microfluidic systems for high- throughput ......high-speed imaging (5,000 frames/sec) captures the...

MICROFLUIDIC SYSTEMS FOR HIGH-

THROUGHPUT BIOPHYSICAL

CHARACTERIZATION OF SINGLE CELLS

by

Yi Zheng

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Mechanical and Industrial Engineering

University of Toronto

© Copyright by Yi Zheng 2014

ii

Abstract

MICROFLUIDIC SYSTEMS FOR HIGH-THROUGHPUT BIOPHYSICAL

CHARACTERIZATION OF SINGLE CELLS

Yi Zheng

Doctor of Philosophy

Graduate Department of Mechanical and Industrial Engineering

University of Toronto

2014

Biophysical (mechanical and electrical) properties of living cells have been proven to play

important roles in the regulation of various biological activities at the molecular and cellular

level, and can serve as promising label-free markers of cells‟ physiological states. In the past

two decades, a number of research tools have been developed for understanding the

association between biophysical property changes of biological cells and human diseases;

however, technical challenges of realizing high-throughput, robust and easy-to-perform

measurements on single-cell biophysical properties have yet to be solved. This thesis focuses

on the development, testing and modeling of microfluidic platforms for biophysical

characterization of single cells.

The proposed microfluidic system for biophysical characterization of red blood cells

(RBCs) achieved a speed of 100-150 cells/second and was capable of quantifying multiple

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parameters as mechanical and electrical signatures of each RBC including transit time,

impedance amplitude ratio, and impedance phase increase. In comparison with previously

reported microfluidic devices for single RBC biophysical measurement, this system has a

higher throughout, higher signal to noise ratio, and the capability of performing multi-

parameter measurements. The microfluidic device consisting of two stages of microchannels

was also developed for measuring mechanical opacity to mitigate the coupled effect of cell

size/volume and deformability. The stiffness/deformability changes of lymphocytes in

chronic lymphocytic leukemia (CLL) patients were, for the first time, studied using this

system.

In order to extract the inherent electrical properties of cells, electrical and geometrical

models are developed to interpret the impedance data and to determine the specific

membrane capacitance and cytoplasm conductivity of individual cells. Results from testing

3,249 AML-2 cells and 3,398 HL-60 cells reveal different specific membrane capacitance

and cytoplasm conductivity values between AML-2 (12.0±1.44 mF/m2, 0.62±0.10 S/m) and

HL-60 (14.5±1.75 mF/m2, 0.76±0.12 S/m) cells. The results also demonstrate that the

quantification of specific membrane capacitance and cytoplasm conductivity can enhance

cell classification results since these parameters contain information additional to cell size.

The progressive deformability changes during blood banking/storage were studied using

a microfluidic system. High-speed imaging (5,000 frames/sec) captures the dynamic

deformation behavior of the cells, and together with automated image analysis, enables the

characterization of over 1,000 RBCs within 3 minutes. Multiple parameters including

deformation index (DI), time constant (shape recovery rate), and RBC circularity were

quantified. Compared to previous studies on stored RBC deformability, our results include a

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significantly higher number of cells (>1,000 cells/sample vs. a few to tens of cells/sample)

and, for the first time, reveal deformation changes of stored RBCs when traveling through

human-capillary-like microchannels.

v

Acknowledgements

I am sincerely grateful to my advisor, Professor Yu Sun, for all his support, advice, and

guidance throughout the past four years at the University of Toronto. His constant

encouragement and understanding gave me tremendous help during my PhD study. His

enthusiasm and dedication for research inspired me to pursuit a career in academia.

My thesis is challenging, and I am glad to have the support from my collaborators. I

would like to express my great appreciation to Dr. Chen Wang (Mount Sinai hospital,

Toronto) for his passionate collaboration and valuable guidance and discussions, who

provided me the necessary samples and clinical knowledge. I also thank Professor Lidan You,

Professor Axel Guenther, Professor Eric Diller and Professor Tianhong Cui for serving on

my Ph.D. supervisory and defense committees.

I had the fortunate chance to be surrounded by many talented people during my graduate

study. I am especially grateful to Ehsan Shojaei, John Nguyen, Jun Chen, Jun Wen and Ji Ge

who have spent endless hours working with me. I would also like to thank all past and

present members of the Advanced Micro and Nanosystems Laboratory for all their helpful

discussions and encouragements throughout the years.

Finally, I wish to thank my parents for their love and support over the years. I also want

to express my deep appreciation to my wonderful wife, Weiwei for everything, and my

lovely son, Renxuan.

This research was supported by the Natural Sciences and Engineering Research Council

of Canada.

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Contents

1. Introduction ........................................................................................................................... 1

1.1 Background ..................................................................................................................... 1

1.2 Mechanical Characterization Techniques ....................................................................... 2

1.2.1 Structure-induced deformation (Constriction channel) ............................................ 2

1.2.2 Fluid-induced deformation ....................................................................................... 5

1.2.3 Electroporation-induced deformation ....................................................................... 8

1.2.4 Optical stretcher........................................................................................................ 9

1.2.5 DEP-induced deformation ...................................................................................... 12

1.2.6 Aspiration-induced deformation ............................................................................. 13

1.2.7 Compression-induced deformation ........................................................................ 13

1.3 Electrical Characterization Techniques ......................................................................... 16

1.3.1 Microfluidic Coulter counter .................................................................................. 17

1.3.2 Electrorotation ........................................................................................................ 18

1.3.3 Micro electrical impedance spectroscopy (µ-EIS) ................................................. 19

1.3.4 Impedance flow cytometry (IFC) ........................................................................... 21

1.4 Research Objectives ...................................................................................................... 24

1.5 Dissertation Outline ....................................................................................................... 24

2. High-Throughput Biophysical Measurement of Human Red Blood Cells ..................... 25

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2.1 Introduction ................................................................................................................... 25

2.2 System overview ........................................................................................................... 28

2.3 Materials and methods .................................................................................................. 30

2.3.1 Device fabrication .................................................................................................. 30

2.3.2 Blood samples and experimental protocol ............................................................. 31

2.3.3 Electrical measurement and data analysis .............................................................. 32

2.4 Results and discussion ................................................................................................... 35

2.4.1 Selection of channel dimension, signal frequency, and applied pressure .............. 35

2.4.2 WBCs and platelets ................................................................................................ 36

2.4.3 RBC measurements ................................................................................................ 36

2.5 Conclusion ..................................................................................................................... 40

3. Electrical Measurement of Red Blood Cell Deformability on a Microfluidic Device ... 41

3.1 Introduction ................................................................................................................... 41

3.2 Measurement Principle .................................................................................................. 46

3.3 Materials and Methods .................................................................................................. 47

3.3.1 Blood sample preparation ....................................................................................... 47

3.3.2 Device fabrication and operation ............................................................................ 48

3.3.3 Signal processing .................................................................................................... 49

3.4 Results and Discussion .................................................................................................. 50

3.4.1 Shear stress effect ................................................................................................... 50

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3.4.2 Mechanical opacity ................................................................................................. 51

3.4.3 System characterization .......................................................................................... 54

3.5 Conclusion ..................................................................................................................... 55

4. Microfluidic Characterization of Specific Membrane Capacitance and Cytoplasm

Conductivity of Single Cells ....................................................................................................... 59

4.1 Introduction ................................................................................................................... 59

4.2 System overview ........................................................................................................... 62



4.4.1 Curve fitting and frequency selection..................................................................... 69

4.4.2 Cell elongation measurement using impedance signal ........................................... 70

4.4.3 Specific membrane capacitance and cytoplasm conductivity ................................ 71

4.4.5 Error analysis .......................................................................................................... 72

4.4.6 Cell type classification ........................................................................................... 74

4.5 Conclusion ..................................................................................................................... 75

5. Characterization of Red Blood Cell Deformability Change during Blood Storage ...... 78

5.1 Introduction ................................................................................................................... 78

5.2 System overview ........................................................................................................... 81

5.3 Methods and materials .................................................................................................. 83

5.3.1 Blood samples ........................................................................................................ 83

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5.3.2 Experimental methods ............................................................................................ 83


5.3.1 Hydrodynamic focusing efficiency ........................................................................ 85

5.3.2 Time constant ......................................................................................................... 85

5.3.3 Changes in RBC deformation index, time constant, and circularity over blood

storage.............................................................................................................................. 89

5.4 Conclusion ..................................................................................................................... 93

6. Decreased deformability of lymphocytes in chronic lymphocytic leukemia .................. 94

6.1 Introduction ................................................................................................................... 94



6.4 Conclusion ................................................................................................................... 104

7. Conclusions......................................................................................................................... 105

7.1 Contributions ............................................................................................................... 105

7.2 Future directions .......................................................................................................... 106

8. Appendix............................................................................................................................. 108

9. Bibliography ....................................................................................................................... 110

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List of Figures

Figure 1.1 (a) A network of bifurcating microfluidic channels for blood cell deformability

measurement. The transit time of the individual cells are measured using a high-

speed camera. Reproduced with permission from [30]. (b) A microfluidic system

for electrical and mechanical characterization of RBCs at a speed of 100-150

cells/sec. The transit time is obtained from electrical impedance data captured

during RBCs flowing through the constriction channel. Reproduced with

permission from [23]. (c) Hydrodynamic stretching microfluidic device. Cells are

focused to the center lines of the channels by inertial force and stretched by fluid

pressure. Cell deformation is measured by analyzing images recorded by a high-

speed camera. Reproduced with permission from [19]. (d) Electroporation-

induced RBC lysis. Time-lapse images of RBCs are recorded by a high-speed

camera as the cells flow through the channel while a constant DC voltage is

applied. Cell lysis time is correlated with RBC deformability. Reproduced with

permission from [45]. ........................................................................................... 10

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Figure 1.2 (a) Optical stretcher. Cells travel along a flow channel integrated with two

opposing laser beams. Cells are trapped and deformed optically, and cell

deformation images are recorded. Reproduced with permission from [50]. (b)

Single-cell microchamber array for electro-deformation. An array of

microchambers integrated with DEP electrodes is used to trap individual RBCs

and apply DEP force. Reproduced with permission from [51]. (c) Microfluidic

pipette aspiration of cells using funnel channel chain. Reproduced with

permission from [52]. ........................................................................................... 11

Figure 1.3 (a) Schematic illustration of PGEs-based DC impedance cell counter. Ionic flows

between the PGEs under low DC bias are interrupted when a cell passes through,

causing a DC impedance change. The sheath flow is used for focusing cells and

preventing cell adhesion to chambers and channels. Reproduced with permission

from [71]. (b) Electrorotation. A cell is placed in the center of four micro-

electrodes connected in phase quadrature (90º difference) and rotated by the DEP

forces generated by the rotating electrical field. Rotation spectra (rotation rate vs.

frequency) are used to extract the electrical properties of the cells. Reproduced

with permission from [72]. (c) Schematic of the μEIS system showing four

impedance analysis sites positioned along the two cell flow channels. The close-

up view illustrates a single cell trapped inside a cell trap. The trapped cell forms a

tight seal with the two integrated electrodes for impedance measurement.

Reproduced with permission from [73]. ............................................................... 15

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Figure 1.4 (a) Impedance flow cytometry with coplanar electrodes. Side view shows a

particle passing over three electrodes (A, C, and B). Impedance signal is

measured differentially (ZAC-ZBC). Reproduced with permission from [113]. (b)

Impedance flow cytometry with parallel facing electrodes. Side view shows a cell

passing between the measurement and reference electrodes. Reproduced with

permission from [114]. (c) Impedance flow cytometry combining impedance

measurements and dielectrophoretic focusing. Reproduced with permission from

[120]. (d) Label-free electrophysiological cytometry. Electrodes are integrated

for both electrical stimulation of cells and recording of their extracellular ......... 23

Figure 2.1 (a) Schematic of the microfluidic system for electrical and mechanical

characterization of RBCs. Two Ag/AgCl non-polarizable electrodes are

connected to a function generator (100 kHz @1.0 Vpp) and a lock-in amplifier.

The two electrodes are inserted into the inlet and outlet ports of the device. As

RBCs are aspirated through the constriction channel, electrical current change is

sensed and amplified. “-P” denotes the negative pressure used to aspirate RBCs

through the constriction channel. (b) Measurements are made when an RBC

passes through the constriction channel. (c) Equivalent circuit model of the

system. .................................................................................................................. 27

Figure 2.2 Experimental amplitude profiles: (a) a single RBC, (b) two RBCs, (c) a single

platelet, and (d) a single WBC within the constriction channel. .......................... 30

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Figure 2.3 Ratio of the amplitude change and the basal amplitude vs. aspiration pressure.

Device sensitivity becomes lower at higher aspiration pressure due to RBC

deformation and larger leakage current. ............................................................... 33

Figure 2.4 Histograms of transit time, amplitude ratio and phase increase attained from an

adult sample (22,669 cells measured), (red)(cyan) before and after the exclusion

of platelets and debris. .......................................................................................... 33

Figure 2.5 Adult RBCs (n=84,073 from 5 adult samples, red), neonatal RBCs (n=82,253

from 5 newborn samples, cyan). (a) 3D scatter plot of transit time vs. amplitude

ratio vs. phase increase. (b) Histograms of transit time, amplitude ratio, and phase

increase fitted with normal distributions. ............................................................. 34

Figure 2.6 (a) Amplitude ratio as a function of RBC volume. (b) Transit time as a function of

RBC volume. ........................................................................................................ 39

Figure 3.1 Electrical measurement of RBC deformability. The schematics show two RBCs

with identical size/volume but different deformability. A voltage signal is applied

across the channel, and current (denoted as yellow arrows) is measured via a

current preamplifier. Shear stress induces the RBCs into a parachute-like shape.

The RBC with higher deformability is more stretched along the flow direction (a),

resulting in larger gaps between the RBC membrane and channel walls than the

less deformable RBC (b). Thus, the RBC with higher deformability blocks less

current (i.e., smaller current decrease in the current profile) than the RBC with

lower deformability. ............................................................................................. 44

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Figure 3.2 (a) Microfluidic device for electrically measuring RBC size and deformability.

Ag/AgCl electrodes are plugged into the inlet and outlet ports via T-junctions.

The cross-sectional areas of the measurement units are 8 µm × 8 µm and 5 µm ×

5 µm, respectively. The transit region (50 µm × 20 µm) is for separating the two

current valleys of the two measurement units. RBCs are driven through the

measurement areas continuously. (b) Experimental data: time series of the current

profile measured within 1 second with 14 RBCs passing through. (c) The zoom-in

view of the profile circled in (b). .......................................................................... 45

Figure 3.3 Current decrease within 8 µm × 8 µm measurement unit (I1, red) and 5 µm × 5 µm

measurement unit (I2, blue) of a controlled RBC sample under various driving

pressures. The error bars represent standard deviation. More than 1,000 RBCs

were measured for each data point. ...................................................................... 50

Figure 3.4 (a) Scatter plot of I2 vs. I1 for controlled RBCs (n=996) and GA-treated RBCs

(n=1,059). (b) Scatter plot of I2 vs. I1 for controlled RBCs (n=996) and heated

RBCs (n=1,179). All RBCs were measured under a driving pressure of 400 Pa. I2

depends linearly on I1. For RBCs with the same I1, GA-treated RBCs and heated

RBCs show a higher and lower I2 than controlled RBCs, respectively. (c) Scatter

plot of I2/I1 (opacity) vs. I1 for controlled, GA-treated and heated RBCs. RBCs

deformability is reflected by the ratio of I2/I1. The data show that GA treatment

decreases RBC deformability and heat treatment increases RBC deformability. 53

Figure 3.5 Histograms of I2/I1 (opacity) of controlled, GA-treated and heated RBCs under (a)

400 Pa and (b) 1600 Pa driving pressures, fitted with normalized distribution. (c)

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Mechanical opacity of controlled RBCs (red), GA-treated RBCs (blue) and

heated RBCs (green) measured under different driving pressures. Results are

mean ± standard deviation. More than 1,000 RBCs were tested for each of the

data points. (d) Standardized difference values calculated using the data present in

(c). Blue: standardized difference between GA-treated and controlled RBCs.

Green: standardized difference between heated and controlled RBCs. ................ 57

Figure 3.6 Comparison of I2/I1 (opacity) of controlled, GA-treated, and heated RBCs within

sub-ranges of I1. (a) 400 Pa driving pressure, (b) 1600 Pa driving pressure. Error

bars represent standard deviation.......................................................................... 58

Figure 4.1 (a) Schematic of the microfluidic system for electrical measurement of single cells.

Within the constriction channel, “green” represents the cytoplasm and “red”

represents the membrane. “i‟ is the current through the channel. Rcyto represents

cytoplasm resistance and Cmem represents membrane capacitance; Rleak represents

the leaking resistance between cell membrane and channel walls; Rch‟ and Cch

represent resistance of the medium and parasitic capacitance of the channel. (b)

Experimental data showing impedance amplitude profiles (at 7 frequencies)

measured within 1 second with 8 cells passing through the constriction channel.

Impedance profiles of different frequencies are plotted in different colors. ........ 63

Figure 4.2 (a) An AML-2 cell is inside the constriction channel. (b) Schematic representation

of the simplified geometrical model (“green” and “red” represent cytoplasm and

membrane, respectively). ...................................................................................... 64

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Figure 4.3 (a) The channel without cells passing is modeled as a resistor Rch and a capacitor

Cch in parallel. (b) The cell is modeled as a resistor Rcyto (cytoplasm) and two

capacitors Cmem (membrane) in series; Rleak represents the leaking resistance

between cell membrane and channel walls; Rch‟ represents the medium‟s

resistance after a portion of the constriction channel is occupied by a cell. G is a

pre-amplifier converting current into voltage signals. .......................................... 64

Figure 4.4 (a)(b) Impedance amplitude and phase of the constriction channel without and

with cells (5 cells of different diameters), measured at 201 frequency points

within the range of 1 kHz to 1 MHz. (c)(d) Impedance amplitude and phase

measured at 7 selected frequency points (1 kHz, 20 kHz, 50 kHz, 100 kHz, 200

kHz, 300k Hz, 400 kHz), on the same 5 cells as shown in (a) and (b).

