vector normalization (150, 151). Peak normalization can be performed by using
the height of a selected peak as the normalization constant. If the highest peak is
chosen, this effectively can set the value of the highest peak to 1.0 and all the other
peaks are scaled accordingly. Peak normalization is most appropriate in situations
where there is a spectral component that is relatively constant across the data set
(internal standard). This situation does not apply for most bacteria and therefore was
not used in this work. Instead vector normalization was done by calculating the sum
of the squared intensity values of the spectrum and using the squared root of this sum
as the normalization constant. Vector normalization may thus be considered as a
Mya Myintzu Hlaing Literature Review/29
method for total intensity normalization and has been widely applied in previous
work (152).
1.4.3.2 Chemometric methods for Raman spectrum data analysis
Raman spectra taken from bacterial samples are generally complex with many
overlapping peaks. Due to this complexity and the subtle changes between the
spectra obtained from different samples, extracting the qualitative and quantitative
information from the spectra is not always straight forward. The application of
chemometric methods has allowed for discrimination of Raman spectra taken from
different samples. Chemometric has been defined as a chemical discipline that uses
mathematical, multivariate statistical and computational methods to extract and
interpret the chemical information in data from analytical instrumentation (153).
Despite the broad definition of chemometric, the most important feature is the
application of multivariate statistical methods to analyse chemistry-relevant data.
The word “multivariate” not only means many variables, but also means that these
variables might be correlated. Statistical methods refer to a range of techniques and
procedures for analysing data, interpreting data, displaying data and making
decisions based on data. Therefore, multivariate statistical methods are collections of
methods and procedures that analyse, interpret, display and make decisions based on
multivariate data. The multivariate statistical methods were gradually developed,
starting from the beginning of the twentieth century (154). Analysis of variance
(ANOVA) is a general technique that can be used to test the hypothesis that the
means among two or more groups are equal, assuming that the sampled populations
are normally distributed. If there is a set of multiple random variables to compare,
the univariate analysis of variance (ANOVA) will become a multivariate analysis of
variance (MNOVA). In MNOVA, the variation in the response measurements is
partitioned into components that correspond to different sources of variation.
Chemometrics using multivariate statistical methods are usually classified as
unsupervised and supervised approaches. The unsupervised or objective
classification method does not require any prior knowledge of the sample and can
provide patterns, grouping and detection of outliers. Principal component analysis
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(PCA) and hierarchical cluster analysis (HCA) are examples of unsupervised
methodologies. On the other hand, the supervised methods require prior knowledge
of the sample for pattern recognition purposes. A model consisting of a set of well
characterised samples can be trained so that it can predict the identity of unknown
samples. Multiple linear regression (MLR), principal component analysis (PCA),
partial least squares regression (PLS) and linear discriminant analysis (LDA) are
examples of supervised methods (155). In this thesis, PCA and LDA were used in
discriminating the Raman spectra taken from different bacterial species, different
metabolic states and biofilm growth (details mentioned in Chapter 3 and results and
discussion sections from Chapters 4 and 5). Another chemometric approach,
principal component logistic regression (PCLR), which is a combination of
unsupervised and supervised methods, was also applied in this study (see details in
Chapter 3). In particular, the leading principal components of the training set are first
established using PCA, followed by constructing a logistic regression model for
classification. This approach was applied to discriminate two bacterial species taken
from dual-species biofilm, as outlined in Section 5.3.4. There are several multivariate
statistical analyses used for Raman data analysis and selections of those that are more
relevant to this study are reviewed in the following Sections.
1.4.3.2.1 Principal component analysis (PCA)
Principal component analysis (PCA) is a statistical procedure that uses orthogonal
transformation to convert the original set of variables into a smaller set of linear
combinations that account for most of the variance in the original data. The purpose
of PCA is thus to determine the data patterns and underlying factors (i.e., principal
components, PCs) that cause the similarities and differences in the original data
without any prior knowledge (154). PCA starts with an eigenvector decomposition of
the original data matrix into eigenvectors and eigenvalues. In particular, the original
data matrix with objects (spectra) and variables (intensity) is decomposed into two
matrices, the scores matrix related to the objects and loadings matrix related to the
variables. The PCs are the eigenvectors of the score matrix and the eigenvalues
represent the data variance captured by the PCs. The first PC is related to the
eigenvector of the highest eigenvalue, so it has the largest variance and the following
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PCs follow the same order (156, 157). PCA with the use of eigenvector-based
methods is the most common approach to identify clusters in Raman spectra (102).
Many reports show that PCA has been widely applied to discriminate between the
different growth phases of a single species and differential identification between
diverse bacterial species (26, 100, 158).
1.4.3.2.2 Linear Discriminant analysis (LDA)
Linear discriminant analysis (LDA) is a statistical method used to predict a
dependent grouping variable based on one or more predictor variables. In brief,
linear combinations of variables are computed to determine directions in the spectral
space in which discriminant functions maximize the variance between groups and
minimize the variance within groups according to Fisher’s criterion (157, 159). As
mentioned previously, LDA is a supervised method and requires a set of well-
characterised known samples which are used for pattern recognition and
classification of the unknown predicted sample. There are several methods to
validate the LDA model and the most common is the leave-one-out cross-validation
(LOOCV). In this method, all spectra except one are used to build a LDA model,
which is then used to classify the left out spectrum. LDA models have been widely
used in Raman spectroscopic analysis for identification and classification of bacterial
species (114, 155, 160).
1.4.3.2.3 Principal component logistic regression (PCLR)
Principal component logistic regression (PCLR) is a logistic regression analysis
technique that is based on principal component analysis (PCA). In general,
regression analysis is similar to discriminant analysis. The main difference is that
regression analysis deals with a continuous dependent variable, while discriminant
analysis acts on a discrete dependent variable. As in many other regression methods,
logistic regression has a very high number of predictor variables so that a dimension
reduction method is required. In PCLR, instead of using the dependent variable on
the explanatory variables as regressors, the principal components of the explanatory
variables are used as regressors. In selecting principal components as regressors, the
principal components with higher variances (the ones based on eigenvectors) are
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often selected. However, for the purpose of predicting the outcome, the principal
components with low variances may also be important, in some cases even more
important (161). A prediction model based on principal component analysis and
logistic regression has been reported for differential identification of bacterial species
from pseudo-mixed biofilm (31). A detailed explanation and discussion of the
multivariate statistical analysis used in this study are discussed in Chapter 2.
1.5 Factors influencing bacterial chemistry
Although prokaryotic bacterial cells are generally believed to be strictly unicellular
as discussed above (Section 1.2.1), most are capable of forming stable aggregate
communities in a polymer matrix, known as biofilm (40, 162). Each biofilm
bacterium lives in a customized micro niche in a complex microbial community that
has primitive homeostasis and metabolic cooperation (163, 164). Each of these
sessile cells in the biofilm matrix reacts to its special environment so that it differs
fundamentally from a planktonic cell of the same species (164). Therefore, biofilm
cells generally have distinct patterns of gene expression (phenotypic differentiation)
compared to their planktonic counterparts. These changes in expression are thought
to result from a cell-to-cell signalling phenomenon known as quorum sensing (162).
It is thus believed that the transition from planktonic state to biofilm is a complex
and highly regulated process. Apart from intercellular and intracellular signalling,
there are many factors that can influence biofilm formation. These factors include the
characteristics of the different species within the microbial community, surface
characteristics, nutrient availability and environmental sensing. Since this literature
review is focusing on bacterial identification in real-world biofilm samples, the
factors influencing biofilm formation can, in turn, influence the identification of
biofilm-forming bacteria. This is a particular concern for identification techniques
based on chemical composition, such as Raman spectroscopy. A brief overview of
some of the main factors influencing biofilm formation is reviewed in this section
based on the followings:
Diversity of bacterial characteristics in communities;
Mya Myintzu Hlaing Literature Review/33
Physical and chemical properties of surfaces which can influence cell
adhesion to surfaces and their development into biofilms;
Chemical communication between bacterial cells which can affect the
organization of biofilm communities.
1.5.1 Bacterial characteristics
The diverse physicochemical characteristics of bacteria within species and strains
can affect the bacterial adhesion properties to different extents during the biofilm
formation process. These characteristics involve the bacterial cell surface
hydrophobicity and charge, the presence of fimbriae and flagella and production of
EPS.
As mentioned in Section 1.2.1, the bacterial cell wall is mainly composed of a
peptidoglycan layer, teichoic and teichuronic acids, lipopolysaccharide, a variety of
polysaccharides and proteins. Some of these molecules are exposed at the cell
surface or extend from the outer membrane of the cell. These cell wall/membrane-
associated proteins are responsible for the hydrophobicity of the bacterial cell surface
which can render it either hydrophobic or hydrophilic. In addition, most bacterial
fimbriae also play a role in cell surface hydrophobicity due to the presence of
hydrophobic amino acid residues in relatively high concentrations (165). This
hydrophobic property of fimbriae and increased bacterial cell wall hydrophobicity
may enhance the bacterial adhesion by overcoming the initial electrostatic repulsion
force between the cell and surface (166, 167). In general, hydrophobic bacteria prefer
to attach to hydrophobic surfaces and the same phenomenon could be seen for
hydrophilic bacteria on hydrophilic surfaces (168, 169). The majority of the reports
regarding bacterial characteristics in biofilm formations are based on the behaviour
of single bacterial species.
In real-world environments, bacteria embedded in an EPS matrix with a multispecies
community tend to adopt a biofilm-specific phenotype which can be radically
different from those expressed in the corresponding planktonic cells. Therefore,
biofilm-growing bacteria undergo a number of complex physiological, metabolic and
phenotypic differentiations. With phenotypic switching, the biofilm bacteria can
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successfully colonise new environments and have a better chance of surviving in
hostile environments. It has been reported that the phenotypic changes that occur in
biofilm can alter the bacterial morphology, bacterial virulence and antimicrobial
resistance profile, all of which would be expected to modify the overall chemical
composition of the cells.
As an example of phenotypic changes in cell morphology, Mangwani et al. reported
that biofilm-forming marine bacterium Paenibacillus lautus within biofilm changed
their morphology from non-motile cocci to motile rods due to competition for space
and survival (170). Kim et al. reported the correlation between bacterial virulence
and spontaneous phenotypic variation, as revealed by a transition from translucent to
opaque colonial morphology (171). Their results showed that the opaque colony
variants of Streptococcus pneumonia exhibited higher virulence than translucent
variants. This phenotypic variation in opacity is associated with differences in
capsular polysaccharide secretion and teichoic acid synthesis on the bacterial cell
wall. The study of Drenkard et al. proposed the impact of phenotypic variation on
other biological process of antibiotic resistance (172).
Apart from the consequences mentioned above, phenotypic variation can affect the
expression of lipopolysaccharides, pili and flagella of the bacterial cell, resulting in
antigenic variations (173, 174). Moreover, Hanlon et al. reported changes in bacterial
cell surface hydrophobicity during biofilm growth (175). To date, it has been
reported that bacterial phenotype heterogeneity is not a genetic variation, but an
alteration in chemical reactions for DNA and protein synthesis, although the
mechanism involved in these changes is still not clear (176, 177). Any variation in
chemical information from bacterial cells may influence bacterial identification
outcomes using traditional phenotypic techniques and emerging spectroscopic
techniques for the detection and characterisation of bacteria. The majority of
experimental reports regarding phenotypic variation in biofilms have been done
under controlled laboratory conditions. However, in real situations, changes within
biofilms occur throughout the biofilm development and are unlikely to remain
constant over time. Therefore, it is of interest to study how phenotype changes affect
Mya Myintzu Hlaing Literature Review/35
bacterial identification by Raman spectroscopy under the real-world setting of
biofilms.
Since biofilm formation is a complex process involving interaction between bacterial
cells and a surface, the factors influencing this process are not only dependent on the
bacterial characteristics, but also on the surface characteristics.
1.5.2 Surface (substratum) characteristics in biofilm formation
Bacterial adhesion to surfaces begins with the initial attraction of the cells to the
surface followed by adsorption and attachment (178). Generally bacteria prefer to
grow on available surfaces rather than in the surrounding aqueous phase. Although
bacterial movement to a surface is believed to be influenced by several physical
forces (e.g. Brownian motion, Lifshitz-van der Waals), other factors (such as
gravitational forces, electrostatic interactions and diffusible or surface bound
chemical factors) also contribute to this process (179, 180). Therefore, the surface
properties that influence biofilm formation can be characterised in terms of their
physicochemical properties such as surface hydrophobicity, roughness, charge and
surface chemistry.
1.5.2.1 Influence of surface hydrophobicity and roughness
Surface hydrophobicity has been reported as one of the important properties involved
in the cell adhesion phenomenon (181, 182). Van Oss et al. stated that, in biological
systems, the hydrophobic interaction is believed to be the strongest of the long-range
non-covalent interactions (183). In general, hydrophobic, non-polar surfaces are
more favourable for bacterial attachment than hydrophilic surfaces (184). It has been
postulated that the preferential association between hydrophobic, low-energy
surfaces and hydrophobic moieties on bacterial cells (i.e. cell wall and extracellular
organelles) result in more stable interactions (185). Sousa et al. reported that
hydrophilic bacteria could also adhere to a higher extent on a silicon surface which
was more hydrophobic than a less hydrophobic material (acrylic) (186). This result
shows the importance of the hydrophobicity of the surface in the bacterial adhesion
process. Moreover, the authors also claimed that surface roughness seemed to exert
an effect on the bacterial adhesion, since the silicon used in their experiment had a
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higher surface roughness than the acrylic material. It is generally accepted that
surface roughness can influence bacterial adhesion. This is because increased surface
area and reduced shear forces on rougher surfaces might enhance the bacterial
colonisation (14, 187).
Although it is known that rougher surfaces promote bacterial adhesion (188), the
degree of surface roughness has a considerable influence on the amount of microbial
adhesion (189). Taylor et al. showed that as roughness increased up to about 1.24
µm, so too was the bacterial adhesion and biofilm formation enhanced, whereas
further increases inhibited adhesion and reduced biofilm formation. The authors
suggested that surfaces with larger pits and gullies corresponding to increased
roughness values offered bacterial cells less protection from shear forces. Therefore,
a high degree of hydrophobicity, as well as a certain extent of surface roughness can
promote bacterial adhesion for biofilm formation. The reports discussed here are
mainly to illustrate the effect of surface characteristics on bacterial adhesion patterns.
The effect of surface hydrophobicity and roughness on identification-related bacterial
phenotype changes, particularly in terms of bacterial chemistry, is still far from being
well understood.
1.5.2.2 Influence of surface charge
In consideration of surface charge involvement in the biofilm formation process, a
positively charged surface favours rapid and tight attachment of negatively charged
bacterial cells (190). On the other hand, electrostatic repulsion between negatively
charged surfaces and bacterial cells can destabilize the cell adhesion. However, this
destabilizing interaction during the initial stages of attachment is often overcome by
extracellular organelles and can be diminished in high ionic strength liquids, as
discussed in Section 1.2.2.
The surface charge is involved not only in initial cell attachment, but also in long
term bio-fouling processes. Terada et al. showed that a positively charged surface
resulted in higher cell adhesion and uniform biofilm formation, while the opposite
effect was found on a negatively charged surface. However, a high bactericidal effect
could be seen in the initial adhesion stage on the positively charged surface, due to
Mya Myintzu Hlaing Literature Review/37
the electrostatic attraction compromising bacterial cell membrane integrity. The
damaged cells can in turn act as a scaffold to initiate and promote a dense and
homogenous biofilm upon later incubation (191).
The correlation between surface charge interaction and cell adhesion is not always
simple. The degree of hydrophobicity and charge of the cell need to be considered as
well. A study by Dai et al. suggests that the effect of surface charge is more
important for adhesion of weak hydrophobic and more negatively charged cells
compared to cells with the opposite character. Their experiment showed a higher
adhesion of cells (stronger hydrophobic and less negatively charged nature) to a
negatively charged, hydrophobic surface compared with a positively charged,
partially-wetted surface. This demonstrated that non-charge based forces, such as a
hydrophobic effect, may overcome the influence of a weaker electrostatic force and
become dominant in cell adhesion (192). Interestingly, a study conducted by Rozhok
and Holz showed that negatively charged E. coli cells managed to attach on
negatively charged surface by experiencing cell wall destruction (193). The authors
claimed that the observed perturbations in the shape of bacteria attached to the
negatively charged surface were likely due to damage in lipopolysaccharide
molecules of the cell wall induced by the surface. Thus, it appears that the surface
charge of the substratum is one of the factors influencing biofilm formation and
altering bacterial chemistry, although it might not be considered a dominant factor.
1.5.2.3 Influence of surface chemistry
Many reports on the effect of defined surface chemistries on bacterial attachment
have been published over the past decades. In particular, Cunliffe et al. tried to assess
bacterial adhesion on modified glass surfaces with different functional groups, such
as an amine and amides of different chain lengths. These different functional groups
on the surfaces provided a range of surface energy, thereby resulting in varying
degrees of hydrophobicity of the surfaces. They found that bacterial adhesion on the
surface with amine groups was very high, whereas less adhesion was seen with the
reduction in chain length of the amide functional group. The higher adhesion can be
explained due to the fact that the surface with amine groups in their experiment has
FSET PhD Thesis/38
lower surface energy, resulting in it being slightly more hydrophobic than the other
modified surfaces (194).
Moreover, Speranza et al. reported that bacterial adhesion is also influenced by
Lewis acid–base interactions. They investigated the role of bacterial adhesion on
polymers having different chemical properties (acid/basic character) and showed that
higher cell adhesion could be seen on the acid moiety of the polymer surface due to a
preferred interaction with the basic nature of the negatively charged bacterial cell
(195). This demonstrates the importance of chemical interactions between the surface
and the bacterial cell. It also raises a question whether the strength of this interaction
during the initial attachment of bacteria to the surface influences the chemical
composition of the bacterial cells for subsequent colonization and biofilm formation.
Different surface chemistries can influence not only the bacterial adhesion, but also
the morphology and viability of attached bacteria (196). In the study of Parreira et
al., the effect of surface chemistry on bacterial adhesion was evaluated using self-
assembled monolayers of alkanethiols on gold surfaces. The alkanethiols exposed
different functional groups such as OH-, ethylene glycol and CH3. The functional
groups on these surfaces provided different wettabilities, ranging from more
hydrophilic surfaces that presented OH- groups, to more hydrophobic surfaces that
presented CH3 groups. They reported that bacteria adhered preferentially to the CH3
exposed surface compared to the OH- exposed surface. A partially-wetted ethylene
glycol coated surface, which was used as a typical non-fouling and protein resistant
surface, showed a significant loss of viability in the few adherent bacterial cells. The
enhanced bacterial adhesion seen on the CH3 coated gold surface can be explained
due to the increased surface hydrophobicity of the CH3 functional group, since the
differences in surface charge and roughness of the surfaces was very low. Although
the viability of the attached cells was not significantly affected by the surface
chemistry, cell morphologies were affected on all of the surfaces with the exception
of the CH3 coated surfaces.
Taken altogether, the effects of surface chemistry on bacterial adhesion are always
linked to the surface hydrophobicity, charge and roughness. The surface charge and
Mya Myintzu Hlaing Literature Review/39
degree of hydrophobicity of the substratum vary depending on its surface chemistry.
Properties such as low electronegative surface charge, high surface hydrophobicity
and a certain extent of surface roughness have been shown to be correlated to high
bacterial adhesion, although this cannot be generalized because the physicochemical
properties of the bacteria and others factors (such as temperature, pH, salt
concentration and presence of signalling molecules) can also influence the adhesion.
1.5.3 Cell-cell interactions in biofilm formation
Since bacterial biofilm is an aggregation of multiple populations of bacterial cells,
cell-cell interactions are a key factor in biofilm formation. Mixed-species biofilms
are dominant in nature compared with single-species biofilm. In order to survive and
proliferate in such complex consortia, bacterial cells have developed cell-cell
communication pathways that govern how they cooperate or compete in their
metabolic activity. These interactions can have synergistic or antagonistic effects on
biofilm formation in terms of structure, development, nature and survival of the
biofilm community (197).
Consortia of bacterial species that influence each other in synergistic ways can
enhance biofilm formation by co-colonisation and metabolic cooperation where one
species utilizes a metabolite produced by another species (198, 199). This synergistic
effect in mixed-species biofilms increases biofilm resistance to the host immune
system, antimicrobial agents and environmental stress (200). Conversely, the
antagonistic effect can decrease biofilm formation due to competition for nutrients
and growth inhibition between the species (201-203). Understanding the bacterial
behaviour and mechanisms of species interactions in the biofilm environment is
important for controlling biofilm formation. Many studies exploring the mechanisms
involved in species interactions have been widely reported. Among them,
communication between bacterial cells via quorum sensing is the most studied
mechanism.
Quorum sensing (QS) is a system of stimulus and response correlated to population
density. Bacteria use the QS system to coordinate gene expression and cell-cell
communication through certain signalling molecules (autoinducers) and receptors
FSET PhD Thesis/40
(inducers). Bacterial cell-cell communication through quorum sensing has important
implications for microbial infections, especially in terms of the virulence and
pathogenic potential of bacteria. The ability of bacteria to communicate and behave
as a group for social interactions through QS has provided significant benefits to
them in colonisation, adaptation to environmental stress and biofilm formation.
Many reports have highlighted quorum sensing and its roles in bacterial social
activities, biofilm formation and infectious diseases over the last decades (65, 204-
206)
Quorum sensing systems in bacteria have been divided into at least three classes: (1)
LuxI/LuxR–type quorum sensing in Gram-negative bacteria, which uses acyl-
homoserine lactones (AHL) as signal molecules; (2) oligopeptide-two-component-
type quorum sensing in Gram-positive bacteria, which uses small peptides as signal
molecules; and (3) luxS-encoded autoinducer 2 (AI-2) quorum sensing in both Gram-
negative and Gram-positive bacteria (207-210). Different signalling molecules are
required for different QS systems in cell-cell communications. During inter-species
communication, several species of Gram-negative and Gram-positive bacteria use
AI-2 signaling molecules (211). Conversely, for intra-species communication, Gram-
negative bacteria usually use AHL signalling molecules while Gram-positive bacteria
use small autoinducer peptides (212). Apart from AI-2 and AHL signals, it has been
reported that a QS system mediated by diffusible signal factor molecules (cis-
unsaturated fatty acids) also plays a role in biofilm formation and influences the
behaviour of bacterial species within a mixed biofilm (213).
The brief literature review presented above has considered factors influencing
biofilm formation and has been summarised in Fig 1.6. Among these factors, targets
for bacterial behaviour and activity in multi-species communities could be
considered as one of the key issues in biofilm management. In order to prevent or
control biofilm formation, effective approaches need to take into account the
complex and obstinate nature of biofilms. Therefore, it is important to obtain specific
identification of organisms and understand their spatial distribution within a mixed
biofilm community. The differential identification and estimation of the relative
proportions of bacteria within a biofilm may also facilitate biofilm management.
Mya Myintzu Hlaing Literature Review/41
However, the overall chemical composition of bacteria can change when the bacteria
form part of a biofilm and this may potentially interfere with efforts to identify the
bacteria by means of Raman spectroscopy.
Figure 1.6 Summarised illustrations of the factors that can influence bacterial
adhesion in the initial stages of biofilm formation. (A) Surface characteristics which
are relevant to the initial interaction between bacterial cell and surface: surface
roughness, positively charged surface and high surface hydrophobicity enhance
adhesion. (B) Bacterial characteristics which can influence adhesion:
polysaccharides, lipopolysaccharides, cellular components on cell wall, EPS and
signaling molecules. (C) Environmental factors which can be involved in adhesion:
temperature, pH, salt concentration.
1.6 Research motivation and thesis scope
The study of microbial biofilms has rapidly risen in prominence recently due to
increased awareness of the occurrence and impact of biofilms in natural
environments, industrial systems and in medical situations. Biofilms cost billions of
dollars every year for equipment damage, product contamination and infections, so it
is desirable to prevent or moderate their growth. The specific identification of
organisms and understanding their spatial distribution within a mixed biofilm
community may facilitate biofilm management.
Because of some limitations in the application of traditional culture-based methods
and molecular methods, Raman spectroscopy has been proposed as a rapid, non-
destructive bacterial identification method. To date, few, if any, studies based on
FSET PhD Thesis/42
vibrational spectroscopic approaches have examined intact biofilm cells in
comparison with planktonic cells and/or in differential identification.
Therefore the overall aims of this study are as follows:
Investigate the ability of Raman spectroscopy to identify different
bacterial cells at different points in their life cycle;
Extend the Raman spectroscopy technique to the identification of
different bacterial cells over their lifetime in micro colony and biofilm
growth;
Test the ability of Raman spectroscopy to identify bacterial cells in the
presence of physiological changes due to cell-cell and cell-surface
interactions during biofilm formation.
To address these goals, the first requirement was to develop the Raman signal
processing model from a combination of enhanced automated algorithms for
fluorescence removal and appropriate multivariate data analysis. During this study, a
novel background subtraction method for improving the fluorescence background
removal using adaptive-weight penalised least squares fitting was applied and
contributed to a report in the Journal of Raman Spectroscopy (147) (see details in
Chapter 3). With this method, Raman spectra taken from bacterial cells and intact
biofilm samples had the background removed successfully, providing a significant
improvement for the performance of further data analysis of the Raman spectra.
Chapters 4-5 present a Raman study of gram-negative and gram-positive bacteria
from different metabolic states, micro colonies and biofilm matrix. A brief
introduction with a literature review and the objectives of each study are presented in
each of these Chapters. The application of Raman spectroscopy to characterize the
chemical composition of single bacterial species at different metabolic growth phases
is discussed in Chapter 4. A model from these Raman spectra of planktonic bacterial
cells at different points in the metabolic cycle is constructed. Once this fingerprinting
system for single bacterial species is established, the analysis moves towards
identifying multiple bacterial species. By applying this model, a complete analysis of
Mya Myintzu Hlaing Literature Review/43
the changes in the individual Raman peaks according to the same type of assigned
biomolecules is conducted in order to gain further insight into the non-destructive
study of bacterial micro colonies and biofilm matrix (see Chapter 5). At the core of
this work, the effect of species interactions on biofilm formation is also examined
using a dual-species biofilm model consisting of Escherichia coli (E. coli) and Vibrio
vulnificus (V. vulnificus). E. coli is selected for this experiment because it is a
widespread bacterial species in clinical and environmental settings and there is a
broad knowledge of its biofilm formation. V. vulnificus is chosen because it is also a
common bacterial species highly abundant in aquatic environments, including
estuaries, marine coastal waters and freshwater environments. Moreover, V.
vulnificus has antibiofilm properties that tend to inhibit biofilm formation of other
bacteria and disrupt established biofilms (58, 203), although they are also believed to
be a serious human pathogenic microorganism (214). This dual-species model
contributes not only to an understanding of species interactions but also to biofilm
formation with species of interest in a more complex community with multiple
environmental species (see details in Chapter 5).
In order to understand the further application of the Raman technique, the study of
cell-surface interaction is discussed in Chapter 6. The interaction of bacterial cells
with allylamine, carboxyl and hydrocarbon rich plasma polymer coatings are studied
in this Chapter. The surface chemistry, namely specific functional groups on the
material, which can alter the adhesion and viability of attached bacteria, is discussed
in this Chapter. The Raman spectral profiles and spectral changes from the cellular
response to the plasma polymer surfaces during biofilm growth provides a better
understanding of factors influencing both biofilm formation as well as secondary
corrosion processes enabled by the biofilm. Finally, the overall conclusions from this
research and future directions are presented in Chapter 7.
In the longer term, following on from the work described here, there is a need to
develop a method which can identify and map the spatial distribution of multiple
species in real-world biofilm samples. If Raman spectroscopy is successful in this
regard, it would use the differences in intrinsic chemical composition of bacterial
cells to create multidimensional maps of microbial structures without extensive
FSET PhD Thesis/44
knowledge of the genetic information of the cell and without requiring any invasive
sample preparation.
Mya Myintzu Hlaing Materials and Methods/ 45
MATERIALS AND METHODS
2.1 Materials
2.1.1 Bacterial species and strains
The ATCC reference strains of Escherichia coli (E. coli), Vibrio vulnificus (V.
vulnificus), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S.
aureus) used in this study are shown in Table 2.1.
Table 2.1 Bacterial species and strains used in this study.
Strain Description References (Source)
E. coli ATCC 25922 Professor Enzo Palombo’s
laboratory, FSET, Swinburne
University of Technology.
V. vulnificus ATCC 27562 BioNovus Life Sciences, NSW,
Australia.
P. aeruginosa ATCC 10145 Professor Enzo Palombo’s
laboratory, FSET, Swinburne
University of Technology.
S. aureus ATCC 25923 Professor Enzo Palombo’s
laboratory, FSET, Swinburne
University of Technology.
2.1.2 Bacterial culture media
The following bacterial culture media were used in this study. The nutrient broth
contains casein peptone, 4.3 g/L, meat peptone, 4.3 g/L and sodium chloride, 6.4 g/L,
adjusted to pH 7.5. The nutrient agar is similar to nutrient broth but with 15 g/L
Bacto agar. Nutrient broth containing 15% (v/v) glycerol was used as a freezing
medium for the preservation of all bacteria used in this study at -80 °C. The nutrient
FSET PhD Thesis/46
media (broth and agar) were purchased from Oxoid Ltd., Basingstoke, Hampshire,
England.
2.1.3 Substrates used for Raman experiments
Fused quartz microscope slides (ProSciTech, Cat. No. G381, Kirwan, Australia) and
calcium fluoride (CaF2) polished slides (Crystran, Raman grade, Poole, Dorset
United Kingdom) were used as substrates for the Raman spectroscopy experiments.
2.1.4 Chemicals and reagents
All chemicals used in this study were analytical grade. Most of them were purchased
from Sigma-Aldrich, Australia unless otherwise specified. Sodium chloride
(molecular formula NaCl, 99.8 % purity, molecular weight (MW) 58.44) was
purchased from Riedel-de Haen. Di-sodium hydrogen phosphate (molecular formula
Na2HPO4, 99 % purity, MW 141.96) was obtained from Chem-supply and potassium
chloride (molecular formula KCl, 99.5 % purity, MW 74.55) and potassium
phosphate monobasic, anhydrous (molecular formula KH2PO4, 99 % purity, MW
136.09) were purchased from Astral Scientific. LIVE/DEAD BacLight Bacterial
Viability Kits were purchased from Life Technologies Australia Pty Ltd. The
fluorescence labelled oligonucleotide probe for 16S rRNA targets of E. coli ATCC
25922 (details in Section 2.2.4.3) and Concanavalin A, Tetramethylrhodamine
conjugate (ConA) probe were purchased from Life Technologies Australia Pty Ltd.
Analytical grade NaCl, KCl, Na2HPO4 and KH2PO4 were used to prepare phosphate
buffered saline (PBS, pH 7.4, 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8
mM KH2PO4). Milli-Q purified water (18.2 MΩ.cm at 25 °C, Millipore, Australia)
was used as ultrapure laboratory grade water for all reagent preparation and sample
washing steps.
Fixative solution for fluorescence in situ hybridisation, FISH, (4 %
paraformaldehyde) was prepared by adding 4 g of paraformaldehyde (P6148, Sigma)
in 100 mL of PBS. The permeabilisation solution for FISH was prepared to get a
final concentration of 70 U/ µL Lysozyme (L-6876, Sigma), 100 mM Tris-HCl
(molecular formula NH2C(CH2OH)3 · HCl, MW 157.60) and 5 mM
Mya Myintzu Hlaing Materials and Methods/ 47
Ethylenediaminetetraacetic acid, EDTA, (molecular formula
(HO2CCH2)2NCH2CH2N(CH2CO2H)2, MW 292.24) in Milli-Q water. Hybridisation
buffer was prepared for a final concentration of 0.9M NaCl, 20 mM Tris-HCl, 0.01
% sodium dodecyl sulphate (SDS) and 30 % formamide (molecular formula
HCONH2, MW 45.04) in Milli-Q water. Washing buffer was prepared in a final
concentration of 20 mM Tris-HCl, 5 mM EDTA, 0.01 % SDS and 159 mM NaCl in
Milli-Q water.
The permeabilisation solution, hybridisation buffer and washing buffer were adjusted
to a pH of 7.5 with HCl. All of the solutions and buffers were sterilised by filtration
using gamma-sterilized Millex-HV Syringe Filter Unit (Cat # SLHV033RS, 0.45
µm, PVDF, 33 mm) purchased from Merck Millipore, Bayswater, Australia. Tris-
EDTA (TE) buffer (pH 7.5 - 8.0, 10 mM Tris-HCl, 1 mM EDTA) was used for
preparation of the 7 µg/ µL stock solution of fluorescence labelled oligonucleotide
probe used for the FISH technique.
2.2 Methods
2.2.1 Bacterial culture and growth conditions
Bacteria from the -80 C stock were isolated on a nutrient agar plate for
approximately 12 hour (h) prior to the experiments. A single bacterial colony was
then inoculated from the plate into 20 mL of nutrient broth medium and then
incubated at 37 °C, with orbital shaking at 200 revolutions per minute (rpm).
2.2.2 Bacterial growth curve and phase measurement
A bacterial growth curve and phase measurement experiment was performed by
detecting the total biomass of the bacteria culture using optical density measurements
at λ= 600 nm with a spectrophotometer. In brief, the overnight culture of bacteria
was diluted to approximately 1×107 cells/mL with fresh sterile nutrient broth in batch
culture. The bacterial growth phases were monitored by verifying the bacterial
culture every 1-2 h time spectrophotometrically using Varian’s Cary 50 Bio, UV-
visible spectrophotometer (Agilent Technologies, Australia). The absorbance values
were plotted against the different functional growth times. A total of nine broth
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cultures evaluating three different growth phases (i.e. early, middle and late of the
exponential phase, stationary phase and decline phase respectively) were
independently harvested for Raman spectral analysis.
For the viable bacteria count, each sample suspension collected at the individual
growth phases was diluted in 0.9% (w/v) sodium chloride (NaCl) in a series up to 10-
6 to determine the number of viable bacteria. A 100 μL aliquot of the diluted sample
was inoculated on nutrient agar and incubated overnight at 37 °C. Each viable unit
(cell) grown as a colony was then counted as a colony forming unit (CFU). The
number of CFU per mL of the sample is related to the viable number of bacteria in
the sample.
2.2.3 Sample preparation for Raman spectroscopy experiments
2.2.3.1 Planktonic sample preparation
After overnight incubation in broth media, the bacterial cells were collected by
centrifugation and washing processes to remove the traces of broth media. A 1 mL
sample of bacterial cells in a microcentrifuge tube was collected by centrifugation for
2 min at 15,294 g (12,000 rpm) (Centrifuge 5804 R, Eppendorf, Australia). The
supernatant was decanted after centrifugation and the cell pellet was washed three
times with sterilised Milli-Q water (ultra-purified water, Millipore) using
centrifugation at the same speed for 2 min. The pellet was then resuspended in 30 µL
sterilised Milli-Q water by repeated gentle pipetting. For the dried-droplet sample
preparation, a 10 µL volume of washed bacterial cell suspension was dropped onto
the CaF2 microscope slide, allowed to air-dry for 3-5 min and finally analysed by
Raman spectroscopy.
2.2.3.2 Bacterial micro colony isolation
The overnight broth cultures were diluted 1:10 in fresh nutrient broth and then
incubated until the optical density at 600 nm (OD at A600 nm) reached 0.3. This
measurement of optical density at λ= 600 nm was performed with a
spectrophotometer and related to the total biomass of the bacteria culture. After that,
the bacterial culture was diluted in a series up to 10-6 to get isolated single bacterial
Mya Myintzu Hlaing Materials and Methods/ 49
(micro) colonies. An aliquot of 100 μL of the diluted sample was then inoculated
onto a pre-warmed nutrient agar and incubated at 37 °C overnight to allow formation
of bacterial colonies. Similarly, another 100 μL aliquot of the diluted sample was
then inoculated onto a sterile nitrocellulose transfer membrane (Prostran, BA 85),
placed on a pre-warmed nutrient agar plate and incubated at 37 °C overnight. After
overnight incubation, colony E. coli cells from the nutrient agar were mixed with 10
µL of Milli-Q water on a CaF2 slide, smeared and air-dried. Raman spectra were
collected from microbial smears on the substrate surface and this method has been
reported on several research publications (94, 215) . For the study of intact single-
species bacterial colony, the colony with the membrane was transferred to the CaF2
microscope slide for Raman spectral analysis.
2.2.3.3 Biofilm cultivation
A static biofilm formation assay was carried out on a sterile quartz microscope slide
in a sterilised bacteria culture petri dish, as previously described (216, 217). In brief,
the optical density of the overnight bacterial broth culture was determined
spectrophotometrically at 600 nm (OD at A600 nm). The bacteria cells were collected
by centrifugation for 5 min at 12,000 rpm and washed three times in sterilised 10
mM PBS to remove the residual nutrient medium. The washed bacterial cells were
resuspended in PBS to a concentration equivalent to an OD at A600 nm of about 0.3.
