development of a microfluidic device and experimental ... · health and disease states. white blood...

Internship Report February 2008- April 2008 Under supervision of Prof. Jaap den Toonder TU/e: Department of Mechanical Engineering; Computational and Experimental Mechanics Project: Cell Diagnostics Supervisors: Ruud van Leeuwen, Murray Gillies

Development of a microfluidic device and experimental setup for measurements

of mechanical properties of cells

Melike Yavuz MT 08.23

Eindhoven University of Technology & Philips Research Internship Report

2

TABLE OF CONTENTS ABSTRACT........................................................................................................................ 3 1. INTRODUCTION .......................................................................................................... 4 2. THEORY ........................................................................................................................ 7 3. MICROFLUIDIC DEVICE............................................................................................ 9 4. EXPERIMENTAL SETUP........................................................................................... 12 5. EXPERIMENTAL PROCEDURE ............................................................................... 14 6. IMAGE ANALYSIS METHOD................................................................................... 16 7. RESULTS ..................................................................................................................... 18 8. CONCLUSIONS........................................................................................................... 23 9. RECOMMENDATIONS.............................................................................................. 25 10. REFERENCES ........................................................................................................... 28 11. APPENDIX A- MECHANICAL CELL EFFECTS CAUSED BY DISEASES ........ 30 12. APPENDIX B- MATLAB CODES FOR IMAGE ANALYSIS ............................... 34


3

ABSTRACT

The deformability of blood cells ensures that the microcirculation functions properly.

Since the diameter of blood cells is often larger than the capillary diameter, they

continuously need to undergo large deformations while passing through the capillary

network.

Especially, the deformability of white blood cells plays a major role in that, since they are

about twice larger in volume per cell and 2000 times more viscous than the red blood

cells, resulting in lower deformability and slower transit through the narrow capillaries.

The onset and progression of many human diseases cause an alteration of the morphology

and/or the activation state of white blood cells and consequently their mechanical

properties change. Measuring and understanding how these properties are influenced by

human diseases may lead to better diagnosis and treatment procedures.

The goal of this research is to physically deform cells by interaction with the walls of

microfluidic channels mimicking the capillary network in vivo. The deformation and

movements of the cells in the microfluidic network is captured using an optical

microscope. The experimental setup and procedure are optimized in order to make proper

image analyses. The image analyses are developed to calculate the local/average velocity

and size of each cell in a defined region of interest of the microfluidic channels.

Hence, we have achieved an optimized experimental setup and procedure that can be

used for future studies of the deformability of cells and establish relationships with

diseases


4

1. INTRODUCTION

White blood cells, or leukocytes, defend the body against both infectious diseases and

foreign materials. Study of the mechanical properties of the white blood cells is important

for an understanding of their function in the circulation and their functional roles in

health and disease states. White blood cells constitute only 1/600th of the blood volume

whereas the red blood cells constitute almost one half of the blood volume. However, the

white blood cells are about twice larger in volume per cell and 2000 times more viscous

than the red blood cells, resulting in low deformability and slow transit through the

narrow capillaries, and therefore the deformability of white blood cells ensures that the

microcirculation functions properly. [1]

The mechanical properties of the white blood cells are determined by their microstructure

which consists of the membrane, cytoplasm, cytoskeleton, nucleus, and granules, and by

their activation state. The cell membrane, a bilayer lipid membrane which forms the cell

surface, has many fine folds or wrinkles which provide about 80-120% excess area to

allow the cell to deform in the narrow capillaries. The cytoplasm of a white blood cell

occupies almost half of the cell volume and contains many granules or organelles which

contribute also to cell deformability. Their cytoskeleton that consists of an actin network

undergoes local rearrangement during cell activation in response to a variety of stimuli.