Experimental data and curve fitting results are plotted in blue and red,

respectively. .......................................................................................................... 68

Figure 4.5 Experimental correlation between cell elongation and impedance increase at 1

kHz of AML-2 (blue) and HL-60 (red). ............................................................... 76

Figure 4.6 (a) Scatter plot of specific membrane capacitance vs. cytoplasm conductivity of

AML-2 and HL-60. Color coding represents data distribution densities. (b)

Histograms of specific membrane capacitance and cytoplasm conductivity of

AML-2 (red) and HL-60 (cyan) fitted with normal distributions. (c) Histograms of

AML-2 (red) and HL-60 (cyan) cell diameters fitted with normal distributions. 77

xvii

Figure 5.1 (a) Schematic of the microfluidic device for studying deformability changes of

stored RBCs. Hydrodynamic focusing centers the cell and adjusts it to the

„standing‟ orientation. The RBC is „folded‟ into a parachute-like shape when

pressure-driven through the 8 µm×8 µm central channel. Deformation index

(DI=L/D) is defined to measure RBC deformation (top-left). Time constant (tc) of

each individual cell is determined by fitting DI value changes during the cell

shape recovery process to an exponential function, after the cell exits the

microchannel. (b) Experimental images showing the centering, orienting, folding,

and shape recovering of an RBC. ......................................................................... 82

Figure 5.2 Efficiency of hydrodynamic focusing. Histogram of central distance (i.e., the

distance between the center of the cell and microchannel‟s central line) of RBCs

before (blue) and after (red) the focusing unit (n=1,500). Central distance of zero

means the cell is perfectly centered within the microchannel. The percentage of

RBCs near the microchannel center was increased significantly by hydrodynamic

focusing................................................................................................................. 84

Figure 5.3 Shape recovery of a biconcave RBC (blue) and spherical RBC (red) fitted to an

exponential model. The spherical shape of the cell was due to morphological

change during blood storage. Time constant (tc) of the spherical RBC is 0.267 ms,

and the tc value of the biconcave RBC is 0.625 ms. The relationship between

deformability change and morphology change is discussed in the next section. . 87

Figure 5.4 (a)(b) Scatter plots of time constant vs. circularity; deformation index (DI) vs.

circularity, for the same blood sample stored for 1 week (n=1,038), 3 weeks

xviii

(n=1,027), and 6 weeks (n=1,001). (c) Distribution of time constant, circularity,

and DI. .................................................................................................................. 88

Figure 5.5 Comparison of time constants for the same blood sample stored for 1 week

(n=1,038), 3 weeks (n=1,027), and 6 weeks (n=1,001). Circularity is divided into

five sub-ranges. Error bars represent standard deviation. In the last circularity

range (>0.8), the group of 1 week old RBCs is not present because no RBC has a

circularity higher than 0.8 in 1 week old sample. ................................................. 91

Figure 5.6 Time constant (a) and circularity-DW (b) alteration over time. Each data point was

obtained from 5 blood samples and over 1,000 cells were tested within each

sample. Significant differences exist between neighbor data points (p<0.05). A

relatively larger change between fresh and 1-2week samples was found,

compared to the steady changes over time during storage. .................................. 92

Figure 6.1 Microfluidic system for electrically measuring lymphocytes volume and transit

time. When a lymphocyte is driven through the measurement units under a

negative pressure (3 kPa) (also see SI Video 1), the total electrical resistance of

the channel increases due to the blockage of electrical current. (b) Experimental

resistance data recorded within 1 second. Zoom-in of the circled area is shown in

(c). Cell volume and transit time are determined by measuring ∆R and ∆t. ........ 97

Figure 6.2 (a) Scatter plots of transit time vs. cell diameter for lymphocytes from a

control/healthy sample (n=1,048) and a CLL sample (n=796). Transit time

exhibits a power law dependence on cell volume/diameter. (b) Histograms of cell

xix

diameter and transit time of the control and CLL samples. Both volume and

transit time follow normal distributions. .............................................................. 99

Figure 6.3 Transit time (a) and cell volume (b) measured from 5 control/healthy samples (red)

and 5 CLL samples (cyan). For each sample, the three lines of the box represent

75 percentile, median, and 25 percentile; the whiskers represent the locations of

maximum and minimum (n≈1,000 for each sample). The average median values

of transit time (c) and cell diameter (d) of the 5 control samples and the 5 CLL

samples are 3.50 ± 0.36 ms vs. 5.13 ± 0.98 ms and 7.36 ± 0.07 µm vs. 7.15 ± 0.13

µm, respectively (*p < 0.05, error bars represent standard deviation of the mean

value). In general, cells in CLL samples are slightly smaller than cells in control

samples but reveal a longer transit time, indicating that they are less deformable.

............................................................................................................................ 100

Figure 6.4 (a) Experimental AFM indentation curves of two representative lymphocytes, one

from control sample and one from CLL sample. The elastic moduli of

lymphocytes were quantitatively determined by fitting force-indentation curves to

standard Hertz model for a pyramidal tip. (b) Elastic modulus values of the

control sample (n = 10) and the CLL sample (n = 14) are 2.92 ± 0.38 kPa and

5.37 ± 0.38 kPa, respectively (*p < 0.05, error bars represent standard deviation

of the mean value). ............................................................................................. 103

Figure 6.5 Lymphocytes from control (a) and CLL (b) samples stained with Wright/Giemsa

Stain. ................................................................................................................... 103

CHAPTER 1, INTRODUCTION

1

Chapter 1

1. Introduction

1.1 Background

The cell, the basic functional unit of living organisms, maintains and senses the physiological

environment within the organism both chemically and physically [1-4]. The unique

biochemical and biophysical properties enable a cell to fulfill its specific functions and adapt

to its surrounding environment. Physiological changes within the cells are accompanied by

chemical and physical modifications and reorganization. Thus, pathological cells can be

identified biochemically and/or biophysically. Biochemical properties of pathological cells

have been under intensive study, and many biochemical markers have been developed to

identify target cells out of a heterogeneous population [5, 6]. However, although research on

physical properties of cells has provided strong evidence about the capability of physical

properties as potential markers for identifying cell types, most of biophysics research was

limited to proof-of-concept demonstrations. The lack of clinical relevance is due to the very

low measurement throughput and tedious operation procedures of conventional techniques

for measuring the biophysical properties of cells [4, 7-9]. Compared to conventional


2

techniques, the advantages of small sample volume, integration capability, biocompatibility

and fast response make microfluidic technologies attractive for studying cells. Recent

decades have witnessed significant advances of microfluidic technologies for biochemical

characterization of cells [10, 11]. Microfluidics is extending its way to the characterization of

single-cell biophysical properties [12-14].

1.2 Mechanical Characterization Techniques

The association between cell deformability and human diseases has been of interest since the

1960s [15, 16]. The deformability of nucleated cells is determined by the membrane, the

cytoskeletal network (actin filaments, intermediate filaments, and microtubules), and its

interaction with the nucleus, while the deformability of red blood cells (RBCs) is determined

by the membrane skeleton network and the interaction between the membrane skeleton and

membrane integral proteins [4, 17, 18]. Physiological and pathological changes can alter the

cytoskeleton composition, reorganize the network structure, and change the protein density.

As a result, cell deformability can be used as an intrinsic marker for identifying pathological

conditions [8, 19, 20]. For example, deformability is known to play a crucial role in the

mobility of cancerous cells [4, 21]; and RBC deformability decrease has been proven

relevant to several human diseases.

1.2.1 Structure-induced deformation (Constriction channel)

Constriction channels, which are marginally smaller than the diameters of tested cells,

provide an efficient method to generate mechanical stimuli. Cells driven through a

constriction channel are squeezed by the walls of the constriction channel. Multiple

parameters, such as transit time, elongation and recovery time, in association with cell


3

deformability can be quantified. Moreover, constriction channels can be easily fabricated

with standard microfabrication techniques and are able to provide an environment to mimic

in vivo capillaries. With the use of high-speed imaging or electrical impedance

measurements, constriction channel devices are capable of achieving a higher throughput

than most other deformability measurement approaches. Due to these merits, constriction

channels have been used to measure the deformability of RBCs [22-29], leukocytes [30] and

cancer cells [31, 32].

Due to the human capillary-like environment and the physiological relevance of RBC

deformability, RBC is studied most in the majority of existing constriction channel-based

devices. The first demonstration of microfluidic constriction channels for RBC deformability

characterization was reported in 2003 [22]. In this study, constriction channels with various

diameters were used to study the deformability changes between healthy RBCs and malaria

parasite infected RBCs at different stages (early ring stage, early trophozoite, late trophozoite,

and schizont). They found that the deformability of malaria-infected RBCs decreases as the

parasite progresses from the early ring stage to a schizont, while healthy RBCs are

exceptionally deformable and even able to travel through the constriction channels blocked

by infected RBCs. In addition to RBC deformability, white blood cell (WBC) deformability

was also studied using microfluidic constriction channels. Rosenbluth et al. demonstrated the

clinical relevance of their microfluidic constriction channel device in sepsis and leukostasis

[30]. The reported device consists of a network of 64 constriction channels. High-speed

imaging was used for measuring cell transit time. They used patient samples to show that cell

transit time increased for diseased samples compared to control samples (Figure 1.1 (a)). In

another study, breast cancer cell lines (MCF-7 and MCF-10A) were flown through a


4

constriction channel to distinguish nonmalignant and malignant cells [31]. According to the

reported results, transit velocity is not significantly affected by cell types, and the transit time

difference is mainly determined by the entry time. Different cell types can be distinguished

on the basis of scatter plots of cell volume and entry time.

Besides imaging, microfluidic constriction channel can also be used together with other

measurement techniques to achieve multiple parameter measurements for cell type

classification. We recently developed a microfluidic device (Figure 1.1 (b)) which combines

a constriction channel and impedance measurements [23]. Detection involves only electrical

signals; hence, it enables a throughput higher than 100 cells/sec. The multiple parameters

quantified as mechanical and electrical signatures include transit time, impedance amplitude

ratio, and impedance phase increase. Histograms compiled from 84,073 adult RBCs and

82,253 neonatal RBCs reveal different biophysical properties across samples and between the

adult and neonatal RBC populations. Bow et al. demonstrated a deformability-based RBC

testing device combining constriction channels and fluorescence measurement. They showed

a population-based correlation between biochemical properties, such as cell surface markers,

and mechanical deformability [24]. They also showed experimentally that entrance geometry

of the constriction channel has a significant impact on RBC transit time, and developed a

dissipative particle dynamics model to interpret the parasite effect on RBC deformability. An

optical stretcher was also introduced to enhance the sensitivity and reliability of a

constriction channel based microfluidic device [25]. Scatter plots compiled from transit time,

elongation, and recovery time measured by this device proved effective for the

discrimination of RBCs from normal donors and leukemia patients.


5

Other applications of microfluidic constriction channels include the usage of wedge-

shaped constriction channels to measure the surface area and volume of a large population of

RBCs [26-28], and a microfluidic manometer to measure the pressure drop due to the

presence of a cell in the constriction region, which correlates with the stiffness of the cell, by

observing the displacement of downstream fluid-fluid interface [29]. Despite the advantages

of the constriction channel technique, cell volume and adhesion between cell membrane and

channel walls are coupled with cell deformability. Consequently, longer transit time does not

necessarily mean lower deformability since larger and stickier cells can also lead to longer

transit time. Efforts have been made to take cell volume/size effect into account. For example,

Adamo et al. reported a microfluidic device for probing both cell volume and transit time.

Comparisons of cell transit time were made among cells with similar volume [33]. They

demonstrated that Hela cells in the control group have longer transit time than Hela cells

treated with latrunculin A and cytochalasin B. The size of the constriction channel can also

be adjusted on demand according to targeted cell size (MCF-7 and MCF-10A) [34]. However,

there exists no technique that is capable of characterizing the adhesion (or friction) between

cell membrane and channel walls. In addition, since the diameter of the constriction channel

is smaller than the diameter of targeted cells, the channel is susceptible to clogging. Using an

array of constriction channels appears to be a potential solution to the clogging issue.

1.2.2 Fluid-induced deformation

RBCs are highly deformable and can easily deform under fluid shear stress inside blood

vessels. Having micrometer dimensions, which are comparable with in vivo capillaries,

microfluidic channels provide an ideal tool for investigating RBC deformability. Compared

to constriction channel, the microfluidic channels used to generate shear stress are larger than


6

RBCs. Thus, RBCs are deformed by fluid shear stress instead of channel‟s confinement

structures. The deformation index (DI) quantified via high-speed imaging is often used to

indicate RBC deformability. Forsyth et al. studied the deformability and dynamic behavior of

chemically “stiffened” RBCs using a simple straight microfluidic channel, revealing three

different types of motion due to the increased shear rate in the microfluidic channel:

stretching, tumbling, and recoiling [35]. Besides straight channels, a hyperbolic converging

microchannel was also developed for assessing RBC deformability by measuring extensional

flow-induced deformation [36]. The results confirmed that extensional flow is more efficient

in causing RBC deformation. Shear stress generated in microfluidic channels was also used

to measure the dynamics of shear-induced adenosine triphosphate (ATP) release from RBCs.

In [37], RBCs were driven through a microfluidic channel with a cross-sectional area of

20m×20m, while the amount of ATP released was measured by counting the photons

emitted from standard luciferase-ATP bioluminescent reaction. Cell deformation and

dynamics were quantified simultaneously using high-speed imaging.

Although shear stress proves effective for investigating RBC deformability, the low

magnitude of shear stress is typically not able to deform other types of cells. Gossett et al.

recently reported a hydrodynamic stretching microfluidic device for identifying malignant

cells in human pleural fluid sample with a measurement speed of ~2,000 cells/sec (see Figure

1.1 (c)) [19]. Cells are focused towards a narrow streamline near the center of the

microfluidic channel and delivered to a junction of two orthogonal channels at a high flow

rate, where the cells undergo mechanical stretching. In the meanwhile, cell deformations are

captured using a high-speed camera and images are then analyzed to extract cell volume and

deformation index (DI). This approach achieved both high testing speed and larger


7

deformation of tested cells. The authors predicted disease states in patients with cancer and

immune activation with a sensitivity of 91% and a specificity of 86%.

In fluid-induced deformation based microfluidic devices, channel dimensions are larger

than cell diameters. DI is used as the deformability indicator and is not affected by the

adhesion between cell membrane and channel walls (vs. constriction channel). However,

since cells of different sizes may experience different forces due to the non-uniformity in the

shear stress and hydrodynamic pressure within the microchannel, DI is still associated with

cell volume. More specifically, shear stress is highest near channel walls and is almost zero

in the center of the channel. As a result, RBCs with larger volumes experience higher shear

stress on the edges causing larger deformation compared to cells with smaller volumes.

Within the hydrodynamic stretching microfluidic device, the highest compressive pressure

appears at the center of the junction where the cells are stretched, which causes the cells with

different volumes or shapes to be exposed under different pressure environment. To address

this issue, cell volume is measured as an independent parameter and used as a reference for

cell type classification [19].

A limitation of fluid-induced deformation based microfluidic devices is the use of high-

speed imaging (tens of kHz) for measuring DI. High-speed cameras are often costly and must

be used on a microscope, making the overall microfluidic system bulky. Furthermore, since

real-time image data transfer from a high-speed camera to the computer hard drive cannot be

achieved, images are stored in the camera‟s on-board memory for later off-line transfer.

Since state-of-the-art high-speed cameras typically have only a few GB on-board memory,

after recoding for a few seconds, the experiment must be stopped for image data transfer.

Processing massive amounts of image data also costs tremendous computational effort and


8

time. Advances in high-speed cameras and hardware-based image processing techniques,

such as FPGA-based techniques, will possibly be able to solve the data transfer problem.

Additionally, opto-microfluidics, microfluidic devices integrated with optical comments, also

seems promising to tackle this problem. Integrating on-chip lenses and CMOS chips can

achieve real-time performance and in the meanwhile, eliminate the need of the use of bulky

microscopes [38].

1.2.3 Electroporation-induced deformation

Electroporation is a technique used for introducing foreign molecules, such as DNA and

proteins, into cells. The concept of electroporation capitalizes on the relatively weak nature

of the phospholipid bilayer‟s hydrophobic/hydrophilic interactions and its ability to

spontaneously reassemble after disturbance [39]. Thus, a voltage shock can disrupt areas of

the membrane temporarily, allowing polar molecules to pass. The membrane then can reseal

and leave the cell intact [39-41]. Swelling or expansion in cell size accompanies the cell‟s

electrical property changes [42], which is caused by the influx of molecules through the open

pores in the cell membrane (vs. dielectric force in DEP-induced deformation). Recently, a

few studies correlated electroporation-induced cell deformation and lysis to cell

deformability changes. Bao et al. [43] developed microfluidic electroporative flow

cytometry to study the deformability of single cells. A constant DC voltage was established

across the microchannel, which concentrated the electric field to yield repeatable cell

exposure to uniform electrical fields. When cells were flown through the microchannel, the

swelling of cells was recorded by imaging and quantified as a deformability indicator.

Deformability changes of breast cancer cell lines [43] and the expansion of the nuclei of

circulating tumor cells (CTCs) were tested using the system [44]. The capability to detect


9

RBCs with deficiencies of cytoskeletal protein network was also demonstrated (see Figure

1.1 (d)) [45]. The achieved throughput was about 5 cells/sec.

It is notable that electroporation efficiency is also dependent on cell volume. Under the

same uniform electrical field, the effective voltage across the cell membrane is a function of

cell diameter [46]. Hence, cells with larger diameters are exposed to higher voltages and are

more easily porated. In addition, the plasma membrane of different cell types (e.g., metastatic

tumor cells vs. non-metastatic tumor cells) can possess different poration properties. In other

words, some cell types may be more susceptible to electrical fields than others. Thus, based

on electroporation-induced expansion or swelling alone, it can be inaccurate to conclude that

cell deformation is caused by cell deformability rather than the membrane‟s poration

susceptibility to electrical voltages.

1.2.4 Optical stretcher

Optical trapping was discovered in 1970 [47] when radiation pressure from laser light was

found to be able to accelerate and trap micron-sized dielectric particles. Light interacts with a

particle by imparting some of its momentum onto it, thus exerting a force on the particle. If

the particle is not centered on the optical axis of the beam, a restoring force is exerted on the

particle to keep it on the optical axis. Optical stretchers utilize two slightly divergent

Gaussian beams to trap an object in the middle. This setup was used to deform and measure

the stress profile of erythrocytes [48], which led to the first optical stretcher device reported

in 2001 [49] as a novel method to stretch biological cells and probe their viscoelastic

properties.


10

Figure 1.1 (a) A network of bifurcating microfluidic channels for blood cell deformability

measurement. The transit time of the individual cells are measured using a high-speed

camera. Reproduced with permission from [30]. (b) A microfluidic system for electrical and

mechanical characterization of RBCs at a speed of 100-150 cells/sec. The transit time is

obtained from electrical impedance data captured during RBCs flowing through the

constriction channel. Reproduced with permission from [23]. (c) Hydrodynamic stretching

microfluidic device. Cells are focused to the center lines of the channels by inertial force and

stretched by fluid pressure. Cell deformation is measured by analyzing images recorded by a

high-speed camera. Reproduced with permission from [19]. (d) Electroporation-induced

RBC lysis. Time-lapse images of RBCs are recorded by a high-speed camera as the cells

flow through the channel while a constant DC voltage is applied. Cell lysis time is correlated

with RBC deformability. Reproduced with permission from [45].

(a) (b)

(c) (d)


11

Figure 1.2 (a) Optical stretcher. Cells travel along a flow channel integrated with two

opposing laser beams. Cells are trapped and deformed optically, and cell deformation images

are recorded. Reproduced with permission from [50]. (b) Single-cell microchamber array for

electro-deformation. An array of microchambers integrated with DEP electrodes is used to

trap individual RBCs and apply DEP force. Reproduced with permission from [51]. (c)

Microfluidic pipette aspiration of cells using funnel channel chain. Reproduced with

permission from [52].