The suspension was then immediately used for initial attachment and biofilm
cultivation.
A 200 µL volume of prepared cell suspension was loaded onto the surface of 12
sterile quartz microscope slides and incubated at room temperature for 1 h. A
negative control for this biofilm cultivation experiment was prepared by loading 200
µL of PBS on a sterile substrate. After that, the substrates were carefully washed
three times by PBS solution to remove unbound bacterial cells. Then, two of the
substrates were kept at 4 C in PBS for further tests such as Raman spectroscopy
measurements and cell viability tests for evaluation of the initial bacterial
attachment. After that, each petri dish containing the remaining 10 washed substrates
and negative control substrate was filled with 15 mL aliquots of the sterilised nutrient
broth respectively. The plates were incubated at 37 °C without shaking at room
FSET PhD Thesis/50
temperature for 4, 8, 24, 79 and 120 h. Old culture media were replaced with fresh
nutrient media after every 24 h of biofilm cultivation. After each cultivation period,
two substrates were gently washed three times with PBS to remove suspended cells
and residual medium and then kept at refrigerated temperature (~ 4-8 C) in PBS
until required for further tests (for 24 h). A previous study by Adetunji et al. for
assessment of biofilm development in different cultural conditions showed that the
biofilm development on the substrates were higher at room temperature compared to
refrigeration temperature (218). Therefore, the subsequent storage for biofilm
samples at the refrigeration temperature in this study was aimed for minimising
further bacterial growth (219) and biofilm development. Finally, the attached cells
and intact biofilm surfaces were rinsed with sterile Milli-Q water to remove the
traces of PBS and were air-dried prior to Raman spectral analysis.
2.2.4 Bacterial visualisation
2.2.4.1 Bacterial viability test
The viability of bacterial cells from each washed biofilm sample was visualised by
using LIVE/DEAD BacLight Bacterial Viability Kits (Life Technologies, Australia).
A mixture of the propidium iodide, PI and SYTO 9 dye components provided with
the kits was first prepared in PBS to get the final concentrations of 30 µM and 5 µM,
respectively. A 200 µL volume of the dye mixture was applied to cover each biofilm
sample and then incubated at room temperature in the dark for 15 minutes. After that,
the samples were gently rinsed twice with PBS to remove the excess stain and
followed by rinsing with ice-cold sterile Milli-Q water to remove any trace of salt
from PBS. Each stained sample was then covered with a coverslip avoiding air
bubbles and stored at room temperature in the dark until microscopic examination.
The SYTO9 stained live cells and PI stained dead cells were viewed and imaged
using a 100× oil immersion lens with a confocal laser scanning microscope (CSLM)
(FV1000 Olympus with IX81). Excitation wavelengths of 488 nm (for SYTO 9) and
543 nm (for PI) were used. Low-speed image acquisition (40 or 100 µs/pixel),
640640 pixel resolution, and three frames filter was used for each image.
Mya Myintzu Hlaing Materials and Methods/ 51
2.2.4.2 Two-dimensional cell counting and colour segmentation
Manual cell counting analysis was performed from the 2-D CSLM images of the
surface-attached E. coli cells using the cell counter plugin installed in ImageJ
software (220). With the application of this cell counter plugin, live and dead cells
were manually marked up as two different groups of cells, and each group was
counted separately via the software.
Figure 2.1 Application of the colour segmentation plugin implemented in ImageJ
software: (A) two-dimensional confocal laser scanning microscope (CSLM) image of
120 hr biofilm with SYTO 9 stained E. coli live cells in green and propidium iodide
stained dead cells in red; (B) green and red pseudochannels segmented from the
original colour image and (C) specification of pseudochannel codes and proportional
ratios which represented area percentages covered by green and red labelled cells
over the total observed area.
FSET PhD Thesis/52
To evaluate the live and dead cell population within the mature biofilms grown on
the surfaces, an analysis based on colour segmentation of 2-D CSLM colour images
were performed using the colour segmentation plugin installed in ImageJ software
(221). This ImageJ plugin allowed performing segmentation of the original colour
image by pixel clustering. Figure 2.1 represents general steps which were performed
to segment the colour images using this plugin for analysing the live and dead
populations of E. coli cells attached to the surface.
First, the colour image (RGB) of a biofilm stained with SYTO 9 (green) and PI (red)
was opened to process (Fig 2.1A). The colour clusters were chosen and defined
manually through the interface and identified by red (R), green (G) and blue (B)
codes. The RGB values of the pixel under the cursor automatically displayed in the
cluster identification box (Fig 2.1C) providing the information used to set the colour
tolerance on a scale which defined the range in foreground pixels. The colour
appearances and mean values on the RGB channels were evaluated and adjusted by
examining the standard deviation values (). The colour segmentation algorithm then
automatically analysed the image, pixel-by-pixel, based on these assigned threshold
values. After the pixel classification was completed, the plugin system created and
displayed a pseudo-coloured segmentation output image with the percentage of the
area covered by the cells (Fig 2.1B and C).
2.2.4.3 Fluorescence in situ hybridisation (FISH)
Fluorescence in situ hybridization (FISH) is a molecular-cytogenetic investigation
method to detect and localize the presence or absence of specific RNA or DNA
sequences on chromosomes. This method uses fluorescent probes that bind to only
those parts of the chromosome with which they show a high degree of sequence
complementarity. FISH was used in this study for species identification of E. coli
ATCC 25922 to detect 16S rRNA targets (Accession: X80724, GI: 1240023, 1452
base pairs, genomic DNA). (Detailed information of the nucleotide sequence are
shown in Appendix A)
Mya Myintzu Hlaing Materials and Methods/ 53
2.2.4.3.1 Preparation of probe
The oligonucleotide for the probe used in this FISH technique is shown in Table 2.2.
As seen in Table 2.2, the probe was designed from E. coli ATCC 25922 16S rRNA
(Accession: X80724. GI: 1240023. 1,452 basepairs, genomic DNA). The
oligonucleotide probe sequence was designed and blasted using the primer tool
software (222). Selection of the oligonucleotide probe sequence was done based on
detailed primer reports such that the probe length is 22 bases where the base
composition is 45% G-C and there is no self-complementarity within the probe
(223). For rapid specificity and coverage evaluations of the probe, the web server,
probeCheck, was applied to check the probe against selected databases of
phylogenetic and functional marker genes (224). The probe labelled with Alexa
Fluor 647 was synthesised and purchased from Life Technologies Australia Pty Ltd.
The 7 µg/µL stock solution of fluorescence labelled oligonucleotide probe (MW;
7641.6 g/mol) was calculated and prepared by dissolving the synthesised probe in 1
mL TE buffer.
Table 2.2 Oligonucleotides probe used in this study
Oligonucleotide
number
Sequence Location/ Description
EC1_485 5 GTATCTAATCCTGTTTGCTCCC -3
E. coli ATCC 25922 16S rRNA
(Accession: X80724. GI:
1240023. 1,452 basepairs,
genomic DNA)
Complementary nucleotide 765-
786
2.2.4.3.2 Sample preparation for FISH
Sample fixation was performed to maintain the cell structure before the
permeabilisation step, which was required to ensure sufficient binding of the probe to
the target. For fixation of the biofilm samples (i.e. substrates with biofilms), the
sample was fixed in 4% paraformaldehyde (3 volumes) in phosphate-buffered saline
FSET PhD Thesis/54
(PBS) (1 volume) for 3 h at 4 C immediately after removal from biofilm cultivation.
For the planktonic cells on substrates, Gram-positive bacteria were fixed with 50%
EtOH and Gram-negative bacteria were fixed with 4% paraformaldehyde. The
samples were subsequently washed with sterile PBS. When the hybridisation steps
could not be performed immediately after the fixation step, biofilms were stored at 4
C in PBS for a maximum of three days. For the permeabilisation step, the samples
were treated with 25 µL of lysozyme (Sigma) [70 U/µL in 100 mM Tris-HCl, 5 mM
EDTA, pH 7.5] for 7-10 min at 37 C in a humid chamber. The samples were rinsed
with sterile Milli-Q water (ultrafiltered water) and dried for 10 min in a vertical
position.
2.2.4.3.3 Pre-hybridization and hybridization
Before the hybridization step, the samples were first pre-hybridized in hybridization
buffer (0.9 M NaCl, 20 mM Tris-HCl, 0.01% sodium dodecyl sulfate, 25%
formamide, pH 7.5) for 15 min at 46 C. After that, the samples were hybridized
with 2 mL of hybridization buffer containing the designated oligonucleotide probe
(5–20 µg/mL) at the annealing temperature of the probe (46 C) for 90 min
(maximum 180 min) in a humid atmosphere in the dark. After hybridization, the
samples were washed in preheated washing buffer (20 mM Tris-HCl, 5 mM EDTA,
0.01% sodium dodecyl sulfate, and 159 mM NaCl, pH 7.5) for 15 min at 48 C.
Finally the samples were rinsed briefly in ice-cold sterile Milli-Q water.
2.2.4.4 Extracellular polymeric substance (EPS) staining
Subsequent to the hybridization steps, to stain the α-mannopyranosyl and α-
glucopyranosyl sugar residues of EPS, Concanavalin A (ConA, Molecular Probes,
Invitrogen) conjugated with tetramethylrhodamine (0.2 g/L) was applied just to cover
the samples and the samples were incubated for another 30 min. Since ConA, a
lectin, is a carbohydrate-binding protein, it will bind exopolysaccharides containing
sugar moieties. ConA can also bind with proteins and glycoconjugate groups
associated with cell walls. After each of these staining stages, the samples were
washed twice with PBS to remove excess stain. Finally, the samples were covered
Mya Myintzu Hlaing Materials and Methods/ 55
with cover slips carefully avoiding air bubbles and could be stored at room
temperature in the dark for up to 6 h prior to microscopic examination.
2.2.4.5 Visualisation of the hybridized E. coli cells and ConA stained EPS
The samples were visualised under confocal laser scanning microscopy (CLSM)
[Olympus FV1000 confocal microscope with IX71 base] with ×100 oil-immersion
lens at an excitation wavelength of 633 nm and emission wavelength of 668 nm for
detection of E. coli cells hybridised with the probe labelled with Alexa Fluor 647.
An excitation wavelength of 543 nm and emission wavelength of 618 nm was used
to visualise the EPS stained with ConA. The images were taken using both “XY
image acquisition” mode for x-y plane and “Z-stack image acquisition” mode for x-
y-z plane. The confocal image processing for 2D images captured with “XY image
acquisition” was performed using FV10-ASW 4.1 viewer software (225). CLSM
generated Z-stack images by a series of 2D images of parallel planes in a sample. To
generate 3D visualization of such scans for the structure of the biofilm sample, Z-
stack image processing was performed with the application of ImageJ software.
2.2.4.6 Probe efficiency test
To determine the optimal hybridisation conditions, the hybridisation efficiency of the
synthesised probe to specific and nonspecific target nucleic acid was optimised over
a range of parameters (64). Specificity tests under stringent FISH conditions using
planktonic cells of E. coli and V. vulnificus species showed that the probe displayed
the anticipated specificity to the E. coli species (Fig 2.2).
Two-dimensional confocal laser scanning microscope (CSLM) images of single-
species biofilm shows successful probe penetration through biofilm grown cells. The
efficiency was evaluated with both the specific FISH rRNA probe and the EPS stain
for the target E. coli strain (Fig 2.3). The cell permeabilisation process of
paraformaldehyde-fixed biofilms by exposure with lysozyme for 9 mins provided
effective fluorescence intensity from FISH hybridized E. coli cells and EPS (-D-
glucopyranose polysaccharide) stained with ConA. Although ConA can also bind
with proteins and glycoconjugate groups associated with cell walls, Alexa Fluor 647
FSET PhD Thesis/56
dye which is a bright, far-red–fluorescent dye from FISH rRNA probe was able to
differentiate FISH hybridized E. coli cells from stained EPS (Fig 2.3).
(A) (B)
Figure 2.2 Specificity test of FISH rRNA probe efficiency with fixed planktonic
cells of E. coli and V. vulnificus species. Panel (A) represents the differential
interference contrast (DIC) confocal images and panel (B) represents fluorescence
confocal micrographs of FISH hybridized cells with an excitation wavelength
633 nm. Scale bar = 10 µm applies to all images.
Taking advantage of the specificity of the FISH rRNA probe and ConA stain, the
next investigations were performed to verify whether the probe and stain could be
used to visualise the labelled E. coli cells and EPS matrix during biofilm
development at different growth time points. As mentioned in the literature Chapter,
biofilm architecture in mature biofilm is believed to become more complex by
additional recruitment and colonisation by planktonic bacteria and increased
synthesis of EPS. Denser groupings of E. coli cells with more EPS content were seen
in the older biofilms (Fig 2.4). Despite an increased thickness of EPS matrix in
mature biofilm, the biofilm sample preparation processes which were applied in this
study (i.e. cell fixation and permeabilisation) provided effective probe penetration
through 120 h old biofilm.
Mya Myintzu Hlaing Materials and Methods/ 57
Figure 2.3 Two-dimensional confocal laser scanning microscope images of single-
species biofilms of E. coli. (A) DIC confocal image; (B) FISH hybridized E. coli
cells labelled with Alexa Fluor 647; (C) EPS (-D-glucopyranose polysaccharide)
stained with Concanavalin A; (D) visualisation of E. coli cells in red with EPS matrix
in blue. Scale bar = 10 µm applies to all images. (White arrow shows labelled E. coli
and red arrow shows stained EPS)
FSET PhD Thesis/58
Figure 2.4 Two-dimensional confocal laser scanning microscope images of single-
species biofilms of E. coli during biofilm growth. FISH hybridized E. coli cells
labelled with Alexa Fluor 647 in red and EPS matrix (-D-glucopyranose
polysaccharide) stained with Concanavalin A in blue can be visualised. Scale bar =
10 µm applies to all images.
2.2.5 Raman spectroscopy experimental set up
2.2.5.1 Instrument set up, calibration and spectrum acquisition
A Renishaw InVia Raman spectrometer, equipped with a Leica microscope plus a
deep-depletion charge-coupled device detector (CCD) and a computer motorised x-y-
z stage was applied. The Raman spectroscopy system was controlled and configured
by the Renishaw WiRE 3.4 software. For acquiring spectra from each sample, the
operational parameters and instrumental specifications such as 2400 lines per mm
grating, a holographic notch filter and ~ 1.5 mW (50 % of laser power) of 514 nm
radiation from an argon-ion laser was used.
One of the benefits of the InVia Raman spectrometer used in this study is the
automation that facilitates automatic alignment and health checks. Once the system
8 h
10 µm
24 h
79 h 120 h
Mya Myintzu Hlaing Materials and Methods/ 59
was commissioned, the health check function was always applied to verify the laser
and spectrometer alignment for optimal performance. If any parameters are starting
to drift then the appropriate auto align or calibration routine was performed
according to the suggestion of the health check program. The system was then
calibrated and monitored using a silicon reference (520.5 cm-1) as an external control
before the measurements. As an internal control, the system was then calibrated and
manually health checked using an internal silicon reference (520.5 cm-1) for
calibration of peak intensity.
For each measurement, a single bacterial cell/biofilm sample was brought into focus
using a 100× microscope objective (NA = 0.85 in air). The accumulation time for
each acquisition was 80 s and three accumulations were collected for a single
measurement on each sample area. The spectra were always collected in the 500 to
2000 cm-1 range that covers the fingerprint region of most biological materials (226).
For calibration and normalisation purposes, the spectra were first collected in the 500
to 3200 cm-1 range with 10 s acquisition and one accumulation to check the
prominent C–H stretching band (associated with polysaccharides and proteins) which
can be observed between 2800–3000 cm-1 of the spectrum (227). This C–H
stretching peak provided confirmation of the bacterial spectral bands as well as the
intensity of the Raman response which correlates with correct positioning of the
focal plane and the number of cells present in the sample (228).
2.2.5.2 Raman signal pre-processing for statistical data analysis
2.2.5.2.1 Cosmic ray removal
For cosmic ray removal, the WiRE 3.4 Raman software integrated in the Renishaw
inVia Raman spectroscopy system was applied. Cosmic ray features (CRFs) are
generally more intense and sharper than Raman bands. With the application of the
WiRE 3.4 Raman software, CRFs were automatically detected and removed during
data collection by using a ‘running median average’ method. This is a highly
effective method, but involves an additional scan (acquisition) resulting in
significantly more time expenditure on the data collection. Therefore, in this study,
methods which can remove CRFs following data collection process were applied.
FSET PhD Thesis/60
These methods are much faster than the ‘running median average’ data collection
method. Two different methods such as ‘nearest neighbour’ and ‘width of feature’
can be utilised within WiRE software to achieve datasets containing no or limited
CRFs. In this study, the ‘width of feature’ method was applied by manually selecting
the minimum bandwidth attributable to a real CRF first and then the selected CRF
was ‘zapped’. The width of feature method was used in every case throughout the
study as CRFs are random and sample-independent events.
2.2.5.2.2 Background removal
In this thesis, enhanced automated algorithms for fluorescence removal based on a
combination of adaptive weighting with penalized least squares (APLS) estimation
were applied (147). A detailed discussion on this algorithm is presented in Chapter 3.
Custom written MATLAB code for the APLS tests was applied. The second-order,
single-weighted background removal scheme (O2W1) was chosen for all fluorescence
background subtraction processes applied throughout the study.
2.2.5.2.3 Smoothing and intensity normalisation
The Savitzky-Golay filter (span = 7, polynomial degree = 2, curve fitting toolbox in
MATLAB) was used to reduce the noise of the spectra. In order to perform
multivariate analysis, the intensities of the background-subtracted spectra were
normalised using total intensity normalisation to account for variations in intensity.
For this total intensity calculation, the data is divided by the sum of the intensities in
the data set. For each single spectrum, the absolute intensity values of each wave
number were further normalized to that of the Raman peak which corresponds to
DNA backbone (O-P-O stretching) at the wave number of 1095 cm-1. The total
intensity normalisation and internal peak feature normalisation was custom written in
MATLAB together with multivariate statistical data analysis.
2.2.5.2.4 Mean-centring the data
The background-subtracted, smoothed and normalized Raman spectra were then
mean-centred to reposition the centroid of the data to the origin. The mean-centred
data is calculated by subtracting the mean of the data from the original data. The
Mya Myintzu Hlaing Materials and Methods/ 61
calculation of mean-centring is set in the MATLAB code together with multivariate
statistical data analysis.
2.2.6 Statistical data analysis
To analyse the Raman spectra obtained from bacterial cells, the multivariate
statistical methods of principal component analysis (PCA) and linear discriminant
analysis (LDA) were applied. Specific peak analysis (univariate analysis) was
performed for the peaks identified from multivariate analysis.
2.2.6.1 Principal component analysis (PCA)
PCA was first performed for data reduction of the 1407 included pixels from each
spectrum of the bacterial cells. In brief, the mean-centred data were analysed by
calculating the principal components (PCs) and then creating scores plots for the first
and second PCs. Mean score values of the first and second principal components,
standard deviations were calculated in MATLAB with custom-written codes. Two-
tailed p-values of each sample group compared to others were calculated in
Microsoft Excel using the TTEST function. A p-value is a measure of evidence
against the null hypothesis which refers to a hypothesis of "no difference" between
two measured phenomena. A small p-value is evidence against the null hypothesis
while a large p-value means little or no evidence against the null hypothesis. The
corresponding loadings plots that relate the scores to specific regions in the original
Raman data were plotted. MATLAB code for the PCA was custom written in this
study.
2.2.6.2 Principal component linear discriminant analysis (PC-LDA)
LDA which can discriminate between groups was further performed based on the
retained principal components (PCs) and the a priori knowledge of which spectra
belonged to each bacterial species. This combined method of principal component
and linear discriminant analysis (PC-LDA) was then performed to maximize between
group variance and minimize within-group variance. After preliminary studies
described in Section 4.3.3, a prediction model based on the first 16 principal
components (PCs) of the four different species of planktonic cells which account for
FSET PhD Thesis/62
approximately 92% of variance in the data set was used for analysis. To validate the
discrimination performed by the PC-LDA model, leave-one-out cross-validation
(LOOCV) was employed. In particular, a single spectrum was removed from the
database and a training data set was created using the remaining spectra. The
classification label of the left out spectrum was determined and the process was
repeated for every single spectrum of the data set. The linear discriminant classifier
in the dimensional space using [obj = ClassificationDiscriminant.fit] was applied
from MATLAB and the custom written code was applied throughout the study.
2.2.6.3 Specific peak analysis (univariate analysis)
For specific peak analysis, the intensity values of Raman peaks were first normalized
by the total intensity values. Normalised Raman spectra were then curve-fitted using
CasaXPS software (229) (version 2.3.15). Mixed Gaussian (Y %) – Lorentzian (X
%) spectral profiles, which are identified as GL(X) in CasaXPS, were used for each
component of the spectra. The best profile of Gaussian–Lorentzian components was
applied to all samples (an example of quantification parameters for the components
and the fitted spectrum are shown in Appendix B). The intensity values of fitted
Raman peaks identified from multivariate analysis were then averaged by adding the
maximum intensity and the intensity values of the two neighbouring channels for
each fitted component. Statistical comparison of the relative mean intensity changes
(log2 fold change) was performed for the selected peaks to compare between the
samples.
Mya Myintzu Hlaing Chapter 3/ 63
OPTIMISATION OF RAMAN SPECTROSCOPY FOR
BACTERIAL CELLS
3.1 Introduction
This chapter investigates the optimisation of experimental and data analysis methods
for Raman analysis of bacterial cells. The initial optimisation step involved the
choice of substrate to minimise spectral interference and optimise the signal-to-noise
ratio. Concurrently, reference spectra were collected from isolated bacterial cell
components (polysaccharides and proteins) to gain some insights into the spectral
features and differences between cells and extracellular matrix signals. Given that all
Raman spectra from biological samples have varying levels of fluorescence and
thermal background, a range of background removal methods and spectral pre-
processing methods for chemometric analysis are explored within this chapter. Due
to the fact that biochemical composition of bacterial macromolecules could be
affected by the sample preparation process and thus create challenges in spectral
analysis, different processing and preparation methods for the bacterial samples were
also explored. This helped to understand the optimal method for obtaining the signal
from individual bacteria and ensuring the repeatability of the sample analysis
methods, both within a sample and across multiple repeat sample sets.
3.2 Experimental set up and spectrum acquisition
3.2.1 Selection of substrate for Raman spectroscopy experiment
The choice of quartz slides and calcium fluoride (CaF2) as the substrates to collect
the Raman spectra from bacterial samples was decided by reviewing the common
practice in the literature (230) and then examining the background scattering signals
from each of the slides. An ideal substrate for bacterial samples would be one with
negligible background scattering, causing little interference with the Raman signal
from single bacterial cells. The most commonly used substrates, such as glass, quartz
and CaF2 were evaluated in this study. The Raman spectra of the glass, quartz and
CaF2 slides are shown in Figures 3.1 (A-C) for comparison. The quartz slide has
FSET PhD Thesis/64
broad features in the range 700-900 cm-1, but they are much less intense than the
peaks from the glass substrate, such as the one at 1100 cm-1. The broad background
signal associated with the quartz can easily be normalized and subtracted from the
Raman signal, as described later in this chapter. The CaF2 slide gave no significant
signal in the spectral range analysed. As such, CaF2 and quartz were selected as the
preferred substrates for the project
(A) (B) (C)
Figure 3.1 Raman spectra of different substrates: (A) glass slide, (B) quartz slide and
(C) calcium fluoride (CaF2) slide.
3.2.2 Raman spectra from reference samples
As mentioned in the literature Chapter, all bacterial cells are composed of water,
macromolecules (proteins, nucleic acids, polysaccharides and lipids), small
molecules (amino acids, nucleotides, fatty acids, carbohydrate and coenzymes, etc.)
and inorganic ions (Section 1.2.1 and Table 1.1). Therefore, before trying to get
typical Raman spectra from bacterial cells, Raman spectra from a range of reference
samples (polysaccharide, proteins and amino acid) that relate to the bacterial cells
were first collected and analysed. Dextran (#31424, Fluka, Mw 410,000), fibrinogen
fraction I from bovine plasma (#F 8630, Sigma, Mw 340,000) and D-tyrosine
(#855456, Sigma, Mw 181.19) were chosen as a reference for polysaccharide and
proteins respectively. 29.4 µM of fibrinogen and 24 µM of dextran were dissolved in
Milli-Q water and used for collection of Raman spectra from individual samples as
well as for optimisation of different molar ratios in mixtures of the various
biomolecules. 3 mM of D-tyrosine in MilliQ was used for collection of Raman
Mya Myintzu Hlaing Chapter 3/ 65
spectra from an aromatic amino acid. Droplets of 10 µL of each sample were air
dried on a quartz slide prior to Raman analysis.
A Renishaw InVia Raman spectrometer equipped with a Leica microscope, deep
depletion charge-coupled device detector, 2400 lines per mm grating, holographic
notch filter and ~ 7 mW of 514 nm radiation from an argon-ion laser was used for
acquiring spectra from the sample. The system was calibrated and monitored
according to the protocol mentioned in Section 2.2.5.1. For each measurement, the
sample was brought into focus using a 50× microscope objective (NA = 0.75 in air).
The accumulation time for one spectrum was 10 s and three accumulations were
collected for a single measurement on each sample area. The spectra were then
averaged over three different sample areas.
Raman spectroscopy on proteins and polysaccharide was hampered by the
intrinsically low scattering cross Section of these molecules. Therefore, mono- or
multi-layers of proteins and polysaccharide adsorbed on the substrate were prepared
to improve signal-to-noise ratios and enable peak assignment. As shown in Fig 3.2,
in mono-layer preparation, the characteristics peak assignments of the dextran were
not seen clearly against the quartz background, while those of fibrinogen and
mixtures were clearly visible. The appearance of the dextran peaks was improved
when multi-layers (in particular, 4 layers) of dextran suspension were applied to the
substrate whereas only small changes were seen in the spectra of fibrinogen and
mixtures of protein/dextran. The multi-layered preparation of dextran, fibrinogen and
dextran-fibrinogen mixture (molar ratio of 1:8) were chosen for further analysis. The
Raman spectrum from a mono-layer preparation of the amino acid (i.e. D-tyrosine) is
shown in Fig 3.2C.
FSET PhD Thesis/66
(A) (B)
(C)
Figure 3.2 Original Raman spectra from reference samples: (A) mono-layer, (B)
multi-layer preparation of dextran, bulk protein fibrinogen fraction I from bovine
plasma and mixed aqueous solutions of dextran and fibrinogen at different weight
ratios (1:6 and 1:8 ratios) and (C) mono layer preparation of D-tyrosine.
500 1000 1500 2000
460
690
920
1150
1200
2400
3600
4800
580
870
1160
14500
410
820
1230
Wavenumber / cm-1
D&F (1:8)
D&F (1:6)
Fibrinogen
Ra
ma
n I
nte
nsity / A
rbitr.
Un
its
Dextran
500 1000 1500 2000
1590
2120
2650
3180
1220
1830
2440
3050340
680
1020
1360
250
500
750
1000
Wavenumber / cm-1
D&F (1:8)
D&F (1:6)
Ram
an Inte
nsity / A
rbitr.
Units
Fibrinogen
Dextran
quartz signal
Mya Myintzu Hlaing Chapter 3/ 67
The characteristic peak assignments of the reference samples are shown in Fig 3.3.
Peak assignments typically associated with polysaccharides (dextran) included
glucose-saccharide peaks at wave numbers 530–540 cm-1 (231) and peaks that are
associated with the glycosidic ring deformation at 1090-1125 cm-1 (120). The
symmetric stretch bands of the carboxyl ion (COO-) appearing at 1460 cm-1 could
also be seen in the Raman spectra of dextran. Similarly, protein-related peaks could
be clearly seen in the bulk protein sample of fibrinogen. The vibrations (deformation)
of amine groups were evident at 838 cm-1. The sharp band at 1001 cm-1 seen in the
fibrinogen spectra is related to the phenylalanine (ring breathing mode). The amide
III peak could be seen at 1240 cm-1 (1295-1230 cm-1). The peak at 1337 cm-1 is
related to the CH vibrations of the protein backbone, while the 1424 cm-1 peak
represents the COO- stretching mode. The peaks at 1448 cm-1 as arise from the
deformation modes of both CH3 and CH2 vibrations. The amide I band, which is
sensitive to the secondary structure in protein, can be observed at 1667 (1620-80)
cm-1 in the fibrinogen sample. As shown in the D-tyrosine Raman spectra, the
strongest band arising from the ring breathing vibration forms a Fermi doublet with
bands located at 828 and 845 cm-1. The ring-O stretching vibration located at 1263
cm-1 could also be seen. These peak assignments for D-tyrosine are based on the
analysis of Raman peaks of amino acids published by Culka et al. (232).
FSET PhD Thesis/68
Figure 3.3 Typical Raman spectra of polysaccharide (dextran), bulk protein
(fibrinogen), a mixture of dextran and fibrinogen in 1:8 molar ratios and D-tyrosine.
Raman peak assignments are based on studies in the references mentioned in the text
and in Table 3.1. Abbreviations: def, deformation; Phe, phenylalanine; symm,
symmetric; str, stretching.
500 1000 1500 2000
1000
2000
3000
2000
3000
600
1200
1800
600
1200
1800500 1000 1500 2000
Wavenumber / cm-1
D-tyrosine
D&F (1:8)
Ra
man
In
ten
sity / A
rbitr.
Un
its
Fibrinogen
Dextran
sid
e g
rou
p d
ef
(CO
H;C
CH
;OC
H)
gly
co
sid
icri
ng
(C-C
;C-O
-C;
C-C
-O)
CH
/CH
2d
ef
Sym
mC
OO
-s
tr
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ide I
Am
ide I
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Am
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II
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Mya Myintzu Hlaing Chapter 3/ 69
3.2.3 Raman spectra from bacterial cells
The overnight broth culture of E. coli planktonic cells was prepared for Raman
spectroscopic analysis. In brief, the washed bacterial cells were smeared and air-
dried on a quartz substrate. Raman spectra were collected from E. coli single cells
following the protocols mentioned in Section 2.2.3.1. A typical Raman spectrum
obtained from E. coli single cells and the dominant peak assignments are shown in
Fig. 3.4 and Table 3.1. The spectrum shows the characteristic Raman bands found in
the literature and associated with the abundant cellular components such as
carbohydrates, lipids, proteins and nucleic acids (94, 215, 233, 234). There was a
relatively high fluorescence background in the data for this planktonic cell sample.
Therefore, attempts were performed for fluorescence background removal using
different approaches and the results are mentioned in the next sub-section.
Figure 3.4 Typical averaged Raman spectrum from planktonic E. coli cells with
characteristics peak assignments. Abbreviations: Phe, phenylalanine; Tyr, tyrosine;
str, stretching; def, deformation. Assignments are based on studies in the references
shown in Table 3.1.
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Table 3.1 Selected Raman frequencies and their peak assignments for the spectra.
Wave number (cm-1) Peak assignment Reference
DNA/RNA
668 T, G (ring breathing) (98, 235)
726 A (ring breathing) (236-238)
746 T (ring breathing) (236)
781 C, U (ring breathing) (94, 239)
785 U, T, C (ring breathing), backbone
O-P-O str.
(98, 235, 236)
811 (808) O–P–O str. RNA (240)
1095 Phosphodioxy group (O-P-O in
nucleic acids)
(240)
1288 Phosphodiester groups in nucleic
acids
(241)
1325-1330 CH3CH2 wagging mode in purine
bases of nucleic acids
(242)
1373 T, A, G (ring breathing), DNA/RNA
bases)
(236)
1485 A, G (ring breathing) (237, 243)
1506-1510 C (244)
1575 A,G (ring breathing) (236)
Proteins/Lipids
622 Phe (C-C twisting) (239, 245)
640 Tyr (C-C twisting), C-S str (105, 236, 239)
838 Amine groups deformation
vibrations
(246)
852 Tyr (ring breathing), Pro (C-C str) (239, 245)
1001 Phe (ring breathing, sym) (120, 239)
1125 C–C, C–N str (94, 120, 236)
1155 C–C, C–N str (236, 239)
1240 (1295-1230) Amide III (22, 94, 237,
247)
1337 CH def (237, 238)
1360 Trp (248)
1440 CH, CH2 and CH3 deformation
vibrations
(231, 249)
1447 CH2 def (94, 236)
1452 CH2 def (94, 236)
1550 (1580-1480) N-H def and C-N str, amide II (243, 247)
Mya Myintzu Hlaing Chapter 3/ 71
Wave number (cm-1) Peak assignment Reference
1602 C=C def, phenylalanine (protein
assignment)
(250, 251)
1615 Tyr, Trp, C=C (236)
1662 (1680-1620) C=O str, amide I (26, 94, 243,
247)
1734-1738 C=O str, lipid (ester) (240, 252)
Abbreviations: A, adenine; G, guanine; T, thymine; C, cytosine; U, uracil; str,
stretching; sym, symmetric; def, deformation; Phe, phenylalanine; Tyr, tyrosine, Pro,
proline; Trp, Tryptophan. Assignments are based on studies in the references.
3.3 Attempts to achieve consistent fluorescence background subtraction
As discussed in Chapter 1, the intrinsic fluorescence background can be orders of
magnitude larger than the Raman scattering and so background removal is one of the
foremost challenges for quantitative analysis of Raman spectra in many samples. A
range of methods anchored in instrumental and computational programming
approaches have been proposed for removing fluorescence background signals.
Because of the challenges associated with instrumental approaches (see Section
1.5.3.1.2), computational methods have become the standard way to correct for
contributions from fluorescence in the background. In this study, several approaches
were initially applied in an attempt to get successful background subtraction from
Raman spectra.
3.3.1 Application of Raman software
The first attempt for background subtraction from Raman spectra was the application
of the baseline subtraction tool from the WiRE 3.4 Raman software integrated in the
Renishaw inVia Raman spectroscopy system. The baseline subtraction was
performed by manually defining the background to be removed from the spectrum
and was initialised by selecting the “Subtract baseline” tool. As shown in Fig 3.5, the
window was split into two panes where the upper pane displays the original spectrum
and baseline. The lower shows a preview of the baseline-subtracted spectrum. For a
linear baseline fit, the baseline could be initialised first to a straight line between the
ends of the spectrum. These two baseline-definition points may be dragged
(vertically) to alternative positions (Fig 3.5A). Additional baseline-definition points
FSET PhD Thesis/72
can be added by clicking at the place where the new point is required and non-linear
baselines based on a polynomial fit can be performed using the “Cubic Spline
Interpolation” function in the software (Fig 3.5B). These additional points can be
dragged (in any direction) to alter their position. The defined points can be selected
based on the subtraction mode and the baseline type. As more points are added, the
baseline subtraction appears to become more consistent with an “expected” result.
Although apparently better results could be obtained by carefully adding more points
and by using the “Cubic Spline Interpolation” function in the software, the manual
selection of the points relies on user intervention and judgement as to where the
curves should be fitted in the data. Clearly different users may have different views
on how the background should be fitted, making this approach subjective and
potentially difficult to reproduce. Therefore, an automatic background remove
method was evaluated to avoid the need for manual curve-fitting.
Mya Myintzu Hlaing Chapter 3/ 73
(A)
(B)
Figure 3.5 Background corrections using the baseline subtraction tool from the
WiRE 3.4 Raman software; (A) linear baseline fit and (B) non-linear baseline based
on a polynomial fit.
3.3.2 Application of polynomial curve fitting
The second attempt for background subtraction from Raman spectra was the
application of the curve fitting tool from MATLAB. The baseline data tips (points)
were first selected and baseline data fitting was then performed using a quadratic
polynomial. A baseline correction algorithm using polynomial fitting was applied to
each spectrum. In brief, the syntax “cftool” (X, Y) creates a curve fit to x input and y
output. These X and Y must be numeric, have two or more elements and have the
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same number of elements. A second-degree quadratic polynomial curve was then
fitted. The curve-fitting coefficients (with 95% confidence bounds) and equation
were applied to calculate the curve-fitting value from each raw spectrum. Finally, the
curve-fitting values were subtracted from the raw spectrum to get the fitted spectrum.
As shown in Fig 3.6, the accuracy of background baseline corrections using the
polynomial curve fitting tool from MATLAB is highly dependent on the baseline
data point selection. Due to inconsistencies between the complex shape of the
fluorescence background and the simplified form of a second order polynomial,
some characteristics of the Raman spectrum were placed below the baseline,
resulting in a poor baseline estimate and severely distorted Raman spectral features.