Several forms of response are observed depending on the type of cell involved, including

oxygen free-radical formation, expression of adhesion molecules and cytokines,

degranulation and the projection of pseudopods, although the majority of circulating

leukocytes are in a relatively quiescent state. Activated white blood cells have an

increased adhesion in order to transmigrate the endothelium, they have reduced

deformability due to polymerization of the actin network which may reduce blood rate

even in the normal circulation, and they are likely to become trapped in the

microcirculation. Thus the mechanical properties of the white blood cells are very

sensitive to their activation.[2][3][4]


5

The onset and progression of a human disease, see Appendix A, may cause an alteration

of the mechanical properties and any change in mechanical properties, thus in

deformability, could directly affect the microvascular flow dynamics, such as blood flow

rate and consequently oxygen transport to tissues. A number of diseases are associated

with alteration of rheological properties of white blood cells, i.e. an alteration of their

adhesion or deformation properties, shown in the table in Appendix A. Many diseases

cause an activation of white blood cells, i.e. they are stiffened due to the polymerization

of cortical actin resulting in a high probability of blocking the capillaries and in an

increased tendency to adhere in order to transmigrate across endothelium.[3] Since the

activation may not be specific, the diseases may not be differentiated from each other just

on the basis of their mechanical properties.

However if we would have some previous knowledge about the present disease, we may

differentiate the phases or subclasses of the disease by a comparison of the change of

deformability. Especially in leukemia, the leukocytes are substantially abnormal, and

every subclass of leukemia shows completely different cytochemistry and different

morphology of leukocytes, thus different deformability. The activation of white blood

cells can influence symptoms or influence progress of a disease, e.g. diabetic patients can

have atherosclerosis as a result of activated monocytes.

There are some limitations to diagnose the diseases via deformability properties. The

proportion of activated leukocytes that can be collected may be a critical limiting factor

in the analysis, because many of the activated leukocytes do not circulate, i.e. they are

trapped in the microcirculation or in target organs. The removal of cells from their native

plasma by the separation techniques available today may shift the state of cell activation,

thus ex vivo analyses may not reflect accurately the real situation in the circulation.

Further developments of methods that analyze the cells in fresh blood are needed.[3]


6

The aim of present research is to design and set up an experiment that would enable us to

physically deform cells by interaction with the walls of microfluidic capillary like

channels that are narrower than the diameter of cells. The deformation of the cells will be

monitored using a (high-speed) camera. To reach our aim the experimental setup and the

experimental procedure have to be optimized in order to make proper image analyses.

The image analyses will be used to calculate the local/average velocity of each cell versus

its size through a defined region of interest. This research is a first step towards our

overall goal, which is to make a distinction between the healthy and diseased cell

velocities which depends on their rheological properties, i.e. cell deformability and cell

adhesion.

In this report, the factors, which the transit time of a cell through a region of interest

depend on, are qualitatively discussed in the chapter 2. The microfluidic device that is

developed for our aim is explained in chapter 3. In chapter 4 and 5, the optimized

experimental setup and procedure are described, respectively. Our dedicated image

analysis method is described in chapter 6 and the results of the experiments are given in

chapter 7.


2. THEORY The velocity of a cell through a channel depends on the applied pressure, on the ratio of

the cell size to the channel width, and on its mechanical properties, i.e. cell deformability

and cell adhesion. The onset and progression of a human disease may cause an alteration

of these mechanical properties of cells which results in different velocities of cells with

the same size. The main mechanical factors that determine the behavior of the cells in

narrow microfluidic channels may be understood with the following basic qualitative

model. Figure 1 illustrates the simplified model:

Figure 1: A very simplified model for a cell flowing through a microchannel The cell is seen in this model as a homogeneous structure having certain deformation

properties such as an effective elastic modulus or a time dependent stiffness. The

interaction between the cell and the wall is modeled with an effective friction force, fF ,

shown in Figure 1. The friction force is the product of a factor called the friction

coefficient, f , the area of contact between the cell and the channel walls, cA , and the

pressure in the cell generated by the cell deformation, p , see Equation 1:

* *f cF f A p= Equation 1

The friction coefficient depends on the physical and chemical interaction between the cell

and the wall. The contact area depends on the stiffness of the cell and also on the physical

and chemical interaction between the cell and the wall. The internal pressure in the cell

that acts on the wall of the channel depends on the stiffness of the cell.

