design of an electrochemical cell cytosensor · design of an electrochemical cell cytosensor mario...

Design of an Electrochemical Cell Cytosensor

by

Mario Moscovici

A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science

The Institute of Biomaterials and Biomedical Engineering University of Toronto

© Copyright by Mario Moscovici 2012

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Design of an Electrochemical Cell Cytosensor

Mario Moscovici

Masters of Applied Science

The Institute of Biomaterials and Biomedical Engineering

University of Toronto

2012

Abstract

A sensitive and simple cell counting method is necessary in many pathologies including HIV [1]

and cancer [2]. Cell counting sensors are used in the clinic for diagnosis of leukemia [3] or HIV

[4]. Furthermore, genetic analysis of these cells is crucial for better prognosis and diagnosis [5].

However, a simple method for cell counting that allows further analysis is still lacking. This

study aims to design a sensor that counts cells in the complex matrix of cell media or in the

presence of non-target cells. The chip designed uses the anti-EpCAM antibody to selectively

count cells via differential pulse voltammetry. The device can selectively count prostate cancer

cells in both complex media with serum and a mixed cell population with a sensitivity of 125

cells per sensor. A simple and sensitive cell cytosensor was designed that can be used to count

cancer cells effectively.

iii

Acknowledgments

First I would like to thank Dr. Shana O. Kelley, my thesis supervisor for her continuous support

and guidance throughout this project. Dr. Kelley has opened the world of research to me and

provided countless ideas throughout the project. It was an unparalleled learning experience and

one that I will not forget.

I would like to thank all of the Kelley Lab members for their support and encouragement

throughout my Master’s degree. Every single one of the Kelley Lab members has helped my

project in a way: through training, moral support and experiment planning. I would like to

specifically mention Brian Lam and Justin Besant for their help with microfabrication and device

design; as well as Alya Bhimji and Dr. Ludovic Live for their continuous help with assay

development and chemical work. I would also like to thank Dr. Jagotamoy Das for introducing

me to electrochemical sensors and getting me started with the project. I would also like to thank

Andrew Sage for proof reading and organization help for this thesis.

I would also like to thank my committee members: Dr. Christopher Yip, Dr. Craig Simmons and

Dr. Ted Sargent for their feedback and guidance with the project.

Finally, I would like to thank my parents Doru and Violet Moscovici for their encouragement

and moral support – for always being there throughout my undergraduate and graduate studies. I

would also like to thank my friends Aaron Rosen, Salva Sadeghi and Simon Sharon Gordon for

constant encouragement and providing motivation during the project. Lastly, I would like to

thank Nika Shakiba for providing constant positivity and showing me support throughout my

Master’s experience.

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Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Figures ............................................................................................................................... vii

List of Appendices .......................................................................................................................... x

List of Equations ............................................................................................................................ xi

List of Terms ................................................................................................................................. xii

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

1.1 Cell counting ....................................................................................................................... 1

1.2 Electrochemical Counting of Biological Particles .............................................................. 4

1.3 Electrochemical Methods and Diagnostic applications ...................................................... 4

1.4 Immobilization of antibodies on a gold surfaces ................................................................ 8

1.5 Antibody conjugation methods to gold surfaces ............................................................... 10

1.5.1 Physisorption ......................................................................................................... 10

1.5.2 Glutaraldehyde coupling ....................................................................................... 10

1.5.3 NHS/EDC chemistry ............................................................................................. 11

1.5.4 Streptavidin-biotin link ......................................................................................... 12

1.5.5 Antibody Reduction .............................................................................................. 14

1.6 The blocking assay methodology ...................................................................................... 14

1.7 Project Summary ............................................................................................................... 16

Thesis Objectives ..................................................................................................................... 17 2

Experimental Methods ............................................................................................................. 18 3

3.1 Cell Culture and Sample Preparation ................................................................................ 18

3.2 EpCAM antibody conjugation to the gold surface ........................................................... 18

3.3 EpCAM Detection Assay .................................................................................................. 19

v

3.4 Cell Counting using macro-electrodes .............................................................................. 19

3.5 Sensor Design and Microfabrication ................................................................................ 20

3.6 Sensor Monolayer formation ............................................................................................ 22

3.7 Counting Cells with Fabricated sensor ............................................................................. 22

Results ...................................................................................................................................... 24 4

4.1 EpCAM detection Assay ................................................................................................... 24

4.2 Cell Counting Using Macro-Electrodes ............................................................................ 24


4.4 Sensor Monolayer Formation ........................................................................................... 29

4.5 Counting Cells with Fabricated Sensor ............................................................................. 30

4.6 Important parameters for electrochemical cell sensors ..................................................... 32

4.6.1 Aperture Size ........................................................................................................ 32

4.6.2 Concentration of Ferrocyanide and Ferricyanide ................................................. 34

4.6.3 Sample Incubation Time ....................................................................................... 35

4.7 Assay Duration and Sensor Storage .................................................................................. 36

4.8 Counting Cells in Complex Samples ................................................................................ 37

4.8.1 Counting Cells in Media ....................................................................................... 37

4.8.2 Counting mixtures of target and non-target cells .................................................. 38

Discussion ................................................................................................................................ 40 5

5.1 EpCAM detection Assay ................................................................................................... 40

5.2 Cell Counting Using Macro-Electrodes ............................................................................ 40


5.4 Sensor Monolayer Formation ........................................................................................... 42

5.5 Counting Cells with Fabricated Sensor ............................................................................. 43

5.6 Important design parameters for electrochemical cell sensors ......................................... 43

5.6.1 Aperture Size ........................................................................................................ 43

vi

5.6.2 Concentration of Ferrocyanide and Ferricyanide ................................................. 45

5.6.3 Sample Incubation Time ....................................................................................... 45

5.7 Assay Duration and Sensor Storage .................................................................................. 45

5.8 Counting Cells in Complex Samples ................................................................................ 46

Conclusions and Future Directions .......................................................................................... 47 6

Bibliography ................................................................................................................................. 48

Appendix A: Kelley Laboratory electrochemical chip ................................................................. 55

Appendix B: List of materials and chemicals used……………………………….……………...56

vii

List of Figures

Figure 1: (a) Hemocytometer from Reichert (b) the hemocytometer grid under 10X

magnification (photo credit: Jeffrey M. Vinocur) ........................................................................... 1

Figure 2: Buffy coat counter for complete blood count [12]. ........................................................ 2

Figure 3: Schematic of an operational flow cytometer [12]. ......................................................... 3

Figure 4: Schematic of a Pt/HBr|AgBr/Ag electrode [27] ............................................................. 6

Figure 5: Current vs. voltage for Pt/H|AgBr/Ag electrode [27]. ................................................... 6

Figure 6: Voltage applied during a typical DPV scan [30]. ........................................................... 7

Figure 7: Peak current response due to applied voltage (shown in Figure 6) for DPV [27]. ....... 8

Figure 8: The structure of a typical antibody where C refers to constant regions and V refers to

variable regions. .............................................................................................................................. 9

Figure 9: Schematic of the glutaraldehyde coupling reaction. .................................................... 11

Figure 10: Complete reaction for EDC/NHS chemistry for biomolecule coupling [39] ............. 11

Figure 11: chemical formulas for (a) mercaptopropioic acid (MPA) and (b) mercaptohexanoic

acid. ............................................................................................................................................... 12

Figure 12: Schematic of the chemistry of biotin-streptavidin interaction. .................................. 13

Figure 13: Blocking assay schematic, showing the physical blocking of ferrocyanide and

ferricyanide from the surface when the antigen binds to the antibody. The physical blocking of

the surface causes an overall current decrease, which is measurable using a potentiostat. .......... 15

Figure 14: Schematic of cell cytosensor device where the attachment of a target cell blocks the

gold surface, reducing the overall current that is produced. ......................................................... 16

Figure 15: Schematic of the fabrication protocol for the cell counting chip; step are shown from

chip side view – i.e. along the chip’s thickness. ........................................................................... 21

viii

Figure 16: Top – Experimental setup for the cell count sensor; bottom – close-up of

experimental setup, including (a) PCB board seen in Figure 20.a; (b) PCB board seen in Figure

20; (c) Cell count chip; (d) BaSI potentiostat; (e) Reference electrode; (f) Platinum wire; and (g)

Scanning solution. ......................................................................................................................... 23

Figure 17: EpCAM antigen detection results. Error bars represent ± standard deviation, n=5. .. 24

Figure 18: DU-145 cell counting using the blocking assay methodology. (a) % current change

vs. cell concentration. (b) Visual cell counts using microscope. Error bars are ± standard

deviation, n=9. .............................................................................................................................. 25

Figure 19: (a) Isometric view of the cell counting chip. (b) Top view of the cell counting chip.