(a) (b)

(c)


12

A typical optical stretcher system consists of a microchannel for loading cells into the

testing region and two laser fibers located on the sides of the passageway (see Figure 1.2 (a))

[50]. Cells flowing through the microfluidic channels are serially trapped and deformed with

the two counter propagating divergent laser beams [53, 54]. For trapping and stretching cells

in the optical stretcher, the alignment of the fibers is crucial. In order to improve alignment, a

platform capable of adjusting fiber positions was developed [55] using pneumatically driven

manipulators. Femtosecond laser technique has been utilized for the fabrication of the optical

stretcher microfluidic devices [56, 57]. By direct writing of both optical waveguides and

microfluidic channels, the alignment problem can be mitigated significantly. The

deformability of human cancer cell lines [53, 54], red blood cells [55] and patient oral

squamous cell [58] was characterized using optical stretchers.

1.2.5 DEP-induced deformation

When polarized in an electric field, biological cells will experience dielectric force, which is

well known as DEP Force [59, 60]. Electro-deformation devices utilize DEP force for

trapping and generating mechanical stimuli to quantify the deformability of individual cells

[61, 62]. The Young‟s modulus of tested cells can be extracted with either analytical models

or numerical simulation. For improving the throughput of electro-deformation devices, a

single-cell microchamber array device [51] was proposed. The device is able to trap

individual RBCs in a large array of micro-wells integrated with DEP electrodes. The ITO

electrodes also allow the correlation of RBC deformation with cell surface and cytosolic

characteristics (Figure 1.2 (b)). Even a single-cell microchamber array was adopted, the

overall throughput is still quite limited due to the time-consuming cell trapping procedure.

On the other hand, the complex physical phenomena involved in electro-deformation and


13

unknown cell electrical properties pose difficulties in extracting forces experienced by an

electro-deformed cell [59, 61, 62]. Since DEP force is determined by the dielectrical

properties of cells, DEP-based techniques can also be used to electrically characterize cells,

which is discussed in the Electrorotation section under electrical characterization.

1.2.6 Aspiration-induced deformation

Pipette aspiration is a conventional technique for studying mechanical properties of single

cells [63]. The mathematical models in conventional pipette aspiration have been adopted in

microfluidic characterization of cells. Figure 1.2 (c) shows a microfluidic pipette aspiration

device [52]. Single cells are infused into a microfluidic channel and deformed through a

series of funnel-shaped constrictions. Malaria infected RBCs were tested using both

membrane cortical tension and threshold pressure as readouts. We developed a microfluidic

device for single-cell electrical and mechanical characterization using impedance

spectroscopy and micropipette aspiration [64]. Cellular deformation was recorded as a

function of increasing pressure while cellular impedance was measured via two Ag/AgCl

electrodes inserted into the culture medium. With pipette aspiration and equivalent circuit

models, both mechanical properties and dielectric properties of single cells were quantified.

In addition to throughput limitation, the rectangle-like cross-section in microfluidic pipette

aspiration devices can cause concerns about the validity of applying conventional pipette

aspiration models.

1.2.7 Compression-induced deformation

Compressive forces can be applied directly to cells through a mechanism similar to bulging

elastomeric valves [65]. In general, such devices consist of multiple layers and a thin


14

elastomeric membrane separating the flow and control channels. The cells within the flow

channels are compressed when a pneumatic pressure is applied to deflect the thin membrane.

Microfluidic devices based on the compression have been used to monitor cell viability and

induce mechanical cell lysis of adherent breast cancer cells (MCF-7) [66]. A more recent

study by Kim et al. has shown that under excessive deformation of the deflecting membrane,

small morphological changes known as “bulges” occur on the cellular membrane as a result

of local detachment of the cytoskeleton lipid bilayer. In addition, differences in the

uniformity of bulge distribution on the cell periphery can be used as a physical indicator to

distinguish different cells [67]. A similar technology can also be used for viscoelastic

characterization of cells of a small population in suspension. Du et al. estimated global

viscoelastic time constants of HL-60 cell line and 3T3 fibroblast by observing the recovery

time upon the removal of the compression force [68]. This design can also be extended to the

mechanical characterization of other specimens such as biofilms [69]. Alternatively,

hydrostatic pressure can apply a uniform compressive force without physical contact [70].


15

Figure 1.3 (a) Schematic illustration of PGEs-based DC impedance cell counter. Ionic flows

between the PGEs under low DC bias are interrupted when a cell passes through, causing a

DC impedance change. The sheath flow is used for focusing cells and preventing cell

adhesion to chambers and channels. Reproduced with permission from [71]. (b)

Electrorotation. A cell is placed in the center of four micro-electrodes connected in phase

quadrature (90º difference) and rotated by the DEP forces generated by the rotating electrical

field. Rotation spectra (rotation rate vs. frequency) are used to extract the electrical properties

of the cells. Reproduced with permission from [72]. (c) Schematic of the μEIS system

showing four impedance analysis sites positioned along the two cell flow channels. The

close-up view illustrates a single cell trapped inside a cell trap. The trapped cell forms a tight

seal with the two integrated electrodes for impedance measurement. Reproduced with

permission from [73].

(a)

(b) (c)


16

1.3 Electrical Characterization Techniques

Besides mechanical deformability, electrical properties of cells are also important physical

properties, serving as the basis of counting, trapping, focusing, separating, and the

characterization of single cells [74, 75]. Early work on cell electrical measurements dates

back to the 1910s [76-78], when approximate hemoglobin conductivity inside RBCs was

reported. The single shell model proposed by Pauly and Schwan in the 1950s laid the

foundation for interpreting electrical properties of cells, where the cell is modeled as a

spherical cytoplasm surrounded by a thin dielectric membrane [79, 80]. Generally, plasma

membrane‟s electrical properties are affected by the membrane morphology, lipid bilayer

composition and thickness, and embedded proteins [81-83]. Cytoplasm‟s electrical properties

are influenced by the intracellular structures and physiological conditions (e.g., nucleus-to-

cytoplasm ratio and ion concentrations inside the cell) [84-86]. Early techniques are only

capable of measuring average electrical properties of a cell population. The most successful

example of electrical measurements on single cells is the Coulter counter. However, direct

current (DC) measurements are limited to counting and sizing single cells. The patch-clamp

technique developed in 1976 is capable of electrical characterization of single cells [87].

However, patch-clamp typically takes tens of minutes for testing one cell. Because of the

capability of manipulating micro-scaled objects and integrating micro electrodes on chip,

microfluidic techniques have gained momentum in single cell electrical characterization in

recent years.


17

1.3.1 Microfluidic Coulter counter

The Coulter counter monitors the DC resistance of a small orifice as micro particles passing

through. Since the cell membrane acts as an insulating layer at DC, the presence of the cell

alters the resistance of the orifice by replacing the conductive liquid. Well established models

are available for correlating DC resistance changes to cell volumes. Several designs have

recently been proposed to improve microfluidic Coulter counter‟s performance. For instance,

two-dimensional sheath flow focusing was demonstrated to overcome the clogging issue [88];

and throughput of microfluidic Coulter counters was improved using multiple-orifice designs

[89].

One challenge for microfluidic Coulter counters lies in the selection of the electrode

material. Ag/AgCl non-polarizable electrodes, which work well in the conventional Coulter

counter, are an ideal choice. Nonetheless, integrating Ag/AgCl electrodes into microfluidic

channels is complex, and Ag/AgCl electrodes have limited lifetime [90, 91]. Although other

metal electrodes can be more readily integrated into microfluidic channels, the electrical

double layers formed between the interface of electrodes and liquid, which are mainly

capacitive, pose difficulties in applying DC signals. Methods for minimizing the electrical

double layer effect include the modification of the electrode surface roughness in order to

increase the surface area [92] and the utilization of polyelectrolytic salt bridges (PSBEs) [93]

or polyelectrolyte gel electrodes (PGEs) [94]. Most recently, a DC impedance-based

microcytometer device integrating PGEs was reported for CTC cell detection. Sheath flow is

used for focusing cells and preventing cell adhesion to chambers and channels (see Figure

1.3 (a)). CTCs were successfully detected in blood samples from breast cancer patients [71].

Besides the electrode design that demands special design consideration, the model used in the


18

conventional Coulter counter to calculate particle volumes is not directly applicable to

microfluidic Coulter counter devices, due to the different electrode configuration and channel

geometry [95, 96].

1.3.2 Electrorotation

When a cell/particle is placed in a non-uniform electrical field, a force is exerted on the

induced dipole and can cause the cell to either move (DEP) or rotate (ROT) [59, 72].

Maxwell‟s mixture theory is commonly used to interpret DEP and ROT data, for associating

the complex permittivity of the suspension to the complex permittivity of the particle/cell [59,

72]. The DEP force and ROT torque are proportional to the real and imaginary parts of the

Clausius–Mossotti factor, respectively [9]. Although DEP and ROT are closely related, ROT

is more suitable for single cell electrical characterization since in ROT, the cells are only

rotated at a certain position in the electric field. Thus, the amplitude of the electric field

remains unchanged, which is suitable for fitting the rotation spectra to determine intrinsic

electrical properties of single cells (e.g., specific membrane capacitance, cytoplasm

conductivity and cytoplasm permittivity). Differently, forces in DEP are determined by the

electric field gradient. A constant electric field gradient is technically challenging to achieve.

Furthermore, the rotation rate is only determined by the rotation force (constant as a cell

rotates) and the viscosity of the suspension medium in ROT, which can be readily measured,

whereas the DEP force (varying as a cell moves) is difficult to quantify. DEP was

demonstrated to electrically characterize cells by curve fitting of the Clausius–Mossotti

factor spectra or cell count spectra. However, since the spectra are not from the

measurements of the same cells, only average electrical properties of a cell population can be


19

obtained [97-99]. More details on DEP and ROT models can be found in other reviews [9,

100].

The most popular ROT setup uses four electrodes connected to sine waves in phase

quadrature [81, 101]. Cells are placed in the center of the four electrodes (e.g., using laser

tweezers). Rotation spectra are obtained by measuring the rotation speed at each frequency

over a frequency range of 1 kHz to around 100 MHz. By fitting the rotation spectra, specific

membrane capacitance, cytoplasm conductivity and cytoplasm permittivity values are

determined [82, 102, 103]. Figure 1.3 (b) illustrates the working mechanism of a microfluidic

electro-rotation device [72]. The ROT technique has been used to test a number of cell types,

such as white blood cells and human cancer cells [81, 82, 102-104]. Electrorotation is a slow

technique. It takes approximately 30 minutes to test a single cell [101, 105]. It is also difficult

to achieve efficient rotation in a high conductivity physiological buffer. Hence, electrical

properties of the tested cell may have already been altered when immersed in the low

conductivity sucrose buffer. Nevertheless, electrorotation is the only method capable of

extracting inherent electrical properties of cells, such as membrane permittivity and

cytoplasm conductivity. The methodology and electrical models can be instrumental for the

development of new techniques of higher throughput.

1.3.3 Micro electrical impedance spectroscopy (µ-EIS)

Micro electrical impedance spectroscopy (µ-EIS) is a technique wherein a frequency-

dependent excitation signal is applied across a trapped cell to measure the corresponding

current response. Various cell trapping mechanisms incorporated with microfabricated

electrodes have been proposed, such as hydrodynamic traps [106, 107], negative pressure

traps [73, 108], and DEP traps [109].


20

Jang et al. developed a microfluidic device which utilizes micro pillars within a

microfluidic channel to physically capture and measure the impedance of a single human

cervical epithelioid carcinoma (HeLa) cell using EIS [106]. Hydraulic trapping devices were

also demonstrated where impedance measurements were accomplished by recording current

from two electrode pairs, one empty (reference) and one containing a cell. The effect of

surfactant and bacterial pore-forming toxins on HeLa cells was monitored continuously [107].

For minimizing current leakage, caused by the current undesirably obviating the targeted cell

through the low resistivity medium, Cho et al. developed an array of planar micro holes for

positioning cells and forming direct contact between cells and electrodes (see Figure 1.3 (c))

[73]. Li et al. used a vertical sub-micrometer opening with embedded recording electrodes.

The device was capable of reducing the serial resistance while keeping the seal resistance for

high sensitivity measurements [110]. Since single cells can be cultured right on the vertical

holes, the capability of monitoring the dynamic changes of singe cell electrical properties

over a period of time is a merit of the vertical hole-based technique [111, 112].

There are two major limitations of the µ-EIS technique. Firstly, since the trapping and

releasing process is time consuming, and the measurement of impedance spectra also takes

time, the throughput of µ-EIS is low. Only tens of cells were tested in reported studies.

Secondly, although the capacitance and resistance of tested cells can be determined by

interpreting impedance spectra with electrical models, these parameters are strongly affected

by electrode size, the cell trapping mechanism, cell volume and the interaction with other

cells (vs. size-independent parameters such as specific membrane capacitance and cytoplasm

conductivity).


21

1.3.4 Impedance flow cytometry (IFC)

Although the Coulter counter has become a widely used technique in clinical instruments

(e.g., hematology analyzers), the conventional Coulter counter is only capable of classifying

cell types that have distinct volume differences. Some of the most recent commercial

hematology analyzers adopted both DC and RF measurements. The ratio of the RF signal to

the DC signal is defined as opacity, which is used as a volume independent electrical

signature of the tested cells. Along with the DC signal (volume signature), WBC differentials

count can be achieved. This technique was recently demonstrated on microfluidic devices. In

addition to WBC count, microfluidic flow cytometry has also been used for analyzing a

variety of particles [113-117].

An impedance flow cytometry design using coplanar electrodes was demonstrated by

Gawad et al. [113]. As shown in Figure 1.4 (a), three microfabricated electrodes were

integrated on the bottom of the microfluidic channel. When micro particles were flowing

through the detection area, impedance change was recorded differentially. The non-uniform

electrical field distribution in the channel resulted in position variations of tested cells may

undesirably affect impedance measurement. To overcome this issue, parallel facing

electrodes were developed (see Figure 1.4 (b)) [114]. When a cell is present in between one

pair of electrodes, the other pair is used as reference, allowing the differential measurement

of electrical signals. The device was demonstrated capable of detecting electrical property

differences between normal RBCs and glutaraldehyde-fixed RBCs.

Homes et al. incorporated a fluorescence measurement unit on their microfluidic flow

cytometry device [116]. This device design enabled direct correlation of impedance signals

from individual cells with their biochemical phenotypes. 3-part WBC differential count of


22

human blood was well achieved. The same group more recently developed a microfluidic

impedance flow cytometry device integrated with RBC lysis and a hemoglobin concentration

measurement unit [118, 119]. Besides counting blood cells, microfluidic impedance flow

cytometry has also found applications in other areas, such as the measurement of cell

viability and physiological differences of cells [117]. More details on impedance flow

cytometry can be found elsewhere [100, 117]. In addition to coplanar and parallel facing

electrode designs, Mernier et al. demonstrated „„liquid electrodes‟‟ to discriminate living and

dead yeast cells (see Figure 1.4 (c)). The larger electrodes recessed in lateral channels allow

measurements at low frequencies (down to 1 kHz), and the same “liquid electrodes” can also

be used for DEP focus of the particles [120]. Recently, the concept of label-free

electrophysiological cytometry was reported. This technique utilizes the nature of

electrically-excitable cells (upon activation by sufficient trans-membrane electric fields,

different cell types generate different extracellular field potential signals) to distinguish

different cell types. As shown in Figure 1.4 (d) , the device is integrated with electrodes for

both electrical stimulation and recording of extracellular field potential signals from

suspended cells in flow [121]. Undifferentiated human induced pluripotent stem cells (iPSC)

and iPSC-derived cardiomyocyte (iPSC-CM) cells were distinguished.


23

Figure 1.4 (a) Impedance flow cytometry with coplanar electrodes. Side view shows a

particle passing over three electrodes (A, C, and B). Impedance signal is measured

differentially (ZAC-ZBC). Reproduced with permission from [113]. (b) Impedance flow

cytometry with parallel facing electrodes. Side view shows a cell passing between the

measurement and reference electrodes. Reproduced with permission from [114]. (c)

Impedance flow cytometry combining impedance measurements and dielectrophoretic

focusing. Reproduced with permission from [120]. (d) Label-free electrophysiological

cytometry. Electrodes are integrated for both electrical stimulation of cells and recording of

their extracellular

(a) (b)

(c) (d)


24

1.4 Research Objectives

The overarching goal of the thesis is to develop novel microfluidic systems for high-

throughput biophysical properties characterization of single biological cells. Specific

objectives include:

To design, prototype, and characterize microfluidic systems for high-throughput

biophysical (mechanical and electrical) characterization of single cells.

To develop microfluidic systems for high-throughput measurement of specific

membrane capacitance and cytoplasm conductivity of single cells.

To study deformability changes of red blood cells during blood storage in human-

capillary-like environment.

1.5 Dissertation Outline

An overview of the ensuing chapters is as follows: Chapter 2 describes the development of a

microfluidic system for simultaneous biophysical properties measurement of red blood cells.

Chapter 3 presents a technique for electrical measurement of red blood cell deformability on

a microfluidic device. Chapter 4 describes the microfluidic system for high-throughput

measurement of specific membrane capacitance and cytoplasm conductivity of single cells.

In Chapter 5, the characterization of red blood cell deformability change during blood storage

is presented. Chapter 6, the stiffness/deformability changes of lymphocytes in chronic

lymphocytic leukemia (CLL) patients are studied. The thesis is concluded in Chapter 7, with

a summary and contributions of this research and suggested future research directions.

CHAPTER 2, HIGH-THROUGHPUT BIOPHYSICAL MEASUREMENT

25

Chapter 2

2. High-Throughput Biophysical Measurement of

Human Red Blood Cells

2.1 Introduction

Because of the physiological and pathological importance of RBCs, their biophysical

properties (mechanical and electrical properties) have been under intensive study over the

past decades [15, 16, 122]. The mechanical property of RBCs is essentially determined by

the membrane skeleton, and the interaction between the membrane skeleton and membrane

integral proteins [123] [17]. RBCs‟ exceptional capacity to deform is of crucial importance to

both macro and microcirculation. The high deformability of normal RBCs enables them to

pass capillaries that are smaller than the diameter of RBCs. A range of diseases have been

described in association with impaired RBC deformability, such as sepsis [124] [125],

malaria [24] [22], sickle cell anemia (hemoglobin disorder) [126] [127], and myocardial

ischaemia and microvascular dysfunction [128]. In the meanwhile, the electrical properties of

RBCs have also been correlated to pathological conditions [129-131]. For example, the ion


26

channel conductance of malaria parasite infected RBCs is lower than the uninfected RBCs

due to the blockage of ion channels by the parasites [132].

A number of technologies have been used for measuring single RBC‟s biophysical

properties [13, 133], for instance, micropipette aspiration [63], atomic force microscopy [134,

135], and optical tweezers [136, 137] for mechanical measurement and patch-clamping for

electrical measurement [14] [16]. However, these technologies are difficult to use and have a

low testing speed. The heterogeneity of RBCs within a sample demands a higher testing

throughput in order to obtain statistically significant data, and thus, determine RBCs‟ genuine

mechanical and electrical properties.