If the physical and chemical interaction between the cell and the wall increases, the

friction coefficient and the contact area are expected to become larger which results in a

higher friction force, and therefore a larger transit time (at equal pressure applied and

PAc

Ff


8

equal geometry). If the stiffness of the cell increases, the contact area may be expected to

become smaller and the pressure acting on the wall of the channel is expected to get

larger. Therefore, the effect of stiffness on the friction force and hence transit time is not

clear a priori, although the effect on contact area is expected to be relatively small. The

effect of increasing the size of cells relative to the channel width, would result in a larger

contact area as well as a higher pressure, and therefore the friction force increases will

cell size and so does the transit time.

In reality, the stiffness and the interaction properties are determined by complex

biological factors, such as the cytoskeleton arrangement of the cell, its activation state, its

development stage, and so forth. These are topics of our future research.


9

3. MICROFLUIDIC DEVICE Our microfluidic device, see Figure 2, consists of a network of continuously splitting and

narrowing microchannels that mimic the capillary network in vivo. This network is

surrounded by larger channels to prevent any change of the pressure drop over the narrow

channels due to any possible blockage. The narrowest channels have a width of 8-12 and

16 µm and the large by-pass channels have a width of 200-300 and 400 um. The height of

all channels is 17.5 µm. The channels are made by positioning PDMS

(polydimethylsiloxane) structures on a glass substrate, shown in Figure 3. Generally,

PDMS can be used to manufacture devices with very narrow channels. Another

advantage of PDMS is that it is permeable to air, which turned out to be convenient in

removing air bubbles from the channels.

Figure 2: Design of the microfluidic device


10

a)

b)

e)

c)

d)

Figure 3: The manufacture process of a microfluidic device Figure 3 shows the manufacture process of our microfluidic device step-by-step. The

general procedure is as follows:

a- First, a homogeneous layer of SU8, a photosensitive polymer, is spin-coated on

top of a glass slide. The viscosity of the polymer and the spinning speed

determine the layer thickness obtained.

b- The SU8 layer is selectively cured (pink) by using a mask and UV light.

c- Uncured SU8 is removed. The cured patches of SU8 function as a negative mould

for the PDMS device.

d- The liquid polydimethylsiloxane (PDMS) is poured over the SU8 mould and the

PDMS is cured.


11

e- The cured PDMS is removed from the mould by careful peeling and the surface is

treated with oxygen plasma to create surface free oxygen radicals. The PDMS

structure is positioned on a glass substrate that is also treated with oxygen plasma.

Covalent bonds will form between the PDMS and the glass. The glass substrate

consists of two glass slides. The first glass slide contains small holes that can be

used to introduce fluids. A second glass slide contains grooves housing hollow

glass capillaries (fused silica, ID 100-200 µm, OD 363 µm) and it was glued to

the first glass substrate. These capillaries could be used to connect tubing to the

device.


12

4. EXPERIMENTAL SETUP The microfluidic device was placed on the mechanical stage of an optical microscope,

Leica DFC490, and was fixed there with adhesive tapes, shown in Figure 4. A high speed

camera, Redlake MotionPro HS-4, was mounted on top of the microscope and connected

to a computer with the required software, shown in Figure 5. Experiments were

monitored with the computer screen placed next to the microscope. The glass capillaries

of the device were connected to a syringe pump via tubing with connectors from

Upchurch Scientific. The syringe pump, Harvard PHD 22/2000, was equipped with

infuse/withdraw configuration, shown in Figure 5. A container with the cell medium was

placed at the outlet of the glass capillaries, as shown in Figure 4. The cells were

introduced into the device by suction.

