Goslar, 09/10/2007
Identification of Microorganisms using MALDI-TOF MS profiling:Adopted sample preparation methods and bioinformatic approaches Dr. Markus KostrzewaBruker Daltonik GmbH, Leipzig
Goslar, 09/10/2007
MALDI-TOF MS microorganism identification
Identified species
Select a colony
Prepare ontoa MALDI target plate
Unknownmicrorganism
?
Data interpretation
Generate MALDI-TOFprofile spectrum
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Target/Acceleration
Time-of-Flight Molecular MassDesorption/Ionisation
DetectorDrift Region
m/z
a.i.
Mass Spectra
Laser
MALDI-TOF mass spectrometry
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Sample preparation
• Direct “cell smear“ methodmost simple method, applicable to many bacteria
• Organic solvent extractionimproved quality for difficult bacteria, yeast, fungi
• Mechanical cell disruption (e.g. sonication)In case of very ridgid cell walls
Compatibility of different procedures
Protocols for inactivation and shipment of microorganisms are available
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E.coli4
36
4.0
6
53
80
.64
62
54
.64
63
15
.49
50
96
.01
71
57
.65
72
73
.87
64
10
.90
78
70
.62
83
68
.99
0
1000
2000
3000
4000
5000Inte
ns.
[a.u
.]
4000 4500 5000 5500 6000 6500 7000 7500 8000
m/z
ribosomal Protein m/zRL36 4364,33RS32 5095,82RL34 5380,39RL33meth. 6255,39RL32 6315,19RL30 6410,60RL35 7157,74RL29 7273,45RL31 7871,06RS21 8368,76
MALDI-TOF MS profile spectrumPositive linear modeMass range 2-20 kDa
Goslar, 09/10/2007
Improved quality by adopted sample preparation
Pichia jadiniiextraction method
0.00
0.25
0.50
0.75
1.00
1.25
1.50
4x10
Inte
ns.
[a.u
.]
Pichia jadiniidirect smear method
0.00
0.25
0.50
0.75
1.00
1.25
4x10
Inte
ns.
[a.u
.]
2000 4000 6000 8000 10000 12000 14000 16000 18000
m/z
Identification score
2.445
1.997
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Psdm. oleovorans B396_Medium 360
0
1000
Psdm. oleovorans B396_Medium 464
0
1000
Psdm. oleovorans B396_Medium 53
0
1000
Psdm. oleovorans B396_Medium 65
0
1000
Psdm. oleovorans B396_Medium 98
0500
1000
Psdm. oleovorans B396_MRS10
010002000
Psdm. oleovorans B396_YPD
010002000
4000 5000 6000 7000 8000 9000 10000 11000m/z
Pseudomonas oleovorans grown on different media
Low influence of culture conditions
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Arthrobacter,effect of storage products
Taken from:Vargha M et al.J Microbiol Methods. 2006
Possible influence of growth state
16h
24h
64h
0
1000
2000
3000
4000
3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 m/z
Clostridium butyricum,effect of sporulation
Coop. with Prof. Krüger, Dr. Grosse-Herrenthey, Leipzig, Germany
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MALDI BioTyper 2.0 - GUI
Unknown samples
Match against microbial reference database
Identification result
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MALDI BioTyper 2.0 – realtime analysis
Wizard guiding through setup from measurement to data analysis
BioTyper Automation Control Wizard
Define Project
Analyte Placement
Select Methods
Start
MALDI BioTyper 2.0: realtime Analysis
• Start Automation Control Wizard.• Use a SOP and do not bother with
instrument settings.• Results available directly after measurement
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MALDI BioTyper – Algorithms
Pattern matchingweighted pattern matching
Principle component analysisCluster analysis Correlation analysis
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Score based pattern matching
Calculation of a matching score based on:
Rel Score% matches of the reference spectrum
(e.g. 6 / 10 = 0.6)
Rel P-Num.% matches of the unknown spectrum
(e.g. 6 / 20 = 0.3)
I-Corr.value of intensity correlation
Unknown microorganism is matched against each Main spectrum in a library. Ranking according to matching score, threshold for IDRobust standard method for species ID
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Pseudo-Gel view
M/Z [Da]
File
no.
4000 5000 6000 7000 8000 9000 10000 11000 12000
1 2 3 4 5 6 7 8 910111213141516171819202122232425262728293031323334353637383940
Neisseria meningitidis serotypes
A
W135
X
Y
How about subtyping?
Pseudo-Gel view
M/Z [Da]
File
no.
6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100
1 2 3 4 5 6 7 8 910111213141516171819202122232425262728293031323334353637383940
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Incorrect hirachical clustering of three Neisseria meningitidis serogroups after PCA
-2
0
2
4
-2-1
01
2-1
-0.5
0
0.5
1
1.5
PC1
3D scatter plot - hierarchical clustering
PC2
PC
3
Principle component analysis
PCA is looking for the largest variations in a given group.If measurement variations are larger than inter-species/ subspecies variations it may fail! Depending strongly on standardization of measurement!