(c) AutoCAD schematic of chip. .................................................................................................. 27

Figure 20: (a) PCB board for easy connection to each individual lead (b) PCB connector for

chip. ............................................................................................................................................... 27

Figure 21: A microscopic image (10X magnification) of the gold electrodes defined by the

yellow colour and the SU-8 apertures defined by the circle on the gold electrodes. Scale bar is

300μm ........................................................................................................................................... 28

Figure 22: Peak current from DPV method after every reaction step in the antibody conjugation

method. Error bars represent ± standard deviation, n=6. Numbers in brackets signify the step

number. ......................................................................................................................................... 29

Figure 23: Image at 10X magnification of cells bound to the gold surface; 150μm aperture

device. Scale bar is 300μm. .......................................................................................................... 30

Figure 24: Cell counting experiment. (a) Current change per electrode vs. cell number present

on electrode. (b) Total cell count added to the chip vs. cell concentration added per sensor. (c)

Total signal for the chip vs. cell concentration added per sensor. Error bars represent ± standard

error of the mean, n=4. Aperture size is 150μm. .......................................................................... 31

Figure 25: Microscopic images (10X magnification) with target cells bound with (a) 300μm, (b)

150μm and (c) 50μm aperture size. Scale bar is 300μm. .............................................................. 32

ix

Figure 27: Graphical representation of the calculated: (a) limit of detection for the sensor vs.

aperture size of the sensor and (b) range – 0 to maximum number of cells that can be counted vs.

aperture size of the sensor. ............................................................................................................ 34

Figure 28: Solution concentration effect on signal per gold electrode. (a) 200μM, (b) 2mM, (c)

20mM and (d) 200mM ferrocyanide and ferricyanide. Error bars represent ± standard error of the

mean, n=6. Aperture size is 50μm. ............................................................................................... 35

Figure 29: Total signal vs. the incubation time with cells. Blue markers indicate total signal for

target cells (DU145) and red indicates non-target cells (U937), both at 625 total cells in sample

per sensor. Error bars represent ± standard deviation, n=3. .......................................................... 36

Figure 30: Chip storage experiment for a 2 week period. Error bars represent ± standard error of

the mean, n=4. No statistical significance found between chips at different weeks. * shows

statistical significance relative to the negative control. ................................................................ 37

Figure 31: Cell counting experiment in the presence of cell media with 10% FBS, the total

signal vs. cells in sample per sensor is presented. Error bars are ± standard error of the mean,

n=3. ............................................................................................................................................... 38

Figure 32: Cell count experiment with 625 U937 non-target cells as background mixed with

target cells at varying concentrations. (a) The total signal vs. target cell concentration (b) Cell

count using immunohistochemistry procedure described in section 3.7. Error bars in figure show

± standard error of the mean, n=3. ................................................................................................ 39

Figure 33: Prefabricated chip used in Kelley Laboratory [59]. ................................................... 55

x

List of Appendices

Appendix A: Kelley Laboratory electrochemical chip………………………………………….55

Appendix B: List of materials and chemicals used……………………………………………...56

xi

List of Equations

Equation 1: Reduction reaction for AgBr/Ag cell ......................................................................... 5

Equation 2: Oxidation reaction for AgBr/Ag Cell ........................................................................ 5

Equation 3: Percent ΔI calculation where I is the peak DPV current at an electrode and Io is the

average blank DPV current – i.e. background current with no target ........................................... 24

Equation 4: Current change, where I is the peak current at an electrode with cells and Io is the

average blank scan current – i.e. no target .................................................................................... 26

xii

List of Terms

CMOS Complementary metal-oxide-semiconductor is a technology that uses

transistors to build integrated circuits for electronics and biosensors

CTC A Circulating Tumour Cell is a cell that is released from the primary

tumour into the systemic circulation. These cells are thought to

contribute to the metastasis process

CV Cyclic voltammetry, an electrochemical technique that linearly sweeps a

voltage range and is used to measure the output current from a chemical

reaction

DPV Differential pulse voltammetry, an electrochemical technique that applies

a square wave as the input voltage, Figure 6, and measures the output

current as a result of a chemical reaction

EpCAM Epithelial cell adhesion molecule, a surface marker present on most

carcinomas and is thought to be used as a cell-cell adhesion molecule

%ΔI % signal change from a chemical reaction calculated by Equation 3

ΔI Signal change from an electrochemical reaction calculated by Equation 4

LOD The limit of detection is the smallest signal detectable usually defined by

statistical difference between the negative control and the smallest signal

with a confidence interval of three standard deviations

IPA Isopropyl alcohol is a solvent commonly used for cleaning or

disinfection of surfaces from biological agents.

1

Introduction 1

1.1 Cell counting

Traditionally, cell counting has been used both in the research setting and the clinical setting for:

cell culturing, blood pathologies, and immunohistochemistry. For example, one of the most

common clinical tests is the complete blood count (or CBC). Counting the number of blood cells

(red blood cells, T cells, lymphocytes, platelets, etc.) is used to monitor pathologies such as HIV,

where T-cell count is important [4], Leukemia, where red blood cell count can be important [3],

and various other anemias where loss of blood cells is common [6].

The most established and simplest method for cell counting is manual counting using a

hemocytometer (Figure 1). Hemocytometers have a calibrated grid that houses a set volume of

sample. Knowing the volume and counting the cells within the grid manually under the

microscope allows for the determination of the cell concentration. This technique is quite simple

and many patents have been applied to improve on this concept [7], [8].

The concept of the hemocytometer was upgraded to include automated cell counters using image

analysis [9] and even more sophisticated methods using quartz crystals as the sensing platform

[10]. However, these particular methods cannot count specific types of cells, requiring additional

processing using antibody labeling. Furthermore, this method uses small sample sizes (generally

under 10uL), which can make the counting inaccurate for low cell numbers. There has been

some work done to use larger volumes for counting; however, this is not extensively used [11].

Figure 1: (a) Hemocytometer from Reichert (b) the hemocytometer grid under 10X

magnification (photo credit: Jeffrey M. Vinocur)

(a) (b)

2

Currently, there are several common techniques for cell counting that are being used in the

clinic. For high concentrations of cells where very accurate counting is not required a simple

method using a buffy coat density rod is used (Figure 2). A capillary tube that floats in a vial of

blood is used to separate out the white blood cells into layers and the height of the layer is used

to determine the overall cell count within one order of magnitude accuracy [12].

Figure 2: Buffy coat counter for complete blood count [12].

Other common counting techniques involve using a flow cytometer with either a fluorescence or

impedance module as the sensing platform. A flow cytometer is a large apparatus that uses

complex flow mechanisms to direct a cell sample into single file stream of cells for analysis. The

flow cytometer uses forward and side scatter profiles resulting from interactions between passing

cells and lasers to detect the cells. Reflectance, or forward scatter, from the laser can detect the

cell size while refractance, or side scatter, from the laser gives insights into cell morphology. In

addition a flow cytometer also includes lasers at different wavelengths for fluorescence studies

which require antibody or molecular labeling of the cells [12]. An impedance platform can also

detect information about the size and internal morphology of the cells. In the hospital these

machines are used in parallel to perform a complete blood count [12]. The flow cytometer can be

augmented by a platform for cell sorting which allows separation of the different cell types for

further analysis; however, this adds to the complexity of operation and cost of the system.

3

Figure 3: Schematic of an operational flow cytometer [12].

Even though flow cytometery techniques are quite accurate there are a few drawbacks. First, this

equipment requires trained staff and an extensive budget for purchasing and maintenance. In

addition, if a sample is formalin-fixed and permeabilized for fluorescent labeling the genetic

material is lost and cannot be analyzed further. Lastly, flow cytometery cannot accurately count

cells that are infrequently represented in the sample.

Considering the drawbacks of the previous systems there has been much effort in the research

community to design a simple-to-use, cost-efficient, and sensitive cell counter.

Microfluidics has been employed to address this problem through many systems that mimic the

inner workings of the flow cytometer. For example, using a simple and cheap laser system a

microfluidic chip was design to count as few as 100 cells/μL [13],[14]. In an effort to integrate

an on-chip analysis system, CMOS sensors have also been integrated into cell counting chips.

CMOS sensors can detect changes in impedance of the transistor due to attachment of cells onto

the surface [15]. Another class of technologies using electrochemical methods for the purpose of

counting cells will be discussed in more detail in section 1.2. However, all these methods require

relatively pure samples (devoid of proteins or non-target cells), and often require complicated

external measurement devices such as a laser system. Moreover, no system combines cell

counting, analysis and cell culturing on the same platform.

4

1.2 Electrochemical Counting of Biological Particles

Electrochemical methods are an attractive option for biosensors. First, electrochemical methods

are generally label-free; in other words, the sensor does not require secondary antibodies or

tagged molecules, which increases the complexity and cost of the assay. Second, electrochemical

apparatuses are lower in cost compared to a microscope system with fluorescence and a camera.

Lastly, fluorescence methods require an established protocol to be developed, which requires

trained personnel. Therefore many researchers are interested in incorporating electrochemical

methods into various sensors with the obvious success story of the glucose sensor in mind [16].

Most methods that are presented in literature use the impedance spectroscopy method which

measures changes in resistance/impedance as a function of frequency [17]. In order to effectively

use this method in measurements, an empirical circuit model is applied to fit the data measured

using the apparatus. From the measurements a Nyquist plot can be made which shows the real

vs. imaginary part of the impedance; changes in the plot are used in biosensors [18].

Several studies show the detection of bacterial species as a potential detector for contaminated

sources of food or water. For example, E. coli was detected using a secondary antibody for as

low as 600 bacteria per milliliter [19]. Other projects show similar results with E. coli (CGSC

5073 K12) [20], Salmonella (Χ3339) [21] or Staphylococcus aureus (MSSA 476) [22].

Other information can be gained from electrical properties of the cell by applying different

models. Successful monitoring of biological cell cycle growth and identification of the different

cycle phases using electrochemical methods was achieved [23]. Some research has even shown

the monitoring of different differentiation states in messenchymal stem cells [24].