Figure 4: Experimental Setup


13

Figure 5: The microscope, the high speed camera and the syringe pump used for

experiments


14

5. EXPERIMENTAL PROCEDURE

The optimized experimental procedure is explained below step by step:

1- The device is filled by flushing 1% w/v (weight per volume) Bovine Serum

Albumin (abbreviated BSA) solution through the device for 2 hours. BSA coats

the surfaces of the channels and prevents any other protein adsorption, i.e. it

prevents cell sticking. The BSA solution has to be filtered with micropores having

diameter of 0.22 µm to prevent any possible blockage of the channels due to BSA

crystals or contaminations in the solution. The infusion flow rate of the syringe

has to be lower than 10 µL/min to prevent delamination of the device, since

higher flow rates result in too high pressures.

2- Cells are taken from Philips Life Science Facilities. THP1 cells are used for the

experiments. These are cells from a human acute monocytic leukemia cell line.

The cell culture medium is replaced with PBS/0.5% w/v BSA/ 2 mM EDTA

solution. Phosphate buffered saline (abbreviated PBS) is a salty solution

containing sodium chloride, sodium phosphate, and (in some formulations)

potassium chloride and potassium phosphate. The buffer helps to maintain a

constant pH. Ethylene diamine tetraacetic acid (abbreviated EDTA) is used as an

agent which binds to calcium and inhibits cell sticking.

3- The cells are introduced by suction from a container at the outlet of the device.

The flow rate of the syringe pump has to be >5 µL/min. With lower flow rates

sedimentation and sticking of the cells in the glass capillaries of the device turned

out to occur, leading to almost zero cell entry in the device.

4- Movies are made by using the high speed camera described earlier, with 1000

frames per second and with 15-25µs exposure time to prevent image blurring.

5- Since the cells also sediment in the container at the outlet, pipetting in the

container up and down every 5 min. helps to maintain a homogenous cell

solution.


15

6- The experiments have to be conducted within 2 hours after taking the cells of their

medium, since the cells are expected to be dead after 2 hours.


16

6. IMAGE ANALYSIS METHOD We developed a dedicated MATLAB routine to analyze the movies taken by the high

speed camera during experiments. The local and the average velocities and sizes of the

cells introduced into the microfluidic device can be determined using this routine. The

MATLAB code is given in Appendix B. The analysis method is explained below:

A region of interest is selected to determine the pixel size (µm/pixel) in the movie as

indicated in Figure 6 and the boundaries of the channels are detected. Since the actual

size and the number of pixels in the region of interest are known, the pixel size can be

simply calculated.

Real Length / Number of Pixels = Pixel Size0.6316 um

Figure 6: The pixel size calculation using MATLAB

The next step is to select a region of interest to carry out image analysis. Mostly these are

the channels through which cells transit, see Figure 7. The region of interest is cropped

from each frame of the movie. A background subtraction method is applied to the


17

cropped part of each frame to identify moving cells. The background is the median of

frames over time. After subtraction of the background from each frame, the pixel values

are integrated along the width of the region of interest for each frame. The integration of

the pixel intensity values results in a typical signal, see the blue curve in Figure 7. The

minima of the signal define the position of the cell boundaries. The distance between

them defines the size in that frame. The position of the cell is defined by the average of

cell boundary positions. Since both position and frame rate are known, the velocity of the

cell can be simply computed.

The local and the average velocities and sizes of the cells through a region can be easily

calculated with the use of this analysis method.

When the resulting signal after the background subtraction for each frame defines one

row of a matrix and this matrix is displayed as an image, then the movement of the cell

boundaries can be seen very well for each frame, shown in Figure 7. The image

represents the position of the cell on the horizontal axes and time on the vertical axis. The

slope of the line relates directly to the velocity of the cell and its width to the size of the

cell.

size

Position (Pixel No)

Fram

e N

o

size

Figure 7: An image analysis example of a movie made at 1000 fps.


18

7. RESULTS The experimental setup and the experimental procedure are optimized, see chapter 5 and

6, so that the developed Matlab code, see chapter 7, is able to determine the sizes and the

velocities of different cells. An example result is given below:

The syringe pump was set up to 5 µL/min and a movie was taken by the high speed

camera with 1000 frames per second. For these particular experiments, the width of the

narrowest channels in the device was 16 µm. THP1- cells were used for the experiment.