While the selected baseline data points can be more accurately fitted by means of a
higher order polynomial, this still results in distortions in spectral regions between
the selected points.
Figure 3.6 Background corrections using the polynomial curve fitting tool from
MATLAB. The solid line is the fitted spectrum and the dotted black line is the raw
spectrum. Baseline data points (X, Y) selected from the raw data are also shown.
Mya Myintzu Hlaing Chapter 3/ 75
3.3.3 Weighted penalized least squares method in “R” language
The poor results derived from polynomial fitting, meant that a more complex
algorithm was required. Zhang et al. have developed an algorithm that combines
wavelet peak detection, wavelet derivative calculation for peak width estimation and
penalized least squares background fitting (146). The background corrections using
this weighted penalized least squares algorithm on bacterial data are shown in Fig
3.7. The result shows that this automated background subtraction method provides
better outcomes than the simpler polynomial methods discussed above. However,
some aspects of this method are unsatisfactory. In particular, the background is
pulled up into some of the peaks and the tails of some peaks appear to be truncated
abruptly at the baseline, rather than smoothly merging into the baseline.
Figure 3.7 Background baseline corrections using the weighted penalized least
squares algorithm, implemented in “R” language.
Wavenumber/cm-1
Raw spectrum
Fitted spectrum
FSET PhD Thesis/76
3.4 Improved methods for fluorescence background subtraction from Raman
spectra
In an effort to address the limitations of the existing background subtraction
methods, an enhanced adaptive weighting scheme for automated fluorescence
removal was developed, applicable to both polynomial fitting and penalized least
squares approaches. This work was performed in collaboration with Professor Peter
Cadusch from the Centre for Quantum and Optical Science, Faculty of Science,
Engineering and Technology, Swinburne University of Technology. MATLAB
codes for improved methods of background subtraction were kindly generated by
Prof. Cadusch. Analysis of the background fitting results from application of this
method and other methods were performed in this study. The efficiency and accuracy
of this enhanced automated algorithm for fluorescence removal was evaluated for
both simulated and experimental data and was published in the Journal of Raman
Spectroscopy (147).
The initial motivation for developing the enhanced algorithm arose from application
of the previously published background-correction algorithm for Raman spectra
using wavelet peak detection, wavelet derivative calculation for peak width
estimation and penalized least squares (PLS) background fitting approach (146). That
approach adaptively separates the measured data samples into peak and nonpeak
(background) values by setting the least squares weights to one for background and
zero for peak regions. The application of these binary-valued weights may cause the
sudden changes in gradient that appear questionable in the context of a Raman
background subtraction. The enhanced automated algorithm for fluorescence
removal was based on a statistically adapted weighting together with either PLS
estimation or polynomial estimation (147). The proposed method significantly
improved the background fit over the range of signal, shot noise and background
parameters tested, while significantly reducing the subjective nature of the process.
This enhanced background subtraction method was applied for all subsequent Raman
analyses in this study.
In order to assess the efficiency and accuracy of the proposed algorithm, it was tested
with simulated Raman spectra. The simulated spectra consisted of a number of
Mya Myintzu Hlaing Chapter 3/ 77
randomly positioned Gaussian peaks (typically up to 20 peaks) and a known variable
background with randomly generated Lorentzian (shot) noise. The results from these
simulated spectra suggest that the method is robust and reliable and can significantly
improve the background fit over the range of signal, shot noise and background
parameters tested, while reducing the subjective nature of the process (Fig 3.8). For a
more detailed explanation of the method and discussion of the simulated spectra, the
reader is referred to (147).
Figure 3.8 Simulated data set fitted with adaptive-weight penalised least squares.
The solid line (red online) is the known background and the dashed black line is the
recovered background.
3.4.1 Experimental data
To demonstrate the application of the fluorescence background correction
algorithms, Raman spectra obtained from a 1:8 mixture of dextran and fibrinogen
were tested. The intention of this mixture was to model a combination of
biomolecules (i.e. polysaccharides and proteins) found in typical biological samples.
As mentioned in Section 3.2.2, the suspension of dextran (Fluka, 24 µM) and
fibrinogen fraction I from bovine plasma (Sigma, 29.4 µM) dissolved in Milli-Q
water were mixed together to get a final molar ratio of 1:8. Droplets of 10 µL of the
sample mixture were air dried on a quartz slide for Raman analysis.
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A comparative study of background removal was performed using five different
methods with similar fitting parameter values. The methods included:
1. The Modified Polyfit method of Lieber and Mahadevan-Jansen (253) which
uses least squares fitting of a polynomial background with (in effect) adaptive
elimination of peak regions from the fit (ModPoly).
2. The Improved Modified Polyfit method of Zhao et al (143) which is similar
to ModPoly but improves the peak removal scheme to allow for statistical variations
in measured quantities and includes an automated iteration cut-off (IModPoly).
3. The probability-based adaptively weighted polynomial fit method proposed
by Cadusch et al. (147) (APoly).
4. The method of Zhang et al (146): a penalised least squares method which
differs from APLS in the way in which the adaptive weights are set (wavelet peak
detection with hard peak / background segmentation) (WPLS).
5. The probability-based adaptive weight penalised least-squares method
proposed by Cadusch et al. (147) (APLS).
The main difference between the methods proposed by Cadusch et al. (APoly and
APLS) and the other similar methods (ModPoly, IModPoly and WPLS) is that the
weighting schemes in the existing methods, which consist of hard background /
foreground segmentation schemes of increasing complexity, are replaced by a single,
simple weighting scheme based on the statistical properties of the signal. MATLAB
code for the ModPoly, IModPoly, APoly and APLS tests was custom written by
Cadusch et al. (147) and the R code of Zhang et al. for Baseline Wavelet version
4.0.1 (254) was used for the WPLS method.
The results of the comparative study are shown in Fig 3.9. Experimental results show
that the proposed methods (APoly and APLS) can automatically identify background
regions and that the results are comparable with or superior to previously reported
methods for fluorescence background subtraction. The APLS approach generally
improves on the method of Zhang et al. (146) while avoiding the questionable
features associated with rapid changes in slope of the fitting curve. With application
Mya Myintzu Hlaing Chapter 3/ 79
of the adaptively weighted algorithms, consistent Raman spectra with significantly
improved background subtraction can be obtained with minimal user input.
Figure 3.9 Experimental data fitted by five different methods: (a) modified
polynomial fit (ModPoly) (b) improved ModPoly, (c) adaptively weighted
polynomial fit (APoly), (d) weighted penalized least squares (WPLS) and (e)
adaptive-weight penalized least squares (APLS). APoly and APLS are probability-
based methods proposed for this study. The extracted background is the dashed line
in red.
The efficiency and accuracy of the proposed background correction algorithms
(APoly and APLS) was further tested on Raman spectra collected from E. coli
bacterial cells. A typical Raman spectrum obtained from a single E. coli cell is
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shown in Fig 3.10, together with the background-corrected result using the APLS
algorithm for fluorescence removal. The adaptive weighting, combined with the
penalised least squares estimation developed for this study, automatically identified
likely background regions and subtracted the background region from the spectra to
leave the Raman scattering component. These background corrected spectra show the
characteristic E. coli Raman bands found in the literature and discussed in Fig. 3.4. It
should be noted that the background correction procedure requires an optimal
“stiffness” parameter which can be kept fixed for consistent analysis of a series of
related spectra, thereby assuring a greater level of repeatability.
Figure 3.10 Typical original and background corrected results for the Raman
spectrum of single planktonic E. coli cells using the APLS method. Abbreviations:
Phe, phenylalanine; Tyr, tyrosine; str, stretching; def, deformation. Assignments are
based on studies in the references shown in Table 3.1.
3.5 Raman signal pre-processing for statistical data analysis
3.5.1 Intensity normalisation
As mentioned earlier, in Raman spectral analysis, the recorded data always contains
some noise. In addition to variance between replicates, measurements can have a
degree of variance due to fluctuations in instrumental parameters. Normalisation can
not only reduce the effect of these variations, but can also improve the quality and
interpretability of the data for further statistical analysis. Ideally, normalisation
Am
ide
I
Am
ide I
I
Lip
id
C-H
de
f
Am
ide
III
Ca
rbo
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Ph
e
Tyr
(C-C
str
)
DN
A/R
NA
Ph
e,
Tyr
Mya Myintzu Hlaing Chapter 3/ 81
would result in identical spectra for replicate samples. From the literature, it is
unclear which normalisation method produces the most consistent results. Therefore,
three different normalisation methods, namely standardisation, root mean square
(RMS) normalisation and total intensity normalisation, were performed as an
optimisation step in this study.
After background subtraction with the APLS algorithm mentioned in Section 3.4 and
prior to normalisation, smoothing was performed with a Savitzky-Golay filter
(span=7, polynomial degree =2, curve fitting toolbox in MATLAB)Section. For
optimisation of the Raman intensity normalisation, four Raman spectra were taken
from different E. coli planktonic cells. The original Raman spectra and background-
subtracted, smoothed spectra are shown in Fig 3.11A. The standard deviations of
these four Raman spectra (before and after background subtraction) and the
corresponding mean spectra are shown in Fig 3.12B. Standard deviation (SD)
normally expresses a dispersion of individual observations about the mean. In other
words, SD characterizes a typical distance of an observation from distribution center
or middle value. If observations are more disperse, then there will be more
variability. Thus, a low SD signifies less variability while high SD indicates more
spread out of data. The results revealed a high SD among pre-processed spectra
indicating that the data points were spread out over a large range of values compared
to those of background-subtracted smoothed spectra Fig 3.11B.
In order to quantify and compare the SDs of the spectra, the average SD was
calculated by the following equation:
𝑚𝑒𝑎𝑛 = √∑ (𝑖)𝑛𝑖=1
2
𝑛−1 (1)
where, 𝑚𝑒𝑎𝑛 is the mean SD, 𝑖 is the SD of each data point i and ∑ (𝑖)𝑛
𝑖=12
𝑛−1 is the
average variance of all data points.
The average SD value of the original Raman spectra was ~25225 while that of
background-subtracted, smoothed spectra was ~1773. The pre- processed spectra (i.e.
after background removal and then smoothing) still showed intensity differences
FSET PhD Thesis/82
among the spectra which were likely due to variation in laser power, differences in
focusing depth and sample volume. Therefore, different normalisation methods
mentioned above were applied and optimised to reduce systematic differences among
measurements.
(A) (B)
Figure 3.11 Raman spectra of before and after signal processing. (i) Original Raman
spectra and (ii) smoothed Raman spectra after background subtraction. The spectra
were taken from four E. coli single cells. Panel A shows individual Raman spectra
and panel B shows the mean spectra. Standard deviations are highlighted in grey
colour.
For optimisation of spectral normalisation step, firstly, a standardisation method
known as “Z-score scaling” was tested on the smoothed and background-subtracted
Raman spectra. This method was performed by subtracting the mean intensity of
each data point from the original data. Then the result was divided by the standard
deviation of the data set (described in equation 2). This standardisation transformed
all variables in the data set to have equal means and standard deviation value to be 1.
This method is a standard way of normalising data but produced poor results in this
case as it reduced the effects of the individual peaks (Fig 3.12(i)).
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𝑋𝑖,1𝜎 = 𝑋𝑖−��𝑠𝜎𝑋,𝑆
(2)
where, 𝑋𝑖,1𝜎 is the normalised intensity of the data point i standardised to 1 (also
known as Z-score scaling), 𝑋𝑖 is the intensity of each data point i, ��𝑠 is the average
intensity of all sample data points and 𝜎𝑋,𝑆 is the standard deviation of all data points.
Another approach is RMS (the root mean square) normalisation, where the original
data is divided by the root mean square of the data set (described in equation 3). This
is a common way of normalising data, especially for data with both negative and
positive values. The average SD values of the spectra using the standardisation
method and RMS were ~1.15 and ~0.24 respectively. The RMS normalization
produced a better result for the normalised data compared to the standardisation
method since it sustained the actual features of the individual peaks and provided the
lower average SD value (Fig 3.12(ii)).
𝑋𝑖,RMS=𝑋𝑖
√∑ (𝑋𝑖)𝑛𝑖=1
2
𝑛−1
(3)
where, 𝑋𝑖,1𝜎 is the normalised intensity of the data point i which was normalised to
the RMS of the intensity of the data set, 𝑋𝑖 is the intensity of each data point i,
∑ (𝑋𝑖)𝑛𝑖=1
2
𝑛−1 is the mean square of the intensity all data points.
Finally, total intensity normalisation was performed by dividing the data with the
sum of the intensities in the data set (described in equation 4). As shown in Figs
3.12(iii), the total intensity normalisation method produced comparable outcomes to
those of RMS. Both RMS and the total intensity normalisation methods provided low
average standard deviations (~0.24 and ~0.00025 respectively). These results
indicate that the data points tend to be very close to the mean and there were less
sample to sample variations after normalisation.
𝑋𝑖,TI=𝑋𝑖
∑ 𝑋𝑖𝑛𝑖=1
(4)
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where, 𝑋𝑖,TI is the normalised intensity of the data point i which was normalised to
the total the intensity of the data set, 𝑋𝑖 is the intensity of each data point i, ∑ 𝑋𝑖𝑛𝑖=1 is
the total intensity of all data points.
(A) (B)
Figure 3.12 Application of different normalisation methods (i) standardisation, (ii)
root mean square (RMS) normalisation and (iii) total intensity normalisation. Panel
A shows individual Raman spectra and panel B shows the mean spectra, with
standard deviations indicated by the grey colour.
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3.5.2 Mean centring the data
Mean centring was performed after normalisation to adjust and reposition the
centroid of the data to the origin. Mean-centred data is calculated by subtracting the
mean of the data from the original data. In brief, a set of normalised spectral
intensities was mean-centred on a wavenumber by wavenumber basis. The mean
intensity over all of the samples at that wavelength was calculated and then this mean
was subtracted from the intensity value at this wavelength measured in each
spectrum of the sample. Thus, the process of mean centring is to calculate the
average spectrum of the data set and subtract that average from each spectrum.
Mathematically, it can be described by the following equation:
𝑋𝑖,c = 𝑋𝑖 − �� (5)
where, 𝑋𝑖,c is the mean-centred intensity of the data point i, 𝑋𝑖 is the intensity of
each data point i, �� is the average intensity of each data point i of all the samples.
After mean-centring, the mean intensity value over all samples of the new data
matrix will become zero and the variances are spread around zero. As can be
observed in Fig 3.13, mean-centring process could not adjust the standardised data to
a centroid whereas this process provided a centroid of both RMS and total intensity
normalised data (Fig 3.13). Therefore, total intensity normalisation and mean-
centring processes were performed throughout this study to provide consistency in
Raman signal pre-processing for statistical data analysis.
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(A) (B)
Figure 3.13 Application of different normalisation methods together with mean-
centring. The spectra were normalised by different normalisation methods (i)
standardisation, (ii) root mean square (RMS) normalisation and (iii) total intensity
normalisation and then the normalised spectra were mean centred. Panel A shows
individual Raman spectra after normalization and mean centring, while panel B
shows the mean spectra.
(i)
(ii)
(iii)
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3.6 Sample preparation and storage for Raman spectroscopy
As mentioned in Chapter 1, the bacterial structural components consist of
macromolecules such as DNA, RNA, proteins, polysaccharides and phospholipids.
The primary structures of these macromolecules are amino acids, carbohydrates,
fatty acids and nucleotides and differences in the relative abundance of these
components can reveal functional aspects of the cells (39). The biomolecules that
make up the cells are responsible for complex biochemical information present in the
Raman spectra taken from bacterial cells (255). During sample preparation, unless
care is taken, these macromolecules, structures or metabolites could be removed
from the cell or altered in other ways. This complicated biochemical information and
the dependence on sample processing presents some challenges when analysing the
Raman spectra from bacterial cells. Furthermore, the study of cellular responses to
antimicrobial agents and the analysis of cell growth behaviour typically require the
bacteria to be cultured over an extended period of days to weeks. Therefore, a
defined and effective sample preparation and sample storage method is essential if
spectral acquisition is to be undertaken throughout the sample test period.
There remains a lack of information regarding appropriate sample preparation and
storage protocols in previous Raman studies, even for a commonly studied bacterium
such as Escherichia coli (E. coli). In this study, the effects of different sample
preparation procedures on E. coli Raman spectra were investigated. Typically, cells
are washed to remove excess media prior to analysis. Two protocols for storage of
the cells, based on freezing at -80 C in glycerol and refrigeration at 4 C, were
investigated and compared to data from freshly prepared cell samples. In a
subsequent study, cells were grown to different stages in the growth cycle and then
frozen at -80 C. After thawing, the cells were analysed by Raman microscopy and
the result of frozen storage was compared to fresh samples at different stages
throughout the metabolic cycle. The detailed analysis of the possible effects of
different sample preparation procedures on bacterial Raman spectra was published in
the International Journal of Integrative Biology (256).
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3.6.1 Materials and methods
3.6.1.1 Bacterial strain, growth conditions and sample preparation
The reference strain Escherichia coli (E. coli ATCC 25922) was used to study the
effect of different sample preparation procedures on the Raman spectra of bacterial
cells. Bacteria from -80 C glycerol stock were grown following the growth
conditions mentioned in Section 2.2.1. The overnight culture of E. coli planktonic
cells was prepared according to the planktonic sample preparation procedure
mentioned in Section 2.2.3.1.
In order to investigate possible changes in the Raman spectra resulting from
metabolic activity or cell damage because of the washing process, the overnight
cultures of E. coli planktonic cells were first kept at 4 C before they were washed
and prepared for Raman measurement. For the study of storage effects at frozen
temperature, the overnight cultures were kept at -80 C in glycerol solution before
the E. coli cells were recovered for Raman analysis. The Raman spectra taken from
these samples were compared with those from fresh samples. An outline of the
preparation of the three different samples is shown as a flow chart in Fig 3.14.
Figure 3.14 Flow chart summarising the different sample preparation procedures for
planktonic E. coli cells: (i) fresh sample, (ii) refrigerated sample before cell washing
steps and (iii) frozen sample.
Mya Myintzu Hlaing Chapter 3/ 89
A bacterial growth curve and phase measurement experiment was also performed in
order to understand the effects of storage-dependent changes in molecular
composition on the Raman spectra of the bacterial cells during the growth cycle. The
overnight culture of E. coli was diluted to approximately 1×107 cells/mL with fresh
sterile nutrient broth in batch culture. The bacterial growth phases were monitored by
detecting the total biomass of bacteria culture and cells from a total of nine different
growth phases were collected as mentioned in the Section 2.2.2. The collected cells
were stored at -80 C in glycerol solution for more than 96 h for frozen sample
preparation. E. coli cells recovered from the glycerol stock and the fresh cells were
processed for the Raman growth/phase experiment described in Section 2.2.2.
3.6.1.2 Raman spectroscopy measurements
Raman spectra from each sample were collected with a Renishaw InVia Raman
spectrometer, equipped with a Leica microscope as mentioned in Section 2.2.5.1. As
described in Section 2.2.5.1, the system was first calibrated and monitored using a
silicon reference (520.5 cm-1) before the measurements. The accumulation time for
each acquisition was 80 s and three accumulations were collected for a single
measurement on each sample area. The spectra were then averaged over six different
cells for each preparation method and growth phase.
3.6.1.3 Raman data acquisition and processing
Raman signal pre-processing for statistical data analysis was performed as described
in Section 2.2.5.2. Commercially available software (MATLAB) was used for all
data processing. Spectra were collected in the 500 to 2000 cm-1 range that covers the
fingerprint region of most biological materials (226). For fluorescence background
removal, an enhanced automated algorithm based on a combination of adaptive
weighting factors with penalised least squares estimation described in Section 3.4
was applied to each spectrum (147).
For multivariate analysis, the intensities of the spectra were normalised using the
total intensity normalisation as mentioned in Section 2.2.5.2.3. The background-
subtracted and normalised Raman spectra were then mean centred as mentioned in
Section 2.2.5.2.4. Finally, the mean-centred data were analysed by calculating the
FSET PhD Thesis/90
principal components and creating scores plots for the first principal component and
loadings plots of the first and second principal components that relate the scores to
specific regions in the original Raman data (Section 2.2.6).
For specific peak analysis, total intensity normalised Raman spectra were curve-
fitted using CasaXPS software (229) (version 2.3.15) as mentioned in Section
2.2.6.3. The intensity values of fitted Raman peaks identified from multivariate
analysis were then averaged by adding the maximum intensity and the intensity
values of the two neighbouring channels for each fitted component. Statistical
comparison of the relative changes in mean intensity (log2 fold change) as mentioned
in Section 2.2.6.3 was performed for the selected peaks to compare the sample
preparation methods.
3.6.2 Results and discussion
3.6.2.1 Raman analysis of planktonic E. coli cells from fresh and stored
samples
Typical background-corrected Raman spectra obtained from fresh and stored samples
and their peak assignments are shown in Fig. 3.15 and Table 3.1. These spectra show
the characteristic Raman bands found in the literature and associated with the
abundant cellular components such as carbohydrates, lipids, proteins and nucleic
acids (94, 215, 233, 234). Changes in Raman peak intensities can be seen between
the cells from fresh samples and stored samples, especially in the 600 to 800 cm-1
region (see shaded regions in Fig 3.15), which relates to DNA/RNA synthesis.
Furthermore, spectral fluctuations attributed to macromolecules containing amide
groups in the protein backbone (1200-1680 cm-1), amino acid containing phenyl
groups (617-640 cm-1) and the ester group of lipids (1734-1738 cm-1) are also
noticeable when comparing both the refrigerated and frozen samples (ii and iii) to the
fresh samples (i).
Mya Myintzu Hlaing Chapter 3/ 91
Figure 3.15 Background subtracted and normalised average Raman spectra from
planktonic E. coli cells taken from (i) fresh sample; (ii) refrigerated sample before
cell washing steps and (iii) frozen sample. The dominant peaks are shown with the
wave number (cm-1). Shaded regions indicate the main spectral changes in different
samples.
3.6.2.2 Principal component analysis for Raman spectra of planktonic E.
coli cells from fresh and stored samples
Principal component analysis of the spectra taken from planktonic cells of fresh and
stored samples is shown in Fig. 3.16. This data shows the variation between the
spectral data sets, which are related to changes in the primary structure and/or
composition of bacterial macromolecules due to the storage process. The average
value plots for the first principal component showed that there is some overlap
between the refrigerated and fresh samples, whereas the frozen glycerol stock
samples vary significantly from the other sample groups (Fig 3.16A). The results
illustrate that keeping the cells at 4 C before the washing step maintains bacterial
EPS and may well induce the encapsulation of microorganisms by encouraging the
production of more EPS, (120, 257) which in turn protects the cells from damage.
Inversely, washing the cells prior to refrigeration can remove the majority of EPS
61
7-7
40
66
8
72
67
46 78
1-7
85
85
28
11
10
01
11
25
11
55 1
23
0-1
295
13
37
14
47
-14
58
14
85
15
75
16
80
-16
20
17
34
-17
38
(i)
(ii)
(iii)
FSET PhD Thesis/92
and significantly affected the resulting Raman spectra (data shown in Appendix C).
The bacterial EPS is believed to protect the cells from environmental stresses such as
temperature, desiccation and pH variation (258-260). On the other hand, the result
seen in the frozen samples creates some potential concerns about the application of
glycerol stock samples for Raman analysis. Ice crystals can damage cells by
dehydration, leading to denaturation of proteins in long-term storage. Although
glycerol is believed to reduce the harmful effects of ice crystals on bacteria, the
loading plots for the first principal component of these data demonstrate significant
changes in specific molecular species between the cells (Fig 3.16B). The dominant
spectral changes can be seen in the range of 1620-1680 cm-1 and 1480-1580 cm-1,
which are attributed to the amide I and amide II bands which are common in the
protein backbone (234, 261). Other significant spectral changes around 1335-1373
cm-1 and 1440-1460 cm-1 suggest changes corresponding to CH2 deformation and C-
H bending modes of structural proteins (94, 261). This may indicate the detrimental
effect of frozen storage on the bacterial cell wall, since the cell wall consists of many
polymers and macromolecules which possess amide, carboxyl, hydroxyl and
phosphate functional groups. The positive and negative values of the loading plots at
1001 cm-1 indicates a frequency shift in the amino acid containing phenyl groups
seen amongst the cells. This frequency shift suggests that there was a modification in
the structural environment of the phenylalanine containing components when the
cells were stored at 4 C and -80 C.
Mya Myintzu Hlaing Chapter 3/ 93
(A) (B)
Figure 3.16 Principal component analysis of Raman spectra for planktonic E. coli
cells taken from (i) fresh sample; (ii) refrigerated sample before cell washing steps;
(iii) frozen sample. (A) Average value plots and (B) loading value plots for the first
principal component (*** p value < 0.005).
Further detailed analysis of the intensity values for specific fitted peaks related to
DNA/RNA and protein synthesis, which were selected from the loading plots (Fig
3.16B), are shown in Fig 3.17. The larger intensity changes of the frozen samples
versus the fresh samples in DNA/RNA-specific peaks, in comparison to the changes
of the refrigerated sample, indicates the cellular response of bacteria to
environmental stress at frozen temperatures (39). The overlap in intensity values (i.e.,
log2 fold change ~ 0) of the DNA/RNA and protein peaks between the fresh sample
and the refrigerated sample before the washing step suggests that there was no
change in the metabolism of bacterial cells when they were kept at 4C in culture
media without washing and the cells remained viable for this time frame (262). The
change in the dominant protein/lipid structure-specific peaks can also be seen to be
most severe in the frozen sample. The dominant spectral variations corresponding to
the C-H vibrational mode of protein (1337 cm-1) and the C-H3 symmetric
deformation of lipids (1379 cm-1) in the frozen sample indicates that some
lipid/protein denaturation may be induced by this sample preparation method (263).
Interestingly, a significant spectral change was seen in the amide II band (1550 cm-1)
for the refrigerated sample, whereas there was no significant variation in the amide I
(i) (ii) (iii)
***
16
20-1
64
0
1447
1663
14851335-1
373
1520
-1550
1001
66
8-8
11
1738
FSET PhD Thesis/94
band (1663 cm-1) between the three different samples. The dominant peaks
corresponding to the amide I and II vibrations are sensitive to subtle changes in the
secondary structure of proteins. Thus, this type of data for DNA/RNA and protein
markers could be used to study biomolecular changes associated with storage and
different methods of sample preparation. Therefore this principal component analysis
allows a well-defined and effective sample preparation methodology to be
established to facilitate subsequent bacterial analysis and identification by Raman
spectroscopy.
Figure 3.17 Intensity changes of DNA/RNA and protein/lipid structure-specific
peaks in the E. coli Raman spectra taken from a refrigerated sample before cell
washing steps and a frozen sample, relative to spectra from a fresh sample. Each
group consisted of five replicates, where each replicate represents the average of six
individual bacterial spectra. The Raman frequencies and their peak assignments are
shown in Table 3.1.
-1.5
-1
-0.5
0
0.5
1
Re
lati
ve
In
ten
sit
y c
ha
ng
e (
log
2fo
ld)
Specific peak (Curve)
Refrigerated sample before washing Frozen sample
DNA/RNA Protein/Lipid
Mya Myintzu Hlaing Chapter 3/ 95
3.6.2.3 Raman spectroscopic analysis of planktonic E. coli cells from fresh
and frozen samples at different phases of the growth cycle
Six different stages in the growth cycle of fresh planktonic E. coli were sampled and
the frozen samples were prepared from the cells after they were frozen at –80 C and
thawed prior to analysis. The Raman spectra of these cells were recorded and scores
plots of the first and second principal components were generated (Fig 3.18). For the
fresh sample, the first principal component is sufficient to separate the cells from the
exponential and early stationary phases from the other phases (Fig 3.18A). However,
the poor group clustering for frozen samples (Fig 3.18B) indicates that there was a
heterogeneous population of cells in each growth stage, especially at the early
exponential and early stationary phases. This may be due to the fact that some cells
were still actively continuing metabolic changes whereas others were approaching
the deterioration stage from cell damage during freezing.
(A) (B)
Figure 3.18 Score plots for the first and second principal components of Raman
spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen samples.
Cells were taken at different phases of the growth cycle: (a) early exponential; (b)
late exponential; (c) early stationary; (d) late stationary and (e) decline.
FSET PhD Thesis/96
The results from the average values plots for the first principal component in Fig
3.19 clearly show that fresh planktonic E. coli cells at different stages of the growth
cycle were significantly differentiated from each other, whereas for the frozen
samples, it was difficult to identify the corresponding stages in the growth cycle. The
variation in the spectra was highest for the early exponential phase of the frozen
samples.
(A) (B)
Figure 3.19 Average value plots for the first principal component of the Raman
spectra of planktonic E. coli cells from (A) fresh samples and (B) frozen samples.
Cells were measured at different phases of the growth cycle; (a) early exponential;
(b) late exponential; (c) early stationary; (d) late stationary and (e) decline.
The loading value plots for the first principal component of the Raman spectra for
planktonic E. coli cells taken from fresh and frozen samples at different stages of the
growth cycle are shown in Fig 3.20. From the loading plots for the fresh samples, the
dominant spectral variation in specific molecular species is seen in the range of 688-
811, 1001, 1447, 1485, 1575, 1620-1680 and 1716-1741 cm-1 which mostly covers
DNA/RNA, protein and lipid synthesis. These bands display the highest absolute
variation during the bacterial growth and account for most of the separation observed
in the score plots. This also suggests that these peaks can be used as Raman markers
to classify the metabolic state of an unknown E. coli bacterial cell. Interestingly,
several of these spectral changes (such as 668-726, 1485, 1575 and 1716-1741 cm-1)
(c) (b) (c) (d) (e) (a) (b) (c) (d) (e)
Mya Myintzu Hlaing Chapter 3/ 97
were not identified to differentiate the corresponding growth stages in the loading
plots for the frozen samples, although some other features remain present.
(A) (B)
Figure 3.20 Loading value plots for the first principal component of Raman spectra
for planktonic E. coli cells taken from (A) fresh samples and (B) frozen samples.
Combining this observation with the results mentioned previously from Figs. 3.16
and 3.17 suggests that the differences between fresh and frozen samples are mainly
due to subtle changes in the structure of proteins during the early exponential phase
of the frozen sample. This can be explained by there being less phenotypic tolerance
to environmental stress in the cells during the exponential phase. This may be due to
bacterial EPS secretion only starting in the exponential phase and maximum EPS
yield only being observed at the beginning of the stationary phase (264). The
presence of a mature EPS matrix at the later growth stages is thought to be
responsible for the cells having enhanced resistance mechanisms (265). As such, the
unexpected spectral fluctuations are thought to be caused by the macromolecules of
the early exponential phase cells being subjected to the harmful effects of ice crystals
at freezing temperatures. Consequently, the direct application of glycerol stock from
long term storage may not be suitable for identifying the growth phase of the bacteria
or for the examination of time-dependent behaviour over the lifetime of the growth
cycle. Changes due to freezing may also interfere with the identification of species
present in unknown samples. A restriction on frozen storage also has implications for
the identification of bacteria that cannot be cultured.
668 7
26
781
811
1001
1335-1
373
1485
1575
1716-1
741
1001
602
-622
781
811
1447
16
20-1
68
0
FSET PhD Thesis/98
3.6.3 Proposed protocol of sample preparation for bacteria identification
Based on these results, a simple protocol for sample preparation for bacterial
identification by Raman spectroscopy has been developed as follows. In brief, an
overnight culture of bacteria is prepared by inoculating a single isolated bacterial
colony from the plate into 20 mL of nutrient broth medium (or selective media for
fastidious bacteria) and then incubated at 37 °C, 200 rpm (unless there is a special
requirement for incubation). After overnight incubation, the cells are collected by
centrifugation and washing processes to remove the traces of the nutrient medium. At
this stage, the overnight bacterial culture can be kept at 4 C in the medium if sample
analysis has to be delayed. Then 1 mL of bacterial cells in a microcentrifuge tube are
collected by centrifugation for 2 min at 12,000 rpm. The supernatant is decanted after
centrifugation and the cell pellet is washed three times with sterilised Milli-Q water
by centrifugation at the same speed for 2 min. The pellet is then resuspended in 30
µL sterilised Milli-Q water by repeated gentle pipetting. For the dried-droplet sample
preparation, a 10 µL volume of washed bacterial cell suspension is dropped onto a
quartz microscope slide, allowed to air-dry for 3-5 min and finally analysed by
Raman spectroscopy. The application of this protocol is very simple, fast and easy to
perform. This protocol is appropriate for reagentless, rapid bacterial identification by
Raman spectroscopy because of its excellent repeatability and reproducibility (Fig
3.18 & 3.19).
3.7 Conclusions
The application of Raman spectroscopy presents challenges in dealing with random
and systematic variations in the spectra. These variations are related to the high
fluorescent background from biological samples, signal intensity variations linked to
changes in optical throughput, cosmic ray spikes and low signal to noise ratio.
Moreover, for applications in the field of microbiology, poor sample preparation
could affect and alter bacterial macromolecules, structures or metabolites in other
ways. Complicated variations in biochemical information might confound the
analysis of Raman spectra from bacterial cells. This chapter explored a wide range of
techniques for optimisation of both the signal pre-processing before multivariate
statistical analysis and the bacterial sample preparation process.
Mya Myintzu Hlaing Chapter 3/ 99
Enhanced methods for fluorescence background removal were investigated using
probability based adaptive-weights. A single, relatively simple parameter is used to
determine the stiffness of the curve used in penalised least squares and polynomial
estimations of the background. The data used to fit the background is adaptively
weighted based on the probability that a point is part of the background given the
Poisson statistics of the signal. Compared to related methods that use a simple binary
classification of peaks and background, this continuously variable weighting helps to
improve the background estimation. The results from experimental spectra provided
a significant improvement on the proposed methods (APoly and APLS) for the
application of the fluorescence background correction algorithms
For pre-processing of the Raman signal before statistical data analysis, the results
proved that total intensity normalisation and mean-centring processes removed
unwanted degrees of freedom from the data, thus allowing further statistical analysis
to focus on the differences between the data points and providing the best outcomes
from principal component analysis.
From the optimisation of bacterial sample preparation and storage, it can be
concluded that washing the bacterial cells collected from culture media with Milli-Q
water eliminated the culture media and provided the typical Raman spectra of E. coli.
The results from comparative PCA for spectra taken from fresh and stored samples
demonstrate that storage of the cells at -80 C can cause spectral alterations
associated with changes in biochemical composition. Storage of bacterial cells at 4
C before the washing step resulted in more typical Raman spectra with no
significant differences from the fresh sample according to Student’s t-test. This
indicates that the bacterial cells remained viable and maintained EPS in the culture
medium, thus providing phenotypic tolerance of the cells to temperature stress.
Monitoring spectral changes during the lifetime of the bacterial growth cycle
indicated that large variations in the biomolecular components of the bacterial cells
can be observed in frozen cells from the early exponential phase. It is plausible to
suggest that these changes may arise from fluctuations in metabolic activity of the
bacterial cells and modification of macromolecules on the cell surface at freezing
temperatures.
FSET PhD Thesis/100
This study of detailed spectral changes owing to different storage methods provides
useful methodological background for Raman applications in microbiology. By
understanding the most effective sample storage as well as preparation methodology,
the techniques presented here can also be beneficial for studying the metabolic status
of bacteria and their growth-rate dependent cellular responses, thereby investigating
whether these responses can affect bacterial identification by Raman spectroscopy.
On the other hand, for bacterial identification, these results suggest that
environmental samples should be analysed promptly, as long term storage may affect
the accuracy of the Raman analysis.
Mya Myintzu Hlaing Chapter 4/ 101
RAMAN ANALYSIS OF PLANKTONIC BACTERIAL CELLS
4.1 Introduction
This chapter investigates the Raman analysis of bacterial cells and construction of
models for differential bacterial identification. The Raman analysis started with a
study of four bacterial species after a specific growth time point. For this initial
study, E. coli, Pseudomonas aeruginosa (P. aeruginosa) and Vibrio vulnificus (V.
vulnificus) were chosen as widespread Gram-negative bacilli of clinical and
environmental importance in their biofilm-forming properties. Staphylococcus
aureus (S. aureus), which is biofilm-forming Gram-positive bacterium, was also
chosen as a potentially life threatening source of nosocomial infection and
community-acquired infection.