1.3 Electrochemical Methods and Diagnostic applications

Electrochemical methods allow sensing that is mediated by an electrochemical reaction (i.e. the

chemical reaction is coupled by electron transfer to a measurement electrode). There are a few

important differences between electrochemical reporters and other sensors. First, electrochemical

sensors require an active metallic surface that serves as a boundary on which the reaction takes

place. Second, an electrochemical sensor requires a reporter mechanism that converts the

5

molecule in question to an electrical signal. Thirdly, the reaction requires a potentiostat or an

impedance analyzer that can measure the changes in electrical signal (voltage, current or

resistance) [25].

Every electrochemical reaction and sensor can be described in terms of a cell where the reactions

at each of the electrodes are described under a set of standard conditions. A typical

electrochemical cell can be seen in Figure 4. A cell is described as either electrocatalytic if a

voltage is applied to drive a reaction or galvanic if the cell generates a voltage due to the reaction

occurring. Next, the reference electrode is selected; this electrode will serve as a ground or

reference from which the voltage will be measured [26]. In the example shown below the

Ag/AgBr electrode is the reference. The composition of silver and silver bromide is in

equilibrium and changes negligibly due to the reaction occurring. The working electrode is

defined as the electrode where the reaction under study is occurring; in this example it is the

Pt/HBr. Note that in this case the electrode is in liquid form. Finally the counter electrode is the

electrode that is used to measure the current in the cells, which in this case is the Pt electrode.

This particular chemical reaction is as follows:

⇔

Equation 1: Reduction reaction for AgBr/Ag cell

⇔

Equation 2: Oxidation reaction for AgBr/Ag Cell

Both of the reactions above occur in pairs as a redox couple – i.e. reduction and oxidation

reactions. The voltage that is produced or that needs to be applied to drive the reaction is

determined by the chemicals being used and the reference electrode which the voltages are

measured against. A characteristic curve of voltage vs. current can be generated for each set of

reactions (see Figure 5).

6

Figure 4: Schematic of a Pt/HBr|AgBr/Ag electrode [27]

Figure 5: Current vs. voltage for Pt/H|AgBr/Ag electrode [27].

7

To generate a typical curve for an electrochemical cell a method of measurement must be

employed in order to detect a change in signal for a sensor. The graph above illustrates a typical

curve for the CV scan. In this particular method a voltage is applied and linearly varied – for the

reaction in Figure 4 between 1.5 to -0.5 volts. Each of the reactions occurs at a particular voltage

range, which generates a current according to the graph above. In electrochemical sensors, a

reporter molecule catalyzes the conversion of a biological event into electrons and a

characteristic CV is seen [28]. A very popular sensor that is widely available in North America is

the glucose sensor. The glucose sensor uses the enzyme glucose oxidase which converts glucose

into hydrogen peroxide. A different enzyme, FAD, releases electrons and forms O2 and H2 from

hydrogen peroxide. The electrons transferred can be measured and are directly proportional to

the concentration of glucose [16]. In order to manage diabetes more easily glucose sensor

research is heading towards implantable technology. The sensor will be inserted under the skin

and constantly take measurements while a pump will release insulin gradually when necessary –

i.e. an artificial pancreas [29].

A second scheme commonly used in electrochemical sensors is DPV. In this method an

incrementally increasing square wave (see Figure 6) is applied resulting in a peak that represents

the change in current due to the electrochemical reaction occurring (see Figure 7).

Figure 6: Voltage applied during a typical DPV scan [30].

8

Figure 7: Peak current response due to applied voltage (shown in Figure 6) for DPV [27].

The DPV method is used due to the increased sensitivity that can be achieved. To produce the

peak seen in Figure 7, the apparatus measures the current from the reaction before the

application of a pulse, see Figure 6, and at the peak of the pulse. By subtracting the peak current

from the current measured before the pulse, the apparatus outputs only the ΔI, or current change.

This can eliminate capacitive currents and non-faradic currents in the scan, thus increasing the

sensitivity [27].

1.4 Immobilization of antibodies on a gold surfaces

When linking a biological molecule to a surface it is important to understand what functional

groups are effective at reacting with the surface and which functional groups react with the

antibody molecule. The second fact to consider is whether the functional groups can be used to

directly conjugate the molecule or whether a second species must be used as a linker between the

surface and the antibody.

Gold has several advantages over other surfaces. First, gold is very unreactive which means it is

not subject to quick oxidation. When a metal oxidizes the chemical bond that was conjugated to

the surface will be disrupted as well and the molecule will not be bound. Gold-thiol bonds are

stable enough that the surface will be stable for a few months [31]. Second, gold-thiol chemistry

has been very well studied which means many established protocols exist for coupling biological

molecules to gold as well as patterned gold with microfabrication [32]. On the other hand, gold

9

surfaces are relatively expensive in comparison with other metals such as chrome or silver. There

are several main methods for conjugating antibodies to a gold surface all using either the primary

amines or primary carboxyl groups in amino acids of antibodies.

The antibody structure can be separated into three main parts: light chain (variable chain), heavy

chain and the hinge. The light chain refers to the binding region, which changes sequences based

on the antigen and antibody being used. The heavy chain is non-variable since it does not depend

on the antigen in question. Generally, antibody type is classified based on the heavy chain which

has 5 different categories: IgM, IgD, IgG, IgA and IgE – every different heavy chain (or

“constant region”) has a different sequence. Finally, the hinge is the area that connects the left to

the right half of the antibody which has several cysteine amino acids to form a di-sulfide bond R-

S-S-R where R is any functional group [33].

Figure 8: The structure of a typical antibody where C refers to constant regions and V refers to

variable regions.

10

1.5 Antibody conjugation methods to gold surfaces

1.5.1 Physisorption

The simplest method that can be used for coupling an antibody to a gold surface is physical

adsorption. This method involves incubating the particular sensor with the antibody either at 4oC

or room temperature. This will cause the antibody to adsorb to the surface and bond via weak

Van der Waals forces [34]. The advantage of this method is that it is quite quick, simple and does

not require additional reagents or surface preparation. However, this method has a low success

rate since the activity of the antibody is not guaranteed once it is bound to the surface. The

tertiary structure of the antibody must remain stable throughout this process in order for it to

remain active, thus making this method unpopular. However, some promising results have been

published with detection of pig serum [35] and E. Coli [36].

1.5.2 Glutaraldehyde coupling

Gluteraldehyde coupling is a reaction (see Figure 9) that requires two steps before the addition

of the antibody using two molecules: first cysteamine and then gluteraldehyde.

After the aforementioned procedure, the surface is active and can be incubated with the antibody,

resulting in a covalent bond. This method has the advantage of using simple reagents that are not

quickly reactive in air or solution, which makes the reaction simpler and the storage easier.

Second, cystamine has an amine group that forms a monolayer on the surface. The amine group

is also positively charged at 7.4 physiological pH, which helps boost the overall signal for

electrochemical sensing (see section 1.6). Third, cystamine reacts with the surface very quickly,

producing an 80% surface coverage in the first 5 minutes of the reaction [37]. On the other hand,

glutaraldehyde has a propensity to self-polymerize which can affect the reaction efficiency [38].

11

Figure 9: Schematic of the glutaraldehyde coupling reaction.

1.5.3 NHS/EDC chemistry

NHS/EDC chemistry along with glutaraldehyde coupling are perhaps the most widely used and

studied methods for biomolecule coupling. The complete NHS/EDC chemistry reaction is

outlined in Figure 10. In this reaction, as compared to the glutaraldehyde coupling, the

Figure 10: Complete reaction for EDC/NHS chemistry for biomolecule coupling [39]

backbone of the reaction is carboxylic acid rather than an amine (cystamine from previous

section). There are several molecules that can be used to create a monolayer of carboxylic acid

12

functional groups so that the EDC/NHS chemistry can be performed. Examples include MPA

(mercaptopropioic acid) and MHA (mercaptohexanoic acid), which can be seen in Figure 11.

MPA or MHA can be added followed by EDC to activate the surface in conjunction with sulfo-

NHS, which activates the surface to a suflo-NHS ester form that can react with an amine-

containing biomolecule as seen in Figure 10.

Figure 11: chemical formulas for (a) mercaptopropioic acid (MPA) and (b) mercaptohexanoic

acid.

NHS/EDC chemistry creates a very reactive surface, which can react with the antibody quickly

and efficiently. Moreover, this method is one of the most studied methods for antibody

conjugation and has seen much success in literature in the biosensor field; for example, in the

DNA detection field [40–42]. On the other hand, activated ester surfaces are quite reactive and

can result in lower efficiency of reaction with antibodies if not properly stored. Secondly, the

reaction requires additional steps as compared to glutaraldehyde coupling. Thirdly, the desired

boost in signal for electrochemical measurements is not possible since carboxylic acid possesses

a negative charge at physiological pH. It is important to note that many coupling methods are

based on this chemistry; for example, DTSSP is a molecule that has a form of the activated NHS-

ester, requiring one single step reaction on the surface. The disadvantage of DTSSP is that its

storage has to be at 4OC under nitrogen and very dry conditions to prevent molecule degradation.