This chapter presents a microfluidic system for simultaneous mechanical and electrical

characterization of single RBCs. Detection involves only electrical signals and has a

throughout of 100-150 cells per second. Since adult and fetal/neonatal RBCs are known to be

different in size and hemoglobin contents, we applied our system to test RBCs in adult blood

(~16,000 cells/sample, 5 samples) and neonatal RBCs (~16,000 cells/ sample, 5 samples) and

revealed their differences in mechanical and electrical properties. We also explored the utility

of biophysical (mechanical and electrical) data for distinguishing neonatal RBCs from adult

RBCs.


27

Figure 2.1 (a) Schematic of the microfluidic system for electrical and mechanical

characterization of RBCs. Two Ag/AgCl non-polarizable electrodes are connected to a

function generator (100 kHz @1.0 Vpp) and a lock-in amplifier. The two electrodes are

inserted into the inlet and outlet ports of the device. As RBCs are aspirated through the

constriction channel, electrical current change is sensed and amplified. “-P” denotes the

negative pressure used to aspirate RBCs through the constriction channel. (b) Measurements

are made when an RBC passes through the constriction channel. (c) Equivalent circuit model

of the system.

(b) (a)

(c)


28

2.2 System overview

Figure 2.1(a) shows the schematic diagram of the single RBCs biophysical characterization

system. The microfluidic chip was constructed by bonding PDMS microchannels on a glass

slide. Two Ag/AgCl non-polarizable electrodes connected to the function generator

(sinusoidal voltage at 100 kHz @1.0 Vpp) and the lock-in amplifier (SR850, Stanford

Research Instruments, USA) were inserted into the inlet and outlet ports of the microfluidic

device. The analog outputs of the lock-in amplifier were sampled with a 16-bit DAQ card (NI

PCI-6229, National Instruments, USA) and data capture software (LabVIEW, National

Instruments, USA). Dilute blood sample was pipetted into the inlet reservoir of the device

and driven through the constriction channel by hydraulic pressure difference (see Figure

2.1(b)).

Figure 2.1(c) shows the equivalent circuit model. The channel is equivalent to a resistor

Rchannel and a capacitor Cchannel in parallel [64]. As RBCs (including membrane capacitance

Ccell and cytoplasm resistance Rcell) [100, 117] pass through the constriction channel, they

perturb the electric field in the volume within the channel generating a current impulse. Rleak

presents the sealing resistance between the RBC membrane and channel walls. In the mean

time, the current change of the circuit loop is sensed via input impedance (Iinner) of the lock-in

amplifier (10 M Ω+25 pF), amplified and recorded by the data capture software. Outputs of

the lock-in amplifier consist of the real component (X) and the imaginary component (Y).

The amplitude (A) and phase (Ф) were calculated according to √

. A processing algorism was used to extract the transit time (the time duration

taken by a cell to travel through the constriction channel, △T), the amplitude ratio (the ratio

between the lowest amplitude value captured during cell‟s squeezing through the constriction


29

channel and the amplitude value with no cell in the constriction channel, ), and

the phase difference between with cells and without cells, were quantified as RBCs‟

mechanical and electrical property indicators.

Compared with previously reported microfluidic devices for biophysical measurements,

this design has a few advantages. Firstly, almost all electrical field lines are forced to

penetrate RBCs‟ membrane and hemoglobin inside, making the device more sensitive to

minute biophysical differences. Secondly, our measurement is purely electrical, eliminating

the need for microscopy imaging and hence, permitting high measurement throughputs.

High-speed cameras (tens of kHz) generate gigabyte data per second and can only record for

a few seconds due to limited on-board memory. Processing this massive amount of image

data also takes tremendous computation efforts and time. Therefore, for microfluidic single

cell measurement systems, high-speed imaging offers high speeds, but total number of data

points (i.e., sample size) is limited [45, 138]. Thirdly, no sheath flow is required on our

device. Since the constriction channel‟s cross-sectional area is smaller than the diameter of

RBCs, only a single RBC is permitted to pass through the constriction channel in a given

time instance simply by tuning the density of the RBC suspension.


30

Figure 2.2 Experimental amplitude profiles: (a) a single RBC, (b) two RBCs, (c) a single

platelet, and (d) a single WBC within the constriction channel.

2.3 Materials and methods

2.3.1 Device fabrication

The constriction channel (first layer) was fabricated with SU8-2002 (5μm×3μm)

(MicroChem Corp., Newton, MA, USA) on a glass slide. A second layer of SU8-25 was then

spin coated on the glass slide covered with the first layer features, soft-baked, and exposed to

UV light with alignment, followed by post-exposure bake, development and hardbake. The

threshold

threshold

transit time △T

two valleys

basal amplitude, A

△A

transit time, △T

threshold

threshold

transit time △T

(d)

(a) (b)

(c)


31

loading channel‟s cross-sectional area (1000μm×30μm) is much larger than that of the

constriction channel. Hence, the impedance of the device is mainly determined by the

constriction channel. The microchannels were molded with PDMS (Ellsworth Adhesives, ON,

Canada), punched to form inlet and outlet ports, and bonded to a glass slide treated with a

corona-treater (Electro-Technic Products Inc., Illinois, USA).

2.3.2 Blood samples and experimental protocol

Peripheral blood samples were obtained following routine blood tests at the hospital

hematology laboratory. Blood samples were collected using commercial vacuum tubes with

EDTA anticoagulant (ethylenediaminetetraacetic acid 1.5mg/ml) (Sigma-Aldrich, Oakville,

ON, Canada). The adult (n=5) and newborn samples (n=5) were of normal individuals with

the complete blood counts performed by a standard commercial hematology analyzer

(Sysmex XE-2100, Kobe, Japan). In the experiments, 10μL blood was diluted in 500 μL PBS

(Sigma-Aldrich, Oakville, ON, Canada) mixed with 0.2% w/v Pluronic (Sigma-Aldrich,

Oakville, ON, Canada) and 1% w/v BSA (New England Biolabs Inc., Herts, UK). Pluronic

and BSA were used for preventing non-specific adhesion to the channel walls. The dilute

samples were placed for 20 minutes before usage. Before the sample was pipetted into inlet

port of the device, the microchannel was filled with PBS solution with 0.2% w/v Pluronic

and 1% w/v BSA. A pressure difference (50 Pa) between the inlet and outlet ports was

applied for 30 minutes to ensure the equilibrium in lubrication of the channel walls. A 5 μL

dilute blood suspension was then pipetted into the entrance of the cell loading channel. The

two Ag/AgCl non-polarizable electrodes were inserted into the inlet and outlet ports of the

device. A negative pressure of 3 kPa was then applied to aspirate cells continuously through

the constriction channel while electrical data was sampled.


32

2.3.3 Electrical measurement and data analysis

A sinusoidal voltage (100 kHz @1.0 Vpp) was applied to the two Ag/AgCl electrodes. When

an RBC is aspirated into the constriction channel, it blocks electric field lines and causes the

current in the circuit loop to drop. During experiments, the real component (X) and the

imaginary component (Y) of the lock-in amplifier output were sampled at 120 kHz. X and Y

were converted to amplitude (A) and phase (Ф). Figure 2.2 shows amplitude profiles of a

single RBC, two RBCs, a single platelet, and a single WBC (white blood cell) within the

constriction channel.

The throughput of our system is 100-150 cells/second. The variation in throughput

depends on cell densitity differences across patient samples. A threshold is defined as 98% of

the basal amplitude (the amplitude without cell presence in the constriction channel) (see

Figure 2.2(a)). Comparing a signal and the threshold amplitude value, the portions where the

signal‟s amplitude is lower than the threshold value were considered as cell passage regions.

A quadratic polynomial peak detector was used to detect the valleys within the cell passage

regions. More than one valley within a cell passage region suggests the passage of more than

one cell (see Figure 2.2(b)). The time period between the two intercepts with the threshold

value was interpreted as cell transit time (△T), which is determined by the cell‟s size and

mechanical stiffness. The amplitude ratio ((A-△A)/A) (see Figure 2.2 (a)) and the phase

increase (△Ф) were quantified as the cell‟s electrical signatures. Since the electrical field

lines are highly concentrated by the constriction channel to penetrate the cell, the electrical

signal generated by a single RBC in our experiments was readily distinguishable from the

background noise (SNR≥29dB). No preamplifier and additional filters were required.


33

Figure 2.3 Ratio of the amplitude change and the basal amplitude vs. aspiration pressure.

Device sensitivity becomes lower at higher aspiration pressure due to RBC deformation and

larger leakage current.

Figure 2.4 Histograms of transit time, amplitude ratio and phase increase attained from an

adult sample (22,669 cells measured), (red)(cyan) before and after the exclusion of platelets

and debris.

aspiration pressure (kPa)

△A

/A


34

Figure 2.5 Adult RBCs (n=84,073 from 5 adult samples, red), neonatal RBCs (n=82,253

from 5 newborn samples, cyan). (a) 3D scatter plot of transit time vs. amplitude ratio vs.

phase increase. (b) Histograms of transit time, amplitude ratio, and phase increase fitted with

normal distributions.

adult

neonatal

(a)

(b)


35

2.4 Results and discussion

2.4.1 Selection of channel dimension, signal frequency, and applied pressure

Constriction channels having a smaller cross-sectional area can form a better seal between

the RBC membrane and the channel walls. However, RBCs collapsed when the cross-

sectional area was much smaller than RBCs‟ diameter. For constriction channels with a

cross-sectional area of 1µm×3µm, almost 100% RBCs collapsed at the entrance of the

constriction channel. For constriction channels with a cross-sectional area of 2µm×5µm, the

majority of RBCs were elongated and then collapsed within the constriction channel. For

constriction channels with a cross-sectional area of 3µm×5µm (chosen for our experiments),

all RBCs showed folded morphology, and no RBCs collapsed.

RBCs are highly deformable. The cell shape in the constriction channel is altered by fluid

shear stress. At higher aspiration pressures, RBCs are further deformed in the shear stress

direction, resulting in a poorer seal between the RBC membrane and constriction channel

walls. Consequently, signal change with and without cell‟s presence in the constriction

channel becomes smaller due to larger leakage current. Figure 2.3 shows the ratio of the

amplitude change and the basal amplitude, measured on the same blood sample at different

aspiration pressures. Each data point in the figure is from approximately 1,000 cells. In our

subsequent experiments, 3 kPa was chosen for a balance between throughput and detection

sensitivity. At this aspiration pressure, the velocity of the fluid in the constriction channel is

approximately 30mm/sec. As to frequency selection, the frequency should be high enough to

enable the electrical field lines to penetrate the RBC membrane such that the electrical

property of RBCs can be reflected by the measured electrical signal. Additionally, the

frequency should not be too high. Too high a frequency would result in extremely low


36

impedance of Cchannel and make electrical field lines undesirably obviate the cell in the

constriction channel [32, 64]. We experimentally selected 100 kHz for subsequent

experiments.

2.4.2 WBCs and platelets

Although RBCs represent over 90% blood cells, white blood cells (WBCs) and platelets

remained in the sample. WBCs and platelets were excluded in data processing. As shown in

Figure 2.2, electrical signals measured from RBCs, WBCs, and platelets are drastically

different, thus using amplitude ratio alone proved highly effective to distinguish WBCs and

platelets from RBCs.

Figure 2.4 shows histograms attained from an adult blood sample (22,669 cells measured).

The red colored histograms show data after the exclusion of WBCs. The relatively small

population (circled) within the blood sample barely perturbed the electrical field lines. This

population was made up of platelets and debris that have diameters smaller than 3μm. The

“cyan” data in Figure 2.4 are transit time, amplitude ratio, and phase increase with fitted

normal distributions after excluding platelets and debris.

2.4.3 RBC measurements

Our microfluidic system (frequency: 100 kHz, pressure difference: 3 kPa, constriction cross-

section area: 3μm × 5μm) tested adult RBCs (5 samples, ~16,000 cells/sample) and neonatal

RBCs (5 samples, ~16,000 cells/sample). As discussed earlier, WBCs and platelets were

easily distinguished from RBCs using their distinct amplitude ratio. Additionally, the

occurrence of two or more RBCs (vs. single RBCs) inside the constriction channel at the

same time was rather rare (<5%). Figure 2.5(a) shows the 3D scatter plot of transit time vs.


37

amplitude ratio vs. phase increase of all adult RBCs (n=84,073 from 5 adult samples) and

neonatal RBCs (n=82,253 from 5 newborn samples). The ellipses in the figure track the

standard deviation of the distribution. Each of the three parameters was identified and fitted

to normal distribution profiles (see Figure 2.5(b)). A back propagation neural network was

used for pattern recognition (MATLAB, MathWork, USA). The input data have three groups

of parameters (transit time, amplitude ratio, and phase increase) measured on RBCs. The

complete dataset was divided into training data (70%), validation data (15%), and testing

data (15%) to quantify adult vs. neonatal RBC classification success rates (i.e., accuracy).

RBC classification success rates were 76.2% (transit time + amplitude ratio), 78.1%

(amplitude ratio + phase increase), 77.9% (phase increase + transit time), and 84.8%

(amplitude ratio + phase increase + transit time), suggesting multiple parameters (transit time,

amplitude ratio and phase increase), when used in combination, can provide a higher cell

classification success rate. Besides the success rate of 84.8%, sensitivity (true positive/(true

positive + false negative)) and specificity (true negative/(true negative + false positive)) were

80.2% and 89.2%, respectively.

Cell transit time is determined by RBCs‟ volume, membrane stiffness, and the friction

between the membrane and channel walls. The electrical impedance amplitude ratio and

phase increase are determined by RBCs‟ volume, ion channels on the membrane, and the

density of hemoglobin. Hence, transit time, amplitude ratio, and phase increase are not

completely independent parameters (e.g., they are all affected by cell volume). The neural

network classification results also indicate that each of the three parameters can reflect

unique properties of RBCs, leading to higher classification success rates when used in

combination.


38

The amplitude change during a cell‟s passage through the constriction channel is caused

by the blockage of the electrical field lines. Figure 2.6(a) reveals a linear trend between the

amplitude ratio and the cell volume for both adult RBCs and neonatal RBCs with different

slopes (-0.0024 vs. -0.00356). Cell electrical properties are determined by membrane

capacitance and cytoplasm (hemoglobin in the case of RBCs) resistance. Since the lipid layer

composition of neonatal RBCs and adult RBCs is very similar [139] and the effective area of

the membrane capacitance is constricted by the cross section of the constriction channel, the

different slope indicates the different conductivity between neonatal RBCs and adult RBCs.

This can possibly be related to the higher hemoglobin density inside neonatal RBCs. The

average RBC volume and hemoglobin density of each sample were obtained from complete

blood counts performed by a commercial hematology analyzer.

As shown in Figure 2.6 (b), transit time as a function of RBC volume can be fitted into a

single line for both adult RBCs and neonatal RBCs. The transit time of RBCs under a

constant negative pressure is determined by the friction force between the membrane and

channel walls (friction force = contact pressure × contact area × friction coefficient, where

contact pressure is related to membrane stiffness and friction coefficient is related to

membrane composition). It is known that the lipid layer composition and membrane stiffness

of neonatal and adult RBCs are very close [139, 140]. Hence, contact pressure and friction

coefficient should be comparable for adult and neonatal RBCs. In the constriction channel,

contact area is proportional to RBC volume. Therefore, it is not surprising that transit time as

a function of RBC volume can be fitted into a single line for both adult and neonatal,

indicating that the transit time difference for adult RBCs and neonatal RBCs were mainly

caused by their volume difference.


39

Figure 2.6 (a) Amplitude ratio as a function of RBC volume. (b) Transit time as a function of

RBC volume.

■ adult: y= -0.00240 x+1.085

● neonatal: y= -0.00356 x+1.245

(a)

■ adult

● neonatal

y= (1.03 x+8.95) × 10-5

(b)


40

2.5 Conclusion

This chapter presented a microfluidic system capable of measuring multiple biophysical

parameters on single RBCs. Compared with previously reported microfluidic devices for

single RBC biophysical measurement, this system has a higher throughput (100-150

cells/second), higher signal to noise ratio, and the capability of performing multi-parameter

measurements. The microfluidic system may have potential applications in drug efficacy

testing and RBC property characterization relevant to clinical conditions. Pattern recognition

confirmed that a combination of measurements of transit time, electrical impedance

amplitude, and impedance phase resulted in a high success rate in classifying fetal/neonatal

and adult RBCs. However, the achieved 89.2% specificity and 80.2% sensitivity for cell

classification require further improvement for diagnostics applications such as rare fetal RBC

enumeration in adult blood.

CHAPTER 3, ELECTRICAL MEASUREMENT OF DEFORMABILITY

41

Chapter 3

3. Electrical Measurement of Red Blood Cell

Deformability on a Microfluidic Device

3.1 Introduction

Red blood cells (RBCs) are highly deformable, allowing them to be able to travel through in-

vivo capillaries with diameters smaller than RBCs‟ size, which facilitates gas transfer

between blood and tissues [17, 141, 142]. Decrease in RBC deformability can disturb blood

flow and oxygen delivery. Essentially, the deformability of RBCs is determined by the

integrity and organization of the membrane cytoskeletal protein network and density, and the

viscosity of the cytoplasm. Pathological condition changes may lead to significant alteration

and reorganization of the protein networks, and consequently compromise RBCs‟

deformability [7, 17, 143]. For example, the polymerized and deoxygenated hemoglobin in

sickled RBCs causes deformability to decrease [144]. In the case of malaria, the impaired

deformability of RBCs is in association with the interruption of the cytoskeleton network by

parasite invasion [145]. In addition to an indicator for pathological states, RBC deformability

can also serve as a criterion for stored/banked blood quality assessment [146, 147].


42

Several standard tools can be applied to measuring RBC deformability. Atomic force

microscopy (AFM) assesses the deformability of a cell by indenting/deforming the cell

through physical contact [13, 148]. Micropipette aspiration applies a negative pressure

through a glass micropipette to aspirate a cell patch for characterizing the cell‟s mechanical

parameters [13, 63]. Optical tweezers stretch a cell by optically manipulating high-refractive-

index dielectric beads attached to the cell membrane [4, 13]. These standard tools involve

high operation complexity and have a limited testing speed of minutes per cell.

Hence, other techniques were developed for testing RBC deformability with improved

speed and/or reduced operation complexity [12, 133, 149]. When RBCs flow through

capillaries, they are deformed by shear stress into a parachute-like shape [141, 150].

Accordingly, microfluidic channels were used to mimic human capillaries and study RBC

deformability. Shear stress-induced cell deformation was captured via a high speed camera

[35, 142, 151]. The use of high-speed cameras also necessitates a microscope and a high

intensity lighting system, making the system inevitably bulky and costly. Furthermore,

present high-speed cameras have limited on-board memory, permitting only a few seconds‟

recording and thus, limiting testing throughput (number of cells per test). Post-processing of

tens of GB image data is also time- consuming.