E. coli and P. aeruginosa are the two most common bacteria associated with biofilm
in hospital-acquired infections and are mainly observed in patients with indwelling
bladder catheters and surgical implants. One of the reasons for including V.
vulnificus in this study was that Vibrio species are gram-negative bacteria highly
abundant in aquatic environments, including estuaries, marine coastal waters and
freshwater environments. Among Vibrio species, V. cholerae, V. parahaemolyticus
and V. vulnificus are serious human pathogenic microorganisms (214). Vibrio
vulnificus is a severe food-borne opportunistic pathogen which can often cause fatal
infections in susceptible persons (266). This bacterium occurs naturally in warm salt
water environments and can cause disease mostly associated with consumption of
raw oysters (267). Another source of V. vulnificus infection is through open wounds
or skin abrasion exposed to warm seawater harboring these bacteria. It has been
reported that people with underlying liver disease, hemochromatosis (iron overload),
diabetes and immune-compromised patients are associated with an increased risk of
V. vulnificus infection (268).
The diagnosis of V. vulnificus infection and isolation of bacteria from seafood require
conventional bacterial identification methods such as plating on selective-differential
FSET PhD Thesis/102
media, followed by confirmation tests using biochemical or molecular techniques
(269). Despite the availability of refined and high-tech instrumental detection
methods, the definitive identification of V. vulnificus involves more than 2 days of
sample processing. Therefore, the development of rapid diagnostic measures that can
identify V. vulnificus within hours is important for effective control and prevention of
food-borne illness. In this study, Raman spectroscopy was applied for rapid
identification of V. vulnificus as well as to distinguish the different phases of the
metabolic cycle (i.e. exponential, stationary and decline phases) from planktonic
bacterial cells. Moreover, it has recently been reported that V. vulnificus have
antibiofilm properties that inhibit biofilm formation by other bacteria as well as
disrupt established biofilm (58, 203), although they are believed to be a serious
human pathogenic microorganisms (214). Therefore, the Raman spectra of V.
vulnificus planktonic cells discussed in this chapter were used as a reference for
Raman spectroscopic analysis of biofilm cells which will be mentioned in the next
chapter.
S. aureus is a major pathogen of increasing importance due to the rise in antibiotic
resistance and biofilm-associated infection. S. aureus has an ability to attach to
indwelling medical devices through direct interaction with polymer surfaces of the
device or by establishing connections to human matrix proteins that have covered the
device (270). For the purpose of strategies to interfere with biofilm formation in the
environment and diagnose human disease by S. aureus, this study investigated the
Raman spectra profiles from planktonic cells throughout growth cycle for rapid
differential identification.
In view of the fact that individual cellular differences in macromolecular
composition contribute metabolic heterogeneity within a bacterial population,
bacterial identification and classification from different time points of the growth
cycle for these four bacterial species was examined in this chapter. A prediction
model based on chemotaxonomic analysis of these Raman spectral profiles was
constructed with the purpose of investigating applications for rapid microbial sensing
in environmental and clinical studies.
Mya Myintzu Hlaing Chapter 4/ 103
4.2 Materials and methods
Each bacterial species (see Table 2.1) was prepared according to the protocols for
planktonic sample preparation discussed in Section 2.2.3.1. To characterise
planktonic bacterial cells at species level, an overnight culture (~ 18 h) was prepared
and the Raman spectra from four individual cells from each species was collected
using the methods described in the Section 2.2.5.
To determine whether the Raman spectra obtained were dependent on the metabolic
growth phase, the four bacterial species were grown and samples were analysed at
different time points through OD measurement (Section 2.2.2). The metabolic
growth phases of the collected cells from different time points were further measured
and confirmed by counting viable units (cells) grown as colony forming units (CFUs)
as described in Section 2.2.2. A total of nine cultures from different growth phases
(i.e. early, middle and late of the exponential phase, stationary phase and decline
phase, respectively) were independently prepared from each of the four species.
Raman spectra were collected randomly from four individual cells of each culture.
Raman signal pre-processing and statistical multivariate data analyses were
performed as mentioned in Section 2.2.6.
4.3 Results and discussion
4.3.1 Raman classification of planktonic cells at species level
The averaged and intensity-normalised Raman spectra for the four bacterial species
(i.e. E. coli, V. vulnificus, P. aeruginosa and S. aureus) are shown in Fig 4.1.
Characteristic peaks determined from the literature (Table 3.1) for abundant cellular
components, such as carbohydrate, lipid, protein and nucleic, were clearly visible in
the spectra of all species. Typical Raman spectra obtained from single cells of E. coli
and S. aureus and their dominant peak assignments were examined based on the
previous published spectra (26, 215). Raman spectra and dominant peaks obtained
from single cells of P. aeruginosa were observed and checked with the previous
published spectra of pseudomonas species (i.e. Pseudomonas putida and
Pseudomonas fluorescens) (26, 30). The tentative Raman peak assignments of V.
vulnificus planktonic bacteria cells were determined based on Raman bands found in
FSET PhD Thesis/104
the literature which are associated with the more abundant cellular components (120,
257-259). To the best of our knowledge, this is the first reported Raman spectrum for
V. vulnificus.
Although the Raman spectral profiles for the different species appeared generally
similar, certain differences in peak intensity could be observed visually (highlighted
in grey box). In particular, the spectral differences could be seen in the regions of
600 to 800 cm-1 and 1055-1135 cm-1 which relates to DNA/RNA synthesis and
carbohydrate peaks respectively. Furthermore, spectral differences attributed to
macromolecules containing amide groups in the protein backbone (1620-1680 cm-1),
amino acid containing phenyl groups (617-640 and 1001 cm-1), tryptophan groups
(1360 cm-1) and the ester group of lipids (1734-38 cm-1) were also noticeable when
comparing the samples between four different species. To analyse these subtle
changes of the Raman spectra obtained from the bacterial cells, multivariate analysis,
in particular, principal component analysis (PCA) was further performed.
Figure 4.1 Averaged, intensity-normalised and background subtracted Raman
spectra from planktonic cells of the four bacterial species. Abbreviations: A, adenine;
G, guanine; def, deformation; Phe, phenylalanine; Trp, tryptophan; Tyr, tyrosine. The
dominant peaks for DNA/RNA and proteins are shown with the peak assignments
mentioned in Table 3.1. Shaded regions indicate the main spectral changes in
different samples.
CH
2 d
ef
A,
G
Ph
e, Tyr
Tyr A
mid
e I
II
Am
ide I
I
Am
ide I
C-H
def
Carb
oh
yd
rate
Ph
e
E. coli
V. vulnificus
S. aureus
P. aeruginosa
DN
A/R
NA
Trp
Mya Myintzu Hlaing Chapter 4/ 105
To determine whether Raman spectroscopy could reproducibly discriminate between
the bacterial species, all 16 collected Raman spectra (4 species × 4 individual cells)
were analysed using principal component analysis (PCA). As seen in Figure 4.2, the
first two principal components (PC1 and PC2) accounted for 69% of the variation in
the data set and were sufficient to differentiate the four different bacterial species.
The PCA scores plot showed distinct clustering of the different species. PC1
differentiated V. vulnificus and E. coli from P. aeruginosa and S. aureus while PC2
differentiated P. aeruginosa and V. vulnificus from S. aureus and E. coli.
Figure 4.2 Scatter plot from principal component analysis (PCA) of four different
bacterial species. The first and second principal components are plotted as a scatter
plot. (Circular regions overlaid on the data points shown in the scatter plot are
included as a visual guide.)
To examine the significance in the separation between species within the data set,
mean score values of the first and second principal components, standard deviations
and p-values of each sample group compared to others were calculated and are
shown in Figs. 4.3A and C. The average value plots of PC1 and PC2 show a
significant separation between the data from each bacterial species with p value <
0.005. The PC1 loadings plot (Fig 4.3B) demonstrates the peaks at 1002, 1447, 1663
cm-1 (associated with proteins/lipids), 781 and 1485 cm-1 (associated with
DNA/RNA) which contributed most to the separation observed in the scatter plot.
P. aeruginosa V. vulnificus
E. coli
S. aureus
FSET PhD Thesis/106
The peaks seen in the positive loading values (i.e. 1002, 1447 and 1663 cm-1) could
be considered as the finger print regions where E. coli and V. vulnificus can be
separated from P. aeruginosa and S. aureus. Similarly, the peaks seen in the negative
loading values (i.e. 781 and 1485 cm-1) are related to the separation of P. aeruginosa
and S. aureus.
(A) (B)
(C) (D)
Figure 4.3 Principal component analysis of four different bacterial species: (A-B)
average value and loading values plots of the first principal component and (C-D)
average value and loadings plots of the second principal component (***p value <
0.005). (The peak assignments are shown in Table 3.1).
Fir
st
pri
nc
ipa
l c
om
po
ne
nt
(a.u
.) (
51
.9%
)
E. coli V. vulnificus P. aeruginosa S. aureus
0
-4
-8
4
8
Different bacterial species
******
***
******
***
1447
1002
781
1680
-1620
1093
1485
X10-3
15
821
48
513
60
12
99
-13
13
78
1
85
2
14
47
16
20
-80
X10-3
Seco
nd
pri
ncip
al co
mp
on
en
t (a
.u.)
(19.2
%)
0
-4
-6
2
4
Different bacterial species
6
******
E. coli V. vulnificus P. aeruginosa S. aureus
-2
Mya Myintzu Hlaing Chapter 4/ 107
Likewise, the PC2 loadings plot (Fig 4.3D) demonstrates the protein/lipid associated
peaks (at 852, 1299-1313, 1360, 1447, 1582, 1663 cm-1) and the DNA/RNA
associated peaks (at 781 and 1485 cm-1) which contributed most to the separation
observed in the scatter plot. The peaks seen in the positive loading values (i.e. 1299-
1313, 1360, 1485 and 1582 cm-1) indicate the regions where V. vulnificus and P.
aeruginosa were separated from E. coli and S. aureus. Similarly, the peaks seen in
the negative loading values (i.e. 781, 852, 1447 and 1620-80 cm-1) were related with
the separation of E. coli and S. aureus. The analysis was further performed between
species in order to understand the key finger print region of individual species.
In routine biochemical tests for bacterial identification, one key difference between
the Vibrio group and enteric bacteria is the oxidase test, which is a test used to
determine whether a bacterium produces a particular cytochrome c oxidase enzyme.
The V. vulnificus are oxidase-positive while E. coli (enteric bacteria) are oxidase-
negative. Bacterial cells are able to generate energy (ATP) from nutrients through
respiration or through fermentation. Different bacteria can ferment a wide variety of
carbon sources (usually sugars) and other compounds. Another important test in the
bacterial identification process is therefore to determine the fermentation pattern for
a series of different energy/carbon sources by an unknown bacterial species.
Although Pseudomonas species are oxidase-positive, their fermentation pattern is
different from lactose fermenter Vibrio and other enteric bacteria as Pseudomonas
species are non-lactose fermenter (39). The clustering results of the PCA (Fig 4.4),
suggest that the specificity of the Raman spectra enabled the differentiation of
oxidase-positive bacteria from oxidase-negative bacteria, lactose fermenter from
non-lactose fermenter and Gram-positive bacteria from Gram-negative bacteria.
FSET PhD Thesis/108
Figure 4.4 Scatter plot of the first and second principal components (PC1 and PC2):
(a-c) Scatter plots comparing the Raman spectra of E. coli with (a) V. vulnificus (b)
P. aeruginosa and (c) S. aureus, then V. vulnificus cells compared with (d) P.
aeruginosa and (e) S. aureus and (f) P. aeruginosa cells versus S. aureus.
The corresponding loadings plots for PC1 reflect the spectral changes between
species (Fig 4.5). The loadings have a spectral dimension, where positive and
negative peaks can be observed. Comparing between E. coli cells and the other three
species (Fig 4.5 a-c), positive peaks in the loadings indicate the corresponding peaks
in the E. coli spectra that contribute to the separation of E. coli cells from the rest. In
contrast, negative loading peaks refer to a contribution of the respective signals in the
Raman spectra of the other species. In particular, an increase in the peaks at 1680-
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Mya Myintzu Hlaing Chapter 4/ 109
1620 cm-1, 1447 cm-1, 1001 cm-1, 852 cm-1 (associated with proteins/lipids), 1093
cm-1 (associated with DNA/RNA) could be seen in E. coli cells. A decrease in the
peaks at 1360 cm-1 (associated with amino acid, tryptophan) and 785 cm-1 (associated
with DNA/RNA) were seen in E. coli cells compared with other species. In
comparing Gram-positive and Gram-negative bacteria, it can be seen that the
DNA/RNA-related peak at 785 cm-1 contributed most in separating S. aureus from
others (Fig 4.5 c, e and f). Further detailed univariate analysis of the intensity values
for specific peaks related to DNA/RNA and protein synthesis, which were selected
from the loading plots (Fig 4.5), are shown in Fig 4.6.
Figure 4.5 Loading plots from the principal component analysis (PCA). (a-c):
Loadings exhibit the spectral differences of E. coli cells compared to (a) V.
vulnificus cells, (b) P. aeruginosa cells and (c) S. aureus cells; (d-e): loadings depict
peak fluctuations of V. vulnificus cells versus spectra of: (d) P. aeruginosa (e) S.
aureus; and (f) P. aeruginosa cells versus spectra of S. aureus.
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As shown in Fig 4.6, E. coli has higher intensity values for the protein/lipid-specific
peaks (i.e. c, 1002 cm-1; f, 1447 cm-1; g, 1663 cm-1) in comparison to the other cells.
The increased intensities of these protein related peaks in E. coli cells indicates that
these cells undergo a change in their intracellular protein concentration at that
particular culture time point (18 h) compared to the other species. This high protein
expression may be related to the secretion level of EPS of E. coli suggesting that EPS
production might be higher than other three bacteria at that time frame. Some
bacterial species have maximum EPS production in the exponential phase (44, 45),
while for others, EPS production is maximized in the stationary phase (46-48).
Figure 4.6 Intensity changes of DNA/RNA and protein/lipid structure-specific peaks
in the Raman spectra of E. coli, V. vulnificus, P. aeruginosa and S. aureus. Raman
peaks (a, 781 cm-1; b, 852 cm-1; c, 1002 cm-1; d,1093 cm-1; e, 1360cm-1; f, 1447 cm-1;
g, 1663 cm-1; h, 1738 cm-1) were selected from the loading plots. Each point was
calculated from four replicates. The Raman frequencies and their peak assignments
are shown in Table 3.1.
800 1000 1200 1400 1600 1800
0.0
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The overlap in the intensity values of the peaks at 852 cm-1 (associated with
tyrosine) and 1360 cm-1 (associated with tryptophan) between the E. coli and S.
aureus cells suggests that a similar expression pattern of amino acid containing
tryptophan and tyrosine groups could be seen in these two bacterial species, both of
which have oxidase-negative properties. In contrast, consistent spectral overlap at
852 cm-1 (associated with tyrosine) was seen in the two oxidase-positive bacteria (i.e.
V. vulnificus and P. aeruginosa). Moreover, the higher intensity value of the peak at
1360 cm-1 (associated with tryptophan) in these two bacterial species further
indicated that amino acid containing tryptophan group expression was more
predominant in the oxidase-positive bacteria compared to the oxidase-negative
bacteria. Interesting, the highest peak intensity at 785 cm-1, which is associated with
ring breathing modes in the DNA/RNA bases (C, cytosine and U, uracil) and DNA
backbone (O-P-O) vibrations, was seen in the S. aureus bacteria compared to the
other Gram-negative bacteria. This high peak intensity at 785 cm-1 was consistent
with increasing peak intensity at 1095 cm-1, which is associated with the DNA
backbone (O-P-O). The increasing intensity of the peaks related with DNA/RNA in
the spectra from S. aureus cells suggests that during the growth phase their DNA
content is higher than other Gram-negative bacteria. These results suggested two
interesting pathways to explore further within the study:
1) Can Raman spectroscopy be used as a method for exploring the changes in
biochemical composition that occur during the growth phases of individual
species?
2) If bacterial Raman signatures vary significantly over the growth cycle, does
this have a deleterious effect on species level identification in mixed growth
phase samples i.e. biofilms?
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4.3.2 Raman classification of planktonic cells at the metabolic phase level
To investigate the potential of Raman microscopy to detect differences in the
physiological state of various bacterial species, Raman spectroscopy analysis was
performed on the cells of four bacterial species. The cells were collected at
appropriate time points within the growth cycle of each species.
Growth curves and phase measurements for planktonic E. coli, V. vulnificus, P.
aeruginosa and S. aureus bacterial populations are shown in Fig. 4.7. The total
biomass of the four bacterial species was measured by optical density at 600 nm
(OD600) and shows the typical bacterial growth phases, which are the lag phase,
exponential phase (2-10 h), stationary phase (14-30 h) and decline phase (after 32 h)
(Fig 4.7). Viable bacterial count experiments confirmed the correct time point of
sample collection from every growth phase. The plotted values, which represent log10
transformed viable bacterial counts (CFU/mL), show that a typical exponential trend
of the bacterial growth curve reached its peak at the stationary phase and declined
after the late stationary phase (Fig. 4.7).
The viable bacterial counts of all of these bacterial species clearly revealed that the
bacterial population reached its maximum level at the stationary phase. The
stationary phase of V. vulnificus was reached after 10 h incubation time, whereas that
of other bacterial species was reached only after 15 h incubation time. Interestingly,
S. aureus took longer to reach to the stationary phase (i.e. 20 h).
During the stationary phase, the viable count of bacteria was at the maximum level
and remained almost constant. After the late exponential phase, the bacterial
population growth rate was counterbalanced by the death rate due to depletion of
essential nutrients and the accumulation of toxic acidic or alkaline waste products in
the medium. As a consequence of comparable growth and death rate, constant viable
count was seen for a few hours. Because of the ongoing depletion of nutrients and
accumulation of metabolic waste, the later stage of the stationary phase approached a
decline phase. The prominent decline phases were seen among E. coli, V. vulnificus
and P. aeruginosa species after 20 h incubation time while S. aureus growth curve
revealed the decline phase only after 30 h incubation time. Because of the somewhat
Mya Myintzu Hlaing Chapter 4/ 113
variable pattern of the growth curves among these four bacterial species, the cells
were prepared for Raman analysis based on growth phases but not on time points.
The collected planktonic cells from different stages of the growth cycle (i.e. early,
middle and late of exponential, stationary and decline phase) were used for Raman
analysis.
(A) (B)
(C) (D)
Figure 4.7 Representative growth curves and viable cell counts of four bacterial
species: (A) E. coli, (B) V. vulnificus, (C) P. aeruginosa and (D) S. aureus. Growth
(total biomass) monitored spectrophotometrically by measuring the optical density
(OD) at 600 nm are shown in blue and viable counts of collected bacteria at different
time points of growth are shown in red. Plotted values for bacterial OD and viable
count are log10 transformed.
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E. coli
The background-subtracted and total intensity normalised Raman spectra of
planktonic E. coli cells at different phases in the metabolic cycle are shown in Fig
4.8. The typical Raman peaks of E. coli planktonic bacterial cells determined from
the literature could be seen in the spectra of the cells from all metabolic growth
phases. Raman peak intensity changes, especially from the 600 to 800 cm-1 region,
which mostly covers DNA/RNA and protein synthesis, were seen between the
exponential phase and other later phases. Furthermore, some spectral fluctuations in
macromolecules (such as lipids and proteins) were also more visible in the
exponential phase than the later phases. In particular, spectral intensity differences
could be seen in the region of amino acid containing phenyl groups (1001 cm-1).
Spectral fluctuations attributed to macromolecules containing amide groups in the
protein backbone, such as the amide II band at 1550 cm-1 and amide I band at 1620-
1680 cm-1, were also identified. Moreover, intensity changes were clearly visible in
regions which are associated with the C-H and C-H2 vibrational modes of proteins
(1337 cm-1 and 1447 cm-1) when comparing the samples between different growth
phases. To analyse these subtle changes of the Raman spectra obtained from the
bacterial cells at different stages of the growth cycle, multivariate analysis, in
particular, principal component analysis (PCA) was further performed.
Mya Myintzu Hlaing Chapter 4/ 115
Figure 4.8 Background-subtracted and intensity normalised Raman spectra of E. coli
cells at different phases of the growth cycle. Cells were collected at early, middle and
late stages of the exponential, stationary and decline phases. Assignments are based
on studies in the references shown in Table 3.1. Abbreviations: Phe, phenylalanine;
Carb, carbohydrate; def, deformation.
From the PCA, the cells from each individual phase of the growth cycle of E. coli
species were reasonably well separated from each other in the early and late phases
of growth, with some overlap between the transitional phases (i.e. late exponential to
early stationary phases and mid-stationary to late decline phases) (Fig 4.9A).
Significant separation (p value < 0.005) of groups at the early metabolic phases (i.e.
early and mid-exponential) was probably due to rather fast metabolic changes in the
cells, whereas there was poor separation in the relatively stable metabolic phases (Fig
4.9B). These differences may be associated with changes in the secretion of EPS,
which varies throughout the growth phases. Some bacterial species have maximum
EPS production in the exponential phase (44, 45), while for others, EPS production is
maximized in the stationary phase (46-48). Moreover, Eboigbodin et.al reported that
the protein content in bound and free EPS extracted from E. coli cells varied
significantly as the cells grew from the exponential to the stationary growth phase
(271).
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Figure 4.9 Principal component analysis of E. coli cells at different phases of the
growth cycle: (A) scatter plot of the first and second principal components, (B)
average value plot and (C) loading values plot of the first principal component. (***p
< 0.005 and **p < 0.05). (Circular regions overlaid on the data points shown in the
scattered plot are included as a visual guide). Abbreviations: E, exponential; S,
stationary; D, decline; C, cytosine; U, uracil; A, adenine; G, guanine; Phe,
phenylalanine; def, deformation.
Early E
Mid E
Late E
Early S
Mid SLate S
Early D
Late D
Mid D
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Exponential Stationary Decline
Different metabolic phases
Early Mid Late Early Mid Late Early Mid Late
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***** ***
Mya Myintzu Hlaing Chapter 4/ 117
The loading values plot of PC1 indicates the Raman peaks which contributed most of
the separation in the scatter plot (Fig 4.9C). The results show that the peaks related to
DNA/RNA represented the highest absolute variance of the exponential phase from
the later phases, whereas the protein-specific peaks were related to the variance of
the later phases. These variations of DNA/RNA and protein-specific peaks over
growth time indicate biochemical or metabolic heterogeneity due to cellular
differences in macromolecular composition or activity during the growth cycle. The
reduced variability between the Raman spectra after the stationary phase, which was
reflected by the overlap in PC1 loading of the later phases (Fig 4.9B) suggests that
the DNA and protein composition stabilised as the metabolism of the bacterial cells
becomes inactive during the stationary phase. These observations, together with the
results mentioned previously from Fig 4.8 and Fig 4.9A, highlight information about
the Raman spectral changes of E. coli species in DNA/RNA synthesis and protein
synthesis all the way through the growth cycle.
V. vulnificus
The background-subtracted and total intensity normalised Raman spectra of
planktonic V. vulnificus cells at different phases in the metabolic cycle are shown in
Fig 4.10. The tentative Raman peak assignments of V. vulnificus planktonic bacterial
cells determined from the literature were seen in the spectra of the cells at all
metabolic growth phases. Similar to the Raman spectral changes seen for the E. coli
growth phases, Raman peak intensity changes, especially in the region associated
with DNA/RNA and protein synthesis (600 to 800 cm-1) were more obvious between
the exponential phase and other later phases. Moreover, growth phase-dependent
spectral fluctuations in macromolecules (such as lipids and proteins) were also
visible in the exponential phase compared to the later phases. The spectral intensity
differences seen in the region of amino acid containing phenyl groups (1001 cm-1),
amide II band at 1550 cm-1 and amide I band at 1620-1680 cm-1 were consistent with
the intensity changes seen in the Raman spectra of the E. coli growth phases.
Interestingly, a similar pattern of intensity changes compared with those of the E.
coli samples were also clearly noticeable in the regions which are associated with C-
H2 vibrational modes of protein (1447 cm-1).
FSET PhD Thesis/118
Figure 4.10 Background-subtracted and intensity normalised Raman spectra of V.
vulnificus cells at different phases of the growth cycle. Cells were collected at early,
middle and late stages of the exponential, stationary and decline phases. Assignments
are based on studies in the references shown in Table 3.1. Abbreviations: Phe,
phenylalanine; Carb, carbohydrate; def, deformation.
From PCA, the first principal component was sufficient to separate the cells in the
exponential phase and stationary phase from those in the decline phase (Fig 4.11A).
In fact, bacterial cells in the early exponential phase (i.e. 2 h) and decline phases (i.e.
58 h and 74 h) could be identified and visualized more clearly whereas the other
groups of cells were pooled together. The poor group clustering for the mid and late
exponential phase (i.e. 10 h and 18 h), stationary phases and early decline phase (i.e.
48 h) indicates that bacterial cells in these phases might have similar population
behaviour and nature of development. The results from the average values plots for
the first principal component (Fig 4.11B) clearly show that planktonic V. vulnificus
cells at early and mid-exponential phases (i.e. 2 h and 10h), mid and late stationary
phases (i.e. 30 h and 38 h) and early and mid-decline phases (i.e. 48 h and 58 h) of
incubation time were significantly differentiated from each other. However, for the
samples at 30-38 h and those at 48-58 h, it was difficult to identify the corresponding
stages according to the growth curve from OD measurement (shown in Fig 4.7B).
However, it appears that Raman spectroscopy is able to detect subtle changes in
macromolecules of bacterial cells at some points in the metabolic growth cycle.
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Figure 4.11 Principal component analysis of V. vulnificus cells at different phases of
the growth cycle: (A) scatter plot of the first and second principal components, (B)
average value plot and (C) loading value plot of the first principal component. (***p
< 0.005 and **p < 0.05). (Circular regions overlaid on the data points shown in the
scattered plot are included as a visual guide). Abbreviations: E, exponential; S,
stationary; D, decline; C, cytosine; U, uracil; A, adenine; G, guanine; Phe,
phenylalanine; def, deformation.
Mid E
Stationary
Early D
Late D
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Early ELate E
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Different metabolic phases
Early Mid Late Early Mid Late Early Mid Late
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FSET PhD Thesis/120
The significant separation of bacterial cells at 2 h from the rest of the growth stages
was probably due to the cells preparing for DNA replication and an increased amount
of DNA associated with cell division, which occurred in the early stage of the growth
cycle. The clustering of bacterial cells at incubation times from 10 h to 30 h may be
related to more consistent cellular population behaviour from maximum cell
reproduction in the later stages of the exponential phase and stationary phase.
Interestingly, the separation of bacterial cells at 36 h from those at 30 h could be
seen, even though there were no significant changes in the optical density
measurement (OD) for the growth curve (Fig 4.7B). The poor separation and high
spectral variations between the later stages of cell growth (i.e. 36 h, 48 h and 58 h)
indicated that the population of cells gradually became more heterogeneous in the
later stages of the growth cycle, with some cells still actively continuing metabolic
changes, whereas others were approaching the deterioration stage. The improved
group separation after the 48 h time point suggests that there was more deterioration
of cell physiology due to depletion of nutrients.
The loading values plot for the first principal component of the Raman spectra for
planktonic V. vulnificus cells sampled at different stages of the growth cycle are
shown in Fig 4.11C. From the loadings plot, the dominant spectral variance in
specific molecular species was seen at spectral positions of 1741-1716, 1663, 1575,
1480, 1447, 1001, 811 and 781 cm-1, which mostly covers lipid, protein and
DNA/RNA. This demonstrated the Raman peaks with the highest absolute variance
during the bacterial growth and most of the separation observed in the score plots.
Combining this observation with the results mentioned previously from Fig 4.10 and
Fig 4.11A provides information about Raman spectral changes in DNA/RNA
synthesis and in protein synthesis during normal cell growth of V. vulnificus.
P. aeruginosa
The background-subtracted and total intensity normalised Raman spectra of
planktonic P. aeruginosa cells at different phases in the metabolic cycle are shown in
Fig 4.12. The Raman peak assignments of P. aeruginosa planktonic bacteria cells
determined from the literature could be seen in the spectra of the cells from all
Mya Myintzu Hlaing Chapter 4/ 121
metabolic growth phases. Similar to E. coli and V. vulnificus species, the main
spectral fluctuations were seen in the regions which are associated with DNA/RNA
(600-800 cm-1), C-H2 vibrational modes of protein (1447 cm-1), amide II band at
1550 cm-1 and amide I band at 1620-1680 cm-1 throughout the growth phases.
Interestingly, intensity changes in the region of amino acids containing tryptophan
groups (1360 cm-1) were noticed clearly in the samples throughout the growth cycle.
In fact, compared to the previous two species (i.e. E. coli and V. vulnificus), Raman
spectra taken from the different growth phases of P. aeruginosa were much more
variable, based on visual inspection. These results suggest that there might be greater
cellular heterogeneity in the samples of P. aeruginosa.
Figure 4.12 Background-subtracted and intensity normalised Raman spectra of P.
aeruginosa cells at different phases of the growth cycle. Cells were collected at early,
middle and late stages of the exponential, stationary and decline phases. Assignments
are based on studies in the references shown in Table 3.1. Abbreviations: Phe,
phenylalanine; Carb, carbohydrate; def, deformation; Trp, tryptophan.
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Figure 4.13 Principal component analysis of P. aeruginosa cells at different phases
of the growth cycle: (A) scatter plot of the first and second principal components, (B)
average value plot and (C) loading values plot of the first principal component.
Abbreviations: E, exponential; S, stationary; D, decline; C, cytosine; U, uracil; Phe,
phenylalanine; def, deformation; Trp, tryptophan.
Am
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Exponential Stationary Decline
Different metabolic phases
Early Mid Late Early Mid Late Early Mid Late
Mya Myintzu Hlaing Chapter 4/ 123
PCA of the Raman spectra of the growth cycle of P. aeruginosa species is shown in
Fig 4.13. From the scatter plot of the first and second principal components, it can be
seen that there is relatively poor clustering within each growth phase and little
separation between growth phases. From the average PC score plot, the overlap
between the samples is visible and individual growth phases cannot be well separated
from each other. Moreover, the relatively large error bars (compared to E. coli and V.
Vulnificus) seen in the samples from later phases of the growth cycle indicated that
there was higher cellular heterogeneity in these phases compared to other earlier
growth phases (i.e. early and mid-exponential phases).
Interestingly, the peak intensity changes in the region of amino acid containing
tryptophan groups (1360 cm-1) were responsible for the separation of decline phases
from the other phases. Tryptophan is known to be a precursor of several important
signalling molecules for cell-cell communication (quorum sensing) and a tryptophan-
dependent pathway is indeed observed in Pseudomonas species (272). It was
reported that tryptophan catabolites accumulated in culture supernatant act as a
quorum sensing signal and are involved in the virulence gene expression during the
transition from a low- to a high-cell-density state of P. aeruginosa (273). In fact,
bacterial cells are believed to exhibit a variety of physiological and morphological
changes upon entering the stationary phase in order to compete and survive in their
living environment. Therefore, the spectral changes discussed above may justify
future investigations to study the relationship between growth phase dependent
physiological differences and the clustering of bacterial identification.
S. aureus
This Section presents PCA of the Raman spectra collected at different metabolic
growth phases of S. aureus, which is a Gram-positive bacteria and one of the model
organisms for biofilm studies. The background-subtracted and total intensity
normalised Raman spectra of planktonic S. aureus cells at different phases in the
metabolic cycle are shown in Fig 4.14. The Raman peak assignments of S. aureus
planktonic bacteria cells determined from the literature were seen in the spectra of
the cells from all metabolic growth phases. Similar to Raman data collected
FSET PhD Thesis/124
throughout the growth phases of E. coli, V. vulnificus and P. aeruginosa species, the
main spectral fluctuation occurred in the regions which are associated with
DNA/RNA (600-800 cm-1), C-H2 vibrational modes of protein (1447 cm-1) and the
amide II band at 1550 cm-1 throughout the growth phases. Interestingly, the Raman
intensity in the region of amino acid containing phenylalanine groups (1001 cm-1) is
at constant level and doesn’t vary significantly at any stage in the growth cycle.
From the PCA shown in Fig 4.15A, it can clearly be seen that the first principal
component separated the exponential phases and early stationary phase from the later
metabolic period. It is also observed that samples taken in the decline phases showed
more heterogeneity than samples taken at the exponential and stationary phases.
Raman spectra of the samples in early exponential phase tended to cluster together.
The samples from mid and late stationary phases also showed sub-clustering
although these phases are reasonably close to those from the decline phase (Fig
4.15A). However, no clear separation was seen between each of the data groups.
Figure 4.14 Background-subtracted and intensity normalised Raman spectra of S.
aureus cells at different phases of the growth cycle. Cells were collected at early,
middle and late stages of the exponential, stationary and decline phases. Assignments
are based on studies in the references shown in Table 3.1. Abbreviations: Phe,
phenylalanine; Carb, carbohydrate; def, deformation.
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The average values plot of the first principal component shows a general trend for
separation of the earlier growth phases (i.e. exponential phase and early stationary
phase) from the later phases of the growth cycle but it was not significant in terms of
p-value (Fig. 4.15B). The significant separation was seen only between the samples
from early and mid-stationary phase (p value < 0.05). Similar to the growth cycle
data of other species, the results show that peaks related to DNA/RNA represented
the highest absolute variance of the exponential phase from the later phases, whereas
the protein-specific peaks, in particular the amide II band at 1550 cm-1, were related
to the variance of the later phases. The dominant peak regions which are associated
with U, T, C (ring breathing) and DNA/RNA phosphate backbone (O-P-O stretching)
were mainly responsible for the separation of the exponential phases and early
stationary phase from the other phases. As mentioned in Section 4.3.1.1, the S.
aureus spectrum displays a relatively intense peak at 780 cm-1 when compared with
other Gram-negative bacteria used in this study (Fig 4.1). The results from univariate
analysis (Section 4.3.1.2) also show that the highest peak intensity at 780 cm-1 was
seen in the spectra of S. aureus in comparison with other bacterial species (Fig 4.6).
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(A)
(B) (C)
Figure 4.15 Principal component analysis of S. aureus cells at different phases of the
growth cycle: (A) scatter plot of the first and second principal components, (B)
average values plot and (C) loading values plot of the first principal component. (**p
< 0.05). (Circular regions overlaid on the data points in the scatter plot are included
as a visual guide). Abbreviations: E, exponential; S, stationary; D, decline; C,
cytosine; U, uracil; A, adenine; G, guanine; Trp, tryptophan; def, deformation.
Early E
Mid S
A,
G
Am
ide
II
CH
3,
CH
2d
ef
Trp
C,
U
Exponential Stationary Decline
Different metabolic phases
Early Mid Late Early Mid Late Early Mid Late
**
Mya Myintzu Hlaing Chapter 4/ 127
4.3.3 Effect of growth phase on the differentiation of four bacterial species
To determine whether the differences in cellular physiology during growth will affect
the clustering of the four bacterial species (E. coli, V. vulnificus, P. aeruginosa and S.
aureus), PCA was first performed on Raman spectra from the same growth phase
(i.e. at early, middle and late of exponential, stationary and decline phase) among the
species. The results are shown in Fig 4.16.
Figure 4.16 Scatter plots of principal component analysis (PCA) comparing the
Raman spectra of four planktonic bacterial species: E. coli, V. vulnificus, P.
aeruginosa and S. aureus at (a-c) early, middle and late exponential phases, (d–f)
early, middle and late stationary phases and (g–i) early, middle and late decline
phases, respectively.
(d) (e) (f)
(g) (h) (i)
(a) (b) (c)
FSET PhD Thesis/128
The scatter plots show that spectra were clustered closer within groups (i.e. replicate
samples) and generally well separated between groups (i.e. different species) based
on similar growth phases. In particular, the clustering of cells in early exponential
phase, stationary phases, early and late decline phases was robust despite temporal
differences in cellular physiology for each species during the phases of growth, as
discussed in the earlier Sections.
However, when PCA was performed on Raman spectra from all growth phases
together, the poor separation between species as well as imperfect clustering of the
same species was seen (Fig 4.17). Since PCA considers all variables and the total
data structure, this method is counting within-group variance as well as between-
group variance. Therefore, these results revealed that PCA clustering of the four
different bacterial species was affected when taking into account physiological
differences of these species all the way through the growth cycles.
Figure 4.17 PCA of the effect of physiological differences due to growth phase on
the clustering of four different bacterial species: E. coli, V. vulnificus, P. aeruginosa
and S. aureus.
Exponential
Exponential
Exponential
Exponential
Stationary
Stationary
Stationary
Stationary
Decline
Decline
Decline
Decline
E.coli
V.vulnificus
P.aeruginosa
S.aureus
Mya Myintzu Hlaing Chapter 4/ 129
In fact, Raman spectral variations were observed for all four species and have been
attributed to growth-phase variations in bacterial membrane compounds,
polysaccharides, proteins, lipids and nucleic acids (see Sections 4.3.2 and 4.3.3). For
these reasons, attempts were made to identify alternative methods of normalising the
spectra in order to accentuate the differences between the species and achieve more
reliable classification. The peak intensities of the spectra were first normalized with
the total intensity. As a normalisation of some internal spectral features for four
bacterial species, the total intensity normalised peaks were further normalised against
the intensities of the peaks associated with amino acid containing phenylalanine
group (1001 cm-1), nucleic acid phosphate backbone (O-P-O stretching) (1095 and
780 cm-1) and the ratio of DNA/RNA to protein (ratio of 780 and 1001 cm-1). These
peaks were chosen to normalise the spectra in order to test whether they may serve as
an “internal standard”, thereby accentuating more relevant changes in biochemical
composition. The normalisation results shown in Fig 4.18 revealed no improvement
in the clustering amongst and separation between the spectra and the outcome
appears to be inferior to the result using the total intensity normalisation process
(shown in Fig 4.17).