1.5.4 Streptavidin-biotin link

A third class of methods that is quite popular involves the use of protein linkers rather than short

molecules. The most used protein link system involves streptavidin-biotin interaction (see

Figure 12). Streptavidin is a bacterial protein that has 4 binding sites for biotin in an especially

strong interaction (among the strongest non-covalent interactions) [43]. Biotin binds to

streptavidin using hydrogen bonds and thus biotin-conjugated antibodies can be purchased and

13

added to the surface during the last step. The molecule presented in Figure 12(a) is a modified

thiol-PEG-biotin molecule that shortens the surface preparation to one step. Without this

molecule NHS-EDC chemistry can be used to create the thiol-PEG to biotin bond, which

requires additional steps. Streptavidin (Figure 12(b)) is added to the surface followed by the

biotin-conjugated antibody (Figure 12(c)). The link system presented is quite robust and easy to

use. Moreover, the biotin-streptavidin system can be used with biomolecules that are not rich in

primary amines, which is a limitation in the previous methods. On the other hand, the system

requires many reagents that are quite expensive compared to the other methods. Also,

streptavidin is a large protein which occludes the surface and reduces electrochemical signals.

This method has been used in several sensors, for example for detecting proteins [44], [45].

Figure 12: Schematic of the chemistry of biotin-streptavidin interaction.

Other protein systems are available that have a unique advantage over the previously presented

methods. Antibodies can bind to the surface in any orientation depending on where the amines

are located. It has been shown that Protein G can be used to orient the antibodies. In other words

14

Protein G is used to bind the heavy chain of the antibody, ensuring that the antigen binding area

is not blocked. Protein G methods for antibody orientation on the surface have been shown to

function more efficiently than non-oriented methods [46], [47]. On the other hand, this method

requires an additional step of conjugating the antibody to the surface, which complicates the

antibody conjugation method. Also, Protein G is an expensive protein, which increases the

overall cost of the assay.

1.5.5 Antibody Reduction

The last class of antibody conjugation methods involves using a different property of the

antibody structure. Since antibody heavy chains are held together via disulfide bonds [47], the

bonds can be broken to create the –S-H functional group. This group, as seen previously, can be

used to directly bind to the gold surface in use. This method has been presented in literature by

using DTT treatment to reduce the disulfide bond [48].

1.6 The blocking assay methodology

For this particular project a gold surface is used that is functionalized with a specific antibody.

The antibody being used for this work is against EpCAM, or epithelial cell adhesion molecule.

The molecule is thought to be expressed in almost all carcinomas in the body and is involved in

cadherin-catenin adhesion; however, the exact details of the mechanism are still under

investigation [49]. It has been proposed as a potential universal marker for carcinomas [50].

The binding event, i.e. the target attaching specifically to the surface via the antibody, must be

converted to an electrically readable signal. In order to achieve this task a secondary reporter

system that is electrochemically active is used.

The blocking assay uses a secondary reporter system, the redox couple ferrocyanide and

ferricyanide, which reacts on the gold surface when in contact with the solution and a voltage is

applied.

When a gold surface has an antibody present there is a high current on the surface due to the

redox reaction of ferrocyanide and ferricyanide. Since the redox reaction can only occur at the

15

boundary layer – i.e. gold surface – when the antigen is bound to the antibody the surface will be

blocked and the reaction will not occur. The binding of the antigen to the antibody will thus

cause the overall current to decrease, which translates to a signal change (see Figure 13).

Figure 13: Blocking assay schematic, showing the physical blocking of ferrocyanide and

ferricyanide from the surface when the antigen binds to the antibody. The physical blocking of

the surface causes an overall current decrease, which is measurable using a potentiostat.

As previously mentioned, the gluteraldehyde conjugation method is used for the purpose of this

assay. One of the advantages of using this particular method is due to the positive charge caused

by the monolayer of cystamine (primary amine group) on the surface. The charge on the surface

attracts ferrocyanide and ferricyanide to the surface via an electrical gradient since both the

molecules are negative. This causes the initial high signal, which increases the sensitivity and

resolution of the assay when using small gold surfaces. This schematic was previously shown to

detect the CA-125 cancer marker with 0.1 U/mL sensitivity [51].

16

1.7 Project Summary

There has been some progress in the field of electrochemical cell counting, most of which has

focused on improving sensitivity of detection. In the field of white blood cell counting for T-

helper cells, single cell detection was achieved [1]. In order to selectively bind T-helper cells an

anti-CD4 antibody is used and impedance spectroscopy is employed. Interestingly, single cell

resolution was achieved by designing the active sensing area (gold electrode) to a size that was

comparable to the cell size – i.e. 15 μm. The drawback to very small electrodes is that a larger

sample where the surface area requirements are greater will require too many connections to

make the device feasible. Some projects have been aimed at miniaturizing a microfluidic

impedance spectroscope for the same application with similar success rates [52]. Recently, in the

area of CTC counting it was shown that the electrode size is a significant contributor to the

sensitivity of the sensor [2]. To augment the current work, this thesis aims to present a

systematic study of the important parameters for designing a simple cell counter that uses the

DPV method. Moreover, this thesis also aims to present electrochemical counting with impure

samples containing various biomolecules and non-target cells. An overview of the proposed

device workflow is shown in Figure 14 below.

Figure 14: Schematic of cell cytosensor device where the attachment of a target cell blocks the

gold surface, reducing the overall current that is produced.

17

Thesis Objectives 2

Purpose Design and validate a sensitive cell cytosensor capable of counting

biological cells without compromising their viability (for cell culturing or

further testing) in the presence of various biomolecules and non-target cells

(see Figure 14).

Objectives (1) Functionalize a gold surface successfully with anti-EpCAM antibody

(2) Detect purified EpCAM antigen

(3) Demonstrate proof-of-principle electrochemical cell counting

(4) Determine critical design parameters that allow for accurate

electrochemical cell counting

(5) Perform counting measurements in a complex matrix with a mix of

target and non-target cells

Research

Motivation

Many applications can benefit from accurate and sensitive cell counting.

One of the problems with the current methodology for cell counting is the

need for trained staff and expensive equipment. Thus, there exists a need for

a method that is simple, cost efficient, quick and has the potential to count

cells in the presence of a mixture of biomolecules and non-target cells.

Moreover, further cell culturing or analysis of the captured cells can be

useful. This requires a device that can count the cells without fixing or

causing degradation of the cells. Having a device that can fill this niche will

further contribute towards an integrated platform, combining cell counting

and further genetic analysis.

18

Experimental Methods 3

All reagents and chemicals used are presented in Appendix B.

3.1 Cell Culture and Sample Preparation

Cells are thawed from -80OC in a 37

OC water bath and placed in 9mL of appropriate media –

MEM α for DU-145 with 10% FBS and RPMI 1670 with 10% FBS for U937 cells. Cells are

centrifuged at 200g for 10 minutes and the media is aspirated and discarded. The cells are

resuspended in 10mL of respective media and placed in a culture flask for either adherent cells

(DU145) or for suspension cells (U937). The cells are cultured until reaching confluence, 80%

surface coverage while replacing media every two days.

DU145 cells are trypsinized in 0.25% trypsin for 5 minutes at 37OC to remove from culture flask.

2.5 mL of cells are recultured in 10mL of media into a new flask while the remaining sample is

centrifuged at 200g for 10 minutes and resuspended in 1X PBS for experiments.

2 mL of U937 cells are extracted and placed in 20 mL of media and recultured into a new flask.

5mL of cells are centrifuged at 200g for 10 minutes and resuspended in 1X PBS for experiments.

The cells are used until passage number 30 or until very large deviation in cell size is observed;

for complex media counting the cells are suspended in 10% FBS with media instead of 1X PBS.

3.2 EpCAM antibody conjugation to the gold surface

The gold surface is cleaned using 5 minutes sonication in acetone, 5 minutes sonication in

isopropanol and thorough rinsing in Millipore H2O followed by drying with N2 stream. The gold

surface is also plasma treated for 60 seconds at 15Watt power before the conjugation procedure

(this procedure is not used for macro electrodes).

10mM cysteamine solution is prepared in DI H2O and incubated on the gold surface for 10

minutes. The surface is washed in a DI H2O water bath for 2 minutes (procedure is repeated

twice). A 10% glutaraldehyde solution is prepared in DI H2O and incubated on the surface for 30

minutes followed by 2 minute washing in DI H2O water bath (wash repeated twice). The gold

surface is thoroughly dried using N2 stream. The EpCAM antibody (or IgG for macro electrode

19

negative control) is diluted to 100μg/mL in 1X PBS and incubated on the surface for 1 hour. A

DI H2O bath is used to wash the surface twice for 5 minutes each time. A 1% BSA solution is

incubated on the surface for 1 hour – used to block the surface from non-specific binding. After

three washes in a DI H2O bath for 5 minutes each the surface is ready for experiments.

3.3 EpCAM Detection Assay

The blocking assay uses a standard three electrode arrangement – reference Ag/AgCl electrode,

platinum counter electrode and a gold electroplated electrode on a chip previously reported by

the Kelley Laboratory [51] (see Appendix A). The electrodes were immersed in a 20mM gold

chloride solution in 0.5M HCl. Each lead of the chip was prepared by applying 30nA of current

with respect to Ag/AgCl electrode and electroplating gold for 50 seconds. The EpCAM antibody

was conjugated to the surface according to section 3.2. The target EpCAM antigen was incubated

at 37OC for 40 minutes at varying concentrations.