Cell Transit Analyzer (CTA) consists of micro pores that are smaller than RBCs. It

measures cell transit time (i.e., the time required by an RBC to pass through) via electrical

resistance changes or high-speed imaging. This approach is able to provide an increased

throughput and is capable of performing multi-parameter measurements, particularly when

electrical measurement is used (100-150 cells/sec) [23]. Recently, the CTA approach has

been adapted for assessing the deformability of other cell types, including human cancer cells


43

[31-33]. An important advantage of CTA is its simplicity [20]; however, cell volume

variations and adhesion between the cell surface and channel walls are coupled with cell

deformability, which together determine cell transit time. In other words, cell transit time is

not only determined by cell deformability but also the volume/size and membrane properties.

In ektacytometry, RBCs are deformed by shear stress, and laser diffraction patterns of

RBCs are analyzed [152, 153]. Diluted blood is loaded into a small gap between two rotating

plates. RBCs are allowed to settle for a period of time to form weak adhesion with the bottom

surface of the test chamber. Shear stress (e.g., 0.5-50 Pa) is controlled by varying the rotation

rate of the plates. Elongation index of the RBCs is measured using laser diffractometry.

Ektacytometers are relatively easy-to-use; however, the measurement principle is difficult to

implement on a miniaturized device design. Importantly, different from CTA wherein RBCs

flow continuously, in ektacytometry, the throughput is determined by the imaging field of the

view. This limits testing throughput to approximately 50-60 RBCs per test (LoRRca MaxSis

user manual, Mechatronics Manufacturing B.V., Netherlands).

Due to the simplicity of electrical measurement, measuring RBC deformability

electrically was attempted [154]. In this reported system, medium viscosity was modified to

achieve sufficient shear stress. It was incapable of decoupling RBC volume/size from

deformability. Thus, RBCs with larger diameters can be mistakenly determined to be more

deformable. This chapter presents a microfluidic system for electrically measuring RBC

deformability. The microfluidic device has two measurement stages, with cross-sectional

areas of 8 µm × 8 µm and 5 µm × 5 µm, respectively. RBCs are pressure driven to flow

through the microfluidic channels while electrical current changes are measured via two


44

Ag/AgCl electrodes. To mitigate the coupled effect from cell volume, mechanical opacity is

defined as an indicator of RBC deformability.

Figure 3.1 Electrical measurement of RBC deformability. The schematics show two RBCs

with identical size/volume but different deformability. A voltage signal is applied across the

channel, and current (denoted as yellow arrows) is measured via a current preamplifier. Shear

stress induces the RBCs into a parachute-like shape. The RBC with higher deformability is

more stretched along the flow direction (a), resulting in larger gaps between the RBC

membrane and channel walls than the less deformable RBC (b). Thus, the RBC with higher

deformability blocks less current (i.e., smaller current decrease in the current profile) than the

RBC with lower deformability.

Vs

Vs (a) (b)

G G

electrode current G preamplifier ground


45

Figure 3.2 (a) Microfluidic device for electrically measuring RBC size and deformability.

Ag/AgCl electrodes are plugged into the inlet and outlet ports via T-junctions. The cross-

sectional areas of the measurement units are 8 µm × 8 µm and 5 µm × 5 µm, respectively.

The transit region (50 µm × 20 µm) is for separating the two current valleys of the two

measurement units. RBCs are driven through the measurement areas continuously. (b)

Experimental data: time series of the current profile measured within 1 second with 14 RBCs

passing through. (c) The zoom-in view of the profile circled in (b).

I1

I2

basal current

(c)

5 µm × 5 µm 8 µm × 8 µm

50 µm × 20 µm

500 µm × 80 µm

(a)

(b)


46

3.2 Measurement Principle

Under shear stress, RBCs change into a parachute-like shape. The cell is stretched along the

flow direction, resulting in gaps between RBC membrane and microchannel walls [141, 151,

155-157]. As shown in Figure 3.1, a voltage signal (10 [email protected] Vpp) is applied across the

microfluidic channel. RBCs have a specific membrane capacitance value of 9 ± 0.8 mF/m2,

and hence act as an insulated layer at low frequencies (e.g., 10 kHz) [33, 158, 159]. When an

RBC is inside the channel, a portion of the conductive medium/liquid is replaced. Thus, only

the conductive medium within the gaps between the cell membrane and channel walls

conducts current, which causes the current passing through the channel to decrease. For two

RBCs have the same volume/size but different deformability, the more deformable RBC

[Figure 3.1 (a)] is stretched more along the flow direction leaving larger gaps between the

cell membrane and channel walls (vs. the less deformable RBC). Therefore, electrical current

within in the channel is less blocked (vs. [Figure 3.1 (b)]), and in the measured current

profile, current decrease is smaller. In other words, current decrease in this case is correlated

to RBC deformation. However, in addition to deformability, current decrease is also

dependent on RBC size/volume, since a larger volume replaces a larger volume of

conductive medium, and thus causes stronger current blockage.

In order to take the cell volume effect into account, our device design has two

measurement units [Figure 3.2 (a)]. Two Ag/AgCl electrodes are inserted into the inlet and

outlet ports via T-junctions to apply a voltage signal and measure current changes. Custom-

made water tanks were used to generate pressure for driving RBCs through the

microchannels. The cross-sectional area of the first measurement unit is 8 µm × 8 µm, and

the cross-sectional area of the second measurement unit is 5 µm × 5 µm. The transit region in


47

between (50 µm × 20 µm) is designed for separating the two current valleys [Figure 3.2

(b)(c)] of the two measurement units for the convenience of signal processing. Figure 3.2 (b)

shows experimental data that are time series of the current profile measured within 1 second

with 14 RBCs passing through the measurement area with a pressure difference of 1600 Pa.

The zoom-in view of the profile circled in Figure 3.2 (b) is presented in Figure 3.2 (c). The

small valley (I1) of the current profile corresponds to the current decrease when an RBC

passes through the 8 µm × 8 µm channel; and the large valley (I2) was generated when the

same RBC passed through the 5 µm × 5 µm channel.

As discussed earlier in this section, the electrical current signals from both 5 µm × 5 µm

channel and 8 µm × 8 µm channel are dependent on cell volume and deformation. The shear

stress within the 5 µm × 5 µm channel is approximately four times as high as that in the 8 µm

× 8 µm channel. As a result, RBCs are much more deformed in the 5 µm × 5 µm channel.

Additionally, due to the smaller cross-sectional area, current signal is more sensitive to

changes of gaps between the cell membrane and channel walls in the 5 µm × 5 µm channel.

In the meanwhile, the current signal generated in the 8 µm × 8 µm channel is more reflective

of the RBCs volume information, as the Coulter counter principle [71, 160]. To mitigate the

coupled effect from cell volume and cell deformability, the ratio of I2/I1 is defined in this

study as mechanical opacity, and is used as a measure of RBC deformability.

3.3 Materials and Methods

3.3.1 Blood sample preparation

Whole blood samples were obtained from healthy donors (Mount Sinai Hospital, Toronto,

Canada). Blood samples were anticoagulated with EDTA anticoagulant


48

(ethylenediaminetetraacetic acid 1.5 mg/ml) (SigmaAldrich, Oakville, ON, Canada) and

washed twice with PBS (Sigma-Aldrich, Oakville, ON, Canada) before further treatment.

Glutaraldehyde (GA)-treated and heated RBCs have been commonly used in the literature for

studying RBC deformability. GA is a nonspecific fixative that lowers RBC deformability by

cross-linking the cytoskeletal proteins [35]. Heating can significantly denature the RBC

cytoskeletal network and make RBCs more deformable [161]. For glutaraldehyde (GA)

treated samples, the washed RBCs were suspended into PBS added with 0.005 %

glutaraldehyde, and incubated at room temperature for 30 min. Following incubation with

GA, the RBCs were washed three times with PBS. For heat treatment, the washed RBCs

were re-suspended in a PBS solution and heated in a water bath maintained at 49 ºC for 30

min. Thereafter, all RBCs samples were suspended in PBS with 1% w/v BSA with

hematocrit of 0.45%, and incubated for 30 min at room temperature to prevent adhesion.

3.3.2 Device fabrication and operation

The device was fabricated using standard multilayer soft lithography [32, 162]. The

microfluidic channel consists of four different cross-section areas: two measurement units (5

µm × 5 µm and 8 µm × 8 µm); the transit unit (50 µm × 20 µm) in between the two

measurement units; and the loading channel (500 µm × 80 µm) for loading RBCs to the

measurement areas. Since the loading channel‟s cross-sectional area is much larger than the

measurement units, electrical current is largely determined by the resistance of the

measurement areas. Before experiments, the microchannel was incubated with PBS (Sigma-

Aldrich, Oakville, ON, Canada) mixed with 0.2% w/v Pluronic (SigmaAldrich, Oakville, ON,

Canada) and 1% w/v BSA (New England Biolabs Inc., Herts, UK) for 30 min. A droplet of

RBC sample was pipetted into the inlet port of the device. The two T-junctions with


49

Ag/AgCl non-polarizable electrodes were then inserted. The T-junctions were connected

with custom-made water tanks for accurately generating pressure differences.

3.3.3 Signal processing

A sinusoidal voltage signal (10 [email protected] Vpp) was applied through the Ag/AgCl electrodes.

A current preamplifier connected in series into the current pathway, amplified and converted

current into voltage signals, which is sampled by a computer. When RBCs flow through the

measurement units, current (RMS) is measured continuously, as shown in Figure 3.2 (b)(c).

At low frequencies, the electrical current signal can better reflect RBCs deformation since

current cannot penetrate the RBC membrane. The frequency of 10 kHz was chosen as a

compromise of system time response and deformation measurement sensitivity. Experimental

data was processed using a custom-built program. Briefly, basal current was extracted from

the raw data using a histogram-based technique. Peak threshold values were calculated as 98%

the basal current. Peak detection was conducted after noise filtering. The blood samples used

in experiments were from whole blood and contained white blood cells (WBCs) and platelets.

In the data processing, WBCs and platelets were excluded from the final results on the basis

of distinct current signals. Data processing also excluded the rare events with multiple cells

within the measurement unit, where consecutive large valleys appear in the measured data.


50

Figure 3.3 Current decrease within 8 µm × 8 µm measurement unit (I1, red) and 5 µm × 5 µm

measurement unit (I2, blue) of a controlled RBC sample under various driving pressures. The

error bars represent standard deviation. More than 1,000 RBCs were measured for each data

point.

3.4 Results and Discussion

3.4.1 Shear stress effect

The pressure difference generated with the water tanks ranged from 400 Pa to 1600 Pa. The

corresponding average shear stress in the 5 µm × 5 µm channel was approximately 8 Pa to 36

Pa. This pressure range was tested to make a comparison with the results from commercial

ektacytometers (shear stress range 0.5-50 Pa). A controlled RBC sample was tested under

several pressures (400Pa, 700 Pa, 1000Pa, 1300 Pa, 1600Pa). The electrical current decrease


51

of the 5 µm × 5 µm channel (I2, blue) and 8 µm × 8 µm channel (I1, red) under these

pressures is summarized in Figure 3.3. When an RBC is in the 5 µm × 5 µm channel, current

change is highly sensitive to pressure since under increasing pressures, the RBC is further

stretched along the flow direction. While in the 8 µm × 8 µm channel, shear stress is lower,

and current change is far less sensitive to pressure variations.

Ideally, the first measurement unit should be much larger than the size of RBCs so that

the low shear stress hardly deforms a cell, and the current signal is only determined by cell

volume/size. However, too large a cross-sectional area in the Coulter counter measurement

unit in experiments caused too small a current change when a cell passed through. Therefore,

8 µm × 8 µm was chosen to achieve a reasonable signal-to-noise ratio and reduce the cell

deformation effect on the measured signal. As shown in Figure 3.3, although the measured

signal shows slight changes as the applied pressure increases, the change is much less

significant than the signal generated in the 5 µm × 5 µm channel. In the 5 µm × 5 µm

channel, current signal is determined by the gaps between RBC membrane and the channel

walls. However, it is challenging to experimentally measure the gaps using standard

microscope imaging.

3.4.2 Mechanical opacity

Controlled, GA-treated, and heated RBCs were tested using this system. The scatter plots

show I2 versus I1 for controlled RBCs and GA-treated RBCs [Figure 3.4 (a)], and controlled

RBCs and heated RBCs [Figure 3.4 (b)]. The driving pressure was 400 Pa. In Figure 3.4

(a)(b), I2 shows a strong linear correlation with I1. Comparing the controlled RBCs and GA-

treated RBCs with the same I1 value (volume indicator), the GA-treated RBCs have clearly

higher I2 values, reflecting the fact that the controlled RBCs are more deformable than the


52

GA-treated RBCs because they are stretched more along the flow direction (larger gaps). The

heated RBCs reveal a lower I2, indicating heated RBCs are more deformable than controlled

RBCs. Here, we borrow the concept of impedance opacity (i.e., ratio of impedance at high

frequency to low frequency) from impedance flow cytometry [116, 117]. Due to the strong

linear dependence, the ratio of I2/I1 was thereby used to normalize RBC volume, which is

defined as mechanical opacity. Based on the mechanical opacity that reflects RBC

deformability, GA-treated RBCs and heated RBCs can be effectively separated from

controlled RBCs [Figure 3.4 (c)].


53

Figure 3.4 (a) Scatter plot of I2 vs. I1 for controlled RBCs (n=996) and GA-treated RBCs

(n=1,059). (b) Scatter plot of I2 vs. I1 for controlled RBCs (n=996) and heated RBCs

(n=1,179). All RBCs were measured under a driving pressure of 400 Pa. I2 depends linearly

on I1. For RBCs with the same I1, GA-treated RBCs and heated RBCs show a higher and

lower I2 than controlled RBCs, respectively. (c) Scatter plot of I2/I1 (opacity) vs. I1 for

controlled, GA-treated and heated RBCs. RBCs deformability is reflected by the ratio of I2/I1.

The data show that GA treatment decreases RBC deformability and heat treatment increases

RBC deformability.

(a) (b)

GA

control

control

heat

control

GA

heat

(c)


54

3.4.3 System characterization

Figure 3.5 (a)(b) shows the histograms of I2/I1 (opacity) of controlled, GA-treated, and heated

RBCs measured under 400 Pa and 1600 Pa, fitted with normalized distribution. These two

pressures were chosen as representatives of low and high pressure. As expected, the less

deformable population (GA-treated) shows an opacity distribution shifted to the right, and

the more deformable population (heated) shows an left shifted opacity distribution, compared

to the controlled population. It was also found that the GA-treated RBCs have a wider

distribution compared to controlled and heated RBCs. As driving pressure increases [Figure

3.5 (b)], the difference in the average opacity values among the populations becomes smaller,

implying lower sensitivity for distinguishing the three populations. Figure 3.5 (c) summarizes

the ratio of I2/I1 (opacity) of controlled RBCs (red), GA-treated RBCs (blue), and heated

RBCs (green) measured under different driving pressures. The GA-treated RBCs (less

deformable) shows consistently higher mechanical opacity than controlled RBCs while

heated RBCs consistently have lower mechanical opacity values (more deformable). As

driving pressure increased, the difference between different populations became smaller. P-

values of the two-group T-test show statistically significant difference between the controlled

and treated RBCs at all applied pressure (p << 0.05).

To evaluate the power to detect differences between controlled and treated RBCs,

standardized difference values in comparison with controlled RBCs were calculated

according to

√


55

where X1 , X2 and S1 , S2 denote the sample mean, standard deviation of each group,

respectively.

As shown in Figure 3.5 (d), standardized difference values of both GA-treated and heated

RBCs decrease as the driving pressure increases. This finding is in agreement with the results

generated using ektacytometry [163, 164]. Ektacytometry results showed that RBC

deformation became less sensitive as shear stress increased from several Pa to 30 Pa, which

caused lower standardized difference values.

We further investigated the effect of RBC volume on the measured mechanical opacity.

Figure 3.6 (a)(b) shows the average and standard deviation of measured mechanical opacity

for different ranges of I1, under 400 Pa and 1600 Pa, respectively. Since I1 is an indicator of

RBC volume, comparing opacity values within each I1 sub-range could reveal the

deformability of RBCs within the same size group. For each RBC population (controlled,

GA-treated, and heated), the mechanical opacity values across the four size (I1) sub-ranges

are all close to the population‟s overall average value; the standard deviation of all controlled,

GA-treated and heated RBCs show very little variation across the four sub-ranges. These

results indicate that the mechanical opacity quantity mitigates the coupled effect from RBC

volume and reflects RBC deformability.

3.5 Conclusion

This chapter presented a microfluidic system for electrically measuring RBC deformability.

The two-stage device design and measurement technique together mitigate the cell volume

effect on RBC deformability. By measuring controlled, GA-treated and heated RBCs, the

system demonstrated the capability of distinguishing RBC populations having known


56

deformability differences. The electrical measurement system has a speed of 10-20 cells per

second. The speed can be further improved by increasing cell density and driving pressure.

However, as shown in Figure 3.5, the ability to distinguish different populations would

decrease at higher driving pressures.


57

Figure 3.5 Histograms of I2/I1 (opacity) of controlled, GA-treated and heated RBCs under (a)

400 Pa and (b) 1600 Pa driving pressures, fitted with normalized distribution. (c) Mechanical

opacity of controlled RBCs (red), GA-treated RBCs (blue) and heated RBCs (green)

measured under different driving pressures. Results are mean ± standard deviation. More

than 1,000 RBCs were tested for each of the data points. (d) Standardized difference values

calculated using the data present in (c). Blue: standardized difference between GA-treated

and controlled RBCs. Green: standardized difference between heated and controlled RBCs.

(a) (b)

(c) (d)


58

Figure 3.6 Comparison of I2/I1 (opacity) of controlled, GA-treated, and heated RBCs within

sub-ranges of I1. (a) 400 Pa driving pressure, (b) 1600 Pa driving pressure. Error bars

represent standard deviation.

(a) (b)

CHAPTER 4, INHERENT ELECTRICAL PROPERTIES MEASUREMENT

59

Chapter 4

4. Microfluidic Characterization of Specific

Membrane Capacitance and Cytoplasm

Conductivity of Single Cells

4.1 Introduction

Each cell type has unique membrane contents and intracellular structures to fulfill their

specific physiological functions. The membrane and intracellular compositions determine the

cell‟s electrical properties that can reflect cell physiological states and can possibly serve as

label-free markers for cell type classification. Compared to conventional biochemical

techniques (e.g., flow cytometry), which rely on the labeling of targeted cells with

fluorescence-conjugated antibodies or molecules to distinguish between cell types, electrical

measurement has distinct simplicity in sample preparation (no labeling), and in data

recording and processing [100, 165, 166].

In order to interpret the electrical properties of a cell, a single shell model is widely used

in various measurement setups [79, 80]. The cell is modeled as a dielectric thin membrane

enclosing homogenous spherical cytoplasm. Accordingly, the specific membrane capacitance

(affected by the membrane morphology, lipid bilayer composition and thickness, and


60

embedded proteins [81-83]) and cytoplasm conductivity (influenced by the nucleus

cytoplasm ratio and the ion concentration inside the cell [84-86]) are depicted as the inherent

electrical properties of the cells‟ membrane and cytoplasm, respectively. In the past decades,

a number of techniques were developed for single-cell electrical measurements. However,

existing technologies are either incapable of characterizing the inherent electrical properties

or incapable of providing statistically significant data due to limited measurement speed.