FSET PhD Thesis/130
Figure 4.18 Normalisation against selected spectral features: (A) phenylalanine peak
at 1001 cm-1, (B-C) nucleic acid phosphate backbone peaks at 780 and 1095 cm-1 and
(D) the ratio of DNA/RNA to protein (ratio of 780 and 1001 cm-1).
The ratios of DNA/RNA related peaks (726, 746, 781, 785, 808, 811, 1095, 1485,
1575 cm-1) to phenylalanine peak (1001 cm-1) were calculated to see whether there
were any growth phase dependent variations between the four different species. The
results from comparative analysis are shown in Fig 4.19.
For all four species, in the exponential phase, the ratios of DNA/RNA to protein
were generally higher than those in the stationary phase. However, in S. aureus, the
ratio of the A, G (ring breathing) region (1575 cm-1) to protein in the stationary phase
was higher than the exponential phase. On average amongst the four species, S.
aureus showed the highest value for the ratios of DNA/RNA to protein in both
phases. Given these relatively high values for the ratios and the increasing intensity
of the peaks related to DNA/RNA (from univariate analysis shown in Fig 4.6), the S.
aureus cells appear to express higher DNA content than the other Gram-negative
bacteria in both the exponential and stationary phases. Conversely, P. aeruginosa
(A) (B)
(D)(C)
Mya Myintzu Hlaing Chapter 4/ 131
showed the highest value for the ratio of the A, G (ring breathing) region (1575 cm-1)
to protein amongst the four species in the decline phases. Based on these results, it
can be concluded that growth phase dependent spectral variations among the four
species have an influence on the PCA clustering of four different bacterial species
seen in Fig 4.17.
Figure 4.19 Comparison of the ratio of DNA/RNA to protein in four bacterial
species: (A-I) ratio of DNA/RNA related peaks (726, 746, 781, 785, 808, 811, 1095,
1485, 1575 cm-1) to phenylalanine peak (1001 cm-1), respectively. Comparative
analysis was performed on the average intensity ratios for all cells from early, middle
and late stages of the exponential, stationary and decline phases. Peak assignments
are based on studies in the references shown in Table 3.1.
With the purpose of maximizing the variance between species and minimizing
variance within species, discriminant analysis (DA) was performed. In particular, the
procedure for DA classification was done by four main steps. The first step was data
pre-processing, which attempted to remove fluorescence background, smooth and
normalise the Raman spectra. PCA was then carried out using MATLAB for data
0.0
0.4
0.8
1.2
Early Exponential
Mid Exponential
Late Exponential
Early Stationary
Mid Stationary
Late Stationary
Early Decline
Mid Decline
Late Decline
Ra
tio
of
nu
cle
ic a
cid
to
pro
tein 0.0
0.4
0.8
1.2
0.0
0.4
0.8
1.2
A B C D E F G H I
0.0
0.4
0.8
1.2
DNA/RNA peak
0.0
0.4
0.8
1.2
A B C D E F G H I
0.0
0.4
0.8
1.2
DNA/RNA peak
0.0
0.4
0.8
1.2
E. coli
V. vulnificus
P. aeruginosa
S. aureus
0.0
0.4
0.8
1.2
1.6
2.0
2.4
A B C D E F G H I
0.0
0.4
0.8
1.2
DNA/RNA peak
FSET PhD Thesis/132
reduction and feature creation from the 1407 included pixels from each spectrum of
the cells from different growth phases. The third step involved canonical linear
discriminant analysis (LDA), which is the classical form of DA on the created
features. This classification algorithm was performed using the discriminant
command tool in OriginPro software (version 9.0.0) and provided the predicted class
of the sample. The final step was the evaluation and validation of the classification
accuracy.
LDA classification was performed based on the first 10, 16, 20 and 30 principal
components (PCs) generated from MATLAB which accounted for approximately 92
%, 93 %, 95 % and 96 % of the variance in the data set. The canonical score was
plotted for the first two canonical discriminant functions, as they reflect the most
variance in the discriminant model. The canonical score plot shows how the first two
canonical functions classify observations between groups. As shown in Fig 4.20,
LDA generally discriminated between different species based on the retained PCs
and the a priori knowledge of which spectra were replicates. With the application of
PCs, LDA effectively discriminated and classified the bacterial taxa into four groups
despite physiological variation within the species.
Mya Myintzu Hlaing Chapter 4/ 133
Figure 4.20 Linear discriminant analysis (LDA) based on the retained principal
components (PCs) for bacterial species differentiation: LDA was performed based on
(A) 10 PCs, (B) 16 PCs, (C) 20 PCs and (D) 30 PCs.
4.3.4 PC-LDA Classification model
To validate the discrimination performed by LDA, projection analysis was also
employed to project test data into the discriminant function analysis (DFA) space
generated by the training set. A principal component linear discriminant (PC-LDA)
prediction model, based on the first 10, 16, 20 and 30 principal components (PCs) of
the four different species which account for approximately up to 96% of variance in
the data set was constructed. For evaluation and calibration of this model, a single
spectrum was removed from the database and a training data set was created using
the remaining spectra. In each of the test cases shown in Figure 4.21, comprising one
cell spectrum from a sub-growth phase of each bacterial species, the “left out
(A) (B)
(C) (D)
-9 -6 -3 0 3 6-8
-4
0
4
8
Dis
crim
inant
function 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
-9 -6 -3 0 3 6-8
-4
0
4
8
Dis
crim
inant
function 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
-9 -6 -3 0 3 6 9
-8
-4
0
4
8
Dis
crim
inant fu
nction 2
Discriminant function 1
E. coli'
V. vulnificus
P. aeruginosa
S. aureus
-9 -6 -3 0 3 6-8
-4
0
4
8D
iscrim
inant fu
nction 1
Discriminant function 1
E. coli
V. vulnificuus
P. aeruginosa
S. aureus
FSET PhD Thesis/134
spectrum” clustered among the data within its respective training set. The example in
Fig 4.21 was calculated with the application of 16 PCs and is shown as an example
for visualisation. The classification label of the test set (left out spectrum) was
determined and the process was repeated for each of the four cells in the nine growth
phases for the four species, evaluated against the training set (108 spectra).
Figure 4.21 Calibration of PC-LDA model using a leave-one-out cross-validation
(LOOCV) with training and testing data. Bacterial species differentiation was
validated by projection analysis. Numbers 1-4 represent test data and the rest of the
points indicate training data used to validate the discrimination. The test samples (1-
4) are the cells from early exponential phases of E. coli, V. vulnificus, P. aeruginosa
and S. aureus respectively.
The cross-validation results of PC-LDA based on the first 10, 16, 20 and 30 principal
components (PCs) of the four different species are shown in Table 4.1. The results
indicate that there were unidentifiable cells from P. aeruginosa and S. aureus with
the application of the PC-LDA model based on the first 20 and 30 PCs, while there
was some misidentification of the species in the other two models. In particular, one
cell sample in S. aureus species could not be identified as any of bacterial species
with the model based on the first 20 PCs. Similarly, in the model of 30 PCs, one cell
1
23
4
-9 -6 -3 0 3 6
-4
0
4
8
Dis
crim
inant
function 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
Test sample
Mya Myintzu Hlaing Chapter 4/ 135
each from P. aeruginosa and S. aureus species were not be able to identified, while
two other cells from S. aureus species were misidentified.
The calibration of the selected PC-LDA model with the first 16 PCs for classification
of four species provides >94% classification sensitivity and >98% classification
specificity in the leave-one-cell-out cross-validation (LOCOCV) (Table 4.2). The
error rate for sensitivity and specificity for four bacterial species are shown in Table
4.3. Given the marginal improvement in performance for the tests with more than 16
PCs, the PC-LDA model using the first 16 PCs was chosen as the training model for
further validation tests.
Table 4.1 Calibration of PC-LDA model based on the first 10, 16, 20 and 30
principal components (PCs) for a total of 144 spectra of four bacterial species.
No.
of
PCs
Predicted Group
E. coli V. vulnificus P. aeruginosa S. aureus Unidentified Total
10 E. coli 36 0 0 0
36
V. vulnificus 0 35 1 0
36
P. aeruginosa 1 5 30 0
36
S. aureus 0 0 1 35
36
16 E. coli 36 0 0 0
36
V. vulnificus 0 35 1 0
36
P. aeruginosa 1 1 34 0
36
S. aureus 0 0 2 34
36
20 E. coli 36 0 0 0
36
V. vulnificus 0 36 0 0
36
P. aeruginosa 1 0 35 0
36
S. aureus 0 0 1 34 1 36
30 E. coli 36 0 0 0
36
V. vulnificus 0 36 0 0
36
P. aeruginosa 0 0 35 0 1 36
S. aureus 1 0 1 33 1 36
FSET PhD Thesis/136
Table 4.2 Calibration accuracy results of PC-LDA model with the first 16 PCs on a
total of 144 spectra of four bacterial species.
Predicted Group Sensitivity Specificity
E. coli V. vulnificus P. aeruginosa S. aureus (%) (%)
E. coli 36 0 0 0 100 99.1
V. vulnificus 0 35 1 0 97.2 99.1
P. aeruginosa 1 1 34 0 94.4 98.1
S. aureus 0 0 2 34 94.4 100
Table 4.3 Error rates for the calibration of PC-LDA model with the first 16 PCs on a
total of 144 spectra of four bacterial species.
Error rate
Sensitivity Specificity
E. coli 0.00% 0.92%
V. vulnificus 2.78% 0.93%
P. aeruginosa 5.56% 1.85%
S. aureus 5.56% 0%
MATLAB code for the PC-LDA model along with the corresponding species labels
was custom written and applied for species identification of cells from a separate
culture batch (see Section 2.2.6.2). The calibrated PC-LDA model using the spectra
from planktonic cells of each species was validated on 10 spectra each from
individual (pure) planktonic cells of E. coli and V. vulnificus achieving 100% and
80% accuracy in prospective classification (Table 4.4). This calibrated PC-LDA
model was then applied to detect the presence of two species in mixed inoculums of
E. coli and V. vulnificus. When a two-species sample from the mixed culture were
examined, the presence of both E. coli and V. vulnificus were detected in 15 and 4
out of 20 sample regions respectively using the PC-LDA model based on the first
16 PCs of four bacterial species (Table 4.4). A visualisation of the classification of
the test samples projected into the DFA space generated by the training set is shown
in Fig 4.22.
Mya Myintzu Hlaing Chapter 4/ 137
Figure 4.22 Validation of the PC-LDA model on 10 new spectra from individual
species and from mixed culture: (A) E. coli cells, (B) V. vulnificus cells and (C)
mixture of E.coli and V. vulnificus. Numbers 1-15 represent the test data and the rest
indicate training data used to validate the discrimination. The test samples which are
misidentified are labelled with (×) symbol in red.
123
45
67 89 10
11 121314151617181920
-9 -6 -3 0 3 6-8
-4
0
4
8
Dis
cri
min
an
t fu
nctio
n 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
Test sample
1
2 3
4
5678910
-9 -6 -3 0 3 6
-8
-4
0
4
8
Dis
crim
ina
nt
fun
ction 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
Test sample
(A) (B)
(C)
X
X
X
1234
5 67
8
910
-9 -6 -3 0 3 6
-4
0
4
8
Dis
crim
ina
nt
fun
ctio
n 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
Test sample
FSET PhD Thesis/138
Table 4.4 Validation of PC-LDA model on new spectra from individual species and
from mixed culture.
Reference Accuracy PC-LDA model
E. coli V. vulnificus Others species (%)
Cla
ssif
ica
tio
n r
esu
lt
E. coli 10 - - 100 4 species’ model
V. vulnificus - 8 2 80 4 species’ model
Cells from
mixed
culture
15 4 1 95* 4 species’ model
* Calculated as the detection accuracy for the presence of two species in the sample.
The next step of the process was to biochemically validate the bacterial identity of
each of the cells that were analysed using Raman to confirm that the PC-LDA model
has identified the bacteria correctly. To confirm the presence and spatial distribution
of the bacterial cells, a fluorescence in situ hybridisation (FISH) technique with
rRNA-targeted oligonucleotide (probe) for E. coli (ATCC 25922) was performed.
The probe efficiency test and FISH techniques have already been optimised in
preliminary work (details in Section 2.2.4.2). The nucleic acid probe (SYTO 9,
Invitrogen) was used to stain the nucleic acid of all bacterial cells in the sample
following the protocols mentioned in Section 2.2.4.1. The results of FISH using
rRNA-targeted probe and nucleic acid probe (SYTO 9) are shown in Fig 4.23. E. coli
cells labelled with rRNA-targeted probe and SYTO 9 appear in yellow and V.
vulnificus labelled with only SYTO 9 is seen as green. As shown in Fig 4.23C,
Raman spectra were collected across the white dotted line (from left to right) with 2
µm steps along the x axis. The first three cells in green colour were likely to be V.
vulnificus species and were identified as V. vulnificus species using the PC-LDA
model. Similarly, the subsequent cells in yellow colour were identified as E. coli
species with the model. Therefore the FISH results support the validity of the
identification provided by the PC-LDA model (data shown in Fig 4.22C and Table
4.4).
Mya Myintzu Hlaing Chapter 4/ 139
Figure 4.23 Confocal laser scanning microscopy images of mixture of E. coli and V.
vulnificus planktonic samples (x–y sections). Bacteria were stained with the rRNA-
targeted oligonucleotide probe for E. coli (ATCC 25922) and the nucleic acid probe
(SYTO 9). Panel A shows bacteria cells with one channel for rRNA-targeted probe
and panel B shows bacteria cells with two channels for both rRNA-targeted and
nucleic acid probes. Panel C shows the enlarged area where Raman spectra were
collected. The sample area used for Raman spectroscopy was relocated with the help
of microscope grid (grid size 300 mesh × 83 μm of pitch, Sigma). Raman spectra
were collected along the horizontal dotted arrow line (in white) with 2 µm steps (x
axis) from left to right. The yellow colour represents E. coli cells labelled with both
rRNA targeted and nucleic acid probes and green represents V. vulnificus cells
labelled with only the nucleic acid probe. The 20 µm scale bar shown in panel (A)
applies to panel (B) as well.
20 µm
20 µm
(A)
(B)
(C)
FSET PhD Thesis/140
4.3.5 PC-LDA Classification model for classification of metabolic phases in
individual species
The capability to investigate the growth phase-dependent differences in physiology
within a single species has important potential applications for control measures in
food and medical microbiology, where single-species communities are common.
Therefore, a PC-LDA prediction model was constructed based on the first 16 PCs of
individual species at 9 different growth points for pattern recognition of the
metabolic phases (i.e. early, mid and late of exponential, stationary and decline
phases respectively). The classification label of the test set (leaving out one spectrum
from each growth point) was determined against the training set (27 spectra) and the
process was repeated for all 36 cells of each individual species. The model provided
>90% classification accuracy in a LOOCV, except for the P. aeruginosa species
(Tables 4.5 and 4.6), which were less accurate.
From the PC-LDA results, the P. aeruginosa cells from different phases of the
growth cycle were poorly clustered and not well separated, with data overlapping
across each growth phase (see Section 4.3.2.2.3). The exponential phase of P.
aeruginosa provides 83.3% sensitivity and 88% specificity. However, this drops to
58% sensitivity and approximately 80% specificity in the stationary and decline
phases. The poor group separation for the later growth phases of P. aeruginosa cells
indicates that there is an increasingly heterogeneous population of cells at different
stages, with some cells actively continuing metabolic changes whereas others are
approaching the deterioration stage. This can be explained by the fact that most
bacteria express changes in gene transcription, cell behaviour and physio-chemistry
between the exponential phase and the stationary phase (274, 275).
Mya Myintzu Hlaing Chapter 4/ 141
Table 4.5 Calibration of the PC-LDA model on a total of 36 spectra of individual
species in different growth phases.
Classification result
E.coli V. vulnificus P. aeruginosa S. aureus
Exp Stat Decl Exp Stat Decl Exp Stat Decl Exp Stat Decl
Exp 11 1 0
E.coli Stat 1 9 2
Decl 0 2 10
Exp
12 0 0
V. vulnificus Stat
0 12 0
Decl
0 0 12
Exp
10 1 1
P. aeruginosa Stat
2 7 3
Decl
1 3 7
Exp
12 0 0
S. aureus Stat
2 10 0
Decl
0 1 11
Abbreviations: Exp, exponential phase; Stat, stationary phase; Decl, decline phase.
Table 4.6 Classification accuracy results of PC-LDA model at metabolic phase level.
Growth
phase
Sensitivity
(%)
Specificity
(%)
E. coli Exponential 91.7 95.8
Stationary 75 95.5
Decline 83.3 91.7
V. vulnificus Exponential 100 100
Stationary 100 100
Decline 100 100
P. aeruginosa Exponential 83.3 88
Stationary 58.3 79.2
Decline 58.3 82.6
S. aureus Exponential 100 92.3
Stationary 83.3 95.7
Decline 91.7 100
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The ten E. coli cells and eight V. vulnificus cells which were identified from the PC-
LDA model at species level (see Table 4.4) were further examined for their
corresponding metabolic phases using the metabolic level PC-LDA model. As shown
in Table 4.7, the results revealed that heterogeneity of cellular metabolic population
was present in both species. In particular, 60% of E. coli cells were classified in the
stationary phase population, whereas 86% of V. vulnificus cells were in the stationary
phase. In fact, the bacterial samples tested in this classification experiment were
collected from overnight incubation and were thus expected to be in the stationary
phase. Although it could not be explicitly confirmed whether the PC-LDA model
was able to detect the correct metabolic behaviour of the cells, the classification
results showed the possible trends of the population behaviour of the tested cells.
Table 4.7 Classification results of 10 new spectra from individual species (spectra
from Table 4.4) using the PC-LDA model at metabolic phase level.
Growth
phase Classification results
E. coli
(10 cells)
V. vulnificus
(7 cells)
Ref
eren
ce (
fro
m m
od
el)
E. coli
Exponential 3
Stationary 6
Decline 1
V. vulnificus
Exponential
1
Stationary
6
Decline
-
Mya Myintzu Hlaing Chapter 4/ 143
4.4 Conclusion
In this chapter, confocal Raman spectroscopy was applied to the identification of
single bacterial cells from different metabolic growth phases. The results have
demonstrated the effectiveness of principal component analysis of Raman spectra for
the discrimination of four species of microorganisms (i.e. E. coli, V. vulnificus, P.
aeruginosa and S. aureus) either in culture at a particular growth time point, or in
culture at a random growth point. This analysis indicates that Raman spectroscopy
can provide reasonably accurate identification of a range of planktonic bacteria,
despite the presence of spectral variations associated with different growth phases.
Bacterial cells from different growth phases can also be classified to varying degrees
of success with the help of principal component linear discriminant analysis (PC-
LDA), although the single-cell spectra are relatively variable between individual
cells. PC-LDA of single cell Raman spectra shows that individual cells can be
discriminated from batch cultures of E. coli and V. vulnificus species at the stationary
growth phase with reasonably high confidence. These results showed that confocal
Raman spectroscopy may be used for rapid environmental sensing of microbial cells
recovered at arbitrary growth time points. Since the PC-LDA model appears to be
able to discriminate between the two species in a mixed culture, it was decided to
investigate further applications of this model on species identification for two-
species grown biofilms.
FSET PhD Thesis/144
Mya Myintzu Hlaing Chapter 5/145
RAMAN ANALYSIS OF BACTERIAL (MICRO) COLONIES
AND BIOFILMS ISOLATED ON SUBSTRATES
5.1 Introduction
While the previous chapters have focused on cells grown in culture, this chapter
investigates the behaviour of bacterial cells grown on two substrates, nitrocellulose
membranes and quartz glass.
Bacterial colonies play an important role in the isolation and identification of
bacterial species and the culture-based method is regarded as the gold standard
screening method in clinical and environmental studies. A bacterial colony, also
known as a colony biofilm, consists of millions of densely packed individual bacteria
along with extracellular materials (such as EPS) (276). During bacterial micro-
colony development, the process of continuous division of bacteria, interactions
among bacterial offspring and between bacteria and their surroundings results in
biochemical or metabolic heterogeneity in a population.
To observe bacterial population behaviour, both membrane grown micro colonies
and the development of biofilms grown on quartz were monitored by collecting
Raman spectra from the four bacterial species (E. coli, V. vulnificus, P. aeruginosa
and S. aureus) over a range of time points. From these Raman spectra collected from
cells in different areas of the colonies, differential identification of bacterial cells
grown on surfaces was performed with the application of the principal component
and linear discriminant (PC-LDA) planktonic model, as described in the previous
chapter.
A prediction model, based on PC-LDA, was subsequently constructed from biofilm
cells of each species. The constructed fingerprinting system for single bacterial
species (PC-LDA biofilm model) was tested on a dual-species biofilm to obtain
specific bacterial identification. A fluorescence in situ hybridisation (FISH)
technique was then used to confirm the identification results using the PC-LDA
biofilm model and to understand their spatial distribution within a mixed biofilm
community.
FSET PhD Thesis/146
5.2 Materials and methods
Colonies of E. coli ATCC 25922 were isolated on a nutrient agar plate following the
protocols mentioned in Section 2.2.3.2. Colonies of all four bacterial species were
grown on nitrocellulose membranes according to the protocols detailed in the same
Section.
To investigate surface-attached and biofilm bacterial cells, a static biofilm cultivation
process on quartz glass samples was prepared for all four bacterial species following
the methods detailed in section 2.2.3.3. For dual-species biofilm cultivation,
planktonic bacterial cultures were collected at the stationary growth phases of E. coli
and V. vulnificus according to the growth curve results discussed in Section 4.3.2.1.
The collected bacterial cultures were then washed and resuspended in PBS to a
concentration equivalent to an OD at 600 nm of about 0.3 as mentioned in Section
2.2.3.3. Each of the diluted samples was then gently mixed by pipette before they
were used for initial attachment and biofilm cultivation. The dual-species biofilms
were grown until 79 hours as stated in Section 2.2.3.3. The bacterial cells from
colonies and biofilm were analysed under Raman spectroscopy with the parameters
mentioned in Section 2.2.5.
The morphology of the E. coli biofilm cells was observed using scanning electron
microscopy (SEM). The images were obtained using a field-emission scanning
electron microscopy (Fe-SEM) instrument (SUPRA 40VP, Carl Zeiss SMT,
Germany) with 3 kV acceleration voltage and ×5000 magnification. Prior to imaging,
biofilm samples were coated with 10-15 nm of gold using a Dynavac CS300 thermal
deposition chamber. For visualisation of E. coli biofilm cells and the extracellular
polymeric substance (EPS) of dual-species biofilm, a fluorescence in situ
hybridisation (FISH) technique was performed using 16S rRNA targeted probe and
EPS staining as detailed in sections 2.2.4.2 and 2.2.4.3. The hybridized E. coli cells
and ConA stained EPS were visualised under confocal laser scanning microscopy
(CLSM) using the protocols detailed in section 2.2.4.4.
Mya Myintzu Hlaing Chapter 5/147
5.3 Results and discussion
5.3.1 Raman analysis of agar-grown bacterial (micro) colonies
As discussed in the literature review (Section 1.5), during biofilm formation, bacteria
have to adapt to their changing environmental conditions by expressing different
phenotypes which are distinct from planktonic growth. These bacterial phenotype
heterogeneities are associated with random alterations in chemical reactions for DNA
and protein synthesis. These chemical changes may alter the Raman spectrum of the
cell to an extent that it interferes with bacterial identification. Therefore, in order to
investigate how significant an effect the chemical changes have on the Raman
spectrum, E. coli cells from micro-colonies were analysed by Raman spectroscopy.
The E. coli colony cells were first isolated on the nutrient agar and then smeared on a
quartz substrate for analysis (Section 2.2.3.2), with the results shown in Fig 5.1.
Figure 5.1 Averaged, intensity-normalised and background subtracted Raman
spectra from planktonic and colony cells of E. coli species. Abbreviations: A,
adenine; G, guanine; def, deformation; Phe, phenylalanine; Trp, tryptophan; Tyr,
tyrosine. The dominant peaks for spectra of DNA/RNA and proteins are shown with
the peak assignments from Table 3.1. Shaded regions indicate the main spectral
changes between the two samples.
Ph
e, Tyr
Tyr
Ph
e
Ca
rbo
hyd
rate
Am
ide
III
CH
2 d
ef A
mid
e I
Am
ide
II
C-H
de
f
DN
A/R
NA
A,
G
Planktonic
Colony
FSET PhD Thesis/148
The Raman spectra of the colony cells were compared with the spectra collected
from the planktonic cells which were cultured under similar conditions. The
characteristic Raman peaks of E. coli cells (determined from the literature and shown
in Fig 4.1), which are associated with carbohydrate, lipid, protein and nucleic acids,
were clearly visible in the spectra of both planktonic and colony cells. The Raman
spectral profiles for the planktonic and colony cells appeared generally similar, but
certain differences in peak intensity could be observed visually (highlighted in the
grey boxes). These spectral fluctuations could be seen in the region of 600 to 800 cm-
1 which relates to DNA/RNA synthesis, the peaks associated with the CH, CH2
deformation mode and the macromolecules containing amide groups in the protein
backbone (1337, 1447-1452, 1620-1680 cm-1). These subtle changes of the Raman
spectra were further investigated by performing principal component analysis (PCA)
and the results are shown in Fig 5.2.
The scores plot from the first two principal components (PC1 and PC2) of PCA
shows a clear separation between the two sample groups accounting for 65% of the
variation in the data set. The average value plot of PC1 shows a significant
separation of the two sample groups with p value < 0.005. The loading plot of PC1
further demonstrates the dominant peaks which contributed to the data separation
seen in the score plots. The results show that the peaks related to DNA/RNA
synthesis represented the main variance of the planktonic cells from the colony cells,
whereas the protein-specific peaks were related to the variance of the colony cells.
The specific peaks selected from the loadings plot of PC1 were further analysed
using univariate statistical analysis, as mentioned in Section 2.2.6.3. As shown in Fig
5.3, the intensity of the DNA/RNA-specific peaks was higher in the planktonic cells
in comparison to the colony cells. It has been reported that the DNA/protein ratio
usually increases during the transition from exponential growth to the stationary
phase because of continuous cell division. Thus, the increased intensities of these
DNA/RNA related peaks in the planktonic cells indicate that these cells may have
been in the stationary phase when they were collected for Raman measurement.
Mya Myintzu Hlaing Chapter 5/149
(A) (B)
(C)
Figure 5.2 Principal component analysis of Raman spectra collected from E. coli
planktonic and colony cells. (A) Scatter plot of the first and second principal
components, (B) average value plot and (C) loading values plot of the first principal
component (***p < 0.005). Abbreviations: A, adenine; G, guanine; Phe,
phenylalanine; def, deformation.
The increased intensity of protein-related peaks in the colony cells might be
associated with more secretion of extracellular polymeric substance (EPS) and higher
protein expression in the colony compared to the planktonic cells. As discussed in
the literature Chapter (Section 1.2.2), it is well established that cells in colonies
secrete more EPS (49, 52). These results suggest that the colony cells at the agar-air
interface were metabolically different from planktonic cells. Therefore, the variations
of DNA/RNA and protein-specific peaks seen here indicate biochemical or metabolic
***
Ph
e
AO
-P-O
, R
NA
DNA/RNA CH
2d
ef
A,G
A,G
Am
ide I
FSET PhD Thesis/150
heterogeneity between planktonic cells and agar-grown colony cells. In this context,
the next step was to see whether this heterogeneity can affect the identification of
colony cells using the PC-LDA planktonic model.
(A)
(B)
Figure 5.3 Analysis of specific peaks from the Raman spectra of E. coli planktonic
and colony cells. Univariate analysis was performed on the normalised intensity of
(A) DNA/RNA and (B) protein/lipid structure-specific peaks in the E. coli Raman
spectra taken from planktonic and colony samples. Each group consisted of seven
replicates. (***p < 0.005, **p < 0.05, *p < 0.1). Abbreviations: A, adenine; G,
guanine; T, thymine; C, cytosine; U, uracil; Phe, phenylalanine; def, deformation.
T, G A C, U U, T, C A,G A,G0.0000
0.0005
0.0010
0.0015
0.0020
Me
an N
orm
alis
ed In
tesn
ity / A
rbitr.
Units
Specific peak (Wavenumber)
planktonic
colony
***
***
*
***
***
***
Phe CH2 def Amide I0.000
0.001
0.002
0.003
0.004
0.005
0.006
Specific peak (Wavenumber)Me
an
No
rma
lise
d I
nte
sn
ity /
Arb
itr.
Un
its
planktonic
colony
*****
**
Mya Myintzu Hlaing Chapter 5/151
The 16 principal components (PCs) of the Raman spectra from E. coli colony cells
were further analysed using the PC-LDA planktonic model. Each test spectrum was
clustered and overlapped with the corresponding species (i.e. E. coli species) within
the training model, thus showing 100% classification accuracy (Fig 5.4). The results
suggested that the subtle changes in macromolecules related with DNA/RNA and
protein synthesis (between planktonic and colony cells) are not related to the key
components identified in the PCA and thus are not critical for the identification of
the samples. Therefore, agar-grown colony cells could be used for identification
purpose despite these subtle changes.
Given the encouraging classification outcomes for agar-grown cells, the next stage of
the study investigated whether surface-attached cells (i.e. intact colony/biofilm cells)
can achieve similar classification accuracy.
Figure 5.4 Classification and identification of spectra from colony cells of E. coli
grown on nutrient agar using the PC-LDA planktonic model. The model
discriminated and identified all test spectra correctly as “E. coli” species. The mean
of each group in the training model is shown with () symbol in yellow.
-9 -6 -3 0 3 6-8
-4
0
4
8
12
Dis
crim
inant fu
nction 2
Discriminant function 1
E. coli
V. vulnificus
P. aeruginosa
S. aureus
Test sample
Group Means
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5.3.2 Raman analysis of intact membrane-grown bacterial micro-colonies
Micro-colonies of each of the bacterial species were inoculated onto a sterile
nitrocellulose transfer membrane which was placed on a pre-warmed nutrient agar
plate as shown in Fig 5.5A. A micrograph of an E. coli bacterial colony from
overnight culture was captured in the x-y and x-z planes with 3D optical profiler
microscopy system (Fig 5.5B). It has recently been reported that the diameter of the
pie-like monolayer of the bacterial colony increases and a two-cell layer appears at
the centre of during colony development. As the growth of bacteria and division
inside the colony continue, the diameter and layers of the bacterial micro-colony are
believed to be expanding outwards on the agar plane and upward in the third
dimension (277). As shown in Fig 5.5B, typical bacterial micro-colony structures,
which are associated with a more dense appearance at the centre core than in the
outer ring, could be seen under the 3D optical profiler microscopy. Raman spectra
were collected from the centre core region (C), the outer ring region (R) and middle
region between the centre and outer ring (M) as shown in Fig 5.5B. This pattern was
seen in colonies of all four bacterial species that were tested in this study.
Raman spectra were recorded from a single cell in the outermost ring (monolayer
region) of an E. coli colony on the membrane, as well as from a region of the
nitrocellulose membrane (shown in Fig 5.5 B and C). Raman spectra were acquired
from upper layers of different regions of the colony. Maquelin et al. detected
different RNA in Raman spectra taken from cells located in different depths within
the colony structure. They speculated that the cells in the higher layers of the colony
were more actively dividing than cells in the deeper layers. Therefore, in order to
minimise biochemical heterogeneity of cells in bacterial colonies, Raman spectra
were consistently taken from upper layers of the colony for all regions in this study.
Mya Myintzu Hlaing Chapter 5/153
(A) (B)
(C)
Figure 5.5 Bacterial micro-colonies isolated on a nitrocellulose membrane placed on
nutrient agar. Micro-colony was observed: (A) with the naked eye (marked with red
circle) (B) on the x-y and x-z planes with 3D optical profiler microscopy system and
(C) a schematic representation of a growing bacterial micro-colony through
overnight incubation. Images were taken after overnight cultivation. A light outer
ring and a dense centre core seen in (B) are labelled with “R” and “C”, respectively;
middle area between the centre core and the outer ring of the whole micro-colony is
labelled ‘‘M’’, in (B and C). Red arrows in (C) indicate cells growing outward and
upward in the micro-colony.
5 mm100 m
Centre core
Outer ring
RMC
(x)
(y)
(x-z plane)
FSET PhD Thesis/154
Figure 5.6 Recovery of Raman spectra from intact colony grown on membrane: (a)
from single cell of intact E. coli colony on membrane, (b) from nitrocellulose
membrane, (c) recovered spectrum of an E. coli single cell by subtracting membrane
spectrum from (a) after normalisation with the nitrocellulose membrane peak at 1282
cm-1, (d) from single cell of planktonic E. coli. The arrows indicate the Raman signal
(846 cm-1 and 1282 cm-1) from the nitrocellulose membrane.
The collected Raman spectra from different regions of the colony and the membrane
spectra were shown in Fig 5.6. The nitrocellulose provided a consistent background
signal at 846 cm-1 and 1282 cm-1 in the spectrum of the membrane (Fig 5.6b). These
peaks did not significantly overlap or interfere with the Raman peak assignments
from macromolecules of the bacterial cells. To recover the spectra from the bacterial
cells, the peak intensities of the bacterial spectra collected from an intact colony
isolated on the membrane were normalized by dividing with the intensity of the
nitrocellulose membrane signal at 1282 cm-1 after background subtraction. This
normalisation process was also performed on the background subtracted membrane
spectra. The spectrum from a single cell of intact bacterial colony was then recovered
by subtracting the normalised nitrocellulose membrane spectrum from the
normalised spectrum of bacterial cell together with membrane. As shown in Fig 5.6,
the peak assignment of the recovered E. coli single cell spectrum is consistent with
that of the E. coli spectrum collected from a planktonic cell analysed on CaF2 (see
Fig 5.6d). The recovered spectrum of bacteria using this normalisation method is
Mya Myintzu Hlaing Chapter 5/155
comparable or better than vector normalisation method mentioned in previous study
(215) (result shown in Appendix D).
The effectiveness of this normalisation and peak recovery process was confirmed by
classification of the spectra using the previously constructed PC-LDA planktonic cell
model (Fig 5.3). The classification results provided the correct identification of the
four bacterial species, showing that the membrane peak correction process was able
to allow for Raman efficiency variations that may have been induced by irregularities
in the scattering from the sample, variations in laser exposure or focal point shifts
due to the introduction of the bacterial layer over the membrane substrate.
(A) (B)
Figure 5.7 (A) Classification and identification of spectra from colony cells of four
bacterial species isolated on nitrocellulose membrane, based on the planktonic PC-
LDA model. (B) Test Raman spectra from the four bacterial species. Numbers 1-4
represent the test data and the rest indicate training data used to validate the
discrimination. The model validated the discrimination and identification of test
spectra (1-4) correctly as “E. coli”, “V. vulnificus”, “P. aeruginosa” and “S. aureus”.
Raman spectra collected from the centre core region (C), the outer ring region (R)
and middle region between the centre and outer ring (M) of micro-colonies of each
bacterial species were also added into the classification analysis. In brief, the
normalisation and peak recovery steps were performed from background corrected
Raman spectra as mentioned above. PCA was then performed on the recovered
spectra. The 16 principal components (PCs) of each species were used for
FSET PhD Thesis/156
classification analysis with PC-LDA planktonic model. The E. coli and S. aureus
appeared to cluster most closely to their respective species within the training set, but
there was some ambiguity in the overlap for V. vulnificus and P. aeruginosa (Fig.
5.8). The reliability of classification was examined by calculating the posterior (post)
probabilities, which indicate the probability of the observations matching the
different groups. The observations of the test samples were then located to the group
with the highest post probability. Moreover, the observed test sample was also
classified to the nearest group (i.e. the smallest Mahalanobis distance value from
each of the group means to the observation). Based on this analysis, all of the
samples were correctly classified, except for 33% of V. vulnificus and 11% of S.
aureus which were incorrectly classified (marked with crosses in Fig. 5.8).