The electrodes are immersed in a scanning solution of 2.5 mM K3[Fe(CN)6] (Potassium

hexacyanoferrate(III)); 2.5 mM K4[Fe(CN)6] (Potassium hexacyanoferrate(II)); 0.1 M KCl in

10mM phosphate buffer (prepared from powder and titrated to pH of 7.4). To scan each

electrode, the DPV method was used with the following scanning parameters: potential range of

0mV to 400mV; pulse width 50msec, pulse period, 100msec, pulse amplitude of 50mV with a

step increase of 5mV.

3.4 Cell Counting using macro-electrodes

A standard gold electrode from BaSI was used to validate electrochemical counting which has a

round and flat surface area with 1.6 mm diameter. The electrode was first cleaned by abrasive

polishing using alumina powder for 1 minute on each electrode. Subsequently, the electrodes

were sonicated in acetone and IPA for 5 minutes each. The electrodes were then cleaned with DI

H2O. A CV was done in 50mM sulphuric acid at a range of -100mV to 1500mV at a scan rate of

100mV/sec, which was repeated for 20 cycles as part of the electrode cleaning.

The antibody was conjugated to the gold surface by using the procedure outlined in 3.2. 50uL of

target cells, DU145, and non-target cells, U937, were incubated for 30 minutes at room

temperature. The three-electrode arrangement with Ag/AgCl reference electrode, platinum

20

counter electrode and the gold macro electrode were immersed in the scanning solution. The

scanning solution was made with 2 mM K3[Fe(CN)6] (Potassium hexacyanoferrate(III)); 2 mM

K4[Fe(CN)6] (Potassium hexacyanoferrate(II)) in 1X PBS solution.

A standard DPV scan was used with the following scanning parameters: potential range of 0mV

to 400mV; pulse width 50msec, pulse period, 100msec, pulse amplitude of 50mV with a step

increase of 5mV. The results were recorded and are shown in section 4.2.

3.5 Sensor Design and Microfabrication

The substrate used is a 4-by-4 inch glass substrate coated with a 5nm chrome layer and 100nm

gold layer as shown in Figure 15. The manufacturer performs the first step (Telic INC): spin-

coating the AZ1500 resist, resulting in a 500nA thickness, followed by metal deposition by the

E-beam evaporation technique. E-beam evaporation directs a strong electron beam onto a gold or

chrome metal pellet, causing the metal to evaporate into a chamber and deposit onto the glass

surface via diffusion. The chrome is deposited underneath the gold via E-beam evaporation for

adhesion purposes since gold cannot adhere to glass directly – the gold surface is 100nm in

thickness and the chrome layer is 5nm. The next steps are completed in the ECTI cleanroom

facilities at the University of Toronto.

To fabricate the chip, standard soft photolithography was used. Photolithography refers to a

process by which UV light is used to pattern features on a substrate in a cleanroom environment.

The entire fabrication is outlined in Figure 15.

The SU-8 resist serves as a passive non-conductive layer on which the apertures are formed.

Scanning solution that is in contact with SU-8 does not produce an electrical current thereby

providing a constant signal from each portion uncovered by SU-8 where gold is present. SU-8 is

a negative resist which requires that portions of the SU-8 desired to remain on the surface be

exposed to UV light. In other words, the apertures of the device are not exposed to UV and SU-8

developer develops the resist.

A mask designed with the gold pattern of the device is used to selectively expose the AZ1500

resist (the AutoCAD drawing of the mask can be seen in Figure 19(c)). MF-321 developer

21

provided by the ECTI facility was used for 1 minute and 40 seconds to dissolve all exposed

AZ1500 resist.

Gold etchant solution was used for 30 seconds to remove the gold that is unprotected by resist

followed by use of CR4 etchant for 15 seconds to remove the chrome layer underneath the gold –

similarly only unprotected portions will be removed. AZ1500 resist was removed from the

surface using AZ300T resist stripper for 5 minutes (provided by ECTI facility) followed by a

spin-coat step of SU-8-3005 resist at 3000 RPM for 46 seconds. The SU-8 layer is then baked at

95 degrees for 2 minutes followed by an exposure step for 30 seconds at 13.5mW/cm2. The SU-8

layer is dissolved by SU-8 developer for 1 minute. Afterwards, the SU-8 is cured completely for

2 hours at 200OC making it permanent and resistant to acetone and IPA.

Figure 15: Schematic of the fabrication protocol for the cell counting chip; step are shown from

chip side view – i.e. along the chip’s thickness.

22

3.6 Sensor Monolayer formation

The fabricated sensor outlined in section 3.5 was prepared with the EpCAM antibody in

accordance with section 3.2. After each step in the conjugation method a DPV scan was used

with the following parameters: potential range of 0mV to 400mV; pulse width 50msec, pulse

period, 100msec, pulse amplitude of 50mV with a step increase of 5mV. The scanning solution

was made with 2mM K3[Fe(CN)6] (Potassium hexacyanoferrate(III)); 2mM K4[Fe(CN)6]

(Potassium hexacyanoferrate(II)) in 1X PBS solution.

3.7 Counting Cells with Fabricated sensor

The fabricated sensor outlined in section 3.5 was prepared with the EpCAM antibody in

accordance with section 3.2. DU145 are referred to as the target cells and are incubated on the

surface for 30 minutes at room temperature. The U937 cells are referred to as the negative

control or non-target cells and are also incubated for 30 minutes at room temperature. The sensor

fabricated was used with an electrode size of 150μm in diameter. After incubation with the

sample a DPV scan was used with the following parameters: range of 0mV to 400mV; pulse

width 50msec, pulse period, 100msec, pulse amplitude of 50mV with a step increase of 5mV.

The scanning solution was made with 2mM Potassium hexacyanoferrate(III); 2mM Potassium

hexacyanoferrate(II) in 1X PBS solution. After every chip was scanned using the DPV method, a

microscope image at 10X magnification was used for counting the cells on the surface.

The complex media experiment used MEM α media instead of 1X PBS. The mixed cell

population experiment used a 1,000,000cells/mL cell concentration with DU145 cells at varying

concentrations mixed into each sample. The experimental setup arrangement can be seen in

Figure 16.

After the DPV scans were done, the chip was incubated with 10μg/mL anti-CD45 antibody for

30 minutes at 4OC; followed by extensive washing steps with PBS. CD45 is a general white

blood cell marker and is also expressed by the U937 cell line [53]. Following the primary

antibody incubation, the chip was incubated with 10μg/mL anti-IgG antibody for 30 minutes at

4OC; followed by extensive washing steps with PBS. The cells were then incubated with

100ng/mL DAPI.

23

Figure 16: Top – Experimental setup for the cell count sensor; bottom – close-up of

experimental setup, including (a) PCB board seen in Figure 20.a; (b) PCB board seen in Figure

20; (c) Cell count chip; (d) BaSI potentiostat; (e) Reference electrode; (f) Platinum wire; and (g)

Scanning solution.

(a) (b)

(c)

(d)

(e)

(f)

(g)

24

Results 4

4.1 EpCAM detection Assay

The results for the assay can be seen in Figure 17; the limit of detection for the EpCAM antigen

is 100ng/mL with respect to the negative control (1% BSA). All conditions are statistically

significant using a one-way ANOVA with p<0.05. It can be noted that the negative control is

also not statistically different than the blank scan (PBS alone) where n represents the number of

experiment repeats for each condition.

Figure 17: EpCAM antigen detection results. Error bars represent ± standard deviation, n=5.

4.2 Cell Counting Using Macro-Electrodes

The results from this set of experiments are presented in Figure 18 for total percent current

change, %ΔI. All values marked with * are statistically significant relative to both negative

controls, using the U937 cell line with EpCAM antibody and using DU-145 cells with IgG

antibody, where n is the number of repeats for each condition The limit of detection (LOD) for

this experiment was calculated as 12,500 cells total in the sample. Note that %ΔI is calculated in

the following way:

Equation 3: Percent ΔI calculation where I is the peak DPV current at an

electrode and Io is the average blank DPV current – i.e. background

current with no target

0

1

2

3

4

PBS BSA 10 ug/mL 1 ug/mL 0.1 ug/mL

Pea

k C

urr

ent

(nA

)

Condition *

25

0

10

20

30

40

50%

ΔI

0

500

1000

1500

2000

2500

3000

3500

Tota

l Nu

mb

er o

f ce

lls

Antibody EpCAM IgG EpCAM EpCAM EpCAM EpCAM EpCAM

Cell type None DU-145 U937 DU-145 DU-145 DU-145 DU-145

Total cells

in sample None 50,000 50,000 50,000 25,000 12,500 5,000

Figure 18: DU-145 cell counting using the blocking assay methodology. (a) % current change

vs. cell concentration. (b) Visual cell counts using microscope. Error bars are ± standard

deviation, n=9.

Antibody EpCAM IgG EpCAM EpCAM EpCAM EpCAM EpCAM

Cell type None DU-145 U937 DU-145 DU-145 DU-145 DU-145

Total cells

in sample None 50,000 50,000 50,000 25,000 12,500 5,000

*

*

*

*

*

(a)

(b)

*

*

26


An image of the SU-8 apertures (150μm in size) on the gold electrode (300μm in size) can be

seen in Figure 21. Note that SU-8 is transparent under a light microscope and only a thin black

line on the gold electrodes – i.e. the edge of the aperture – can be seen. The area seen in the

middle of the gold electrode defined by the circular line is exposed gold that can sense cells.