Goslar, 09/10/2007
Weighted pattern matching
• Batch weighting:Automated generation of a weighted main spectra library; every main spectrum of a library is compared with all the other main spectra
• Manual weighting:Weight of each peak in a main spectrum can be edited manually
Combination of both procedures is possible
Hierachical approach in combination with standard pattern matching
Characteristic peaks are selected and weighted by occurence in subgroups, intensity, and frequency
Goslar, 09/10/2007
Weighted pattern matching
Identification Results weighted
Detected Species log(Score)--------------------------------------------------------------Sp. 1 Serogruppe_A 2.677Ser.A Serogruppe_Y 2.150 Serogruppe_W135 2.044 Serogruppe_X 2.026Sp. 2 Serogruppe_W135 2.339Ser.W135 Serogruppe_Y 2.123 Serogruppe_X 1.784 Serogruppe_A 1.571Sp. 3 Serogruppe_X 2.665Ser.X Serogruppe_W135 2.033 Serogruppe_Y 1.902 Serogruppe_A 1.136Sp. 4 Serogruppe_Y 2.294Ser.Y Serogruppe_W135 2.126 Serogruppe_X 1.958 Serogruppe_A 1.617
Neisseria meningitidis serogroups
Correct identification of subspecies through weighting of specific peaks.
Expansion of pattern matching towards subspecies detection.
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Correlation analysis
Composite Correlation Index Map
Species
Spe
cies
2 4 6 8 10 12
2
4
6
8
10
12
Color code:dark red – highest correlationdark blue – lowest correlation
Correlation analysis of different Salmonella enterica serovars:Correlation analysis according to Arnold & Reilly, RCMS, 1998, modified
1. 1849_Hadar_VAB 2. 371_enteritidis_VAB 3. 042_typhimurium_O5_VAB 4. 104_enteritidis_VAB 5. 123_typhimurium_O5_VAB 6. 163_Virchow_VAB 7. 188_Dublin_VAB 8. 202_Infantis_VAB 9. 242_Infantis_VAB 10. 285_Virchow_VAB 11. 506_Hadar_VAB 12. 754_Agona_VAB
Goslar, 09/10/2007
Microorganism databases
Acetobacter aceti subsp. acetiAcetobacter pasteurianus subsp.lovaniensisAcetobacter pasteurianus subsp.pasteurianusActinomadura aurantiacaActinomadura libanoticaActinomadura lividaAgrobaterium tumefaciensArthrobacter globiformisArthrobacter oxydansArthrobacter pyridinolisArthrobacter sulfureusBacillus alcalophilusBacillus cohniiBacillus sphaericusBrevibacillus brevisBrevibacterium linensCellulomonas flavigenaCellulomonas turbataCorynebacterium glutamicumComamonas testosteroniiGluconobacter oxydans subsp. oxydansGluconobacter oxydans subsp.oxydansGordonia amaraeGordonia rubropertinctaGordonia terraeHalomonas denitrificansHalomonas elongataHalomonas elongataHalomonas halmophilaHalomonas halmophilaHydrogenophaga flavaHydrogenophaga pseudoflava
Methylobacterium mesophilicumMethylobacterium organophilumMethylobacterium radiotoleransMethylobacterium rhodesianumParacoccus versutusParacoccus versutusPseudomonas balearicaPseudomonas fluorescensPseudomonas fluorescensPseudomonas oleovoransPseudomonas putidaPseudomonas stutzeriPseudonocardia hydrocarbonoxydansRhizobium leguminosarumRhodococcus coprophilusRhodococcus fasciansRhodococcus globerulusRhodococcus rhodniiRhodococcus rhodochrousRhodococcus ruberSinorhizobium melilotiStarkaya novellaStarkaya novellaStreptomyces albusStreptomyces avidiniiStreptomyces azureusStreptomyces badiusStreptomyces griseusStreptomyces hirsutusStreptomyces lavendulaeStreptomyces phaeochromogenesStreptomyces violaceoruberStreptomyces viridisporus
Libraries:
• Generation of reference pattern for new microorganisms by users
• Ready-to-use libraries with microbial strains for direct identification
Goslar, 09/10/2007
• Minimal sample preparation
• Powerful bioinformatic approaches
• Species to strain resolution, mixture detection
• High throughput at low costs per analysis
• Non-expert identification possible
• Dedicated databases of high quality
Conclusions
Goslar, 09/10/2007
The BDAL BioTyper team:
Thomas MaierKristina SchlosserThomas WenzelThorsten MieruchStefan KlepelUwe RennerJan-Henner WurmbachKarl-Otto KräuterAlexander Rueegg
Thanks to:
… and many cooperation partners!
In particular:Prof. Stackebrandt,Dr. Schumann