Given that surface-attached bacterial cells are believed to be different in gene
expression pattern from those of planktonic cells (such as from proteomic and
transcriptomic analysis) (32, 33, 278), it was not surprising to find some incorrect
classification results. In fact, the classification generally relied on the smallest
Mahalanobis distance due to relatively poor clustering of the data with the training
set. Since there were only four bacterial species (groups) in the constructed PC-LDA
model, it is possible that the test data sample may be classified as another more
closely related group if more groups (bacterial species) were added to the training
set. Another possibility is that the membrane peak removal method for membrane-
grown colonies cells was not perfect, thus leaving some residual of the membrane
signal in the test spectra. Thus, the spectra of membrane-grown colony cells were in
the right general area for the corresponding species, but they were not overlapping
with the training data set.
Nevertheless, the results reported here provide preliminary data for using a PC-LDA
planktonic training model for classification of surface-attached bacterial cells. These
results have highlighted that more bacteria species need to be included in the training
data set in future to evaluate the reliability of the outcomes. Furthermore, the results
suggested that a more reliable computational method may be required to
automatically and completely remove the membrane peak. The results from
calculation of classification accuracy are shown in Table 5.1. The identification-
Mya Myintzu Hlaing Chapter 5/157
accuracy achieved in the present analysis shows that Raman spectroscopic
techniques can be applied in rapid bacterial identification of micro-colonies on
nitrocellulose membrane, despite some loss in classification accuracy due to changes
in the colony spectra in comparison with planktonic cells.
(A) (B)
(C) (D)
Figure 5.8 Classification and identification of spectra from cells in different regions
of micro-colonies of four bacterial species isolated on nitrocellulose membranes with
the application of the PC-LDA planktonic model. Raman spectra were collected from
the outer ring, centre core and middle area between the core and outer ring of whole
micro-colony cells of (A) E. coli (B) V. vulnificus (C) P. aeruginosa (D) S. aureus.
Cyan-labelled points represent the test data and the rest indicate the training data of
the PC-LDA planktonic model used for classification. The mean of each group in the
training data set is shown with () symbol in yellow. The test samples which were
misidentified are labelled with (×) symbol in red.
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Table 5.1 Classification of colony cells from four bacterial species isolated on
nitrocellulose membrane with the application of the PC-LDA planktonic model.
Reference Accuracy
E. coli V. vulnificus P. aeruginosa S. aureus
Miss
classifi
cation
(%)
Cla
ssif
ica
tio
n r
esu
lt E. coli 9
- 100
V. vulnificus
6 3 66.7
P. aeruginosa 9 - 100
S. aureus 8 1 88.9
In an attempt to investigate the metabolic growth phases within colony development,
the retained 16 PCs of Raman spectra collected from different regions of whole
micro-colony cells were further analysed for every species. The constructed PC-LDA
planktonic model was used for this analysis and the results of E. coli colony cells are
shown in Figure 5.9 and Table 5.2.
Based on the first discriminant function, the results suggest that the outer ring region
of the E. coli colony might have contained cells that were in the early exponential
phase. This finding can be explained by a fact that the outer ring of the colony is a
monolayer of newly divided cells, that the cells in this region are exposed to an
enriched nutrient environment from the culture agar and that the cells undergo
exponential cycles of cell growth and division. The exponential growth of bacterial
cells could also be seen in the middle region of E. coli colony, whereas some cells in
the middle regions of other species might have ceased their exponential increase in
biomass, thus entering a stationary culture phase (data shown in Appendix D). The
population of cells in the middle regions of P. aeruginosa and S. aureus colonies
might face starvation conditions due to very limited access to nutrients and they were
thus in stationary phase.
Interestingly, the centre core region of the E. coli colony showed heterogeneous
population behaviours with observations of some cells being in the exponential phase
Mya Myintzu Hlaing Chapter 5/159
while some in the decline phase. It can be postulated that bacterial growth in the
central core of the bacterial colony may be entering the stationary phase and decline
phase because of inadequate nutrient levels. From these results, it can be assumed
that some of the cells in the centre core region might be in the starved condition.
Eventually, when a required nutrient became exhausted (or the concentration of toxic
waste products becomes too high), the cessation of reproduction and growth will
occur. During this condition, cell population growth may become unbalanced and
more heterogeneous (i.e., some cells are still growing and dividing while others are
deteriorating). These findings are consistent with previous studies for colony
development of in the literature (279, 280). Meunier et al. reported that
Saccharomyces cerevisiae cells in the centre of a colony gradually enter stationary
phase and later the bacterial growth occur predominantly at the periphery.
Figure 5.9 Investigation of population behaviours of E. coli cells from spectra of
different regions of colony cells isolated on nitrocellulose membrane with the
application PC-LDA planktonic model. Numbers 1-9 represent 3 Raman spectra
collected from each of the outer ring (1-3), centre core (4-6) and middle area between
the core and outer ring of whole micro-colony cells (7-9) respectively.
Abbreviations: EE, early exponential; ME, mid exponential; LE, late exponential; ES,
early stationary; MS, mid stationary; LS, late stationary; ED, early decline; MD, mid
decline; LD, late decline.
1
2
3
4
5
6
789
-60 -30 0 30 60 90
0
25
50
Dis
crim
inant
function 2
Discriminant function 1
EE
ME
LE
ES
MS
LS
ED
MD
LD
Test sample
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Table 5.2 Analysis of population behaviour of E. coli colony cells.
Reference
(PC-LDA
planktonic
model)
Met
ab
oli
c
ph
ase
Classification results
E. coli
Growth region
Outer ring Middle Centre core
E. coli
(9 cells)
EE 3
ME
3 1
LE
ES
MS
LS
ED
2
MD
LD
Abbreviations: EE; early exponential, ME; mid exponential, LE; late exponential, ES;
early stationary, MS; mid stationary, LE; late stationary, ED; early decline, MD; mid
decline, LD; late decline.
Moreover, a previous study from Naumann group on cell growth in microbial
colonies using FTIR spectroscopy showed that cell population heterogeneity could
be seen in even in relatively young colonies (279). Therefore, the results seen in this
study using PC-LDA model of Raman spectra can further provide the detailed
information of population behaviour of colony cells. To confirm these findings in
future work, the morphological changes and DNA contents of bacterial cells in the
outer ring and other regions of the colony could be investigated by microscopic
techniques (i.e. fluorescence microscopy for nucleic acid labelling and scanning
electron microscopy). These techniques would provide evidence for the presence of
larger cells in exponential phase compared to stationary-phase cells and for different
DNA contents among the cells from different growth phases (281). However, these
detailed analyses of the heterogeneity of bacterial cells from micro-colonies
(particularly in terms of their physiology) were beyond the scope of this study.
5.3.3 Raman analysis of bacterial cells in developing biofilms
Biofilms grown on sterile quartz microscope slides under static conditions were
monitored for up to 5 days for biofilm matrix structures, which were then analysed
with Raman spectroscopy. Optical micrographs of E. coli ATCC 25922 biofilms at
Mya Myintzu Hlaing Chapter 5/161
different growth time points are shown as examples of the biofilm formation and
morphology in Fig 5.10A). The initial attachment and cell adherence of E. coli could
be seen at 1 h incubation. Bacterial cell division and growth (increase in cell biomass
and number) was visible after 4 h cultivation with nutrient media. After continuous
cultivation, the adhered bacteria cells aggregated and developed a micro-colony at 8
h growth time. More cells aggregated and early biofilm structures were formed after
8 h incubation time. In this phase, E. coli bacterial cells might generate more self-
produced EPS matrix since the morphology of single cells was hard to differentiate
inside micro-colonies. It is believed that the amount of EPS synthesis within the
biofilm may depend greatly on the availability of nutrient status of the growth
medium (such as excess available carbon and limitation of nitrogen, potassium, or
phosphate promote EPS synthesis) (53). The slow bacterial growth observed in most
biofilms promote the synthesis of EPS (53).
Given that fresh nutrient media were replaced every 24 h to induce continuous
growth leading to biofilm formation and minimise nutrient starvation, more cells
were produced and more complex architectures of mature biofilm were formed at the
79 h growth time. After 120 h of culture, a mature biofilm developed on the quartz
surface and a large amount of EPS could be observed for all the four bacterial species
in this study. Field-emission scanning electron microscopy (Fe-SEM) micrographs
further demonstrated the initial attachment of E. coli to the surface (shown in Fig
5.10B) and the aggregation of E. coli cells enclosed in an EPS matrix.
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(A)
(B)
Figure 5.10 (A) Optical micrographs of E. coli ATCC 25922 biofilms at different
time points. Observation of initial attachment to surfaces after 1 h and 4 h incubation;
cell aggregates (early biofilm forming) after 8 h and 24 h incubation; and mature
biofilm after 79 h and 120 h incubation, were detected with ordinary light
microscopy. (B) Field-emission scanning electron microscopy (Fe-SEM)
micrographs of E. coli attached to surfaces and E. coli biofilm with 5000 times
magnification. The arrows indicate the individual cells that are typically selected for
Raman analysis. The scale bars for 10 µm and 1 µm represent all of the images in
series (A) and (B), respectively.
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5.3.3.1 Single-species surface-attached cells
A tentative assignment of the peaks that appeared in the average Raman spectra of E.
coli, V. vulnificus, P. aeruginosa and S. aureus surface-attached bacteria is shown in
Figure 5.11. Raman spectra were collected from single cells of each biofilm grown at
cultivation periods of 1, 4, 8, 24, 72 and 120 hours on quartz substrates. The Raman
spectra of biofilm cells from the four bacterial species contained the peaks which are
associated with cellular components, such as carbohydrate, lipid, protein and nucleic
acids, as determined from the literature (94, 215, 233, 234) (Table 3.1). However, the
peak features were not as prominent compared as those of planktonic cells and
colony cells.
This can be explained by two possible reasons. Firstly, the spectral features of
bacterial cells are overlaid with the background signal of the quartz slides. The effect
of the quartz signal on bacterial spectral quality was explained in Chapter 3.
Secondly, it may be due to the EPS production during biofilm development. As
discussed in Chapter 1, in the mature biofilm, the majority of biofilm matrix (70-
95%) is occupied by EPS secretions enclosing the surface-attached cells inside.
Moreover, the EPS matrix from the mature biofilm recruits more bacterial cells to
attach to the biofilm surface. The amount and thickness of EPS may influence the
intensity of the background signal of the quartz substrate, thereby complicating the
removal of the quartz background. In order to minimise chemical information from a
complex EPS which can interrupt bacterial identification within the biofilm matrix,
Raman experiments could be designed to analyse on recovered (collected and
washed) cells from biofilm (30). However, the motivation of this study is to
investigate the behaviour of surface-attached bacterial cells and to facilitate the
bacterial identification from biofilm samples. Because of the heterogeneity of
bacterial cells from different locations of the biofilm even within a single species, it
was difficult to analyse the chemical variation during biofilm formation of the four
species as a function of time. Therefore, Raman spectral changes were analysed and
compared amongst the biofilm cells of individual species.
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Figure 5.11 Averaged, intensity-normalised and background subtracted Raman
spectra from biofilm cells of the four bacterial species. Abbreviations: Phe,
phenylalanine; Carb, carbohydrate; def, deformation. The dominant peaks for
scattering from DNA/RNA and proteins are shown with the peak assignments
mentioned in Table 3.1.
E. coli
The average Raman spectra of E.coli biofilm cells at different biofilm phases (e.g., 1
h and 4 h for initial attachment; 8 h and 24 h for bacterial colony and early biofilm;
79 h and 120 h for mature biofilm) are shown in Fig. 5.12. The results indicate that
the features of the Raman spectra mainly change in the DNA/RNA related region at
685–800 cm-1, phenylalanine region at 1001 cm-1 and proteins/lipids associated peaks
at 1002, 1239, 1447, 1663 cm-1. These spectral variations are more distinctive in the
comparison between 120 h old biofilm cells and the cells from the earlier biofilm
phases.
From the scatter plot of PCA shown Fig 5.13A, a good separation between these 120
h biofilm cells and the other cells can clearly be seen. The average value plot of the
first principal component revealed a significant separation (p value < 0.05) of biofilm
cells at the later mature biofilm phases (i.e. at 79 h and 120 h) (Fig 5.13B). The
significant separation of the 120 h data set from the rest was probably due to
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distinctive spectral intensity changes resulting from the increased density of
microorganisms and more EPS secretion during biofilm formation.
The loading values plot of the first principal component displays the Raman peaks
which contributed most of the separation in the scatter plot (Fig 5.13C). The results
show that the peaks related to DNA/RNA corresponded to the variance of the earlier
phases of biofilm from the later phase, while the protein-specific peaks were related
to the variance of 120 h old biofilm from other phases. These variations of
DNA/RNA and protein-specific peaks indicate the biochemical and/or metabolic
heterogeneity of bacterial cells and self-secreted EPS throughout biofilm
development.
Figure 5.12 Averaged, intensity-normalised and background subtracted Raman
spectra of E. coli surface-attached cells during biofilm development. Abbreviations:
Phe, phenylalanine; Carb, carbohydrate; def, deformation.
As mentioned in Section 1.2.2, EPS are composed of polysaccharides, proteins,
nucleic acids, lipids and humic-like substances. It is believed that the level of
polysaccharides in biofilm-associated EPS was much higher than that in planktonic
cells (282). Moreover, because of increased EPS synthesis and more complex
architecture in mature biofilm, it has been reported that the protein content of mature
biofilm was also higher than that in younger stages of multispecies biofilms (120).
1 hour
4 hour
8 hour
24 hour
79 hour
120 hour
DN
A/R
NA
syn
thes
is
Ph
e
Am
ide
I
Am
ide
III
CH
2d
ef
Ca
rb
CH
de
f
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(A) (B)
(C)
Figure 5.13 Principal component analysis of Raman spectra collected from E. coli
surface-attached cells during biofilm development: (A) scatter plot of the first and
second principal component, (B) average values plot and (C) loading values plot of
the first principal component (**p < 0.05). Abbreviations: Phe, phenylalanine; def,
deformation.
In order to investigate the DNA/RNA and protein content of the E. coli biofilm,
further detailed univariate analysis of the intensity values for specific peaks which
were selected from the loadings plot were performed (Fig 5.14). As shown in Fig
5.14, the intensity values of protein/lipid-specific peaks (i.e. phenylalanine peak at
1002 cm-1, amide III peak at 1239 cm-1, CH2 deformation peak from protein
backbone at 1447 cm-1, amide I peak at 1663 cm-1) were higher in the E. coli cells
from the mature biofilm than in the other biofilm phases. Conversely, the intensity of
**
**
Am
ide IP
he
CH
2d
ef
DNA/RNA
Mya Myintzu Hlaing Chapter 5/167
DNA/RNA-specific peaks decreased in the E. coli biofilm cells from the mature
biofilm when compared with the other biofilm phases.
(A)
(B)
Figure 5.14 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of E. coli surface-attached cells during biofilm
development. Raman peaks were selected from the loadings plot (Fig 5.13(C)). Each
group of biofilm cultivation was an average of four replicates. The Raman
frequencies and their peak assignments are shown in Table 3.1. Abbreviations: T,
thymine; G, guanine; C, cytosine; U, uracil.
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V. vulnificus
The average Raman spectra of V. vulnificus biofilm cells at different biofilm phases
are shown in Fig. 5.15. The results indicate that the main changes in the Raman
spectra occur in the DNA/RNA related region at 685–800 cm-1, phenylalanine region
at 1001 cm-1 and protein/lipid associated peaks at 1002, 1242, 1452, 1663 cm-1.
Unlike E. coli biofilm cells, these spectral variations are more distinctive when
comparing 1 h old biofilm cells with cells from later biofilm phases.
Figure 5.15 Averaged, intensity-normalised and background subtracted Raman
spectra of V. vulnificus surface-attached cells during biofilm development.
Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def, deformation.
The scores plot from PC1vs PC2 also showed the separation of these 1 h substrate
grown cells from later time points (Fig 5.16A). A significant separation (p value <
0.05) of biofilm cells between 1 h old biofilm phase and other older phases was seen
from the average value plot of the first principal component (Fig 5.16B). The loading
values plot of the first principal component shown in Fig 5.16C indicates the Raman
peaks which contributed most of the separation in the scatter plot. Interestingly, the
results show that the peak fluctuations related to DNA/RNA synthesis corresponded
to the later phases of biofilm growth, while the protein-specific peaks were related to
the difference between initially attached cells at 1 h and the later phases of biofilm
development. The variation in the DNA/RNA related peaks in the later phases could
be explained by the release or accumulation of extracellular DNA from bacterial
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cells into the biofilm matrix in mature biofilm development (see further discussion
below).
(A) (B)
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Figure 5.16 Principal component analysis of Raman spectra collected from V.
vulnificus surface-attached cells during biofilm development: (A) scatter plot of the
first and second principal component, (B) average values plot and (C) loading values
plot of the first principal component (**p < 0.05, *p < 0.1). Abbreviations: Phe,
phenylalanine; carb, carbohydrate; def, deformation.
The results seen in Fig 5.16 were further confirmed by detailed analysis of specific
peaks related to DNA/RNA synthesis (Fig 5.17A). A higher intensity of DNA related
peaks was seen in the later biofilm phases. In particular, higher intensity values
started to appear from 4 h old biofilm and the highest intensity was seen in the 79 h
old biofilm. Extracellular DNA has recently been reported as a major structural
component in the biofilm matrix and found to play various roles in biofilm
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development, including enhancement of adhesion and cohesion of biofilm, as well as
exchange of genetic information (283-285). Moreover, it has been further reported
that the release of extracellular DNA was mediated by certain genes (such as lytic
transglycosylase and cytoplasmic N-acetylmuramyl-L-alanine amidase genes in
Neisseria meningitides) to facilitate initial biofilm formation (286). Therefore, it can
be concluded that higher concentrations of extracellular DNA might exist in the
mature biofilm matrix than in earlier phases of V. vulnificus biofilm (i.e., initial
attached cells and bacterial colonies) due to DNA accumulation or release as
observed in this study.
Judging from the appearance probabilities of the spectra (Fig 5.15) and the univariate
analysis of intensity changes for the selected peaks (Fig 5.17B), it can be seen that
the protein content was significantly higher in early biofilm cells compared with
mature biofilm (at 4 h cultivation and onwards). This increased level of protein
synthesis in early biofilm cells differs from the opposite trend observed in the E. coli
biofilm cells (mentioned in Section 5.3.2.1.1). Although it cannot be explained
exclusively, this raised level in V. vulnificus might be related to the high expression
level of lipopolysaccharides, capsular polysaccharide and adhesion proteins on the
outer membrane of the bacteria during initial attachment to the surface.
Mya Myintzu Hlaing Chapter 5/171
(A)
(B)
Figure 5.17 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of V. vulnificus surface-attached cells during
biofilm development. Raman peaks were selected from the loadings plot. Each group
of biofilm cultivation was an average of four replicates. The Raman frequencies and
their peak assignments are shown in Table 3.1. Abbreviations: A, adenine; C,
cytosine; U, uracil; T, thymine.
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P. aeruginosa
The average Raman spectra of P. aeruginosa cells from biofilm growth are shown in
Fig. 5.18. As was seen in the E. coli and V. vulnificus data, key features in the Raman
spectra are associated with the DNA/RNA related region at 685–800 cm-1,
proteins/lipids and phenylalanine associated peaks at 1337 and 1602 cm-1. Similar to
E. coli biofilm cells, these spectral features are more distinctive in the 120 h old
biofilm cells compared to the cells from earlier biofilm phases.
Figure 5.18 Averaged, intensity-normalised and background subtracted Raman
spectra of P. aeruginosa surface-attached cells during biofilm development.
Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def, deformation.
Univariate analyses were performed to investigate the trends in DNA and protein
synthesis during biofilm growth. The intensities value of DNA-related peaks selected
from the loading plots showed a decreasing trend in DNA content during biofilm
growth (Fig 5.20A). Although the overall spectral variation were insignificant (p >
0.05) as shown in the average values plot, the detailed analysis of specific peaks
revealed a significant increasing trend in the protein-related peaks, which were again
similar to the trend of E. coli cells during biofilm development (Fig 5.20B).
Moreover, the large error bars seen in the samples from mature biofilm indicated that
there was higher cellular heterogeneity in this phase compared to other earlier
biofilm growth phases. This phenomenon was also seen in the planktonic cells of P.
aeruginosa, but it was even more striking in the biofilm cells. This suggests that P.
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aeruginosa might exhibit a variety of physiological and morphological changes from
a low- to a high-cell-density state in mature biofilm. As discussed in the literature
Chapter, biofilm cells are likely to encounter nutrient and oxygen limitations, as well
as higher levels of waste products and secondary metabolites. Moreover, it is
believed that the production of EPS by surface-attached cells and the mechanisms of
the biofilm development process are quite different from species to species (287).
Because of this, it is perhaps not surprising that there was more intrinsic cellular
heterogeneity in P. aeruginosa biofilm cells compared to other species tested in this
study.
(A) (B)
(C)
Figure 5.19 Principal component analysis of Raman spectra collected from P.
aeruginosa surface-attached cells during biofilm development: (A) scatter plot of the
first and second principal component, (B) average values plot and (C) loading values
plot of the first principal component (***p < 0.005). Abbreviations: Phe,
phenylalanine; def, deformation.
***C
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(A)
(B)
Figure 5.20 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of P. aeruginosa surface-attached cells during
biofilm development. Raman peaks were selected from the loadings plot. Each group
of biofilm cultivation was an average of four replicates. The Raman frequencies and
their peak assignments are shown in Table 3.1. Abbreviations: T, thymine; G, guanine
C, cytosine; U, uracil.
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S. aureus
The average Raman spectra from biofilm cells of the Gram-positive bacterium, S.
aureus, are shown in Fig 5.21. The main changes in the Raman spectra throughout
biofilm growth were seen in the DNA/RNA related region at 685–800 cm-1,
phenylalanine and protein/lipid associated peaks at 1002, 1452 and 1663 cm-1.
Raman spectral features in these regions are actually more distinctive in the initial
attached biofilm cells (i.e. 1 h and 4 h old biofilm) compared to the cells from
bacterial colonies or mature biofilm.
Figure 5.21 Averaged, intensity-normalised and background subtracted Raman
spectra of S. aureus surface-attached cells during biofilm development.
Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def, deformation.
The good separation between biofilm cells and cells at initial attachment can be seen
in the scatter plot of the first and second principal components (Fig 5.22A). The
average values plot of the first principal component revealed significant spectral
variations between earlier biofilm cells and mature biofilm cells (Fig 5.22B). The
loadings plot of the first principal component provides the peak regions which
contributed to the separation of cells during biofilm growth, as seen in the scatter plot
(Fig 5.22C). The results indicate that the peaks which are associated with
lipid/protein synthesis represented the main spectral variation of surface attached
bacterial cells compared to the biofilm cells. From the analysis of specific peaks, it
can be seen that the DNA peak intensities were relatively stable during biofilm
growth. As mentioned in the earlier sections, the intensity of the protein-related
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peaks increased significantly for Gram-negative bacteria from initial bacterial
attachment to mature biofilm. However, for S. aureus, this increase was different to
that of E. coli and P. aeruginosa, as a decreasing trend was observed along with the
biofilm cultivation. This increased intensity of protein-related peaks during initial
attachment might be due to the higher expression of surface proteins in S. aureus
bacteria for cell adherence to the surface. As mentioned in Section 1.2.2, bacterial
cells that do not have extracellular organelles (such as fimbriae, flagella and pili)
normally produce adhesion proteins to overcome the interfacial repulsive forces and to
promote stable attachment to the surface. The role and expression of surface adhesion
proteins in S. aureus for adherence to surfaces has been recently reported (288).
(A) (B)
(C)
Figure 5.22 Principal component analysis of Raman spectra collected from S. aureus
surface-attached cells during biofilm development: (A) scatter plot of the first and
second principal components, (B) average values plot and (C) loading values plot of
the first principal component (***p < 0.005). Abbreviations: Phe, phenylalanine; def,
deformation.
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Figure 5.23 Intensity changes of (A) DNA/RNA and (B) protein/lipid structure-
specific peaks in the Raman spectra of S. aureus surface-attached cells during
biofilm development. Raman peaks were selected from the loadings plot. Each group
of biofilm cultivation was an average of four replicates. The Raman frequencies and
their peak assignments are shown in Table 3.1. Abbreviations: T, thymine; G, guanine
C, cytosine; U, uracil.
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5.3.4 PC-LDA models for classification of biofilm cells
5.3.4.1 Single-species surface-attached cells
As discussed in the literature review, the identification of microbial species within an
intact biofilm sample is important for microbiological studies of biofilm formation in
clinical and environmental settings. Based on the limitations of standard methods for
species identification, a measurement method that could detect the presence of
bacteria and map the spatial distribution of multiple species in intact biofilm samples
in a label-free, reagentless fashion would be invaluable. Therefore, an attempt was
made to create a model which could be used in longitudinal studies of environmental
biofilm samples.
The results from the previous sections have shown significant variations in the
Raman spectra of cells at different growth points in single species biofilms. A first
attempt was made to classify the surface-attached cells (biofilm cells) using the
previously constructed PC-LDA planktonic model. The results revealed that 72%
classification sensitivity was achieved for the Raman spectra collected from surface-
attached cells of E. coli species during biofilm growth (Fig 5.24).
Figure 5.24 Classification and identification of spectra from surface-attached cells of
E. coli grown on a quartz substrate with the planktonic PC-LDA model. The mean of
each group in the training model is shown with () symbol in yellow. The test
samples which were misidentified are labelled with (×) symbol in red.
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V. vulnificus
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Although 72% correct identification was achieved, it can be seen that the surface-
attached cells were clustered near the general area of the corresponding species
within the model without overlapping. As discussed in Section 5.3.2, the
classifications results of surface-attached cells using PC-LDA planktonic cells were
imperfect due to the differences between surface-attached and planktonic cells. In
fact, efforts to classify the other species (i.e. V. vulnificus, P. aeruginosa and S.
aureus) were a failure as a consequence of these variations between surface-attached
and planktonic cells and intrinsic cellular heterogeneity among biofilm cells.
The next step in the study investigated whether the differences between cells at
different biofilm growth points impacted on the classification and identification of
isolates from intact biofilm using a biofilm model. To explore this, Raman spectra
from the three bacterial species E. coli, V. vulnificus and S. aureus were analysed at
different points of biofilm growth using principal component and linear discriminant
analysis (PC-LDA) since biofilm data of P. aeruginosa showed more intrinsic
cellular heterogeneity compared to other species (shown in Fig 5.19). PCA was first
performed for data reduction of the 1407 included wavenumbers from each spectrum
of the cells from different phases of biofilm growth using MATLAB. With the
application of OriginPro software (version 9.0.0), LDA was further performed based
on the first 10 principal components (PCs) generated from MATLAB which
accounted for approximately 98 % of variance in the data set. As shown in the
canonical scores plot, which was plotted against the first two canonical discriminant
functions, the PC-LDA classification method effectively discriminated and classified
the bacterial taxa into three groups, despite physiological variations between the
same species cells during biofilm development (Fig 5.25).
For evaluation and calibration of the PC-LDA model, leave-one-out cross-validation
(LOOCV) was performed. To perform LOOCV, a single spectrum was removed as a
test spectrum from the database and a training data set was created using the
remaining spectra. The classification label of the test set (left out spectrum) was
determined and the process was repeated for all 18 cells against the training set (36
spectra). Clustering of each test set (one cell from every biofilm growth phase of
each bacterial species) was observed among the data within its respective training
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set, thus validating the model. The cross-validation results of PC-LDA based on the
first 16 PCs of the three different species are shown in Table 5.4. The results provide
100 % classification sensitivity and specificity in a leave-one-cell-out cross-
validation (LOCOCV) for all three species.
Figure 5.25 Linear discriminant analysis (LDA) based on the retained principal
components (PCs) for bacterial species differentiation during biofilm growth: LDA
was performed based on 16 PCs of the Raman spectra collected from the surface-
attached cells of E. coli, V. vulnificus and S. aureus biofilms. The mean of each
group in the training model is shown with () symbol in yellow.
Table 5.3 Calibration accuracy results of the PC-LDA model with the first 16 PCs on
a total of 54 spectra of three bacterial species from their different biofilm growth
points.
Predicted Group Sensitivity Specificity Error rate
E. coli V. vulnificus S. aureus (%) (%) (%)
E. coli 18 0 0 100 100 0
V. vulnificus 0 18 0 100 100 0
S. aureus 0 0 18 100 100 0
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MATLAB code for the PC-LDA model along with the corresponding species labels
was custom written and applied for species identification of the E. coli biofilm cells
from separate batch culture. The calibrated PC-LDA model using spectra from
biofilm cells of each species was validated on 9 spectra of E. coli biofilm cells
achieving 100% accuracy in prospective classification. As discussed in Section 5.3.2,
the PC-LDA classification was based on the smallest distance value from each of the
group means to the sample. The classification of the test samples projected into the
DFA space generated by the training set is shown in Fig 5.26. Since this PC-LDA
biofilm model correctly identified the species of origin of single-species biofilms, the
next step was to apply this model for detection of two bacterial species in mixed
biofilms.
Figure 5.26 Validation of PC-LDA biofilm model on 9 new spectra of E. coli cells
from a single-species biofilm: the numbers 1-9 represent the test data and the rest of
the points indicate training data used to identify the bacterial cells.
5.3.4.2 Raman- Fluorescence in situ hybridisation (FISH) analysis of
bacterial cells from dual-species biofilm
Techniques based on FISH and confocal laser scanning microscopy have become
well-established for the analysis of the spatial organization of in vitro bacterial
biofilms (18, 64, 65, 69). There are many advantages of using rRNA as the main
target molecules for FISH, because ribosomes can be found in the cells of all living
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organisms for translation processes. They are relatively stable and occur in high copy
numbers. Each prokaryotic ribosome (i.e. ribosomes of bacteria and archaea)
contains 5S, 16S and 23S rRNA with lengths of approximately 120, 1500 and 3000
nucleotides, respectively (20, 87).
Experiments were set up to enable Raman and FISH analysis of individual bacterial
cells from dual-species biofilm. Raman spectra were collected to determine the
chemical fingerprints of the individual microbial cells within the biofilm. These
spectra were then projected into the PC-LDA biofilm model in an attempt to identify
the species. The FISH technique was then used to confirm the location of E. coli
within the biofilm. The FISH technique also allowed in situ visualization of the
complex biofilm structure and spatial distribution of the cells.
Raman spectra were collected from 79 h old dual-species biofilm and signal pre-
processing was performed as mentioned in Section 2.2.5.2. To track the location of
the biofilm cells, which were selected to collect the Raman spectra, an electron
microscopy grid (#G4901, grid size 300 mesh × 83 μm of pitch, Sigma) was used.
The microscope grid was mounted on the back of the quartz slide where the biofilms
were grown. A single bacterial cell from biofilm sample was brought into focus to
collect the Raman spectra. After each Raman acquisition, the grid was brought into
focus and microphotographs were taken for tracking the location of the cell.
The custom-written MATLAB code for the PC-LDA model, along with the
corresponding species labels of biofilm cells from each single-species biofilm, was
applied for species identification of cells from a dual-species biofilm. The model was
applied to detect the presence of two species in dual-species biofilms of E. coli and
V. vulnificus. When 12 spectra collected from dual-species biofilms were examined,
the presence of both E. coli and V. vulnificus was detected in 5 and 4 out of 12
sample regions respectively and providing 75 % for overall sensitivity using the PC-
LDA model based on the first 16 PCs of three bacterial species (Table 5.4). The
classifications of the test samples projected into the DFA space generated by the
training set are shown in Fig 5.27. As discussed in Section 5.3.2, the PC-LDA
classification was based on the smallest distance value from each of the group means
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to the sample. Thus, the test samples labelled with (x) symbol in red (shown in Fig
5.27) were probably allocated to S. aureus species, which was the nearest group to
the test data point. To confirm the presence and identification of bacterial cells with
the PC-LDA biofilm model, fluorescence in situ hybridisation (FISH) technique with
rRNA-targeted oligonucleotide (probe) for E. coli (ATCC 25922) was performed.
The probe efficiency test and FISH techniques are discussed in Section 2.2.4.3.
Table 5.4 Application of PC-LDA model to 12 spectra from a dual-species biofilm.
Reference Sensitivity
E. coli V. vulnificus Misidentification (%)
Cla
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Figure 5.27 Application of the PC-LDA biofilm model to 12 spectra from dual-
species (E. coli and V. vulnificus) biofilm culture: Numbers 1-12 represent the test
data and the rest of the points indicate training data used to identify the bacterial
cells. The test samples numbered 1, 4-6, 8 were identified as E. coli and the test
samples numbered 2, 10-12 were identified as V. vulnificus. The test samples which
were misidentified are labelled with (×) symbol in red.
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In this study, FISH was undertaken with 16S rRNA targeted oligonucleotide (probe)
for E. coli. The rRNA targeted oligonucleotide probe and its hybridization conditions
were discussed in Section 2.2.4.3. The probe sequences used (EC1_485) were
designed to specifically target the 16S rRNA of E. coli ATCC 25922 (Accession:
X80724, GI: 1240023, 1452 base pairs, genomic DNA). The probe was
manufactured by Life Technologies Australia Pty Ltd and was labelled with Alexa
Fluor 647. Probe sequences were designed and pre-validated using the Primer-blast
tool from National Center for Biotechnology Information (NCBI) before synthesis.
The probe specificity, efficiency and EPS staining protocols with Concanavalin A
(ConA, Molecular Probes, Invitrogen) were optimised (details in Sections 2.2.4.3,
2.2.4.4 and 2.2.4.6).
The optimised FISH techniques were applied to the dual-species biofilms of E. coli
and V. vulnificus after Raman analysis. Two-dimensional (2-D) confocal laser
scanning microscope (CLSM) images of dual-species biofilms are shown in Fig 5.28.
The results show that the target E. coli cells were generally labelled with both FISH
rRNA probe and ConA, while the cells which were stained with ConA only were
considered to be V. vulnificus species. This strategy is based on the expectation that
the cells that were identified indirectly by ConA stain outnumber the cells labelled by
the FISH rRNA probe, which is the case here (Fig 5.28B and C).
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Figure 5.28 Two-dimensional confocal laser scanning microscope images of dual-
species biofilms of E. coli and V. vulnificus. (A) DIC image, (B) FISH hybridized E.
coli cells labelled with Alexa Fluor 647, (C) Concanavalin A stained EPS (-D-
glucopyranose polysaccharide) and proteins/glycoconjugate groups associated with
bacterial cell walls and (D) visualisation of E. coli cells in red with EPS matrix and
bacterial cell wall in blue. (White arrow shows labelled E. coli and red arrow shows
stained EPS and bacterial cell wall)
In order to investigate the cellular densities, spatial distribution of bacterial species
and structural information of the biofilm matrix, CLSM images of 79 h old dual-
species biofilm were captured with the z-stack function tool from CLSM (Fig 5.29).
The three-dimensional (3-D) reconstruction of the confocal z-stack images was
performed with ImageJ software. Selected 2-D cross-sectional images of CLSM
showed the spatial distribution of bacterial species across the surfaces of dual-species
biofilms in the x-y, y-z and x-z planes. The two species were equally distributed
across the surfaces of biofilms in the x-y plane, whereas more than two defined layers
of cells could be seen in the vertical distribution of the z axis. The top layer was
mainly composed of labelled E. coli cells and another layer close to the substrate
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surface was mostly V. vulnificus cells/EPS matrix stained with ConA and. In terms of
species interactions in these dual-species biofilms, the cell density of E. coli was
sightly affected by the presence of V. vulnificus (Fig 5.29A). The higher cell density
of V. vulnificus can be seen more clearly in the 3-D CLSM image and the formation
of differentiated 3-D structures like ‘‘stacks’’ of micro-colonies could also be
observed (Fig 5.29B).
(A)
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Figure 5.29 Spatial organisation of 79 h old dual-species biofilms. (A) 2-D confocal
laser scanning microscope (CLSM) images of dual-species biofilms showing spatial
distribution of E. coli and V. vulnificus species on x-y, y-z and x-z planes, (B) 3-D
CLSM images showing dual-species biofilms with FISH hybridized E. coli cells
labelled with Alexa Fluor 647 in purple (blue + red) and concanavalin A stained EPS
(-D-glucopyranose polysaccharide) and proteins/glycoconjugate groups associated
with bacterial cell walls of V. vulnificus species in blue.
x-y plane
10 µm
x-z plane
y-z plane
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The identification results from the PC-LDA biofilm model suggested a homogeneous
population of E. coli and V. vulnificus cells (i.e. approximately 50% of each of the
identifiable cells). However, the data from FISH technique showed a smaller cell
density of E. coli in the dual-species biofilm. From the results of the planktonic
growth curve experiments which were mentioned in Section 4.3.2.1, the growth rates
of E. coli and V. vulnificus, were found to be similar (approximately 0.77/h and
0.65/h respectively). On account of the same number of initial loaded cells on the
substrate and the similar growth rate, the cell densities of the two bacterial species
were expected to be similar in the dual-species biofilm. The reduced number of E.
coli in the biofilm in the presence of V. vulnificus is probably due to competition for
nutrients during biofilm growth. Moreover, it has been reported that the capsular
polysaccharide of V. vulnificus inhibits attachment and biofilm formation (203).