A simple 4-by-4 array of circular gold electrodes was designed – see Figure 19. The electrode

arrangement allowed for sampling of cells on the sensor as well as allowing for changes in the

aperture size for future optimization studies. The chip is comprised of a glass substrate with gold

electrodes and an SU-8 resist layer on top for defining the aperture size. The top of the chip was

designed to fit a PCB board connector for easy connections with the BaSI potentiostat. The PCB

boards used were designed by Brian Lam in the Kelley Laboratory and can be seen in Figure 20.

Note that the chip has two areas of active electrodes: the left side where a 4-by-4 grid of circular

electrodes can be seen and the right side where a 2-by-2 grid of circular electrodes can be seen

(see Figure 19(b)). The 4-by-4 grid is used as a sensing platform on which the cell sample is

placed while the 2-by-2 grid is used for a blank scan with PBS only. The blank scan is used to

measure a background current to which the sensing electrode results are compared. All graphs

reporting current vs. cell count in this chapter show ΔI, or the change in current relative to the

background.

Equation 4: Current change, where I is the peak current at an electrode with cells and Io is the average blank scan current – i.e. no target

The dimension of each circular gold electrode is 300μm, while the connections between the

electrodes to the top of the chip are 100μm in width. The SU-8 layer insulates most of the gold,

excluding the exposed apertures on the gold electrode. The top gold connections that fit inside

the PCB connector have dimensions of 400μm by 1cm with a spacing of 250μm between each

connection.

27

Figure 19: (a) Isometric view of the cell counting chip. (b) Top view of the cell counting chip.

(c) AutoCAD schematic of chip.

Figure 20: (a) PCB board for easy connection to each individual lead (b) PCB connector for

chip.

(a)

(b)

(c)

(a) (b)

28

Figure 21: A microscopic image (10X magnification) of the gold electrodes defined by the

yellow colour and the SU-8 apertures defined by the circle on the gold electrodes. Scale bar is

300μm

29

4.4 Sensor Monolayer Formation

The experimental results can be seen in Figure 22. Each step (1)-(8) represent the DPV scan

after the conjugation step. Statistical significance is reported by * by two-way ANOVA as

compared only to the previous step of the conjugation – i.e. step (3) is significant in comparison

to step (2) by ANOVA and so on. Only step (8) is compared to step (6) instead of the previous

step. The statistical test reports statistical significance for p<0.05 where n is the number of

repeats for each condition.

Figure 22: Peak current from DPV method after every reaction step in the antibody conjugation

method. Error bars represent ± standard deviation, n=6. Numbers in brackets signify the step

number.

-50

0

50

100

150

200

250

300

350

400

Aftercleaning (nA)

Acid Scanwith noplasma

Acid Scanafter plasma

AfterCysteamineTreatment

After GATreatment

AfterAntibody

After BSAand Pluronic

After BSAand Pluronic

withoutAntibody

scan

Pea

k C

urr

ent

(nA

)

(1) (2) (3) (4) (5) (6) (7) (8)

*

* *

*

*

30

4.5 Counting Cells with Fabricated Sensor

Figure 24(a) reports cells detected per electrode and the peak current corresponding to the cell

count. Using ANOVA by comparing every possible combination of cell counts to its respective

current, the sensitivity and limit of detection was found to be 8 cells per electrode. The total

current (sum of all electrodes per individual chip) was compared to the concentration of the cells

applied to the surface and is reported in Figure 24(a). In addition, the total number of cells

visually counted using the microscope images is graphed with respect to concentration of the

cells applied to the sensor in Figure 24(b). It can be observed that the 150μm chip has detected

125 cells per sensor. All statistically significant values are marked with * and are reported when

p<0.05. A simple linear regression for Figure 24(c) revealed a correlation coefficient of 0.98

where n is the number of experimental repeats for each condition.

ΔI is calculated according to Equation 4. All data is analyzed, sorted and analyzed using

MATLAB 2008 software.

Figure 23: Image at 10X magnification of cells bound to the gold surface; 150μm aperture

device. Scale bar is 300μm.

31

0

20

40

60

80

100

120

140

0 10 20 30 40

ΔI (

nA

)

Number of cells detected per electrode

0

500

1000

1500

2000

625 125 62 Negative

Tota

l Sig

nal

(n

A)

Number of cells in sample per sensor

0

100

200

300

400

500

625 125 62 Neg

Tota

l Nu

mb

er o

f ce

lls


Negative

Figure 24: Cell counting experiment. (a) Current change per electrode vs. cell number present

on electrode. (b) Total cell count added to the chip vs. cell concentration added per sensor. (c)

Total signal for the chip vs. cell concentration added per sensor. Error bars represent ± standard

error of the mean, n=4. Aperture size is 150μm.

(a)

(b)

(c)

*

*

*

*

32

4.6 Important parameters for electrochemical cell sensors

4.6.1 Aperture Size

The results for each aperture size is presented in Figure 26 where (a) through (c) represent the

current measured for each electrode vs. the number of cells per electrode that were counted using

a microscope. A simple linear regression revealed a correlation coefficient of 0.99. Figures (d)

through (f) show the total current measured as a sum of the electrodes vs. the total number of

cells in the sample per sensor. Statistical significance is marked with * and represents

significance with p<0.05. Sensitivity and range was calculated using MATLAB 2008 and the

summary results are shown in Figure 27. Figure 25 shows sample images of the active sensor

area using 10X magnification after incubation of target cells for different sized apertures.

Figure 25: Microscopic images (10X magnification) with target cells bound with (a) 300μm, (b)

150μm and (c) 50μm aperture size. Scale bar is 300μm.

(a) (b)

(c)

33

0

50

100

150

200

250

300

350

0 20 40 60 80 100 120

ΔI (

nA

)


(b)

0

2

4

6

8

10

12

0 1 2 3 4

ΔI (

nA

)


(a)

0

20

40

60

80

100

120

140

0 10 20 30 40

ΔI (

nA

)


(c)

0

20

40

60

80

100

120

625 125 62 Negative

Tota

l Sig

nal

(n

A)


(d) *

0

500

1000

1500

2000

625 125 62 Negative

Tota

l Sig

nal

(n

A)


(e) *

*

0

500

1000

1500

2000

2500

3000

3500

625 125 62 Negative

Tota

l Sig

nal

(n

A)


(f) *

Figure 26: Results from aperture size experiment (a)-(c) Peak current per electrode vs. cell number

per electrode for 50μm, 150μm, 300μm – top to bottom, (d)-(f) Total peak current vs. cell

concentration for 50μm, 150μm, 300μm – top to bottom. Error bars represent ±standard error of the

mean, n=3.

34

0

5

10

15

20

25

300 μm aperture

150 μm aperture

50 μm aperture

Nu

mb

er

of

cells

pe

r e

lect

rod

e

0

20

40

60

80

100

120

300 μm aperture

150 μm aperture

50 μm aperture

Nu

mb

er

of

cells

pe

r e

lect

rod

e

Figure 27: Graphical representation of the calculated: (a) limit of detection for the sensor vs.

aperture size of the sensor and (b) range – 0 to maximum number of cells that can be counted vs.

aperture size of the sensor.

4.6.2 Concentration of Ferrocyanide and Ferricyanide

The experimental results can be seen in Figure 28 and are represented as DPV signal per

electrode vs. the number of cells bound to each electrode. A simple linear regression showed that

the correlation coefficient is 0.87, 0.99, 0.92, 0.91 for Figure 28(a), (b), (c), and (d) respectively.

The lowest concentration in Figure 28(a) results in signals that are quite variable and a closer

analysis of the raw data revealed that the signal was decreasing with time, implying local

depletion of the solution. If the solution was left to equilibrate for a few minutes the scans

returned to normal (data not shown). The sensitivity of the sensor was 0 cells, 1 cell, 2 cells and

2 cells for 200 μM, 2mM, 20mM and 200mM solutions respectively. It is important to note that

at 200mM ferrocyanide and ferricyanide there was noticeable cell death.

(a) (b)

35

0

2

4

6

8

10

12

0 1 2 3 4

ΔI (

nA

)


0

0.5

1

1.5

2

2.5

0 1 2 3 4

ΔI (

nA

)


0

20

40

60

80

100

120

140

0 1 2 3 4

ΔI (

nA

)


0

50

100

150

200

250

300

0 1 2 3 4

ΔI (

nA

)


Figure 28: Solution concentration effect on signal per gold electrode. (a) 200μM, (b) 2mM, (c)

20mM and (d) 200mM ferrocyanide and ferricyanide. Error bars represent ± standard error of the

mean, n=6. Aperture size is 50μm.

4.6.3 Sample Incubation Time

Lastly, in order to minimize the non-specific binding, which is significantly higher for the 50μm

aperture, and maximize target cell binding, the incubation time with the cells was varied from 1

minute to 1 hour. The results of different incubation times vs. the total signal seen for the 150μm

apertures can be seen in Figure 29. No significance was observed with incubation times less than

15 minutes. It can be seen that the total signal saturates at 30 minutes with no statistical

(a) (b)

(c) (d)

36

significance between 30 minutes to an hour. The signal from non-specific cell binding does not

significantly increase even with 1 hour incubation.