The most successful technique performing electrical measurement on single cells is the

Coulter counter, where direct current (DC) impedance measurements are applied for counting

and sizing single cells [167]. Impedance flow cytometry is an extension of the Coulter

counter technique. It implements both DC and RF impedance measurements. The ratio of the

RF signal to DC signal is defined as opacity, which is applied in commercial hematology

analyzers for three differential counts of white blood cells [168, 169]. However, the opacity

value reflects combined effects from both cell membrane and cytoplasm. It is not an inherent

electrical signature of cells and can differ among different testing configurations (e.g., co-

planar electrodes vs. parallel facing electrodes) [100, 119]. µ-EIS (Micro Electrical

Impedance Spectroscopy) is a technique in which a frequency-dependent excitation signal is

applied across a trapped cell to measure the corresponding current response [108, 110, 111].

Similar to opacity, capacitance and resistance of a cell are not inherent electrical parameters

(vs. specific membrane capacitance and cytoplasm conductivity) and are strongly affected by

electrode size, cell trapping mechanism, and cell volume.

The patch-clamp technique was originally developed for measuring current through ion

channels on the cell membrane, and can be used for characterizing the specific membrane

capacitance by aspirating a cell membrane patch into a micropipette [170, 171]. However, it


61

is a highly delicate procedure and has strong operator skill dependence. Consequently, the

testing speed of patch-clamp is typically tens of minutes per cell, and at most tens of cells can

be tested in a study. Electrorotation is capable of quantifying electrical properties of single

cells by measuring the rotation rate of the targeted cells induced by the rotating electrical

field (e.g., specific membrane capacitance, cytoplasm conductivity and cytoplasm

permittivity). Nonetheless, electrorotation is also a rather tedious and slow technique, and it

typically takes approximately 30 minutes to test a single cell [101, 105]. Another drawback is

the difficulty to achieve efficient rotation in high conductivity physiological buffer. Hence,

electrical properties of the tested cells may have already been altered when immersed in the

low conductivity sucrose buffer. At present, no techniques exist for measuring the specific

membrane capacitance and cytoplasm conductivity values at a reasonable speed.

This chapter presents a new technique for measuring single-cell specific membrane

capacitance and cytoplasm conductivity at a speed of 5-10 cells/second. A microfluidic

constriction channel that is marginally smaller than the diameters of tested cells concentrates

the electrical current to penetrate the cell membrane and confines the geometry of individual

cells. Electrical impedance was measured at seven frequencies simultaneously. By fitting

experimental impedance measurement data to equivalent circuit models, the specific

membrane capacitance and cytoplasm conductivity values of over 6,000 cells were quantified.

AML-2 and HL-60 cells were selected for their comparable sizes to assess the effectiveness

of distinguishing these size-comparable cell types using their electrical parameters.

Compared to the impedance measurement system we previously reported [32], the new

multi-frequency measurement technique and the improved equivalent circuit model with cell

geometry estimation enable, for the first time, the determination of the specific membrane


62

capacitance and cytoplasm conductivity values on a high number of cells (3,249 AML-2 cells

and 3,398 HL-60 cells).

4.2 System overview

Figure 4.1 (a) shows the schematic diagram of the single cell electrical measurement system.

The microfluidic chip was constructed by bonding PDMS microchannels to a glass slide. Cell

suspension was pipetted into the inlet reservoir of the device and driven through the

constriction channel by hydraulic pressure difference (500 Pa). Two Ag/AgCl non-

polarizable electrodes are plugged into the inlet and outlet ports. A sinusoidal excitation

voltage with 7 frequency components (1 kHz, 20 kHz, 50 kHz, 100 kHz, 200 kHz, 300 kHz,

400 kHz, @0.2 Vpp) is generated by the function generator. As cells are aspirated through the

constriction channel continuously, current within the channel is pre-amplified, demodulated

and sampled at 14.4 kHz per frequency. By fitting the impedance spectroscopy (7

frequencies) to the equivalent circuit models, membrane capacitance (Cmem) and cytoplasm

resistance (Rcyto) can be obtained. Within the constriction channel, the cells‟ shape is well

confined; hence, the Cmem and Rcyto values can be used to determine the cell‟s specific

membrane capacitance and cytoplasm conductivity.

Figure 4.1 (b) is the impedance amplitude profiles (7 frequencies) measured within 1

second with 8 cells passing through the constriction channel. The excitation voltage of a

single frequency is 0.2 Vpp, which is intentionally selected to provide a measurable current

signal and avoid possible electroporation (electroporation threshold voltage is ~0.5 V [172,

173]). A threshold is defined in this study to be 1.05× basal amplitude (i.e., the amplitude

without cell presence in the constriction channel) at 1 kHz (see dash-dotted line in Figure 4.1

(b)). Comparing a signal amplitude value with the threshold, the portions where the


63

impedance amplitude is higher than the threshold value are considered as cell passage

regions. The maximal values of each frequency within the cell passing region are extracted

and used to calculate the electrical parameters of the cell.

Figure 4.1 (a) Schematic of the microfluidic system for electrical measurement of single cells.

Within the constriction channel, “green” represents the cytoplasm and “red” represents the

membrane. “i‟ is the current through the channel. Rcyto represents cytoplasm resistance and

Cmem represents membrane capacitance; Rleak represents the leaking resistance between cell

membrane and channel walls; Rch‟ and Cch represent resistance of the medium and parasitic

capacitance of the channel. (b) Experimental data showing impedance amplitude profiles (at

7 frequencies) measured within 1 second with 8 cells passing through the constriction

channel. Impedance profiles of different frequencies are plotted in different colors.

(b)

(a)


64

Figure 4.2 (a) An AML-2 cell is inside the constriction channel. (b) Schematic representation

of the simplified geometrical model (“green” and “red” represent cytoplasm and membrane,

respectively).

Figure 4.3 (a) The channel without cells passing is modeled as a resistor Rch and a capacitor

Cch in parallel. (b) The cell is modeled as a resistor Rcyto (cytoplasm) and two capacitors Cmem

(membrane) in series; Rleak represents the leaking resistance between cell membrane and

channel walls; Rch‟ represents the medium‟s resistance after a portion of the constriction

channel is occupied by a cell. G is a pre-amplifier converting current into voltage signals.

10 µm

(a) (b)

(b) Model 2

(a) Model 1


65


()AML-2 (acute myeloid leukemia) and HL-60 (human promyelocytic leukemia) cells were

chosen for testing in this study. Their comparable sizes permit the evaluation of using their

electrical parameters (i.e., specific membrane capacitance and cytoplasm conductivity) for

cell classification. Furthermore, the characterization of electrical properties of white blood

cells (WBCs) has been reported (e.g., using the electrorotation technique), providing

reference data for comparison with our measurements. WBCs behave like a droplet of

viscous liquid and are highly deformable [174, 175]; therefore, they can easily pass through

the constriction channel (cross-sectional area: 9µm×9µm, the channel is slightly smaller than

the cells‟ diameter and was lubricated by incubating with 1% BSA to minimize the friction

between cell membrane and channels walls). When a WBC is inside the constriction channel,

its geometry is well confined by the channel (Figure 4.2 (a)). To quantify the geometry of the

tested cells, a simplified geometrical model is adopted [26]. The cell is modeled as a

rectangular cube with two quasi-spherical caps fitted smoothly onto the rectangle (Figure 4.2

(b)). The caps of the cell are taken as a quasi-sphere, with an effective radius (r) of 1/2 the

side of the constriction channel‟s cross-sectional area (4.5 µm in this case). Under this

simplification, the effective area of the membrane (red thin shell around the cells contour)

that contributes to the cell‟s capacitance (Cmem) includes only the two quasi-spherical caps‟

area (determined by the cross-sectional area of the constriction channel); and the elongation

L (see Figure 4.2 (b)) is the only geometrical parameter that varies across different cells,

which is only determined by the cells‟ volume, assuming Poisson‟s ratio is approximately 0.5

[63].


66

Figure 4.3 (a) and Figure 4.3 (b) show the equivalent circuit models of the system without

and with cells in the constriction area. The electrical models are modified from the equivalent

models used in patch-clamp [110] and proves effective for interpreting our experimental

setup [176]. The impedance amplitude Z of the system at a specific frequency can be

calculated as

in

out

VZ G

V (1)

where Vin is the amplitude of the excitation voltage (0.2 volt in this case), Vout is the output of

the pre-amplifier, and G (10,000 ohm in this case) is the gain of the pre-amplifier.

When there is no cell present in the constriction region, the channel is equivalent to a

resistor Rch and a capacitor Cch connected in parallel. Rch is the resistance of the medium in

between the electrodes determined by the conductivity of the medium and geometries of the

channel. Cch is the parasitic capacitance of the channel. Rch and Cch can be obtained by fitting

the impedance amplitude profiles to Model 1, before aspiration pressure is applied.

As a cell is present inside the constriction channel, it has an elongation length, L and has

membrane capacitance, Cmem and cytoplasm resistance, Rcyto. The cell blocks the current

within the channel causing the impedance to increase. When a portion of the constriction

channel is occupied by a cell, Rch is also changed due to the change of the medium volume.

The resistance of the medium in the channel with the presence of a cell (Rch’) can be

calculated as

2

1'

4ch ch

LR R

r (2)


67

where Rch is the resistance of the channel without the presence of cells, Rch’ is the resistance

of the channel occupied by a cell with elongation L , r is the radius of the quasi-spherical caps

(4.5 μm in this case), σ is the conductivity of the medium (1.6 S/m).

At low frequencies, all the capacitive components can be perceived as an open circuit.

Thus, the measured low frequency impedance, Rlow, only reflects Rleak and Rch’ (Eq. 3). Rleak

can be calculated as

'leak low chR R R (3)

In Model 2, Cch remains unchanged with or without the presence of cells in the

constriction channel. Thus, only the electrical components related to the tested cell remain

unknown (Rcyto and Cmem), which can be obtained by fitting the impedance spectroscopy data

to Model 2. Under the geometrical assumption, the specific membrane capacitance and

cytoplasm conductivity can be estimated according to

2specific membrane capacitance =

2

memC

r (4)

2

2 1cytoplasm conductivity =

4 cyto

L r

r R

(5)

where Rcyto and Cmem are the resistance and capacitance of the tested cell obtained from curve

fitting, L is cell elongation, and r is the radius of the quasi-spherical caps (4.5 μm in this

case). The quasi-spherical caps‟ surface area is used as the effective area for specific

membrane capacitance, and the cross-sectional area of the channel is used as the effective

area for cytoplasm conductivity calculation.


68

Figure 4.4 (a)(b) Impedance amplitude and phase of the constriction channel without and

with cells (5 cells of different diameters), measured at 201 frequency points within the range

of 1 kHz to 1 MHz. (c)(d) Impedance amplitude and phase measured at 7 selected frequency

points (1 kHz, 20 kHz, 50 kHz, 100 kHz, 200 kHz, 300k Hz, 400 kHz), on the same 5 cells as

shown in (a) and (b). Experimental data and curve fitting results are plotted in blue and red,

respectively.


69


4.4.1 Curve fitting and frequency selection

Cells of various diameters were first „parked‟ in the constriction channel with careful

application of very low aspiration pressure. This allows an impedance spectroscopy (201

frequency points within the range 1 kHz to 1 MHz) of the parked cells to be attained. In the

meanwhile, images of the tested cells were taken via microscopy imaging (Olympus IX81,

Olympus Canada Inc., Canada), and cell elongation was measured with a custom-designed

image processing program. Figure 4.4 shows the impedance amplitude (a) and phase (b)

spectroscopy of the constriction channel without and with cells having different diameters.

Nonlinear least squares curve fitting (MATLAB, Mathworks, USA) was used to fit the

measured impedance to the equivalent circuit models. Before a cell was parked, the

impedance profile was measured and fitted to Model 1(see Figure 4.4 (a)) to determine Rch

(0.85 Mohm) and Cch (0.18 pF). When a cell was parked in the channel, the impedance

spectroscopy was measured and fitted to Model 2 to determined Cmem and Rcyto. We tested 20

AML-2 cells with 201 frequency points spectroscopy, and the Cmem and Rcyto were

determined to be 1.838±0.240 pF and 0.300±0.057 Mohm, respectively, with all fitting

regression coefficients higher than 0.99.

Generally, more frequency points are preferred for impedance spectroscopy to obtain

more comprehensive information of the tested system. However, due to the limit of

instruments (e.g., HF2IS, Zurich Instrument, Switzerland), measurements can only be made

at 7 frequencies. Thus, the 7 frequency points must be properly selected. As shown in Figure

4.4 (a) and Figure 4.4 (b), the system is almost purely resistive at low frequencies (near 1

kHz-10 kHz), wherein the cell membrane acts as an efficient insulator. This can be seen in


70

the impedance phase of the system (close to 0 degree). In the range of 1 kHz and 10 kHz, the

impedance amplitude value does not vary much. Hence, the impedance amplitude at 1 kHz is

selected to reflect the resistive characteristic of the system. High frequency (near 1 MHz)

results in low impedance of Cch, and electrical field lines undesirably obviate the cell in the

constriction channel. As a result, the impedance amplitude of the channel with a cell and

without a cell becomes rather close, which can barely reflect the cell‟s electrical properties.

Therefore, more frequency points (20 kHz, 50 kHz, 100 kHz, 200 kHz, 300 kHz, 400 kHz)

are selected within the middle frequency range for well capturing the cell‟s electrical

properties. The impedance amplitude at the selected 7 frequency points of the same 20 cells

was fitted to Model 2 (see Figure 4.4 (c) and Figure 4.4 (d)). The calculated Cmem and Rcyto

are 1.836±0.218 pF and 0.316±0.051 Mohm, which are fairly close to the values obtained

from 201 frequency points fitting (1.838±0.240 pF and 0.300±0.057 Mohm). These results

prove the validity of the selected 7 frequencies.

4.4.2 Cell elongation measurement using impedance signal

Cell elongation can be measured using high-speed microscopy imaging. However, high-

speed cameras (tens of kHz) generate gigabyte data per second and can only record for a few

seconds due to limited memory. Processing this massive amount of image data takes

tremendous computation efforts and time. Hence, high-speed imaging for measuring cell

elongation (L) is not a desirable approach. Since the cell membrane acts as an insulate layer

at low frequencies (lower than 1 kHz), the impedance increase at 1 kHz is only determined

by Rleak, which is proportional to cell elongation (L). Before high-speed measurements, we

correlated the elongation of cells to the impedance increase (∆R) at 1 kHz by microscopy

imaging and manually measuring tens of cells‟ elongation in the captured images (see Figure


71

4.5). The fitted relationship was then used to estimate cell elongation in the subsequent all-

electrical high-throughput experiments. Compared with HL-60, AML-2 is less deformable

[177], leading to a slightly larger gap between AML-2 cell membrane and the corners of the

rectangle-like microchannels. Thus, as shown in Figure 4.5, with the same elongation, AML-

2 cells caused a lower impedance increase compared to HL-60 cells, resulting in the different

slopes of linear trends between AML-2 and HL-60 cells.

4.4.3 Specific membrane capacitance and cytoplasm conductivity

Our microfluidic system tested AML-2 cells and HL-60 cells. Impedance profiles were

recorded and fed into an automated curve fitting program. The program outputs values of

specific membrane capacitance, cytoplasm conductivity, cell elongation and norm of the

curve fitting. Although intact cells represent the majority in the tested cell suspension, there

were also debris, clusters, and apoptotic cells. All the data points shown in Figure 4.5 were

measured on intact single cells as verified in experiments via imaging. The data revealed that

impedance increase at 1 kHz (∆R) falls into the range of 0.3 Mohm to 3.5 Mohm for the

intact single cell events. Thus, the events with impedance increase at 1 kHz (∆R) out of this

range were regarded as debris or clusters and were excluded from the final data. In the

meanwhile, since the Cmem values of the apoptotic cells are so low that the curve fitting

program is not able to locate a solution within the reasonable range, the events with

exceptionally high curve fitting norm are regarded as apoptotic cells (confirmed via imaging)

and excluded from the final results. Using these criteria, approximately 5% of events were

excluded. Figure 4.6 (a) shows the scatter plot of specific membrane capacitance vs.

cytoplasm conductivity of AML-2 (n=3,249) and HL-60 (n=3,398). Both parameters were

quantified and fitted to normal distributions (see Figure 4.6 (b)). The determined specific


72

membrane capacitance and cytoplasm conductivity of AML-2 and HL-60 are 12.0±1.44

mF/m2, 0.62±0.10 S/m and 14.5±1.75 mF/m

2, 0.76±0.12 S/m, respectively. The difference in

specific membrane capacitance values can be attributed to the differences in the two cell

types‟ surface morphology [178]. Membrane morphology was confirmed to be a primary

factor influencing cells‟ specific membrane capacitance [81, 82]. Additionally, the different

nucleus-to-cytoplasm ratios of these two cell types, as we observed in imaging, could have

contributed to the measured differences in cytoplasm conductivity.

4.4.5 Error analysis

Cell elongation is estimated by using the correlation of cell elongation and the impedance

increase at 1 kHz (Figure 4.5). The error can be quantified to be

1 N

i i

i

L y fN

(6)

where L is the average error of cell elongation measurement, iy is cell elongation measured

via microscopy imaging, if is the predicted value calculated via the fitted relationships, and

N is the total number of data points. According to Eq. 6, the average elongation measurement

errors of AML-2 and HL-60 are 2.03 µm and 1.48 µm, respectively. When the cell

elongation is used for the estimation of Rch‟ (Eq. 2), the error in elongation estimate can

cause an error of 0.01-0.03 Mohm of Rch‟, which accounts for less than 3.5% of Rch‟.

Furthermore, Rch‟ is much smaller compared with the impedance value of Rcyto and Cmem.

Thus, the elongation errors‟ effect on the electrical component calculation (Rcyto and Cmem) is

negligible. However, the elongation error can lead to 7-12% errors in cytoplasm conductivity

estimation (Eq. 5).


73

We further investigated the effect of cell size on the measured specific membrane

capacitance and cytoplasm conductivity of AML-2 and HL-60. The diameters of the cells

were estimated on the basis of the geometrical model with Poisson‟s ratio of 0.5 (Figure

4.6(c)). The cells within the diameter range of 10.5µm to 13µm were chosen, since the

majority of these two types of cells fell in this range (>70% for AML-2 and >80 for HL-60),

and within this range, the cell diameters of these two cell types heavily overlapped. The

range of interests was divided into five sub-ranges (10.5-11µm, 11-11.5 µm, 11.5-12 µm, 12-

12.5 µm 12.5-13 µm. For a certain cell type, the specific membrane capacitance and

cytoplasm conductivity values of the five diameter sub-ranges are all fairly close to the

population‟s average, and the differences of the specific membrane capacitance and

cytoplasm conductivity values for AML-2 and HL-60 are almost the same across all the sub-

ranges, indicating the different electrical properties of AML-2 and HL-60 measured using

our device truly result from the different electrical properties of the cells, rather than the

diameter variation.