FISH hybridized E. coli cells labelled with Alexa Fluor 647 probe were seen as red
cells clusters. EPS (α-D-glucopyranose polysaccharide) and proteins/glycoconjugate
groups associated with cell walls labelled with ConA stain were seen as in blue.
Thus, E. coli cells aggregation in the biofilm were seen as in purple coloured cells
cluster (blue plus red) because of labelling with both FISH probe and ConA. As a
consequence of having overlapped bacterial cells within biofilm matrix (as seen in
Fig 5.29), the cells cluster of the micro-colonies could not be identified clearly
whether they were E. coli or V. vulnificus. Since unstained cells were not excluded
from undetectable cells, the cell densities could not be accurately enumerated or
estimated from visualisation using only one species-specific fluorescence-labelled
probe in this experiment.
The results from confocal imaging underscored the involvement of extracellular
polysaccharide of V. vulnificus, which may relate to both the initial attachment and
biofilm development of E. coli cells when these two species were co-cultured. For
the reason that the two bacterial species were on the top of each other within the
multi-layered biofilms, the Raman spectra collected from the same focal plane may
have mixed spectral features from the two bacterial species. Therefore, the
identification results from dual-species biofilms may be unpredictable. Thus, the
classification and identification results of the cells from the mature dual-species
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biofilms were not successfully confirmed by FISH. If less mature biofilm samples
were used instead, the outcomes may be different as the Raman spectra of surface-
attached cells from the monolayer biofilms may be more reliable for PC-LDA
classification.
The first obstacle in performing the FISH technique was that some of the collected
Raman spectra from the multi-layered biofilm had mixed spectra features from the
two bacterial species. This issue may be minimised if Raman spectra were collected
from cells of mono-layered biofilm samples. Thus, this problem could be solved in
future studies, possibly by collecting “z” profile Raman spectra using a combination
of FISH with Raman spectroscopy instrumentation, providing that the fluorescence
doesn’t swamp the Raman spectral region.
Another challenge for the FISH technique applied here is that the presence of other
charged particles in the biofilm matrix, such as DNA and sugar acid residues could
impede the penetration of probes to the target cells. Therefore, it is very difficult to
rely on the labelling efficiency of a one species-specific probe in a mixed biofilm by
FISH. This difficulty could be revised in future, possibly by using species-specific
fluorescence-labelled probes for every species in the consortium biofilm. By
performing multiplex FISH analysis, it will assure that only CoA stained cells in the
biofilm were due to substantial losses of the probe penetration to the target cells
during the staining procedure. Then a sample preparation step for hybridisation (i.e.
permeabilisation using lysozyme) could be optimised to achieve the best outcomes.
Although we were not able to confirm that the model detected the correct species, to
the best of our knowledge, this is the first study in which two different bacterial
species have been detected and identified in dual-species biofilm using Raman
spectroscopy.
5.4 Conclusion
In conclusion, the application of Raman spectroscopy to bacterial identification in
intact bacterial colonies has been developed in this study. This approach allows for
simple sample preparation for direct investigation of bacterial micro-colonies
isolated on nitrocellulose membranes, which were laid on pre-warmed nutrient agar.
Mya Myintzu Hlaing Chapter 5/189
Moreover, the ability to detect and identify the bacteria that were loaded on the
membrane can be applied in food-processing environments and water analysis.
The nitrocellulose membrane provides a very narrow and sharp Raman peak at a
wavenumber of 1282 cm-1 and the Raman signal from this peak was used as an
internal standard for normalization of the spectra. Although the membrane peak
removal method for membrane-grown colonies cells was challenging and not so
perfect at this point, high accuracy in differential identification was achieved using
PC-LDA planktonic model except for V. vulnificus species. These results encouraged
that the nitrocellulose membrane could be used in routine Raman analysis as it is
cost-effective and commercially available. As a preliminary approach, the population
behaviours of bacterial cells isolated on the membrane were analysed with the PC-
LDA planktonic model and promising results were obtained. With the appropriate
reference method to provide confirmation of the population behaviour of bacterial
cells, this approach shows good potential for use in analysis of membrane attached
cells. Overall, this technique allows the simple creation of a Raman biosensor for
differential bacterial identification.
Raman spectroscopy has recently proven to be a promising technique for
characterizing the chemical composition of the biofilm matrix (119, 120). In the
present study, to fully understand the chemical variations during biofilm formation,
Raman spectroscopy was applied to evaluate the chemical components in the biofilm
matrix at different growth phases, including initial attached bacteria, colonies and
mature biofilm. Meanwhile, field-emission scanning electron microscopy (Fe-SEM)
was also applied to study the changes in biofilm morphology. Four model bacteria,
including E. coli, V. vulnificus, P. aeruginosa and S. aureus, were used for Raman
analysis of surface grown biofilm cells. Single-species biofilms of these species were
studied as a simplified model of biofilm forming bacteria in clinical and
environmental studies. The results showed that the content of carbohydrates, proteins
and nucleic acids in the biofilm matrix changed significantly during the biofilm
growth of the four bacteria, as demonstrated by the univariate analysis of related
marker peaks which were selected from PCA. The findings suggest that Raman
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spectroscopy has significant potential for studying chemical variations during biofilm
formation.
Despite the Raman spectral variations of cells from different growth points for a
single species, confocal Raman spectroscopy combined with chemometric statistical
analysis (PCA and LDA) provided a good discrimination between different species
of biofilm cells. Analyses were performed using three bacterial species which
included Gram-negative and Gram-positive bacteria (i.e. E. coli, V vulnificus and S.
aureus). A prediction model based on principal component and linear discriminant
analysis (PC-LDA model) was calibrated using single spectra from biofilm cells of
each species and validated on pure E. coli biofilms grown separately, achieving
100% accuracy in classification although PC-LDA planktonic model provided poor
classifications for biofilm cells.
When the PC-LDA biofilm model was applied to a dual-species biofilm, the presence
of E. coli or V. vulnificus was detected in nine out of twelve biofilm regions,
providing 75% sensitivity. Performing FISH was the next motivation to confirm the
species identification results from dual-species biofilm with the PC-LDA biofilm
model. However, many challenges were encountered in performing the FISH
technique. In future studies, collecting “z” profile Raman spectra using a
combination of FISH with Raman spectroscopy instrumentation could solve some
difficulties of having mixed spectra features from the two bacterial species.
Moreover, performing multiplex FISH analysis will be able to confirm whether the
PC-LDA model detect the correct species within dual or multi-species biofilm
samples.
Mya Myintzu Hlaing Chapter 6/191
RAMAN ANALYSIS OF BACTERIA ON DIFFERENT SURFACE
CHEMISTRIES
6.1 Introduction
This chapter presents a Raman spectroscopy study of E. coli grown on hydrocarbon
rich, amine-terminated and carboxyl-terminated plasma polymer surfaces (i.e. 1, 7-
octadiene, allylamine and acrylic acid). It is believed that surface-attached bacteria
normally sense and respond to a substratum surface, resulting in adaptive responses
(i.e. subtle changes in morphology and chemical composition) during their struggle
for survival (289). This reaction/response from bacterial cells is due to the adhesion
forces that make bacteria aware of their adhering state on a surface and drive the
change from a planktonic to a biofilm phenotype (290). As also discussed in Chapter
1, physical and chemical properties of surfaces can influence bacterial cell adhesion
to surfaces and their development into biofilms (181, 182, 194). In this Chapter, the
primary intention was to evaluate the effect of different surfaces on the ability to
identify the bacteria using planktonic and biofilm models. The initial attachment and
viability of attached bacteria and subsequent growth and biofilm formation on
different plasma-polymerized surfaces are also discussed. In order to observe cell-
surface interactions during biofilm development, multivariate and univariate analyses
of Raman spectra collected from bacterial cells attached to plasma polymer films
were performed.
6.2 Materials and methods
Fused quartz slides (dimension 76 × 25mm; thickness 1 mm) purchased from
ProSciTech, Australia (detailed in Section 2.1.3) were used as a substrate for
deposition of the plasma polymerised thin films. 1, 7-octadiene (molecular formula
C8H14, 98.50% purity, MW 110.20, d = 0.740) and allylamine (molecular formula
C3H7N, 98+%, extra pure, MW 57.09, d = 0.763) were purchased from Acros
Organics, USA. Acrylic acid (molecular formula C3H4O2, 99% purity, MW 72.06, d
= 1.051) was purchased from Sigma-Aldrich, USA. Isopropyl alcohol (IPA,
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molecular formula C3H8O, 99.8 % purity) and ammonia 30% solution (molecular
formula NH4OH, MW 35.05, d = 0.89) were obtained from Chem- Supply Pty Ltd,
Australia. Hydrogen peroxide 30% (molecular formula H2O2, MW 34.01) was
purchased from Ajax Finechem Pty Ltd, Australia.
Plasma polymerisation experiments were performed in collaboration with Ms.
Hannah Askew who is a PhD candidate in the McArthur group. The optimal
deposition conditions for plasma polymerisation, i.e. radio frequency, input power,
monomer flow rate and deposition time were established from previous studies
undertaken within the McArthur group (291).
Prior to plasma polymerisation, quartz substrates were first sonicated in IPA for 10
minutes. The substrates were then cleaned by incubating in a solution containing
Milli-Q water, 30% ammonia and 30% hydrogen peroxide (ratio of 5: 1: 1) at 70 °C
for 10 min, followed by extensive rinsing with Milli-Q water. The cleaned substrates
were blown dry with N2 gas before treatment in a UV Ozone Cleaner (Bioforce
Nanosciences) for 10 min. Plasma polymerisation was carried out in a custom-built
stainless steel T-shaped reactor with stainless steel end plates that were sealed with
Viton O-rings, as previously described (291). The gas pressure was controlled using
a fine (CMV-VFM-2-P-KK) or medium (CMV-VFM-3-P-KK) flow needle valve
(Chell Instruments Ltd, UK) depending on the monomer being used and was
monitored with a Pirani gauge (Edwards, UK). The reactor was pumped down to a
base pressure of 1 × 10-3 mbar. Monomers were degassed using a minimum of three
freeze-thaw cycles. Stabilisation of the defined monomer flow rates was performed
according to the standard operating procedure established by the McArthur group
(291). Plasma was ignited via an aluminium internal disc electrode connected to a
radio frequency (13.56 MHz) power source (Coaxial Power Ltd.) for 20 mins once a
stable flow rate was reached. Plasma deposition conditions for the different
monomers are summarised in Table 6.1, which includes deposition power, monomer
flow rate and deposition time.
Mya Myintzu Hlaing Chapter 6/193
Table 6.1 Plasma polymerisation conditions for 1, 7-octadiene, allylamine and
acrylic acid.
Monomer Structure Power
(W)
Flow rate
(sccm)*
Time
(min)
1, 7-octadiene
20 1.5 20
Allylamine 20 1.5 20
Acrylic acid
20 1.5 20
* Standard cubic centimeters per minute (sccm) units for the monomer flow rate
The choice of polymer coated thin films for this study was based on previous studies
of the McArthur group revealing that surface chemistry plays a critical role in
bacterial attachment (292, 293). Therefore, this study was conducted to further
understand the interaction of E. coli cells to the hydrocarbon rich, amine-terminated
and carboxyl-terminated plasma polymer surfaces using Raman spectroscopy.
The surface chemistry of plasma polymerised thin films (i.e. ppOD (1, 7-octadiene),
ppAAm (allylamine) and ppAAc (acrylic acid)) was determined by X-ray
photoelectron spectroscopy (XPS) with the help of Dr. Deming Zhu. The XPS
measurements were carried out with an AXIS-NOVA XPS spectrometer (Kratos
Analytical Inc., Manchester, UK) using a monochromated Al Kα source with a
power of 150 W (15 kV × 10 mA) at a pass energy of 20 eV for high resolution scans
and 160 eV for wide scans. The total pressure in the sample analysis chamber during
analysis was on the order of 10-8 Torr (1.33 × 10-8 mbar). Three different positions on
each sample were analysed. CasaXPS software (Casa Software Ltd., Cheshire, UK)
was used to determine the elemental composition and the main components present
in the plasma polymers using sensitivity factors supplied with the instrument.
Surface wettability of plasma polymerised thin films was determined by static
contact angle measurements using a contact angle goniometer (Ramé-Hart, Inc.,
Mountain Lakes, NJ, USA). These contact angle measurements were performed in
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collaboration with Ms. Hannah Askew. Contact angles were measured statically on
each of the samples using a contact angle goniometer. A droplet (~ 2μL) of Milli-Q
water was lowered manually into the sample using a needle. The contact angle was
then measured on the right side of the droplet. The measurements were repeated in
three different positions on the sample.
To investigate cell attachment and growth on different polymer coated surfaces, a
static biofilm cultivation process was undertaken on the quartz glass slides using E.
coli and following the methods detailed in Section 2.2.3.3. The samples were
collected after 1 h, 24 h and 120 h incubation time points. The biofilm samples were
kept in PBS and rinsed with Milli-Q water just before Raman measurement. For
Raman spectroscopy measurements of the transferred cells, the bacterial cells from
the polymer coated surfaces and quartz substrate were removed from the surfaces by
scrubbing with inoculation loops. The harvested cells were then mixed with 10 µL of
MilliQ water on a CaF2 slide, smeared and air-dried.
The surface-attached bacterial cells/biofilm cells from the intact biofilm samples, as
well as the transferred cells on the CaF2 slides, were analysed by Raman
spectroscopy with the parameters mentioned in Section 2.2.5. For visualisation of E.
coli live and dead cells within the biofilm matrix, bacterial viability tests were
performed as mentioned in Section 2.2.4.1. The SYTO9 stained live cells and
propidium iodide (PI) stained dead cells were visualised under confocal laser
scanning microscopy (CLSM) using the protocols detailed in Section 2.2.4.
6.3 Results and discussion
6.3.1 Characterisation of the plasma polymer thin films
6.3.1.1 Surface wettability
The contact angles (surface wettability) of the plasma polymer thin films (ppOD,
ppAAm and ppAAc) and the control quartz substrate were determined using a
goniometer. The results confirmed that the hydrocarbon rich, ppOD thin film was
hydrophobic with a contact angle of 90°±0.8 compared to the more hydrophilic
oxygen-rich ppAAc coated surface and quartz slide with contacts angles of 50°±2
and 20°±5 respectively. The ppAAm thin film displayed moderate surface wettability
Mya Myintzu Hlaing Chapter 6/195
with a contact angle of 60°±3. These results correlated well with the contact angle
data for both of these plasma polymers and clean quartz substrate found in the
literature (294-296).
6.3.1.2 X-ray photoelectron spectroscopy
The surface chemistry of each of the plasma polymers was determined by XPS. A
survey spectrum of each plasma polymer showing each of the elements (i.e. oxygen,
O 1s; carbon, C 1s; and nitrogen, N 1s) present on the sample surfaces is presented in
Fig 6.1. The atomic composition and O/C and N/C ratios for each polymer thin film
are shown in Table 6.2. The results indicated that as expected the 1, 7-octadiene thin
film (ppOD) was hydrocarbon rich with low levels of oxygen incorporation due to
oxidation of the film (297). The acrylic acid thin film (ppAAc) contained the
expected levels of both carbon and oxygen. The plasma polymerised allylamine thin
film (ppAAm), contained oxygen, nitrogen and carbon as expected with oxygen
again incorporated due to oxidation. The results obtained in this study were
correlated with previous studies reported by the McArthur group (293). Based on the
survey spectra which showed all elements present on the sample surfaces, subsequent
high-resolution XPS spectral acquisition was performed for O 1s, N 1s and C 1s to
analyse the functionality of the components of the peak. These XPS results were just
for validation of surface chemistry on polymer surfaces demonstrating that the
coatings were not contaminated and could be correlated with coatings produced
previously in the group.
FSET PhD Thesis/196
Figure 6.1 XPS survey spectra of plasma polymerised 1, 7-octadiene (ppOD),
allylamine (ppAAm) and acrylic acid (ppAAc) deposited on quartz slides.
Table 6.2 XPS Atomic composition and atomic ratios of plasma polymerised thin
films deposited on quartz substrates.
Atomic composition (%) Relative ratio to carbon
C 1s O 1s N 1s O/C N/C
ppOD 96.8 ± 0.1 3.2 ± 0.1 - 0.03 -
ppAAm 84.7 ± 0.4 2.1 ± 0.3 13.1 ± 0.1 0.02 0.15
ppAAc 77.0 ± 0.2 23.0 ± 0.2 - 0.29 -
* Listed are the mean values (± standard deviation) based on 3 analyses performed
on each sample.
wide
x 104
2
4
6
8
10
CP
S
1000 800 600 400 200 0
Bi ndi ng E nergy (eV)
Binding Energy (eV)
10
8
6
4
2
x104
Inte
nsity /
Arb
itr.
un
its
O1s C1sppAAc
wide
x 104
2
4
6
8
10
12
14
CP
S
1000 800 600 400 200 0
Bi ndi ng E nergy (eV)
1000 800 600 400 200
10
8
6
4
2
12
ppOD
Binding Energy (eV)
Inte
nsity /
Arb
itr.
un
its
O1s C1s
x104wide
x 104
2
4
6
8
10
12
CP
S
1000 800 600 400 200 0
Bi ndi ng E nergy (eV)
10
8
6
4
2
12
Binding Energy (eV)
Inte
nsity /
Arb
itr.
un
its
O1s C1sN1sppAAm
x104
1000 800 600 400 200
1000 800 600 400 200
Mya Myintzu Hlaing Chapter 6/197
6.3.1.3 Raman spectroscopy measurement
In order to examine the Raman spectra of the plasma polymer thin films deposited on
quartz substrates (ppOD, ppAAm and ppAAc), spectra were collected from the films
and the control quartz slide. A previous study from the McArthur group showed that
the thickness of the polymeric thin films was 40-50 nm (292). Since the same
standard operating procedure established by the McArthur group was applied, the
polymer films in this study were expected to have the same thickness. The Raman
spectrum of each plasma polymer film is shown in Fig 6.2.
Figure 6.2 Averaged, intensity-normalised and background subtracted Raman
spectra collected from the plasma polymerised thin films and the control quartz slide.
The dominant peaks regions covering 700-900 cm-1 and 1000-1100 cm-1 were
consistently seen in the spectra of all polymer films. These peak regions can be
assigned to the broad Raman features from the quartz slide and there is almost no
information associated specifically with the film evident in the spectra. Given that
the thickness of the polymer film is relatively thinner than the quartz substrate and
smaller than the height of the confocal sampling region, Raman spectra taken from
the polymer coating is expected to be dominated by background signal from quartz.
Thus, it can be seen that the Raman spectra of the plasma polymer films were quartz
spectra creating difficulties for the analysis of Raman spectra from polymer surface-
attached cells.
ppOD
ppAAm
ppAAc
Quartz
FSET PhD Thesis/198
6.3.2 Bacterial adhesion to plasma-polymerised surfaces
As discussed in the literature Chapter, the effect of surface chemistry on the cell-
surface interaction could be determined by investigating the bacterial adhesion and
proliferation on the surface. Live and dead cell staining was performed and the
viability of E. coli adhered to the polymer surface was examined by confocal laser
scanning microscopy (CLSM). Representative images for viability tests of the cells
adhering to the polymer surfaces in comparison with the control quartz slide (from
initial attachment throughout biofilm development) are shown in Figs 6.3-6.5.
From CLSM images of E. coli cells adhered to the surfaces after 1 h incubation time,
a relatively large number of adhered cells were observed on the ppAAm surface (Fig
6.3). In contrast, the smallest number of adhered cells was seen on the ppAAc
surface. The individual cell morphology of E. coli was noticed on the quartz,
whereas the cells attached to the polymer surfaces were seen as aggregated cells. As
discussed in Section 1.2.2, it is generally believed that stressful conditions induce an
enhanced bacterial EPS production. Due to cellular responses to the functional
groups of the polymer surfaces, the cells on the polymer surfaces were likely to
produce more EPS secretion in comparison with those on the quartz surface.
Consequently, cell aggregations with indistinct cell morphology were seen on the
polymer surfaces as the cells might be embedded in EPS.
Among the cells attached to the polymer surfaces, the cells from ppOD displayed
larger bacterial colonies on the surface. Deposition of plasma polymerised 1, 7-
octadiene (ppOD) onto fused quartz slides increased the hydrophobicity of the
surface due to the introduction of non-polar hydrocarbon functionalities (CH2) on the
surface. As discussed in the literature review Chapter, hydrophobic moieties on E.
coli cells (i.e. cell wall, fimbriae and extracellular organelles) are expected to result
in more stable interaction and stronger adhesion with the hydrophobic hydrocarbon
rich ppOD polymer surface. These attached bacteria then continued to grow into
micro colonies on the ppOD surface. The large number of cells seen on ppAAm after
initial attachment can be explained due to either an increase in the number of
attached cells on the surface or rapid cell growth within the attached cells.
Mya Myintzu Hlaing Chapter 6/199
Figure 6.3 Two-dimensional CSLM images of E. coli attached to the surfaces at
initial attachment. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)
ppAAc and (d) quartz slide were stained with BacLight Live/Dead staining kit.
SYTO 9 stained E. coli live cells are in green and propidium iodide, PI stained dead
cells are in red. Scale bar = 20 µm applies to all images.
Figure 6.4 shows CLSM images of E. coli colonies which were continuing grow on
the surfaces after 24 h of incubation. Similar increasing trends of bacterial
colonisation with respect to the initial attachment were observed. In particular, more
bacterial colonisation was seen on the ppAAm surface than any other surface.
Interestingly, similar patterns of biofilm development were seen on both ppAAm and
the quartz slide, whereas large bacterial clusters (colonies) were seen on the ppOD
surface. In the case of the surface-attached cells on ppAAm, a small number of PI
stained dead cells were seen mixed together with the live cells (yellow coloured). In
20 µm
(b)
(c) (d)
(a)
FSET PhD Thesis/200
contrast, no dead cells were seen at this growth time point among the cells attached
to the other polymer surfaces including the control.
Figure 6.4 Two-dimensional CSLM images of E. coli attached to the surfaces after
24 h of incubation. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)
ppAAc and (d) quartz slide were stained with BacLight Live/Dead staining kit.
SYTO 9 stained E. coli live cells are in green and propidium iodide, PI stained dead
cells are in red. Scale bar = 20 µm applies to all images.
(a) (b)
(c) (d)
20 µm
Mya Myintzu Hlaing Chapter 6/201
Figure 6.5 Two-dimensional CSLM images of E. coli attached to the surfaces after
120 h of incubation. Cells from the polymer films (a-c): (a) ppOD, (b) ppAAm, (c)
ppAAc and (d) quartz slide were stained with BacLight Live/Dead staining kit.
SYTO 9 stained E. coli live cells are in green and propidium iodide, PI stained dead
cells are in red. Combinations of live and dead cells appear yellow. Scale bar = 20
µm applies to all images.
The biofilm development at 120 h of incubation is shown in Fig 6.5. Distinctive
biofilm features and significantly higher numbers of bacterial colonies were observed
on all surfaces, except for the ppAAc surface where the number of initially attached
cells was the least. In terms of the live and dead cell populations, it can be visualised
that the ppOD and control quartz slide harboured nearly equal numbers of live and
dead E. coli cells, while higher dead cell counts were observed on the ppAAm
surfaces. Thus, based on these observations, it can be concluded that the ppAAm
surface supported the highest level of initial cell adhesion but also displayed the
highest dead cell population in subsequent biofilm formation. In order to study
(a) (b)
(c) (d)
20 µm
FSET PhD Thesis/202
quantitatively the effect of surface chemistry on bacterial adhesion, cell viability and
biofilm formation, the total number of live and dead cells attached on the surfaces
was counted. Bacterial viability, defined as the percentage of the area covered by live
attached bacterial cells versus the total area of adhered cells or total biofilm area,
were calculated.
6.3.3 Two-dimensional cell counting and quantifying cell viability
Cell counting was performed from the 2-D CSLM images of the attached E. coli cells
on different plasma-polymerised surfaces and control quartz slide after 1 h
incubation time. Manual cell counting analysis was performed using the cell counter
plugin installed in ImageJ software (220) and following the protocols mentioned in
Section 2.2.4.1. A comparison of the initial attachment of E. coli on the different
polymer surfaces after 1 h of incubation time is shown in Fig 6.6. The results show
that there was a significantly higher total number of cells attached on the ppAAm
surface compared to any of the other surfaces analysed (p value <0.05 for all
samples). It can be noticed that almost equal numbers of cells adhered on the ppOD
and the quartz slide and this number was significantly higher than the number of
cells attached to ppAAc.
Figure 6.6 E. coli adhesion to different plasma-polymerised surfaces and quartz
substrate at 1 hour incubation time. Abbreviations: ppOD, 1, 7-octadiene; ppAAm,
allylamine; ppAAc, acrylic acid. (*, ** significant differences between surfaces p<0.1
and p<0.05 respectively)
ppOD ppAAm ppAAc quartz0
1x1010
2x1010
3x1010
4x1010
5x1010
Bacte
ria c
ount /
m2 A
rea
Different surfaces
*
**
****
**
Mya Myintzu Hlaing Chapter 6/203
To evaluate the live and dead cell population within the mature biofilms grown on
each of the surfaces, an analysis based on colour segmentation of 2-D CSLM colour
images was performed using the colour segmentation plugin installed in ImageJ
software (221) (detailed protocols mentioned in Section 2.2.4.1). The percentages of
live and dead cell populations in the biofilm were calculated based on the percentage
of the total area covered by attached cells on the surfaces and plotted against those on
quartz surface for comparative study. The viability of E. coli cells in 120 h old
biofilm was evaluated from the ratio of the area covered by green and red labelled
cells.
The results of total biofilm area and cell viability of 120 h old biofilms which were
grown on the plasma polymerised surfaces and quartz slides are shown in Fig 6.7.
The maximum total area covered by biofilm cells was found on the quartz slide,
although the number of initial attached cells on ppAAm was found to be the highest
among the surfaces (Fig 6.7A). As expected, cell coverage on the ppAAc surface was
significantly lower than all other surfaces. When comparing between the biofilm
areas of the ppOD and ppAAm surfaces, it can be noticed that E. coli cells
preferentially adhered and developed biofilm on ppAAm surfaces.
In evaluating cell viability, it is interesting to see that the percentage of the area
covered by live cells on the ppOD surface was the highest with more than 75% of the
total area containing cells. In fact, less cell viability was seen on the ppAAm surface
compared to that on ppOD, although a higher number of adherent cells and more
biofilm covered area were seen on the former surface (Fig 6.7B). Moreover, the
lowest percentage of live cells was found on the ppAAc surface (Fig 6.7B). These
results reveal the trend of cell viability on surfaces which have different wettabilities,
ranging from more hydrophobic to hydrophilic. Based on these results, it can be
concluded that there was higher cell viability on the surface which had more
hydrophobicity (i.e. ppOD surface). These findings are correlated with the study of
Parreira et al. revealing that bacteria adhered preferentially to the more hydrophobic
surface compared to more hydrophilic OH- exposed surface (196). Moreover, as
discussed in Section 6.3.2, more cell viability may be due to stronger and stable
FSET PhD Thesis/204
adhesion of hydrophobic moieties on E. coli cells with the hydrophobic hydrocarbon
rich ppOD polymer surface.
(A)
(B)
Figure 6.7 Viability of E. coli cells from 120 h old biofilm grown on plasma
polymerised surfaces and quartz substrate. Cell viabilities are shown as a percentage
of the area covered by green labelled live cells and red labelled dead cells in the total
area of biofilm cells. (*, ** significant difference between surfaces p<0.1 and p<0.05
respectively)
0
20
40
60
80
100
ppOD ppAAm ppAAc quartz
Are
a c
ove
red
by b
iofi
lm c
ell
s (
%)
Different surfaces
Dead cell
Live cell
Mya Myintzu Hlaing Chapter 6/205
In conclusion, the effect of surface chemistry on bacterial adhesion, subsequent
biofilm formation and cell viability was evaluated in this study using plasma
polymerisation of hydrocarbon-rich (ppOD), amine (ppAAm) and carboxyl (ppAAc)
thin films on quartz surfaces. These different coatings expose different functional
groups such as CH2, NH2 and OH. The functional groups on these surfaces provide
different wettabilities, ranging from a more hydrophobic surface presenting CH2
groups to a more hydrophilic surface presenting OH-groups.
The total cell count results revealed that there was a significant increase in initial
bacterial adhesion to the ppAAm surface compared to the other polymer surfaces and
the quartz control. As discussed in Chapter 1, bacteria normally secrete a complex
variety of extracellular polymeric substances (EPS) including polysaccharides,
proteins and nucleic acids while they are in both planktonic and surface-attached
states. These EPS substances play an important role in bacterial colonisation of
surfaces by enhancing initial cell adhesion and aggregation with each other once they
attach to surfaces. The ppAAm surface, which has amine (NH2) functionality, might
interact favourably with both extracellular DNA from secreted EPS and attached
bacterial cells, providing an increase in the initial cell adhesion and subsequent
bacterial colonisation. These findings agree with the results reported by Hook et al.
for high DNA binding and adsorption efficiency to ppAAm surfaces (298). Another
possible reason for the higher cell adhesion seen on the moderately hydrophilic
ppAAm surface could be due to a favourable interaction between the basic behaviour
of the negatively-charged bacterial cell and the positively-charged surface, which
promotes the initial attachment and subsequent biofilm formation. However, the
amide functional group on ppAAm surface might be toxic to bacterial cells and thus
leads to a decrease in cell viability in later phases of biofilm growth compared to
those on ppOD and the control.
In contrast to the ppAAm surface, the lowest bacterial adhesion was seen on the
more hydrophilic ppAAc surface at initial attachment. The number of adherent cells
to the surface was relatively unchanged for at least 24 h. Finally, the lowest viability
of attached cells (i.e. highest proportion of dead cells) was found on ppAAc,
although the number of attached cells to remained almost stable even after 120 h of
FSET PhD Thesis/206
biofilm growth. Given that plasma polymers are known to conform to the substrate
where they are deposited (292, 293), the enhanced bacterial adhesion seen on the
ppOD surface compared to the ppAAc surface can be explained by an increased
surface hydrophobicity on ppOD due to the CH2 functional groups.
These investigations with model surfaces demonstrated that E. coli exhibits
differences in adhesion, biofilm properties and cell viability that depend on the
surface chemistry and specific functional groups exposed. Therefore this study raises
the question whether these changes due to cell-surface interactions have an influence
on bacterial identification by Raman spectroscopy.
6.3.4 Raman analysis of bacterial cells grown on polymer surfaces
In order to investigate whether Raman spectroscopy can still be used to identify
bacterial cells that have been affected by cell-surface interactions, Raman spectra
were collected from E. coli cells attached to the surfaces (i.e. polymer coated
surfaces and control quartz substrate) and from biofilms grown on these surfaces.
A tentative assignment of the peaks that appeared in the average Raman spectra of E.
coli cells attached to the surfaces after 24 h incubation time are shown in Fig 6.8. As
a reference spectrum, Raman spectra from planktonic E. coli samples after 24 h
incubation were also collected. Raman spectra of planktonic cells which were
smeared on the substrates (CaF2 and quartz) and the spectra of cells attached to the
control quartz substrate showed prominent peaks at 700-800, 1001, 1240 and 1447,
1663 cm-1, which could be characterized as nucleic acids, carbohydrates, proteins
and lipids, according to previous studies (26, 29, 100). However, the Raman spectra
of cells attached to the polymer surfaces displayed broad peaks and poor resolution
of the spectral features. These peaks were overlaid with background signals from the
quartz substrate (especially in the regions of 700-800 cm-1) and background signal
from the plasma polymer surfaces (shown in Fig 6.2). This phenomenon was most
severe in the spectra of the cells attached to ppAAm surface. The intense background
drowned out the signal from the cells, thereby making bacterial identification
impossible.
Mya Myintzu Hlaing Chapter 6/207
Figure 6.8 Averaged, intensity-normalised and background subtracted Raman
spectra from 24 h-old surface-attached cells and planktonic cells. The spectra were
collected from E. coli cells on different surfaces (a: ppOD, b: ppAAm, c: ppAAc and
d: quartz) and planktonic cells which were smeared on the substrates (e: CaF2 and f:
quartz). The dominant peaks for spectra of DNA/RNA and proteins are shown with
the peak assignments mentioned in Table 3.1. Abbreviations: Phe, phenylalanine;
Carb, carbohydrate; def, deformation.
Attempts were made to identify E. coli cells which were grown on the plasma-
polymerised surfaces using the PC-LDA biofilm model (details discussed in Chapter
5). The PC-LDA prediction model, which was constructed from biofilm cells of E.
coli and V. vulnificus species grown on quartz substrates, was tested for the direct
identification of E. coli grown on polymer surfaces. The preliminary intention was to
validate the constructed PC-LDA model for differential identification of surface-
grown bacteria. The classification and identification results were shown in Table 6.3.
The results provided a very low sensitivity (< 50%) in accurate identification of E.
coli cells for all tested cells from the polymer coated surfaces. As discussed above,
these poor classification results were probably due to interference from the polymer
and quartz background signals in the bacterial spectra.
(a)
(b)
(c)
(d)
Ph
e
Am
ide I
II
CH
def
CH
2d
ef
Am
ide I
DN
A/R
NA
syn
thesis
Carb
(e)
(f)
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Table 6.3 Identification of E.coli biofilm cells from different polymer surfaces using
dual-species biofilm model.
Test run True (+) False (-)
Sensitivity
(%)
E. coli cells from 1, 7-octadiene 9 3 6 33.3
E. coli cells from Allylamine 9 4 5 44.4
E. coli cells from Acrylic acid 9 3 6 33.3
E. coli cells from Quartz 9 5 4 55.5
Another possible explanation for these poor results is the nature of cellular
heterogeneity of biofilm cells. As discussed in the previous Chapters (Chapters 4 and
5), Raman spectra of individual bacterial cell within the population can be
fundamentally different, even though a population of bacteria may be genetically
identical. Compared to planktonic form, this phenomenon is more significant in the
bacteria’s struggle for survival, particularly when they are grown on a surface.
Therefore it was unsurprising for the poor classification results of polymer surface-
attached cells using PC-LDA biofilm model, which were constructed from the cells
grown on another substrate. The next step was to investigate whether biofilm cells
can behave differently from each other depending on surface where they attach and
continue cell growth thereby affecting their identification. In order to perform this
detailed spectral analysis, the background signal from polymer and quartz substrate
has to be avoided or minimised. Therefore, the results shown in Fig 6.8 and Table 6.3
suggest a need for an alternative way of transferring the surface-attached cells to a
substrate which can provide low background signal disturbance.
6.3.5 Raman analysis of bacterial cells from different polymer surfaces
In order to minimise or avoid the Raman background signal associated with the
plasma polymer surfaces interfering with the bacterial spectra (previously shown in
Fig 6.8), the 24 h-old bacterial cells from the polymer coated surfaces and quartz
substrate were transferred to CaF2 substrates with the help of sterilised inoculation
loops. Averaged intensity-normalised Raman spectra taken from the transferred cells
Mya Myintzu Hlaing Chapter 6/209
(n=4 cells for each polymer surface and quartz control) are shown in Fig 6.9A. The
Raman spectra of the transferred E. coli cells were comparable to those collected
from planktonic cells, enabling peak assignment of the standard features in the
spectra. Although the Raman spectral profiles for the cells from different surfaces
appeared generally similar to those of planktonic cells, certain differences in peak
intensity could be observed especially in the DNA/RNA related regions, the peaks
associated with macromolecule containing amide groups in the protein backbone
(1620-1680 cm-1) and the peaks attributed to the deformation mode of CH2
vibrations. Principal component combined with linear discriminant analysis (PC-
LDA) was performed to classify and identify the attached cells and the results are
shown in Fig 6.9B. All collected 16 Raman spectra (4 individual cells × 4 different
surfaces) were classified as E. coli species with the application of the PC-LDA
planktonic model (mentioned in Section 4.3.4). Although the surface-attached cells
were classified as E. coli, they group more closely together with each other without
completely overlaying the planktonic data from the model.
These classification results supported the potential application of the PC-LDA model
to identify surface-attached cells after they are transferred to a CaF2 substrate which
provides no significant signal disturbance in the spectral range analysed. However,
the visualisation of the classification results highlighted that the surface-attached
cells appeared to form their own cluster in PC space, which suggests some
biochemical changes in comparison with planktonic cells. The collected Raman
spectra from surface-attached cells and those from planktonic cells were further
analysed using principal component analysis (PCA) in order to investigate whether
Raman spectroscopy could reproducibly discriminate between the surface-attached
cells and planktonic cells.