Figure 29: Total signal vs. the incubation time with cells. Blue markers indicate total signal for

target cells (DU145) and red indicates non-target cells (U937), both at 625 total cells in sample

per sensor. Error bars represent ± standard deviation, n=3.

4.7 Assay Duration and Sensor Storage

To test the stability of the chip, an experiment was conducted. For validation purposes one cell

concentration is used, including a negative control for every experiment. The data is presented in

Figure 30 showing comparable results between the different weeks with statistical significance

of p<0.05 between the positive and its respective negative control; n represents number of

repeats for each condition. The experiment was carried out in accordance with section 3.7;

however, instead of 1% BSA blocking the chip was stored at 4OC with 0.1% sodium azide and

1% BSA, which resulted in the sensor being viable for 2 weeks.

37

Figure 30: Chip storage experiment for a 2 week period. Error bars represent ± standard error of

the mean, n=4. No statistical significance found between chips at different weeks. * shows

statistical significance relative to the negative control.

4.8 Counting Cells in Complex Samples

4.8.1 Counting Cells in Media

The sensor was able to count the cells in the presence of serum and proteins. To illustrate this,

the same experiment shown in section 4.5 is repeated in cell media (MEM α media) with 10%

FBS – a mixture that has high concentrations of proteins, small molecules and nutrients as

potential agents to interfere with the sensor. The experimental result is shown in Figure 31. It

can be seen that in comparison to data presented in

Figure 24, there is no statistical difference. Moreover, the media-only data also shows that the

proteins and serum in the solution do not affect the results. The data showed statistical

significance of p< 0.05 relative to the negative controls (media only and U937 cells). The limit of

detection was calculated as 125 cells per sensor, which is comparable to measurements without

the media.

0

200

400

600

800

1000

1200

1400

1600

1800

625 Negative

Tota

l Sig

nal

(n

A)


0 weeks

1 week

2 weeks

* * *

38

Figure 31: Cell counting experiment in the presence of cell media with 10% FBS, the total

signal vs. cells in sample per sensor is presented. Error bars are ± standard error of the mean,

n=3.

4.8.2 Counting mixtures of target and non-target cells

The procedure for the experiment can be seen in section 3.7. Data for the experiment is presented

in Figure 32. Statistical significance for Figure 32(a) is determined relative to the negative

controls of the experiment in the previous section and show significance with p<0.05. Figure

32(b) shows cells counted using the microscope and immunohistochemistry procedure in section

3.7. Statistical significance can be seen as compared to the negative cell count with p<0.05. Error

bars in figure show ± standard error of the mean with total n=3, where n is the number of repeats

for each condition.

As with previous sections the limit of detection was calculated as 125 total cells per sensor,

which is comparable to previous sections.

0

200

400

600

800

1000

1200

625 312 125 62 Media Negative

Tota

l Sig

nal

(n

A)


*

*

*

39

Figure 32: Cell count experiment with 625 U937 non-target cells as background mixed with

target cells at varying concentrations. (a) The total signal vs. target cell concentration (b) Cell

count using immunohistochemistry procedure described in section 3.7. Error bars in figure show

± standard error of the mean, n=3.

0

200

400

600

800

1000

1200

1400

625 312 125

Tota

l Sig

nal

(n

A)


0

50

100

150

200

250

300

350

400

625 312 125

Tota

l Ce

ll N

um

ber


Positive Cells

Negative Cells

(a)

(b)

*

*

*

*

*

*

40

Discussion 5

5.1 EpCAM detection Assay

The redox couple in the solution is [Fe(CN)6]3-

and [Fe(CN)6]4-

where ferricyanide ([Fe(CN)6]3-

)

reduces to ferrocyanide ([Fe(CN)6]4-

). The oxidation reaction occurs between ferrocyanide to

ferricyanide on the gold electrode, which produces the current seen in Figure 17. However,

when the antigen is bound to the surface the redox couple did not react on the surface, thus

producing a smaller signal.

The limit of detection for the assay is not as low as previously reported [51]. EpCAM antigen is

significantly smaller than the CA-125 antigen previously tested. Therefore, EpCAM blocks a

smaller portion of the gold surface, resulting in a smaller signal change as compared to the blank

scan. This idea is consistent with the blocking assay methodology which favors larger antigens.

Due to this fact, it can be expected that cells would produce larger signal changes on comparable

electrode sizes. In other words, a cell would completely block the 8μm electrode used for

EpCAM detection which would result in a large signal change.

Alternatively, since cells are larger and can elicit a larger signal change a small electrode is not

necessary to create a sensitive sensor. As part of the project goals the electrode size was

investigated to determine its effect on sensor sensitivity and range.

5.2 Cell Counting Using Macro-Electrodes

In order to transfer the sensing scheme to cellular detection for proof-of-concept purposes the

scanning solution and gold surface was reconsidered. A short experiment (incubating cells in

scanning solution for 10 minutes) revealed that the target cells (DU-145) die in the scanning

solution. The most likely reason for this is the 0.1 M KCl concentration in the solution.

Alternatively, as seen in the experimental methods section, a scanning solution of 2mM

K3[Fe(CN)6] and 2mM K4[Fe(CN)6] in 1X PBS solution provides sustainable conditions for the

cells throughout the scanning procedure. However, the overall conductivity was reduced since

the KCl concentration was decreased, thus decreasing the initial signal.

41

Considering DU-145 cells are significantly larger (12±2 μm diameter average) than the EpCAM

antigen, a larger electrode can be used for the experiment. Since the total current is also

proportional to the electrode size, a larger electrode helps increase the overall signal. This is

done in order compensate for the decreased conductivity of the solution. Considering the changes

to the scanning solution the active ferrocyanide and ferricyanide concentrations are investigated

to determine their effects on sensor sensitivity and range in subsequent sections.

From this experiment it is important to observe important parameters that can affect assay

design. One, the sample volume affects assay sensitivity since the total cell count will be reduced

with smaller samples. Second, centrifuging the sample being used can alter the limit of detection.

However, it is important to note that impurities in the sample will also be concentrated following

centrifugation; therefore, the sensor must also count cells in the presence of high non-target cell

numbers. Third, important design parameters must be optimized: such as gold surface size,

ferrocyanide and ferricyanide solution concentrations and cell incubation time.


There are a few considerations that need to be taken into account when designing a cell counting

sensor using electrochemical methods. First, cells settle and will not diffuse to the active sensing

area in contrast to protein or DNA targets. In order to sense a cell sample and count it effectively,

an evenly spaced array of active sensing area must be distributed across the location where the

cells are deposited. Cells can also be dispensed to the sensor via microfluidic channels.

Biological cells, more so for epithelial cells such as the DU-145 cell line used here, have several

mechanisms that enable them to effectively bind non-specifically to surfaces. A multitude of

proteins that are displayed on the cell membrane make the cell easily adherent to surfaces that

are hydrophobic. Moreover, cells extend their membrane in order to make better contact with the

surface since without adhesion to the surface epithelial cells will die [54]. Both these facts make

cells challenging to deal with and a careful non-specific blocking method must be employed.

42

5.4 Sensor Monolayer Formation

In the following paragraphs all steps (1) through (8) are referring to the scans conducted after

each step of the antibody conjugation procedure, represented in Figure 22. As noted, there is no

signal present without any treatment. Afterwards, a procedure known as acid scanning is

performed for 20 cycles [55]. The purpose of the procedure is to clean the gold surface from

contaminants, which in this case did not make any changes to the signal peak.

It should be noted that SU-8 is quite hydrophobic, thus preventing any hydrophilic solutions

(including the scanning solution) from reaching the surface, which can be the cause of the signal

loss. To clean the SU-8 surface as well as the gold and make the SU-8 hydrophilic, oxygen

plasma is applied at 15 W for 60 seconds before proceeding to the next step. As can be seen in

Figure 21, the plasma treatment increases the DPV signal on the gold to typical values for

150μm apertures. Plasma treatment also increases the hydrophilicity of the surface, which helps

reduce non-specific binding [56].

As per the glutaraldehyde coupling procedure, cysteamine is added to the surface. Since

cysteamine is positively charged the signal from the scanning solution should significantly

increase (refer to section 1.6) – this change is seen from step (3) to (4). Next, the added

glutaraldehyde reacts with a portion of the amine groups on the surface which decrease the

overall charge and therefore decrease the signal – this effect is also consistent between step (4)

and (5).

Next, the addition of the antibody physically blocks a portion of the surface which decreases the

overall signal, similar to the effect of antigen addition in section 1.6. Next, 1% BSA and 5%

pluronic F-68 is used to block the surface from non-specific binding. The scan after blocking

(step (7)) produced a quite variable scan, which can be interpreted as damage to the antibody

from excessive scanning. The addition of cells to the surface indicate no binding, which is

consistent with inactive antibody (data not shown).

To rectify this situation, no electrochemical scan is performed after the antibody addition step

since the antibody is not stable enough under those conditions. As seen in step (8) there is no

large variation and the signal is consistent – i.e. blocking with BSA should reduce the signal due

43

to physical blocking. Since the scans after each step are consistent with the chemistry the

antibody activity and the assay must be tested before continuing to the next thesis goal, which

aims to identify the important parameters for designing a cell counting device.