Specific membrane capacitance and cytoplasm conductivity values are inherent electrical

properties of the cells, which should ideally be completely independent of cell size. However,

as the cell diameter increases, our measurement results show a very minute increase trend of

the specific membrane capacitance and cytoplasm conductivity values for both AML-2 and

HL-60 cells. This trend may result from measurement errors caused by the use of the

simplified geometrical model. The quasi-spherical cap‟s surface area is used as the effective

area for all the cells to calculate the specific membrane capacitance. Cells with larger

diameters may result in slightly larger effective area (vs. cells with smaller diameters) due to

the squeeze of the channel, which can cause increase in specific capacitance membrane


74

calculation (Eq. 4). The cytoplasm conductivity estimation assumes a uniform current

distribution inside the cell membrane. The different quasi-spherical caps may alter the

current distributions inside the cytoplasm, and consequently affect the calculated cytoplasm

conductivity value. Overall, the measurement errors caused by cell diameter variation on the

determined specific membrane capacitance and cytoplasm conductivity values are small.

Thus, the electrical property values are valid for representing the population of a certain cell

type. In fact, the results obtained using our technique are consistent with the values reported

in electrorotation studies, which are also based on the single shell model (specific membrane

capacitance of HL-60: 15 ± 1.9 mF/m2, 15.6 ± 0.9 mF/m

2; specific membrane capacitance of

human granulocytes: 11.0 ± 3.2 mF/m2; and cytoplasm conductivity of human granulocytes:

0.60 ± 0.13S/m [81, 179]).

4.4.6 Cell type classification

Since AML-2 (diameter: 12.5±1.3 µm) and HL-60 (diameter: 11.4±0.93 µm) cells have

comparable diameters, the Coulter counter technique produced a low classification success

rate of 67.7%. With additional information on cells‟ electrical properties, these two cell types

were better classified. A back propagation neural network was used for pattern recognition

(MATLAB, MathWork, USA). The input data have three parameters (diameter, specific

membrane capacitance, and cytoplasm conductivity) measured on each cell. Cell type

classification success rates were 67.7% (diameter only), 84.4% (diameter + cytoplasm

conductivity), 88.6% (diameter + specific membrane capacitance), and 93.0% (diameter +

cytoplasm conductivity + specific membrane capacitance), suggesting that using specific

membrane capacitance and cytoplasm conductivity can significantly improve the

classification success rate (vs. only using cell diameter/size as in Coulter counter). This result


75

is not surprising, for the two cell types within the same diameter range, their inherent

electrical properties (specific membrane capacitance: P value <10-150

, cytoplasm conductivity:

P value <10-70

) are significantly different. These inherent electrical properties contain

additional information (cell membrane and cytoplasm properties) to cell size.

4.5 Conclusion

This chapter demonstrated a new technology for rapid characterization of single-cell

electrical properties (specific membrane capacitance and cytoplasm conductivity). The cells

are aspirated through a constriction channel, and impedance profiles at 7 different

frequencies are measured simultaneously. Geometrical and electrical models were developed

to quantify the specific membrane capacitance and cytoplasm conductivity from the

experimental impedance data. Compared with previously reported techniques for single-cell

electrical characterization, the speed of our system is significantly higher (5-10 cells per

second vs. minutes per cell). A total of 3,249 AML-2 cells and 3,398 HL-60 cells were tested,

and the specific membrane capacitance values were determined to be 12.0±1.44 mF/m2 and

14.5±1.75 mF/m2, respectively, while cytoplasm conductivity values were determined to be

0.62±0.10 S/m and 0.76±0.12 S/m. The measurements can be used for cell type classification

or provide additional information on cells‟ physiological conditions.


76

Figure 4.5 Experimental correlation between cell elongation and impedance increase at 1

kHz of AML-2 (blue) and HL-60 (red).


77

Figure 4.6 (a) Scatter plot of specific membrane capacitance vs. cytoplasm conductivity of

AML-2 and HL-60. Color coding represents data distribution densities. (b) Histograms of

specific membrane capacitance and cytoplasm conductivity of AML-2 (red) and HL-60 (cyan)

fitted with normal distributions. (c) Histograms of AML-2 (red) and HL-60 (cyan) cell

diameters fitted with normal distributions.

AML-2

HL-60

(a) (b)

(c)

CHAPTER 5, DEFORMABILITY CHANGE DURING BLOOD STORAGE

78

Chapter 5

5. Characterization of Red Blood Cell Deformability

Change during Blood Storage

5.1 Introduction

In transfusion medicine, red blood cells (RBCs) collected from blood donors are stored and

preserved in a blood bank. Every year in the U.S. and Canada, over 14 million units of RBCs

are administered to more than 5 million patients [180]. Present regulations in Canada and the

U.S. specify 42 days as the shelf life for stored blood [180, 181]. However, it has been

suggested that during storage red blood cells undergo morphological, structural, and

functional changes, which may induce clinical complications and adversely affect patient

mortality [182-185]. For instance, transfusion of RBCs stored for more than two weeks was

found associated with a significantly increased risk of postoperative complications as well as

reduced short-term and long-term survival of cardiac surgery patients [186].

The degradation of stored RBCs is known as the storage lesion [183]. Although the

clinical consequences of storage lesions remain controversial [182], intensive research has

shown how parameters, which govern RBCs‟ metabolic ability and oxygen delivery capacity,


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such as 2,3-DPG, potassium, pH, HbO2 saturation, RBC ATP, RBC NO, SNO-Hb, and

haemolysis, change over the life span of stored RBCs [146, 180, 187, 188]. In addition to

these biochemical properties, the biconcave shape and high deformability of RBCs are also

crucial for their physiological activities and functionality.

The study on the deformability of stored RBCs dates back to the 1960s. La Celle and

Weed investigated the progressive alteration of stored RBC deformability by using a

micropipette [189, 190]. The hydraulic pressure required to aspirate the RBCs through the

micropipette was used as a deformability indicator. Their results show that the RBCs that

remained biconcave disc-shaped during storage have a deformability similar to that of fresh

RBCs; however, those RBCs that became more spherical had decreased deformability.

Optical tweezers were also applied to characterize the deformability of 0 days and 35 days

stored RBCs by optically stretching the cells, wherein higher membrane elasticity and

viscosity were observed in 35-day old RBCs [137]. More recently, ektacytometry was used

to study the deformability of stored RBCs. Ektacytometry consists of two rotating plates with

a small gap in-between. RBCs adhere to the bottom of the gap, and shear stress elongates the

RBCs. The extent of cell elongation is measured as an indicator of RBC deformability. Using

ektacytometry measurements, the progressive elongation change of stored RBCs over 42

days storage period was reported, demonstrating that RBCs become harder to elongate when

stored longer [180, 191-193].

Measuring the deformability of stored RBCs using micropipette and optical tweezers is

tedious and skill-dependent. More importantly, the slow measurement speed (minutes to tens

of minutes for testing one cell) makes these techniques infeasible to obtain sufficient

information of the highly heterogeneous blood cell population [13, 17, 194]. As stored RBCs


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age, the cells change their morphology progressively from biconcave to more spherical

(spheroechinocytes) [184, 188, 195]. Even for the cells within the same stored blood sample,

their morphology change varies significantly. Hence, testing only a few cells from a blood

sample cannot objectively reveal deformability changes of the sample.

Compared to micropipette and optical tweezers, ektacytometers are relatively easy-to-use.

However, ektacytometry measurement is limited to approximately 50-60 RBCs per test. In

ektacytometry, elongation index measured via laser diffraction is the only parameter for

indicating RBC deformability [164, 196]. The definition of elongation index becomes

improper when RBCs‟ shapes become less regular [197-199]. Furthermore, ektacytometry

requires RBCs to settle and adhere to the bottom of the ektacytometer chamber in order to

elongate the cells under shear stress. This deformation mode is not physiologically relevant

since in vivo RBCs are folded when flowing through human microcapillaries with diameters

comparable to or smaller than RBCs [141, 155, 157]. It is known that the mechanical

properties of RBCs can differ significantly when deformed under different modes (e.g.,

extension or folding) [200, 201].

This chapter describes the folding of stored RBCs on a microfluidic device. Fresh and

stored RBCs were pressure-driven to flow through microfluidic channels (cross-sectional

area: 8 µm× 8 µm). Hydrodynamic focusing within the microchannel controls the orientation

and position of individual RBCs. High-speed imaging (5,000 frames/sec) captures the

dynamic deformation behavior of the cells, and together with automated image analysis

enables the characterization of over 1,000 RBCs within 3 minutes. Multiple parameters

including deformation index (DI), time constant (recovery rate after an RBC exits the

channel), and circularity of the individual cells were quantified. Compared to existing stored


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RBC deformability studies, our results include a significantly higher number of cells (>1,000

cells/sample vs. a few to tens of cells/sample) and, for the first time, reveal deformation

changes of stored RBCs when traveling through human-capillary-like microchannels. The

correlation between deformability and morphology of stored RBCs is also reported.

5.2 System overview

Figure 5.1 (a) shows a schematic of the microfluidic device for studying RBC deformability

changes during storage. The central channel is 160 µm long and has a cross-sectional area of

8 µm×8 µm. The recovery region has a cross-sectional area of 200 µm×8 µm, where the

deformed RBCs gradually recover to their original shape. Two focusing channels are

integrated to center and orient the RBCs. The loading and focusing channels are connected to

a custom-developed precision pressure source using water tanks. When RBCs flow through

the central channel, RBC images are captured by a high-speed camera (5,000 frames/sec)

through an inverted microscope. Figure 5.1 (b) shows the dynamic process of RBC

deformation. When the cell entered the channel, its position was close to one of the

microchannel walls. After the focusing unit, the cell was centered and adjusted to the

„standing‟ orientation. It then underwent symmetrical shear stress, resulting in a parachute-

like shape near the exit. When the cell exited the microchannel, shear stress was released, and

the RBC gradually recovered to its original shape.

In order to quantify the deformation behavior of RBCs, deformation index (DI) is defined

as DI=L/D [see Figure 5.1 (a)] in the last frame of image right before the cell exits the

microchannel and enters the recovery region. Recovery time constant (tc) is determined by

exponentially fitting DI values with respect to time. Circularity, defined as

circularity=4π×Area/Perimeter2 (circularity=1 means a perfect circle), is measured


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experimentally in the last frame of image right before the cell exits the region of interest to

depict the morphology of each stored RBC.

Figure 5.1 (a) Schematic of the microfluidic device for studying deformability changes of

stored RBCs. Hydrodynamic focusing centers the cell and adjusts it to the „standing‟

orientation. The RBC is „folded‟ into a parachute-like shape when pressure-driven through

the 8 µm×8 µm central channel. Deformation index (DI=L/D) is defined to measure RBC

deformation (top-left). Time constant (tc) of each individual cell is determined by fitting DI

value changes during the cell shape recovery process to an exponential function, after the cell

exits the microchannel. (b) Experimental images showing the centering, orienting, folding,

and shape recovering of an RBC.

(a)

(b)


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5.3 Methods and materials

5.3.1 Blood samples

Stored blood samples were collected using techniques consistent with the Technical Manual

of the AABB [202]. Briefly, venous blood samples (500 ml ±10%) were collected from

healthy donors in CP2D anticoagulant. After separation of plasma and buffy coat, the RBCs

were suspended in saline-adenine-glucosemannitol (SAGM) (110 ml), and then stored in a

blood bank refrigerator at 4ºC. For each test, an RBC aliquot was removed and tested at

desired time intervals [180, 188]. After an RBC sample was drawn from the aliquot, it was

diluted with PBS with 1% w/v BSA (RBC sample:PBS=1:100) and incubated for 10 min at

room temperature to prevent adhesion to channel walls. Fresh blood samples were obtained

from healthy donors (Mount Sinai Hospital, Toronto, Canada) and tested following the same

procedure with the stored RBC samples. All samples were tested within one hour after

incubation.

5.3.2 Experimental methods

The microfluidic device was constructed with PDMS using standard soft lithography, as

described elsewhere [203]. The device consists of two layers of different thickness. The

thickness of the testing areas is 8 µm, while the thickness of the loading channels is 50 µm

for reducing flow resistance. After the RBCs were loaded into the device inlet, a custom-

developed water tank applied pressure (2.5 kPa) to drive the cells through the microchannels.

Cell images were captured at a speed of 5,000 frames/sec (416 pixel by 142 pixel) via a high-

speed camera (HiSpec 1, Fastec Imaging Corp., U.S.), under a 60× objective of an inverted

microscope.


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A custom-designed MATLAB image processing program was developed for automated

RBC image analysis. For each video, the region of interest (ROI) is defined as the

microchannel region excluding the PDMS area. After Gaussian filtering, canny edge

detection with adaptive thresholding is applied to extract cell edges. All the edge points

inside the ROI are accumulated along the X and Y axes, respectively. The peak regions in the

X and Y curves indicate the coarse cell region, based on which the fine cell region is

obtained by iterations of localizing the active contours of the cell. Length, width, area, and

perimeter are measured for each RBC to quantify its DI, time constant (tc), and circularity.

Figure 5.2 Efficiency of hydrodynamic focusing. Histogram of central distance (i.e., the

distance between the center of the cell and microchannel‟s central line) of RBCs before (blue)

and after (red) the focusing unit (n=1,500). Central distance of zero means the cell is

perfectly centered within the microchannel. The percentage of RBCs near the microchannel

center was increased significantly by hydrodynamic focusing.


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5.3.1 Hydrodynamic focusing efficiency

The shape of RBCs flowing through small capillaries is a function of flow rate, initial

position relative to the central line of the capillary, and the diameter of the capillary [141,

151, 204, 205]. For consistency, RBCs need to be centered in the microchannel and deformed

symmetrically. If cells are asymmetrically deformed, the definition of DI=L/D becomes less

appropriate for describing their deformation behavior [141]. Experiments demonstrate that

RBCs close to the central line of the microchannel reveal a more symmetrically deformed

shape while cells near channel walls usually are asymmetrically deformed. Cells near the

channel walls at the entrance typically stay near the channel wall until exiting the channel.

When RBCs enter the channel, their positions are random. Hence, hydrodynamic

focusing near the inlet (see Figure 5.1) was used to better position RBCs close to the central

line of the microchannel. Figure 5.2 shows a histogram of central distance (i.e., the distance

between the cell center and the microchannel‟s central line) of RBCs before (blue) and after

(red) the hydrodynamic focusing unit (n=1,500). The percentage of RBCs near the

microchannel center was increased significantly by hydrodynamic focusing, and the number

of cells more than two pixels away from the channel center was significantly reduced. The

data presented in this chapter only include those RBCs that were no more than two pixels

away from the central line of the microchannel.

5.3.2 Time constant

After the RBC enters the recovery region, the time constant (tc) of each RBC is determined

by fitting its DI values over time to an exponential function, in order to characterize the


86

recovery rate of the cell. The exponential model is adapted from the standard Kelvin–Voigt

model for describing the recovery properties of RBCs [206, 207]. Figure 5.3 shows two sets

of data, one from a biconcave RBC (blue) and the other from a spherical RBC (red) due to

storage. The first data point on each curve was obtained from the image frame right after the

cell exits the microchannel. For the RBCs shown in Figure 5.3, time constant (tc) of the

spherical RBC is 0.267 ms, while the tc value of the biconcave RBC is 0.625 ms.

It is worth noting that the time constant of RBCs reported in the literature was

approximately 100 ms [208-210]. In contrast, the time constant we quantified ranges from

0.1 ms to 1 ms. In previous studies, DI was typically measured starting from 100 ms after an

RBC was released from deformation until the shape completely recovered (several seconds)

[208, 209]. In our experiments, enabled by high-speed imaging, the rapid recovery process

immediately after an RBC is released was captured (from 0.2 ms to 10ms). During this

period, RBCs recover much faster than in the later period (e.g., after 100 ms as in earlier

studies). As a result, the captured fast dynamics of RBC deformation more authentically

revealed the cell shape recovery behavior, and exponential fitting resulted in time constant

values orders of magnitude lower than those from earlier results in the literature.


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Figure 5.3 Shape recovery of a biconcave RBC (blue) and spherical RBC (red) fitted to an

exponential model. The spherical shape of the cell was due to morphological change during

blood storage. Time constant (tc) of the spherical RBC is 0.267 ms, and the tc value of the

biconcave RBC is 0.625 ms. The relationship between deformability change and morphology

change is discussed in the next section.


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Figure 5.4 (a)(b) Scatter plots of time constant vs. circularity; deformation index (DI) vs.

circularity, for the same blood sample stored for 1 week (n=1,038), 3 weeks (n=1,027), and 6

weeks (n=1,001). (c) Distribution of time constant, circularity, and DI.

(a)

(b)

(c)


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5.3.3 Changes in RBC deformation index, time constant, and circularity over blood

storage

Figure 5.4 summarizes results from testing the same blood sample stored for 1 week

(n=1,038), 3 weeks (n=1,027), and 6 weeks (n=1,001). The scatter plots show tc vs.

circularity [Figure 5.4 (a)] and DI vs. circularity [Figure 5.4 (b)]. Within each sample, both tc

and DI show a decreasing trend as circularity increases, indicating that morphological

changes of RBCs are a factor that causes RBC deformability changes over blood storage.

Figure 5.4 (c) shows the distribution of these three parameters. It can be seen that the average

time constant becomes lower as the RBCs are stored longer. The lower time constant may be

attributed to ATP loss. It has been proven that the depletion of ATP alters its binding to

spectrin–actin, which modifies RBC‟s cytoskeleton structure [211-213], resulting in RBC

stiffening and faster recovery [208, 214]. During storage, RBCs undergo a number of

biochemical changes, including ATP depletion [215, 216], which may be a cause of the

lower time constants of older RBCs.

The average circularity of fresher and older RBCs is not significantly different; however,

the distribution (i.e., standard deviation) becomes wider. Thus, the distribution width of

circularity (circularity-DW) can possibly be used as an indicator of stored RBC quality or age.

The alteration of circularity distribution over time is mainly contributed by RBC morphology

changes. Based on experimental observation, as stored RBCs age, they first swell and then

progressively change to a sphere-like shape. A portion of the aged RBCs became more

spherical, making their circularity approach one. There were also RBCs appearing

isotropically enlarged from storage, and these RBCs typically revealed a higher DI value.


90

When they pass through the microchannel, their deformed shape resulted in low circularity.

The more spherical cells and isotropically swelling cells increased circularity-DW.

No significant difference in DI was found (DI: 1.227 for 1-week, 1.221 for 3-week, and

1.219 for 6-week) [Figure 5.4 (c)]. This implies that when stored RBCs are transfused into

patients, they are able to flow through microcapillaries with a similar folding capability.

However, their stretching capability might become poorer, according to the lower elongation

index (EI) measured with ektacytometry [180, 191-193]. The insignificant folding DI change

of older RBCs (vs. fresher RBCs) as quantified in our work, and the significant stretching EI

change of older RBCs (vs. fresher RBCs) as reported in ektacytometry measurements can be

due to the fundamentally different cell deformation modes [200, 201]. Although the depletion

of ATP alters RBC cytoskeleton, the stretching EI change might not be a concern in

transfusion medicine since the stretching mode is not in vivo like and can be physiologically

irrelevant.