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(A)
(B)
Figure 6.9 (A) Averaged intensity-normalised and background subtracted Raman
spectra of 24 h-old transferred cells from the surfaces (a: ppOD, b; ppAAm, c;
ppAAc and d; quartz) and (e) planktonic cells smeared on CaF2 substrate and (B)
classification of surface-attached cells which were transferred from the surfaces. The
dominant peaks for DNA/RNA and proteins are shown with the peak assignments
mentioned in Table 3.1. Abbreviations: Phe, phenylalanine; Carb, carbohydrate; def,
deformation.
Ph
e
Am
ide I
II
CH
def
CH
2d
ef
Am
ide I
DN
A/R
NA
syn
thesis
Carb
(a)
(b)
(c)
(d)
(e)
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(A)
(B) (C)
Figure 6.10 principal component analyses of Raman spectra from E. coli planktonic
cells and those from transferred E. coli surface-attached cells after 24 hour
incubation. (A) Scatter plots of the first and second principal components (PC1 and
PC2), (B) average values plot and (C) loadings plot of PC1. (p<0.005 for all surface-
attached cells comparing with the planktonic cells in the average value plot)
The scatter plots of PCA analysis revealed that cells collected from the plasma
surfaces were clearly separated from the planktonic cells (Fig 6.10A). The first two
principal components (PC1 and PC2) accounted for more than 60 % of the separation
between the data sets. The average value plot of PC1 showed a significant separation
between planktonic cells and surface-attached cells (p<0.005 for all samples). The
loadings plot of PC1 revealed the dominant peaks which are associated with the
separation seen in the scatter plot (Fig 6.10B and C). The results show that the peaks
related to DNA/RNA synthesis represented the highest absolute variance of the
ppOD ppAAm ppAAc quartz planktonic
DN
A
RN
A
Am
ide
I
CH
2d
ef
Ph
e
A,G
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planktonic cells from the surface-attached cells, whereas the protein-specific peaks
were related to the variance of the surface-attached cells. In particular, the peaks at
1001 cm-1, 1447 cm-1, 1680-1620 cm-1 (associated with proteins/lipids) tended to be
higher in the E. coli cells grown on the quartz and plasma polymers substrates than in
the planktonic cells. The peaks region at 700-852 cm-1 (associated with DNA/RNA),
1093 cm-1 (associated with PO2 stretching vibration of the DNA backbone) and 1485,
1575 cm-1 (associated with DNA/RNA) were seen as those which contributed mostly
in separation of planktonic cells from surfaced-attached E. coli cells. These
variations in DNA/RNA and protein-specific peaks indicate the biochemical or
metabolic heterogeneity due to cellular differences in macromolecular composition
or activity during the transition from the planktonic phase to the surface-attached
phase. Therefore, the next step was to investigate the differences between planktonic
cells and cells attached to each polymer surface.
The PCA shown in Fig 6.11 revealed the differences between planktonic cells and
cells attached to the individual surfaces. The first principal component (PC1) of each
PCA accounted for more than 50 % of the separation between planktonic cells and
surface-attached cells (Fig 6.11). The highest PC1 value was seen in PCA between
cells from the more hydrophobic ppOD surface and planktonic cells, whereas lower
PC1 values were seen from the analysis of cells on the more hydrophilic surfaces
(i.e. ppAAm, ppAAc and quartz). Because adhesion to a surface is a survival
mechanism for bacteria, many previous studies implicated that bacterial cell surface
components (such as adhesins, polysaccharides, and proteins) play major roles in cell
modification to adhere to a surface (290, 299). The findings in this study thus raise
the question whether the cells might have modified their macromolecular
composition more intensely in order to attach to the hydrophobic surface.
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(A) (B)
Figure 6.11 Principal component analyses of Raman spectra from E. coli planktonic
cells and those from E. coli surface-attached cells transferred to CaF2 after 24 hour
incubation. (A) Scatter plots of the first two principal components (PC1 and PC2)
and (B) loadings plots of PC1.
(i)
(ii)
(iii)
ppOD
ppAAm
ppAAc
quartz
planktonic
(iv)
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Interestingly, the loadings plots for the first principal components from PCA between
planktonic cells and the moderately hydrophilic surfaces (i.e. cells on ppAAm and
the quartz slide) revealed that the corresponding peaks for the separation seen in
PCA score plots were similar for both types of attached cells (Fig 6.11(ii) and (iv)).
These loadings plots suggest that the cell populations on ppAAm polymer surfaces
and quartz show similar differences to their planktonic counterparts. In order to
investigate similarities and differences among the bacterial cells which were grown
on polymer surfaces and the control quartz slide, PCA was further performed from
the collected Raman spectra.
Figure 6.12 Scatter plots of the first and second principal components (PC1 and PC2)
comparing the Raman spectra of E. coli cells from the control quartz slide with those
from polymer surfaces: (a) ppOD (b) ppAAm and (c) ppAAc.
ppOD
ppAAm
ppAAc
quartz
(a) (b)
(c)
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PCA of Raman spectra from E. coli cells attached to different polymer surfaces
(ppOD, ppAAm and ppAAc) in comparison with those from the control quartz slide
after 24 hour incubation time points were performed. From the scatter plots shown in
Fig 6.12 (a-c), it can be seen that the first two principal components (PC1 and PC2)
described about 63% of the variation in the data set for comparison between cells
from ppOD and cells from the control quartz slide, whereas there was some overlap
between the data points from the other surface-attached cells. The significant
separation seen between the cells from hydrocarbon-rich ppOD surface and the
control quartz slide suggests that there were Raman identifiable changes between
these spectra (Fig 6.12a). The functional groups on the tested polymer surfaces
provided different wettabilities, ranging from the more hydrophobic CH2 exposed
surface (ppOD) to more hydrophilic surface (ppAAc) that presented OH- groups.
Overlapping and non-significant sample separations could be seen among the spectra
of the cells from the moderately hydrophilic ppAAm surface and more hydrophilic
ppAAc and quartz surfaces. Since PCA was applied to separate the data points, the
clustering of the data seen on the hydrophilic polymer surfaces can be explained due
to similarities in the spectra. These findings suggested that attached cells from more
hydrophilic surfaces might have similar cellular modifications.
Taken together with the results from Fig 6.11, Raman spectral changes of the
surface-attached cells might depend on the degree of surface hydrophobicity.
Therefore, these finding suggested that the surface-attached cells on more
hydrophilic surfaces could be used as the model to investigate Raman detectable
cellular changes in biofilm cells resulting from cell-surface interactions. The
dominant peaks from the loadings plots (Fig 6.11), which contributed to the data
separation seen in the score plots (Fig 6.11), were selected for univariate analysis to
investigate the relative intensity changes of surface-attached cells from the polymer-
coated surfaces to those of control planktonic samples.
The univariate statistical analyses of the selected specific peaks were performed
following the methods mentioned in Section 2.2.6.3. The intensity values of curve-
fitted Raman peaks identified from multivariate analysis were then normalised by the
total intensity values and averaged by adding the maximum intensity and the
FSET PhD Thesis/216
intensity values of the two neighbouring wavenumbers (Raman shifts). Statistical
comparison of the relative mean intensity changes (log2 fold change) for surface-
attached cells compared to planktonic cells were calculated and plotted. The results
of intensity values analysis for the specific peaks related to DNA/RNA synthesis are
shown in Fig 6.13. Lower intensity values of the DNA related peaks were seen in
surface-attached cells compared to planktonic cells (i.e. negative log2 fold change).
In response to changing environmental conditions, bacterial cells are able to adapt to
allow them to persist through time. One of the adaptive changes consists of
modifying its growth rate, which is accompanied by adjusting mechanisms to control
the timing of the cell-cycle (300). Under favourable conditions, bacteria often strive
towards cell growth and reproduction thereby initiating DNA replication.
Conversely, the cells shift from growth to survival functions under stressful
conditions. Thus, the lower intensity values of the DNA related peaks seen in the
surface-attached cells can be explained by the fact that these cells might experience
the stressful environment where the cells probably delayed DNA/RNA synthesis.
These results agree with a comprehensive study of transcriptomic analysis in biofilm
and planktonic cells by Lo et.al (301). Their study reported that the genes involved in
DNA replication were down-regulated in biofilm cells as opposed to planktonic cells.
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Figure 6.13 Intensity changes of DNA/RNA specific peaks in the Raman spectra of
E. coli surface-attached cells transferred from different surfaces, measured relative to
planktonic cells. Abbreviation: ppOD, octadiene; ppAAm, allylamine; ppAAc, acrylic
acid; T, thymine; G, guanine; C, cytosine; U, uracil; A, adenine. (*, *** significantly
different to planktonic with p<0.1 and p<0.005, respectively)
The relative intensity changes in the surface-attached cells were not significantly
different among themselves for all of the tested DNA specific peaks, except for the
peak at 1575 cm-1. The overlapping in log2 fold change value of the DNA/RNA
peaks especially in the peaks related with ring breathing modes of cytosine, uracil,
-1
-0.5
0
ppOD ppAAm ppAAc quartz
A,G (1575 cm-1)
-2
-1.5
-1
-0.5
0
ppOD ppAAm ppAAc quartz
A,G (1485 cm-1)
-2.5
-2
-1.5
-1
-0.5
0
ppOD ppAAm ppAAc quartz
O-P-O backbone (811 cm-1)
-2
-1.5
-1
-0.5
0
ppOD ppAAm ppAAc quartzRela
tive In
ten
sit
y C
han
ge
(lo
g2 f
old
)
T, G (668 cm-1)
-1.5
-1
-0.5
0
ppOD ppAAm ppAAc quartz
C, U (781 cm-1)
*
*** *** *** ***
*** *** ***
****** *** *** ***
***
******
****** *** ***
***
***
*
*
-2.5
-2
-1.5
-1
-0.5
0
ppOD ppAAm ppAAc quartz
U, T, C (785 cm-1)
FSET PhD Thesis/218
thymine were noticed among the cells from polymer surfaces. These results
suggested that there were some similar differences between the cells from each
polymer surface and the planktonic cells. The cells from the ppAAm polymer surface
showed a significant change in the intensity value for the peak related to the ring
breathing mode of adenosine and guanine (1575 cm-1). This finding provides a
potential application of this DNA/RNA marker for identification of cells from this
polymer surface.
The results for analysis of intensity values of specific peaks related to protein
synthesis are shown in Fig 6.14. In contrast to DNA/RNA related peaks, higher
intensity values can be seen for the dominant protein/lipid structure-specific peaks in
all biofilm cells from surfaces, compared to planktonic cells. Interestingly, the
relative intensity changes for these protein-related peaks in the cells from the
ppAAm surface were the highest among the surface-attached cells. However, a
significant decrease in the relative intensity of the phenylalanine peak was seen in the
cells from the ppAAm surface compared to those of the cells from ppOD. The
increased intensity values of peaks associated with protein/lipid synthesis might be
related to EPS secretion due to the cellular response of bacteria to environmental
stresses during biofilm development. The log2 fold change values of the peaks
attributed to the deformation mode of CH2 vibrations and amide I were similar
among the cells from ppOD and ppAAc polymer surfaces.
The intensity fluctuation seen in the dominant peaks corresponding to the ring
breathing mode of phenylalanine (1001 cm-1), the CH2 deformation of protein (1447
cm-1) and amide I (1663 cm-1) in the biofilm samples from the polymer surfaces
indicated that some lipid/protein denaturation or up-regulation of protein synthesis
may be induced by the functional groups of the polymer-coated surfaces.
Interestingly, smaller relative intensity changes were seen at the phenylalanine peak,
CH2 deformation band and amide I band in all ppAAc samples. This can be
explained by two factors: either the protein synthesis in the cells attached to ppAAc
was not significantly different from the control planktonic samples or protein
synthesis in these samples was much lower than the other surface-attached cells. This
finding was consistent with the results observed in Fig 6.7, illustrating the smallest
Mya Myintzu Hlaing Chapter 6/219
biofilm area and lowest cell viability on the ppAAc surface. Given that the highest
protein content should be found in mature biofilm compared to younger biofilm, the
decreased intensity of the protein-related peaks in the cells from ppAAc surfaces
might be due to the unfavourable environment of the OH- exposed surface inhibiting
mature biofilm development. The results obtained within this work were comparable
with the results reported by Parreira et al., that bacteria adhered less favourably to
the OH- exposed surface than to the CH3 exposed surface.
Figure 6.14 Intensity changes of protein/lipid specific peaks in the Raman spectra of
E. coli surface-attached cells transferred from different surfaces, relative to
planktonic cells. Abbreviation: ppOD, octadiene; ppAAm, allylamine; ppAAc, acrylic
acid; def; deformation; phe, phenylalanine. (*, **, *** significantly different to
planktonic with p<0.1, p<0.05 and p<0.005, respectively)
The results shown in this Section using microscopic and spectroscopic techniques
have demonstrated that surface chemical properties (i.e. functional groups) and
surface wettability can alter the initial cell adhesion, viability of attached bacteria and
0
0.1
0.2
0.3
0.4
ppOD ppAAm ppAAc quartz
Amide I (1663 cm-1)
0
0.1
0.2
0.3
0.4
ppOD ppAAm ppAAc quartzRela
tive In
ten
sit
y C
han
ge
(lo
g2 f
old
)
Phe (1001 cm-1)
******
****
**
******
*
***
***
*** ***
0
0.1
0.2
0.3
0.4
0.5
ppOD ppAAm ppAAc quartz
CH2 def (1447 cm-1)
FSET PhD Thesis/220
subsequent biofilm formation. These variations in physiochemical properties of
bacteria due to their interactions with different surfaces could be detected by Raman
spectroscopy. The results showed that surface-attached cells were significantly
different from planktonic cells. However, the relative changes of attached cells (from
polymer surfaces) to planktonic cells did not significantly distinguish them from cells
grown on a quartz substrate except for the peak related to ring breathing mode of
adenosine and guanine (1575 cm-1) (Fig 6.13).
6.4 Conclusion
Given that bacteria can attach and form biofilm on any natural and synthetic surface,
scientific investigations of bacterial biofilm formation have become popular in
medical, industrial and environmental applications. Many studies on biofilms and
effects of surface modifications have been applied for better understanding of cell-
surface interaction and thereby finding means of controlling biofilm formation.
In this study, the effect of surface chemistry on bacterial adhesion and subsequent
biofilm formation was first evaluated using plasma polymerisation of hydrocarbon-,
amine- and carboxyl-rich precursors on quartz surfaces, thereby exposing different
functional groups such as CH2, NH2 and OH. These investigations with polymer
surfaces revealed that the E. coli strains exhibit differences in adhesion, biofilm
properties and cell viability that depend on the surface chemistry and specific
functional groups exposed. Detailed analysis of Raman spectra collected from E. coli
biofilm cells from different polymer surfaces revealed the DNA/RNA and protein
markers which were related to these subtle changes.
Despite subtle changes in macromolecular composition within the same species due
to cell-surface interactions, the classification results using the PC-LDA planktonic
model were highly accurate for the cells which were transferred from the surfaces.
These findings encourage the use of transferred cells for Raman spectroscopic
analysis of any surface-attached cells. On the other hand, Raman spectra taken
directly from the cells on the polymer surfaces, without transferring them to CaF2
substrate, were swamped by background signals and showed no characteristic peak
features of E. coli cells. For this reason, attempts at direct identification of biofilm
Mya Myintzu Hlaing Chapter 6/221
cells from polymer surfaces with the application of the PC-LDA biofilm model were
not successful.
While it may have been unsurprising to get poor classification results for these
spectra because of the nature of cellular heterogeneity in biofilm matrix, the success
of the transfer technique suggests that the background contributions from the quartz
substrates and fluorescence from the polymer surfaces were a major limitation in the
direct measurements. However, with the success of the transfer technique, the results
of this chapter support the potential for Raman spectroscopy to be used as a
substantial technique for identifying bacteria recovered from biofilm. In particular,
the results suggest that it might be possible to analyse environmental biofilm samples
from any surface by transferring cells to a substrate, with relatively high Raman
intensity and low fluorescence background, thereby providing an efficient and
reliable approach for bacterial identification. This application needs to be further
validated with a larger database of bacterial species. The results also demonstrate the
ability of Raman spectroscopy to evaluate phenotypic variability within species and
identifying the diversity of macromolecules that may play a role in initial cell
attachment and biofilm growth.
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Mya Myintzu Hlaing Conclusions/223
CONCLUSIONS
To implement the appropriate treatment and control measures for problems
associated with biofilm formation, there is a need for rapid bacterial identification.
The work presented in this thesis has focused on exploring the application of Raman
spectroscopy identification techniques to the challenging systems of life cycle
analysis, co-culture environments and biofilm formation. Much of the existing
Raman literature has focussed on the identification of bacteria in the planktonic state,
whereas it is known that bacteria actively modify their behaviour in order to adapt to
the constraints of biofilm consortia and survive in a range of environmental settings.
These adaptations generally involve changes in biochemical activity in the cell,
which are expected to modify the bacterial Raman spectrum and therefore may
interfere with the successful identification of the species. Therefore this work aimed
to evaluate the capacity of Raman spectroscopy to identify bacteria in biofilm and
when attaching to surfaces with a range of surface chemistries.
First, the Raman spectroscopy experimental methods were optimised in order to
analyse the Raman spectra quantitatively and consistently throughout this study.
These steps involved the implementation of a fluorescence background removal
method and validation of sample preparation methods for bacterial identification. An
improved background subtraction method using adaptive-weight penalised least
squares fitting was evaluated and implemented. With this method, the background
was successfully removed from Raman spectra taken from planktonic cells, colonies
and biofilms, providing a significant improvement for the performance of further
quantitative analysis of the Raman spectra (including multivariate analysis). From a
detailed analysis of the possible effects of different sample preparation procedures on
bacterial Raman spectra, the sample to sample variations were minimised during the
study.
In order to construct a database to model the individual cellular differences in
macromolecular composition within a bacterial population, Raman spectroscopy
experiments were set up for different time points of the planktonic growth cycle.
FSET PhD Thesis/224
With principal component analysis (PCA), the results revealed Raman spectral
changes in DNA/RNA synthesis and protein synthesis all the way through the growth
cycle for four species of interest (E. coli, V. vulnificus, P. aeruginosa and S. aureus).
Although PCA was not perfect in detecting all of these subtle changes in the Raman
spectra of P. aeruginosa cells during the growth cycle, it should be noted that such
differentiation (between stationary and decline phase) was achieved solely on the
basis of multivariate analysis of Raman data without a priori expectation of chemical
differences or an understanding of the biochemical-physiological pathways.
Moreover, the PCA presented here should still be considered as preliminary analysis
for differential identification. Nevertheless, the results from E. coli and V. vulnificus
species highlighted that Raman spectroscopy together with PCA analysis can detect
cellular differences from metabolic growth phases of single bacterial species.
As a consequence of cellular heterogeneity during the growth cycle, poor PCA
classification results were obtained for the data set collected from the whole growth
cycles of four bacterial species, although they were well-discriminated at a particular
growth time point. In contrast, analysis based on principal component and linear
discriminant analysis (PC-LDA) could successfully discriminate and classify the
diverse species in spite of these growth phase dependent physiological differences.
The validation of the constructed prediction model (PC-LDA planktonic model) with
new spectra from each individual species provided >80% classification accuracy.
Moreover, this PC-LDA model could detect the presence of E. coli and V. vulnificus
species from mixed culture. The results from the fluorescence in situ hybridisation
(FISH) technique with rRNA-targeted oligonucleotide (probe) for E. coli (ATCC
25922) further supported the validity of the identification results using the model.
These findings demonstrated that Raman spectroscopy with the application of a PC-
LDA model that incorporates chemotaxonomic data may provide valuable
applications in the rapid sensing of microbial cells in environmental and clinical
studies. However, the classification accuracy may be affected by intra-species and
inter-species variability. The effect of such inaccuracy may be more pronounced if
more species were added to the database. It is important to evaluate the application of
the PC-LDA planktonic model to the identification of real-world biofilm samples.
Mya Myintzu Hlaing Conclusions/225
Raman spectroscopy experiments were thus performed on intact bacterial colonies
and biofilm as a step towards realistic settings by examining the cellular changes of
surface-attached bacterial cells. Analysis of Raman spectra taken from intact
bacterial colonies isolated on nitrocellulose membrane were complex. The
background peak removal of nitrocellulose membrane was extremely challenging.
Attempts were made to remove the peak by normalising with the intensity of the
nitrocellulose membrane signal at 1282 cm-1. While the membrane peak removal
method for membrane-grown colonies cells remains somewhat subjective and
requires further improvement, a high accuracy in differential identification was
achieved using the PC-LDA planktonic model for all species, except V. vulnificus.
These results are encouraging further extension of the Raman spectroscopy
application to detect and identify membrane-grown bacteria in food-processing
environments and water analysis. Furthermore, with this model, it was possible to
evaluate the population behaviour of the membrane-attached cells from intact colony.
If reference methods can be applied to confirm the classification results in future
work, this approach will have a great potential to study the bacterial population
behaviour resulting from different nutrient utilisation.
To apply the PC-LDA classification approach for rapid bacterial identification from
biofilm consortia in real-world situations, Raman spectra taken from throughout the
stages of biofilm growth were analysed, using a similar approach to the analysis done
in planktonic cells. It was found that there were Raman identifiable changes in
DNA/RNA and protein-related peaks in surface-attached cells of the individual
species. Given that biofilm cells are believed to be different from their planktonic
counterparts, it was perhaps unsurprising that ineffective classification results were
obtained using the PC-LDA planktonic model. Instead, a PC-LDA biofilm model
that could match the surface-attached biofilm cells was constructed. The PC-LDA
biofilm model was validated on new spectra of E. coli surface-attached cells from
single-species biofilm, which were grown on quartz substrates and provided high
accuracy in prospective classification. This interpretation and classification result did
not take into account the possible misclassification with closely related species that
were not included in the current database and hence may overestimate the accuracy
FSET PhD Thesis/226
of the model. Furthermore, it is possible that the model can only be applied to a
specific substrate where the biofilm was grown. As a change in the surface chemistry
will probably alter the accuracy of the model, it was highlighted that a new model
might be required for each surface of interest, significantly reducing the viability of
the direct surface analysis approach. A validation of the constructed PC-LDA biofilm
model was performed with the new spectra collected from dual-species biofilms. The
results revealed 75% sensitivity in detecting the presence of E. coli and V. vulnificus
species in dual-species biofilms. With this approach, species interactions could
potentially be better understood, thereby assisting in the study of biofilm formation
with species of interests in more complex situations.
The positive results from the surface-attached E. coli cells on quartz substrate
encouraged us to examine the effects of different surface chemistries on bacterial
identification by Raman spectroscopy and to examine the Raman identifiable
macromolecular changes in the cells. Therefore, the interaction of E. coli cells with
plasma polymer thin films containing hydrocarbon, amine and carboxyl groups were
investigated. The functional groups on these surfaces provided different wettabilities,
ranging from a more hydrophobic hydrocarbon-rich surface to more hydrophilic
surface carboxyl/ester containing films. The results from microscopic examinations
using CLSM illustrated the differences in cell attachment phenotypes, cell viability
and subsequent biofilm formation, which were associated with different surface
chemistries and surface hydrophobicity. These results showed that bacteria adhered
preferentially to the more hydrophobic CH2 exposed surface (i.e. ppOD) than to the
more hydrophilic OH- exposed surface (i.e. ppAAc). The same phenomenon was
seen for biofilm formation and cell viability. However, the less cell viability was
seen on the amine exposed containing polymer surface (i.e. ppAAm) compared to
ppOD although there were higher cell adhesion and biofilm formation. Therefore,
the next step was to investigate whether these cellular differences in the surface-
attached bacteria influenced their identification by Raman spectroscopy.
While investigating the Raman spectra taken from cells attached to the polymer
surfaces, it was found that the spectra were convoluted with polymer and quartz
background. The identification of surface-attached cells from polymer surfaces using
Mya Myintzu Hlaing Conclusions/227
PC-LDA model was thus challenged by weak spectral features resulting from
background interference. The lack of success with the direct identifications of the
cells from the polymer surfaces prompted another approach that used transferred
cells for identification purposes. In particular, cells from the polymer surfaces were
transferred to a CaF2 substrate for Raman measurement. Raman spectra from these
transferred cells showed the characteristic peak assignments of E. coli cells and the
results were comparable with those from planktonic cells. Correct identification
outcome were also achieved for these surface-attached cells using the PC-LDA
planktonic model. This outcome suggested that transferred cells should be used to
increase the chance of successful Raman spectroscopy analysis of any surface-
attached cells.
By using the transferred cells, a detailed analysis of specific peaks was possible
without any disturbance from superimposed polymer and quartz background. The
results of this analysis showed that the spectral profiles of the surface-attached cells
were subtly different from those of the planktonic cells. Furthermore, there were
spectral intensity changes among the surface-attached cells due to their interactions
with the different surfaces. It was found that the relative intensity changes for
DNA/RNA and protein-related peaks in the cells from the ppAAm surface were the
highest among the surface-attached cells. This finding correlated with the results of
reduced cell viability seen on the ppAAm surface compared to those on ppOD. It will
be interesting to evaluate phenotypic variability between species and to identify the
diversity of macromolecules throughout biofilm development on these polymer
surfaces.
In summary, this thesis illustrated the potential to apply Raman spectroscopy in
combination with multivariate analysis for bacterial identification in real world
settings. The results from optimising the effective sample storage and preparation
highlighted the role of bacterial EPS in response to environmental stresses. This
finding further suggested studying EPS-specific Raman markers to understand their
role in biofilm formation process of different bacterial species. The constructed PC-
LDA planktonic model of four bacterial species in this study showed promising
outcomes for differential identification. Some factors which might influence the
FSET PhD Thesis/228
identification, such as cellular heterogeneity throughout the life cycle, cell-cell
interactions within consortium biofilm and cell-surface interactions, were studied.
The ongoing challenge lies on the development of this approach for application on
the industrial scale. Therefore, further studies to validate the PC-LDA planktonic
model with more bacterial species are suggested. The effectiveness and accuracy of
the PC-LDA planktonic model for the identification of biofilm forming bacterial
species can also be studied by adding more bacterial species which are involved in
specific processes, such as microbiologically-influenced corrosion. More bacterial
species can be added to the database by categorising the groups which include EPS-
producing bacteria, acid-producing bacteria, sulphur oxidising bacteria, iron-
precipitating bacteria and sulfate-reducing bacteria. In addition, environmental
factors (such as temperature, pH and nutrient composition) can also be taken into
accounts for future study of factors influencing bacterial identification. The method
for membrane peak removal from the Raman spectra of intact colonies could also be
improved. The method conducted in this thesis was done by manual normalisation of
the intensity of the membrane peak. If it is possible, future work should develop a
reliable computational method which can automatically remove the membrane peak.
With a range of parameter settings for good performance in membrane peak removal,
the approach of using membrane-grown cells will be useful for a wide range of
applications in food-processing industries.
The promising classification results from the study of surface chemistry effects on
bacterial identification suggested a further study to be tested with Gram-positive
bacteria. Finally, the Raman spectral fluctuations observed among surface-attached
cells suggest the need to test the model with other antimicrobial coated thin films to
characterise the patterns of spectral variation between surface-attached cells and
planktonic cells. These fingerprint patterns can be applied to assess the stability and
antimicrobial property of coated films in clinical and environmental applications.
Finally, it should be noted that the time taken to collect high quality Raman spectra
from bacteria remains a limiting factor, particularly in terms of collecting a
significantly larger number of spectra for more reliable PCA training sets. Future
improvements in Raman spectroscopy equipment might assist in this regard.
Mya Myintzu Hlaing References/229
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Mya Myintzu Hlaing Appendix/259
APPENDIX
FSET PhD Thesis/260
Mya Myintzu Hlaing Appendix/261
APPENDIX A
Nucleotide sequence of E. coli ATCC 25922, 16S rRNA (X80724, GI: 1240023)
The DNA sequence presented starts with nucleotide number as described in 16sRNA
sequence of E. coli ATCC 25922 (Accession: X80724, GI: 1240023, 1452 base
pairs, genomic DNA). The primer, EC1_485, 5’ GTATCTAATCCTGTTTGCTCCC
-3’ which were used in Section 2.2.4.3 is indicated by horizontal small arrow and
highlighted in grey colour.
1 AGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAACGG
61 TAACAGGAACGAGCTTGCTGCTTTGCTGACGAGTGGCGGACGGGTGAGTAATGTCTGGGA
121 AACTGCCTGATGGAGGGGGATAACTACTGGAAACGGTAGCTAATACCGCATAACGTCGCA
181 AGACCAAAGAGGGGGACCTTCGGGCCTCTTGCCATCGGATGTGCCCAGATGGGATTAGCT
241 AGTAGGTGGGGTAAAGGCTCACCTAGGCGACGATCCCTAGCTGGTCTGAGAGGATGACCA
301 GCCACACTGGAACTGAGACACGGTCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTG
361 CACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGT
421 AAAGTACTTTCAGCGGGGAGGAAGGGAGTAAAGTTAATACCTTTGCTCATTGACGTTACC
481 CGCAGAAGAANNACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGC
541 GTTAATCGGAATTACTGGGCGTAAAGNGCANGCAGGCGGTTTGTTAAGTCAGATGTGAAA
601 TCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGG
661 GGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAA
721 GGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGGATT
781 AGATACCCTGGTAGTCCACGCCGTAAACGATGTCGACTTGGAGGTTGTGCCCTTGAGGCG
841 TGGCTTCCGGANNTAACGCGTTAAGTCGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAA
901 CTCAAATGAATTGACGGGGGCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAAC
961 GCGAAGAACCTTACCTGGTCTTGACATCCACGGAAGTTTTCAGAGATGAGAATGTGCCTT
1021 CGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGT
1081 TAAGTCCCGCAACGAGCGCAACCCTTATCCTTTGTTGCCAGCGGTCCGGCCGGGAACTCA
1141 AAGGAGACTGCCAGTGATAAACTGGAGGAAGGTGGGGATGACGTCAAGTCATCATGGCCC
(See overleaf for continued sequence)
FSET PhD Thesis/262
1201 TTACGACCAGGGCTACACACGTGCTACAATGGCGCATACAAAGAGAAGCGACCTCGCGAG
1261 AGCAAGCGGACCTCATAAAGTGCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCAT
1321 GAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCT
1381 TGTACACACCGCCCGTCACACCATGGGAGTGGGTTGCAAAAGAAGTAGGTAGCTTAACCT
1441 TCGGGAGGGCGC
Mya Myintzu Hlaing Appendix/263
APPENDIX B
Curve-fitted spectrum and quantification parameters for the components using
CasaXPS software
Figure 1. Curve-fitted spectrum and the components.
0
10
20
30
40
50
10-2
2000 1600 1200 800
Wavenumber / cm-1
No
rma
lise
d R
am
an In
tensity
(a.u
)
FSET PhD Thesis/264
Figure 2. Quantification parameters for the components shown in Fig 1.
Mya Myintzu Hlaing Appendix/265
APPENDIX C
PCA of Raman spectra for planktonic E. coli cells taken from refrigerated sample
before cell washing steps
Figure 1. Flow chart diagram for the different sample preparations for planktonic E.
coli cells: (i) fresh sample, (ii) refrigerated sample after cell washing steps, (iii)
refrigerated sample before cell washing steps and (iv) frozen sample.
FSET PhD Thesis/266
Figure 2. Background subtracted Raman spectra from planktonic E. coli cells taken
from (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)
refrigerated sample before cell washing steps and (iv) frozen sample. The dominant
peaks for spectra of DNA/RNA and proteins are shown with the wave number (cm-
1). The change in peak position attributed to phenylalanine is shown in the enlarged
picture with dotted line.
Figure 3. Principal component analysis of Raman spectra for planktonic E. coli cells
taken from (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)
refrigerated sample before cell washing steps. (A) Average values plots and (B)
loading value plots for the first principal component (*** p value < 0.005).
Ram
an Inte
nsity (
Norm
alis
ed)
Wavenumber/cm-1
(i)
(ii)
(iii)
(iv)72
6
78
1-7
85
74
6
81
18
52
10
01
11
25
12
40
13
3714
47
-14
58
1900 1700 1500 1300 1100 900 700 500
0.5
0
1.5
1
2
66
86
17
-64
0
14
85
15
73
16
63
(i)
(ii)
(iii)
(iv)
First prin
cip
al co
mp
on
en
t (a
.u.)
(2
8%
)
(ii) (iii) (i)
(A)6
4
2
0
-2
-4
-6
***
1900 1700 1500 1300 1100 900 700 500
Ra
ma
n In
ten
sity
Wavenumber/cm-1
(B)0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
1680
-1620
1391-1
260
1001
1573
1489-1
443
1125
640-6
20
Mya Myintzu Hlaing Appendix/267
Figure 4. Principal component analysis of Raman spectra for planktonic E. coli cells
taken from a (i) fresh sample; (ii) refrigerated sample after cell washing steps; (iii)
refrigerated sample before cell washing steps; (iv) frozen sample. (A) Average
values plots and (B) loading value plots for the first principal component (** p value
< 0.01).
(A)F
irst princip
al com
ponent (a
.u.)
(32.3
4%
)
(i) (ii) (iii) (iv)
6
4
2
0
-2
-4
-6
8
(B)
Ram
an Inte
nsity
Wavenumber/cm-1
0.2
0.1
0
-0.1
-0.2
1900 1700 1500 1300 1100 900 700 500
1573(1
580
-1520)
1673 (
1699
-1657)
1337 (
1323
-1375)
1001
1447 (
1469-1
434)
**
1640-1
620
FSET PhD Thesis/268
APPENDIX D
Figure 1. Comparison of normalised Raman spectra from E. coli ATCC 25922
colony grown on membrane after membrane peak normalisation and vector
correction.
500 1000 1500 2000
0.00
0.43
0.86
1.29
-2500
0
2500
5000N
orm
alis
ed Inte
nsity / A
rbitr.
Units
Wavenumber (cm-1)
Peak Intensity normalisation
Vector projection normalisation
Mya Myintzu Hlaing Appendix/269
Analysis of population behaviour of colony cells with the application PC-LDA
planktonic model
V. vulnificus
Reference
(PC-LDA
planktonic
model)
Met
ab
oli
c
ph
ase
Classification results
V. vulnificus
Growth region
Outer ring Middle Centre core
V. vulnificus
(6 cells)
EE 3 2
ME 1
LE
ES
MS
LS
ED
MD
LD
P. aeruginosa
Reference
(PC-LDA
planktonic
model)
Met
ab
oli
c
ph
ase
Classification results
P. aeruginosa
Growth region
Outer ring Middle Centre core
P. aeruginosa
(9 cells)
EE 2
ME
LE 1 1
ES
MS 1 1
LS 1 2
ED
MD
LD
FSET PhD Thesis/270
S. aureus
Reference
(PC-LDA
planktonic
model)
Met
ab
oli
c
ph
ase
Classification results
S. aureus
Growth region
Outer ring Middle Centre core
S. aureus
(8 cells)
EE 1 2
ME
LE 2
ES 2 1
MS
LS
ED
MD
LD
Abbreviations: EE; early exponential, ME; mid exponential, LE; late exponential,
ES; early stationary, MS; mid stationary, LE; late stationary, ED; early decline, MD;
mid decline, LD; late decline.
Mya Myintzu Hlaing Lists of Publications/271
LISTS OF PUBLICATIONS
1. Hlaing MM, Cadusch PJ, Wade SA, McArthur SL, Stoddart PR. Method for
Fluorescence Background Subtraction from Raman Spectra. International
Conference on Raman spectroscopy. August, 2012, India. Poster presentation
2. Cadusch PJ, Hlaing MM, Wade SA, McArthur SL, Stoddart PR. Improved
methods for fluorescence background subtraction from Raman spectra. Journal of
Raman Spectroscopy. 2013; 44(11):1587-95.
3. Hlaing MM, Dunn M, McArthur SL, Stoddart PR. Sample Preparation and
Optimization for Bacterial Identification by Raman Spectroscopy. AVS International
Symposium and Exhibition (60th ). October, 2013, California. Oral presentation
4. Hlaing MM, Dunn M, McArthur SL, Stoddart PR. Raman spectroscopy for
bacterial identification: Effects of sample preparation and storage. International
Journal of Integrative Biology. 2014; 15(1):11-7.
5. Hlaing MM, Dunn M, Stoddart PR, McArthur SL. Raman Spectroscopy for
Differential Identification of Bacterial Species. International Conference on Raman
spectroscopy (14th). August , 2014, Germany. Poster presentation
6. Hlaing MM, Dunn M, Wade SA, Stoddart PR, McArthur SL. Raman
Spectroscopy for Differential Identification of Bacterial Species. DMTC Students
Conference. October, 2014, Melbourne, Australia. Oral presentation