5.5 Counting Cells with Fabricated Sensor

As a potential application for the sensor, circulating tumor cells can be counted. A typical CTC

sample of 1,000cells/mL in 10mL of whole blood can be concentrated when extraction is

performed from blood [50]. However, for CTC extraction technologies as an example, the

samples are not pure and therefore the sensor must count cells in the presence of many non-target

cells. Since the cells that were counted maintain cell viability throughout the procedure the assay

can be used in conjunction with other molecular marker analysis, such as DNA or RNA.

5.6 Important design parameters for electrochemical cell

sensors

In order to maximize the sensitivity of the chip while minimizing the non-specific binding of the

non-target cells (U937 cell line) several important factors that affect the assay sensitivity are

investigated. Aperture size and concentration of ferrocyanide and ferricyanide were previously

mentioned to be important and results are shown in this section. Moreover, the incubation time

of the sample on the surface is also optimized in order to increase cell binding and minimize

non-specific adhesion.

5.6.1 Aperture Size

Since the reaction peak current is dependent on the surface area of the conductive gold it is

reasonable to hypothesize that the size of the gold electrodes will affect sensor sensitivity. As

previously mentioned, when a target cell binds to the surface it will reduce the overall signal (see

section 1.6 for further details). If the target cell covers a larger percentage of the electrode

surface, as is the case with smaller electrodes, the signal change will be larger. Therefore, it can

be expected that decreasing the size of the electrode will increase the sensitivity of the sensor –

consistent with previously published data [2].

44

Figure 24 shows that the large aperture (Figure 24(a)) requires numerous cells to cover the

active gold area while the small aperture (Figure 24(c)) requires only a few cells. However,

there is a trade-off between the sensitivity and the range of the sensor – i.e. the maximum

number of cells that the sensor can count is significantly smaller when the sensitivity is

increased.

From the data presented in Figure 25, it can be seen that as the aperture size decreases less cells

are required to elicit a statistically significant response. In other words, Figure 25(a)-(c) show

that the 50μm aperture can detect 1 cell per aperture while with 300μm aperture 22 cells produce

the minimum detectable signal. It is also important to note that the limit of detection of the

overall device changes to 125 cells per sensor when decreasing the aperture from 300μm to

150μm size. However, from 150μm to 50μm the device is not able to detect 125 cells even

though the sensitivity of the assay increases significantly.

This can be explained by considering the dead space of the sensor. When decreasing the aperture

to 50μm the sensor has less exposed gold area than with 300μm apertures. Since cells are not

able to diffuse to the active gold area and only settle, a significantly large non-sensing space will

result in many cells not being detected. In order to take advantage of the large sensitivity

increase of the 50μm apertures the number of electrodes can be increased. These will cover the

same area and will also take advantage of the sensitivity increase. It can be noted that the

negative control signal increases with decreasing aperture size.

Considering the total number of cells that non-specifically bind is similar between aperture sizes,

when the sensor becomes more sensitive, the non-specific signal will increase. To decrease the

non-specific binding, efficient molecules such as PEG molecules [57] or pluronic [58] should be

investigated in the future. The summary of the calculated sensitivity and sensor range for each

aperture size can be seen in the results section in Figure 27.

Therefore, when designing a cell electrochemical sensor the aperture size should be made small

and the non-sensing area should also be minimized. Theoretically an aperture size that is of an

equal size to a cell will elicit a 100% signal response; however, practically that will require more

numerous connections, making individually addressed electrodes a difficult challenge.

45

5.6.2 Concentration of Ferrocyanide and Ferricyanide

Noting from the previous section that the most sensitive signal was achieved for 50μm electrodes

the optimization experiments were done with the aforementioned aperture size. The principle

idea is that, as with any chemical reaction, the rate is proportional to the reactant concentration.

This means that the current produced will also vary with the reaction concentration. It can be

hypothesized that there will be an optimal concentration where the signal will have maximum

sensitivity. If the concentration is too low the reaction will be diffusion-limited and will have too

great a variability from lead-to-lead while if the concentration is too high the signal will saturate

with high cell numbers.

5.6.3 Sample Incubation Time

The incubation time should be minimized considering that cells at room temperature will slowly

lose viability, causing the count to be inaccurate. On the other hand, short incubation time

decreases the number of cells that can bind onto the surface. Antigen-antibody interactions with

the surface require time. Also, when studying a mixed cell population where cells can adhere to

each other the incubation time should be minimized since a mixture of cells has higher likelihood

for non-specific binding. Therefore, from Figure 29, it can be concluded that 30 minute

incubation at room temperature is optimal without compromising the cells or increasing the non-

specific binding significantly.

5.7 Assay Duration and Sensor Storage

An important factor that affects the practicality of a sensor is the assay time and the stability of

the sensor while not in use. For the cell count sensor presented in this thesis the assay time

involves a 30 minutes incubation period with the sample and a 10 minute period for scanning the

chip using the DPV method as well as a setup time. However, the chip preparation time for

antibody conjugation involves a 4 hour procedure. In order to ensure that the sensor can be

practically used in the future as a potential clinical tool it must have a reasonable shelf life.

Moreover, storage of the chip also ensures adequate stock if necessary.

In order to increase the storage time a different storage media such as 5% trehalose can be used.

Alternatively, a glycerol-based solution can be used to prevent freezing of the liquid on the

46

sensor with storage at -20OC which could increase the shelf life to a few months rather than a

few weeks [52].

5.8 Counting Cells in Complex Samples

Normally, real clinical samples in multiple fields shown previously – T-cell counting for HIV or

CTCs for cancer – present with impurities such as proteins, DNA and small molecules. As a

proof-of-concept a sensor should be able to count in conditions where multitudes of molecules

are mixed with the target sample. For cell counting this can imply two separate conditions: a

mixture of proteins and small molecules and a mixture of non-target cells. The cell counter in

this thesis is shown to be able to count target cells in the presence of serum from media and in

the presence of non-target cells.

Currently, identification of non-target cells in a cell population requires immunohistochemistry

which involves the use of multiple antibodies and molecules that are fluorescently labeled. As

previously mentioned, this can require trained technicians to develop a protocol for a particular

cell target as multiple reagents. This makes immunohistochemistry costly and impractical for

clinical application. However, with the cell sensor presented in this thesis that requires 40

minutes total for the assay, bringing such a tool to both a research and clinical setting can be

more feasible.

47

Conclusions and Future Directions 6

This thesis project has shown the potential of a cell counting sensor to be used with a complex

sample and a mixed cell population using the DPV method, which has not been previously

shown in literature. The thesis also presented important design parameters that were optimized

and considered for effective cell counting.

Considering the parameters optimized, a chip will be designed in the future that includes more

gold electrodes with the smaller aperture size of 50μm in diameter. This will move the project

towards more efficient counting with higher sensitivity. Moreover, the new chip design can be

potentially used with gold standard methods available for CTC isolation such as the OncoQuick

system, which extracts CTCs in the presence of many non-target cells. This application could be

ideal in the future since it exploits the sensitivity of the sensor and the ability to count cells in the

presence of a high background signal.

48

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55

Appendix A: Kelley Laboratory electrochemical chip

Figure 33: Prefabricated chip used in Kelley Laboratory [59].

56

Chemical Supplier Catalog number

cysteamine Sigma Aldrich 60-23-1

glutaraldehyde Sigma Aldrich 111-30-8

DI H2O Invitrogen 10977023

EpCAM R&D systems 960-EP-050

Anti-EpCAM antibody R&D Systems MAB960

Ag/AgCl reference BaSI MF-2078

Pt wire Omega Electronics SPPL-001

Potassium hexacyanoferrate(III) Sigma Aldrich 13746-66-2

Potassium hexacyanoferrate(II) Sigma Aldrich 13746-66-1

KCl Sigma Aldrich 7447-40-7

Phosphate monobasic Sigma Aldrich 13472-35-0

BSA Sigma Aldrich 9048-46-8

DU-145 cell line ATTC HTB-81

U937 cell line ATTC CRL-1593.2

Standard gold electrode 1.6mm diameter BaSI MF2014

Gold on glass substrate 4x4’’ TELIC Custom quote

Gold etchant Transene GE-8110

Chrome etchant Cyantek CR4 etchant

AZ300T stripper Capitold scientific INC AZ300TSTRIP

SU-8 Microchem INC SU-8-3005

SU-8 developer Microchem INC SU-developer

Pluronic F-68 Sigma Aldrich 9048-46-8

MEM alpha media Invitrogen 12571089

FBS Invitorgen 16000044

Anti CD45 antibody R&D systems MAB1430

DAPI Invitrogen D1306

Anti IgG antibody R&D systems NL009

Gold chloride solution 1.45M Sigma Aldrich 27988-77-8

Appendix B: List of materials and chemicals used

57

Hydrochloric Acid Sigma Aldrich 7647-01-1

Acetone Sigma Aldrich 67-64-1

IPA Sigma Aldrich 67-63-0

Sulphuric Acid Sigma Aldrich 7664-93-9

Adherent culture flasks VWR (BD Falcon) CABD353133

Suspension culture flasks VWR (BD Falcon) CABD353136

Trypsyn EDTA Invitrogen 15050057

design of an electrochemical cell cytosensor · design of an electrochemical cell cytosensor mario...

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