We further investigated the effect of RBC morphology change (circularity) on time

constant. Figure 5.5 shows the average and standard deviation of measured tc values within

each circularity range (divided into five sub-ranges). Both fresher and older RBCs with

higher circularity reveal lower time constant. Due to intrinsic property changes, within each

circularity range, older RBCs show consistently lower time constants, compared to fresher

RBCs.

Finally, five blood samples were tested from fresh to 8 weeks‟ storage, at time intervals

of every two weeks (n>1,000 per time interval per sample). As shown in Figure 5.6, time

constant decreases and circularity-DW (distribution width) increases over blood storage.

Significant differences exist between neighboring data points (p<0.05), demonstrating that


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time constant and circularity-DW can possible be used as indicators of RBC storage age or

stored RBC quality. Standard deviations shown in Figure 5.6 can be attributed to blood donor

variations and variations in blood processing procedures [217, 218].

Figure 5.5 Comparison of time constants for the same blood sample stored for 1 week

(n=1,038), 3 weeks (n=1,027), and 6 weeks (n=1,001). Circularity is divided into five sub-

ranges. Error bars represent standard deviation. In the last circularity range (>0.8), the group

of 1 week old RBCs is not present because no RBC has a circularity higher than 0.8 in 1

week old sample.


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Figure 5.6 Time constant (a) and circularity-DW (b) alteration over time. Each data point was

obtained from 5 blood samples and over 1,000 cells were tested within each sample.

Significant differences exist between neighbor data points (p<0.05). A relatively larger

change between fresh and 1-2week samples was found, compared to the steady changes over

time during storage.

(a) (b)


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5.4 Conclusion

The deformability changes of stored red blood cells (RBCs) were studied using a human-

capillary-like microfluidic channel. High-speed imaging system and automated image

processing were used to quantify multiple parameters, enabling a higher measurement speed.

Fresh and stored blood samples (up to 8 weeks) were tested. Besides large sample sizes, our

study, for the first time, revealed deformation behavior changes of stored RBCs when

traveling through human-capillary-like microchannels. Although existing literature

consistently reported stretching deformability change of stored RBCs, our results show that

no significant difference exists in their folding deformability. Furthermore, we report that

significant changes in time constant (i.e., recovery rate) and circularity distribution width (i.e.,

heterogeneity of morphology) can be useful parameters for quantifying stored RBC quality or

age.

CHAPTER 6, LYMPHOCYTES IN CHRONIC LYMPHOCYTIC LEUKEMIA

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Chapter 6

6. Decreased deformability of lymphocytes in

chronic lymphocytic leukemia

6.1 Introduction

Over the past decade, there is growing evidence showing that cells undergo biophysical

property changes during disease progression [219, 220]. For example, the increased

deformability of malignant cancer cells, as compared to benign cells, facilitates their

migration and invasion [194, 221]; and in sickle cell disease, sickled red blood cells due to

their higher stiffness can cause vaso-occlusion [222]. Biophysical property changes of

various leucocytes under a number of pathological conditions have been reported.

Neutrophils were found to be significantly stiffened by formylmethionyl-leucyl-

[223]. Neutrophils‟ deformability can be severely impaired in sepsis patients, and thus

migration capability was consequently jeopardized [224, 225]. Research has also shown that

immature granulocytes and lymphocytes‟ inability to traverse micropipettes due to

compromised deformability, which can cause leukocclusive events, abnormal intravascular

leukocyte aggregation and clumping in vasculature [226]. A recent micropipette aspiration


95

study of lymphocytes from diabetic mice revealed that diabetic lymphocytes are stiffer than

control cells, which is associated with several clinical complications [227].

Leucocytes are nonadherent cells and can be tested with atomic force microscopy (AFM)

when mechanically immobilized in microfabricated wells [228, 229]. Using this technology,

the stiffness alteration of acute lymphoblastic leukemia (ALL) cell lines (HL-60 and Jurkat)

caused by chemotherapy was quantified [177]. The study concluded that after exposure to

chemotherapy, leukemia cells possess increased stiffness, and the type of chemotherapy can

affect stiffness kinetics. The increased cell stiffness in ALL patients with leucostasis

symptoms was also shown by using the AFM technique 12

. Most recently, data obtained

using optical tweezers provided preliminary evidence that leukemic hematopoietic cell

populations in normal and leukemia patients with distinct primitiveness exhibited differential

deformability [230].

To understand the pathophysiology involving biophysical properties of cancerous

leukocytes (leukemia), existing studies either performed measurement on cell lines instead of

patient samples or tested a very limited number of cells. This chapter presents the first study

of stiffness/deformability changes of lymphocytes in chronic lymphocytic leukemia (CLL)

patients based on the measurement of more than 1,000 cells per patient sample. The results

reveal that lymphocytes from CLL patients have a higher stiffness (i.e., lower deformability),

as compared to their counterparts in healthy samples, which is different from the known

knowledge on other types of metastatic cells (e.g., breast and lung cancer cells) whose

stiffness becomes lower as metastasis progresses.


96


Peripheral blood samples were collected from patients diagnosed with CLL following the

protocol of standard blood tests (anticoagulant with 1.5 mg/ml EDTA

(ethylenediaminetetraacetic acid)). Mononuclear cells were isolated using a standard density-

gradient protocol (Ficoll-Paque PLUS, GE Healthcare Bio-science, AB). Briefly, 4 ml of

diluted whole blood (2 ml blood : 2 ml PBS) was carefully layered on 3 ml Ficoll-Paque

PLUS, and then centrifuged at 400 g for 30 mins, which leaves the lymphocyte layer at the

interface. The lymphocyte layer was re-suspended in 6ml PBS and centrifuged at 80 g for 10

min to remove platelets. After removing the supernatant, the cell pellet (<5% monocytes and

erythrocytes, >95% lymphocytes) was re-suspended in PBS with 1% w/v BSA (with a final

density of 3 M/ml) and kept at room temperature for 30 min to reduce adhesion to channel

walls.

Microfluidic devices were fabricated via standard PDMS soft lithography. The two stages

of the microchannel [Figure 6.1(a)] have cross-sectional areas of 5 µm×5 µm and 8.5 µm×8.5

µm, respectively [205]. Two Ag/AgCl electrodes were used to measure resistance changes.

Due to the blockage of electrical current [203], a single cell generates a pair of resistance

peaks [Figure 6.1(b)(c)] when driven through the channel under a negative pressure (3 kPa).

The small peak and the large peak in the resistance profile correspond to the resistance

increase when a cell passes through the 8.5 µm×8.5 µm channel and the 5 µm×5 µm channel,

respectively. The height of the smaller peak (∆R) was used to measure cell volume according

to the Coulter counter principle; and the width of the larger peak was used to determine

transit time, ∆t.


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Figure 6.1(b) shows experimental data measured within 1 sec (11 cells). Figure 6.1(c) is

the zoomed-in view of the circled area in Figure 6.1(b). To extract the actual cell volume,

finite element modeling was conducted using COMSOL. In finite element modeling,

dielectric spheres with known sizes (diameter ranging from 5.0 µm to 8.5um) were simulated

and corresponding electrical resistance values were determined. Cells with a diameter larger

than 8.5 µm or smaller than 5 um were monocytes and red blood cells, and hence excluded in

data processing.

Figure 6.1 Microfluidic system for electrically measuring lymphocytes volume and transit

time. When a lymphocyte is driven through the measurement units under a negative pressure

(3 kPa) (also see SI Video 1), the total electrical resistance of the channel increases due to the

blockage of electrical current. (b) Experimental resistance data recorded within 1 second.

Zoom-in of the circled area is shown in (c). Cell volume and transit time are determined by

measuring ∆R and ∆t.

(a)

(b) (c)


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Figure 6.2(a) shows scatter plots of transit time vs. cell volume for lymphocytes from a

control/healthy sample (n=1,048) and a CLL sample (n=796). The distributions of cell

volume and transit time are presented in Figure 6.2(b). Transit time is mainly affected by cell

volume and cell deformability. We first investigated whether cell volume alone can explain

the transit time variation. Within the same sample, lymphocytes with larger volume indeed

took longer time to travel through the microfluidic channel. Our measured transit time

exhibited a power law dependence on cell volume in agreement with previous studies where

cell is modeled as a viscous liquid drop [231, 232]. It is worth noting that within the same

sample, a 2 µm change in cell diameter caused transit time to span more than an order of

magnitude, which indicates the dominant effect of cell size/volume on transit time. However,

with the same diameter, transit time of the lymphocytes varies by 3-4 folds, which indicates

the effect of cell deformability differences. As shown in Figure 6.2(a), cell diameter and

transit time of the control sample were measured to be 7.20±0.56 µm and 4.88±1.35 ms;

whereas the volume and transit time are 6.96±0.62 µm and 5.41 ±1.50ms for the CLL sample.

Cells in the CLL sample are smaller compared to cells in the control sample but take longer

time to pass through the 5 µm × 5 µm channel. This fact led us to hypothesize that

lymphocytes in CLL patients have decreased deformability.


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Figure 6.2 (a) Scatter plots of transit time vs. cell diameter for lymphocytes from a

control/healthy sample (n=1,048) and a CLL sample (n=796). Transit time exhibits a power

law dependence on cell volume/diameter. (b) Histograms of cell diameter and transit time of

the control and CLL samples. Both volume and transit time follow normal distributions.

(a) (b)


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Figure 6.3 Transit time (a) and cell volume (b) measured from 5 control/healthy samples (red)

and 5 CLL samples (cyan). For each sample, the three lines of the box represent 75 percentile,

median, and 25 percentile; the whiskers represent the locations of maximum and minimum

(n≈1,000 for each sample). The average median values of transit time (c) and cell diameter (d)

of the 5 control samples and the 5 CLL samples are 3.50 ± 0.36 ms vs. 5.13 ± 0.98 ms and

7.36 ± 0.07 µm vs. 7.15 ± 0.13 µm, respectively (*p < 0.05, error bars represent standard

deviation of the mean value). In general, cells in CLL samples are slightly smaller than cells

in control samples but reveal a longer transit time, indicating that they are less deformable.

(a)

(b)

(c)

(d)

*

*


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Cell volume and transit time from 5 control samples and 5 CLL samples were measured

using the microfluidic system. As shown in Figure 6.3(a)(b), for each sample, the three lines

of the box represent 75 percentile, median, and 25 percentile. The whiskers represent the

locations of maximum and minimum. Figure 6.3(c)(d) summarize average group median

values of transit time and cell volume with error bars representing standard deviation of the

mean. Compared to control samples, CLL samples have a smaller cell volume (7.15 ± 0.13

µm vs. 7.36 ± 0.07 µm) but a higher median transit time (5.13 ± 0.98 ms vs. 3.50 ± 0.36 ms).

The volume measurement results fall in the same range as reported in previous studies[233].

Statistical difference exists between two populations (*p < 0.05).

Furthermore, as shown in Figure 6.3(a), 75 percentile, median, and 25 percentile cell

transit time values were all higher for CLL samples, demonstrating that the measured transit

time difference reflects entire cell populations tested in this work. Cell transit time variance

is more prominent in CLL samples, which is likely attributed to the increased heterogeneity

caused by accumulated mutations during malignancies [234]. Note that since none of the

CLL patients showed leukostasis symptoms, all the lymphocytes were able to pass through

the microchannel [30].

Surface properties can influence friction between cell membrane and microfluidic

channel walls, and hence possibly contribute to transit time differences. It has been reported

that changing the coating of channel surfaces from PEG to PLL can cause cell entry velocity

and transit velocity to decrease [231]. In our work, the isolated lymphocytes were incubated

in PBS with 1% BSA for 30 min to reduce friction [205]. Before loading cells, the

microfluidic channel was also perfused with PBS+1% BSA for 30 min to further reduce

friction force of control and CLL lymphocytes. To confirm the measured transit time


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difference (control vs. CLL) truly reflects cell deformability/stiffness, lymphocytes from a

control sample and a CLL sample were tested using AFM indentation.

Lymphocytes‟ stiffness was measured when the cells were immersed in PBS at room

temperature using an AFM (Bioscope Catalyst, Santa Barbara, CA) mounted on an inverted

microscope. The cells were mechanically immobilized within microwells [228, 229]. AFM

indentations were conducted using silicon nitride cantilevers with a nominal spring constant

of 0.03N/m. The elastic moduli of the lymphocytes were quantitatively determined by fitting

force-indentation curves using the Hertz model for a pyramidal tip. Figure .4(a) shows

experimental indentation curves of two representative lymphocytes from the control sample

and the CLL sample. The elastic moduli of control and CLL samples are 2.92 ± 0.38 kPa and

5.37 ± 0.38 kPa, respectively, as summarized in Figure 6.4(b). This difference in elastic

modulus confirms that lymphocytes in CLL patients have a higher stiffness than lymphocytes

in control samples, and the higher stiffness contributes to the longer transit time of CLL

lymphocytes.

Difference in actin filament density was often thought responsible for deformability

differences between two cell types [231, 235]. However, a decrease in actin content of

lymphocytes from CLL patients was previously found [236]. In addition, no connection was

found between actin level and cell deformability in human leukemia cell lines [228].

Recently, vimentin (intermediate filaments) was shown to have dominant influence on

mouse lymphocyte deformation [237]. In our study, we noticed that the nuclei of CLL

lymphocytes occupy almost the entire cell (Figure 6.5). As a result, when they flow through

the microfluidic channel, the nuclei of CLL lymphocytes could have contributed more

significantly to transit time since cell nuclei are known to be significantly stiffer compared to


103

cytoplasm and cell membrane [226, 238, 239]. Thus, the enlarged nuclei may be a factor that

contributes to the longer transit time observed for CLL lymphocytes. However, the exact

pathological causes and the underlying mechanism of the observed deformability difference

in lymphocytes from healthy and CLL patients necessitate further investigation.

Figure 6.4 (a) Experimental AFM indentation curves of two representative lymphocytes, one

from control sample and one from CLL sample. The elastic moduli of lymphocytes were

quantitatively determined by fitting force-indentation curves to standard Hertz model for a

pyramidal tip. (b) Elastic modulus values of the control sample (n = 10) and the CLL sample

(n = 14) are 2.92 ± 0.38 kPa and 5.37 ± 0.38 kPa, respectively (*p < 0.05, error bars represent

standard deviation of the mean value).

Figure 6.5 Lymphocytes from control (a) and CLL (b) samples stained with Wright/Giemsa

Stain.

(a) (b)

*

(a) (b)

control CLL


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6.4 Conclusion

This chapter reports stiffness/deformability differences of lymphocytes from healthy donors

and chronic lymphocytic leukemia (CLL) patients. Microfluidic measurement was used to

quantify cell volume and transit time of thousands of lymphocytes from control and CLL

samples. The results reveal that CLL lymphocytes have higher stiffness (i.e., lower

deformability), as compared to lymphocytes in healthy samples. Their higher stiffness was

also confirmed via AFM indentation. This study demonstrates that at the single cell level,

leukemic metastasis progresses are accompanied by biophysical property alterations.

CHAPTER 7, CONCLUSIONS

105

Chapter 7

7. Conclusions

7.1 Contributions

1. Developed microfluidic systems for high-throughput biophysical (mechanical and

electrical) characterization of single cells (i.e., red blood cells, leukocytes). Compared

to existing techniques, the proposed systems improves the testing throughput by about

two orders of magnitude and are capable of simultaneous characterization of

mechanical and electrical parameters of single cells. It also proves that quantification

of multiple biophysical parameters significantly improves the cell type classification

success rate.

2. Designed a microfluidic system for electrically measuring the deformability of red

blood cells (RBCs). The experimental results proved the capability of the system for

distinguishing different RBC populations based on their deformability with a

throughput of ~10 cells per second.

3. Demonstrated a technique for single-cell electrical property (specific membrane

capacitance and cytoplasm conductivity) characterization at a speed of 5–10 cells/s.


106

The results also demonstrate that the quantification of specific membrane capacitance

and cytoplasm conductivity can enhance cell.

4. Quantified the deformability changes of red blood cells during blood storage in

human-capillary-like environment. The results demonstrate that the deformation

index of RBCs under folding does not change significantly over blood storage.

However, significant differences exist in time constants and circularity distribution

widths.

5. Studied stiffness/deformability differences of lymphocytes from healthy donors and

chronic lymphocytic leukemia (CLL) patients. Microfluidic measurement was used to

quantify cell volume and transit time of thousands of lymphocytes from control and

CLL samples. The results reveal that CLL lymphocytes have higher stiffness (i.e.,

lower deformability), as compared to lymphocytes in healthy samples.

7.2 Future directions

1. To further improve the throughput and sensitivity of the microfluidic systems for red

blood cells (RBCs) deformability measurements. To model the dynamic process of

the red blood cells passing through microchannels and extract material properties

(such as shear modulus and the viscosity) of the RBCs from experimental data.

2. To conduct biophysical measurement of diseased RBC samples (such as malaria and

sickle cells). To reveal the biophysical property differences between diseased samples

and healthy samples. And to explore the feasibility of utilizing biophysical properties

for diagnostic application.

3. To design portable, easy-to-use and low-cost microfluidic system for single-cell

electrical properties measurement. To implement the system for various clinical


107

applications (such as complete blood counting, leukemia cells classifications). To

improve the system by adding cell volume measurement unit.

4. To systematically conduct deformability study of the stored red blood cells samples.

To reveal the intrinsic association between the biochemical properties (such as ATP,

NO and 2,3 DPG) and deformability alteration. To study the effect of donor

variations (such as ages and gender) on the stored RBCs‟ quality.

5. To reveal the underlying mechanisms of the altered biophysical properties of the

lymphocytes in chronic lymphocytic leukemia patients. To further study the

association between the clinical complications and the lower deformability of the

leukemic lymphocytes.

APPENDIX

108

8. Appendix

Figures and text in chapter 1, 2, 3 4, 5 are from the following publications, permission not

required for use in a thesis/dissertation:

Y. Zheng, J. Nguyen, Y. Wei, and Y. Sun, "Recent advances in microfluidic

techniques for single-cell biophysical characterization," Lab on a Chip, Vol. 13, pp.

2464-2483, 2013.

Y. Zheng, E. Shojaei-Baghini, A. Azad, C. Wang, and Y. Sun, "High-throughput

biophysical measurement of human red blood cells," Lab on a Chip, Vol. 12, pp.

2560-67, 2012.

Y. Zheng, J. Nguyen, C. Wang, and Y. Sun, "Electrical measurement of red blood

cell deformability on a microfluidic device," Lab on a Chip, Vol. 13, pp. 3275-83,

2013.

Y. Zheng, E. Shojaei-Baghini, C. Wang, and Y. Sun, "Microfluidic characterization

of specific membrane capacitance and cytoplasm conductivity of single cells,"

Biosensors and Bioelectronics, Vol. 42, pp. 496-502, 2013.

APPENDIX

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Y. Zheng, J. Chen, T. Cui, N. Shehata, C. Wang, and Y. Sun, "Characterization of red

blood cell deformability change during blood storage," Lab on a Chip, Vol. 14, pp.

577-83, 2014.

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