Figure 8 shows the region of interest chosen by the user and selected frames of the movie

that show two different cells flow through that region at different times.

As can be seen from Figure 8, the first cell is smaller than the second cell, which is

expected to result in different cell velocities (see section 2).

Region of interest

cell 1 cell 2

Figure 8: Two cells flowing through the region of interest chosen by the user.


19

According to the image analysis with the developed Matlab code, the first cell has an

average size of 12.1 µm and its average velocity is 3034 µm/s in the region of interest.

The second cell has a size of 15.2 µm and its velocity is 2640 µm/s, see Figure 9.

Figure 9: Image analysis results of the movie.

In the experiments with the microfluidic channels with a width of 16 µm, the diameter of

many THP1 cells is smaller than the channel width, see Figure 8. No significant cell

deformation was therefore observed. The image analysis was carried out for 58 observed

cells with the syringe pump set at a flow rate of 5 µL/min. Figure 10 shows the overall

result of the image analyses. It shows the expected trend, i.e. the velocity of the cell

decreases when its size increases (see section 2). However the points show substantial

scatter. There are several possible explanations for this effect. First, the position of the

cell in the height and width direction is ambiguous. As mentioned before, the height of

the channels is 17.5 µm for each microfluidic device, which is somewhat larger than the


20

diameter of many cells. The same argument holds for the width in the case of 16µm

channels. As explained before, the velocity of a cell depends on its position in the

channel, i.e. the velocity of the cell near the wall is expected to be smaller than if it would

be in the center. This rule holds in the height and in the width direction of the channel. A

second reason for the spread may be the change of the flow velocities due to the

blockages in the other channels. Finally, there may be a variation of the cell properties,

since the cells are taken from a cell population with mixed stages of development.

Figure 10: Average velocity versus cell size in the microfluidic channel with a width of

16 µm with the syringe pump set at a flow rate of 5 µL/min, for a total of 58 cells

measured.

In the experiments with the microfluidic channels with the narrowest width of 12 µm, the

diameter of many THP1 cells is just as large as the channel width, see Figure 11. A small

number of significant deformation events are observed, see Figure 12. Since the movies

are made using the high speed camera with only 100 frames per second, the number of

frames taken per cell travelling through a channel was not sufficient to analyze the size

and the velocity of cells accurately and to obtain good statistics.

Average Velocity vs Cell Size

0500

10001500200025003000350040004500

0 5 10 15 20 25

Cell Size (um)

Ave

rage

Vel

ocity

(um

/s) Movie1

Movie2Movie3Movie4Movie5Movie6Movie7


21

Figure 11: An example event without cell deformation in the microfluidic channel with a

width of 12 µm with the same syringe pump flow rate of 5 µL/min

Figure 12: An example event with cell deformation in the microfluidic channel with a

width of 12 µm with syringe pump flow rate of 5 µL/min

In the experiments with the microfluidic channels with the narrowest width of 8 µm,

significant cell deformations are observed but they could not be quantitatively analyzed

because the movies were not made using the high speed camera, i.e. the number of

frames per cell-transit event was not sufficient for accurate analysis. Moreover, the

magnification was not enough to analyze cell deformations in the correct way, see Figure

13.


22

Figure 13: An example event with cell deformation in the microfluidic channel with a

width of 8 µm


23

8. CONCLUSIONS

This research was the first step towards our overall goal, which is to make a distinction

between healthy and diseased cells with the use of a microfluidic device. This distinction

depends on the mechanical properties of cells, i.e. cell deformability and cell adhesion

that are affected by diseases.

A microfluidic device has been developed to physically deform cells by interaction with

the walls. It consists of a network of continuously splitting and narrowing microchannels

that mimic the in-vivo capillary network. The narrowest channels have a width of 8-12

and 16 µm. The height of all channels is 17.5 µm. The channels are made by combining

PDMS (polydimethylsiloxane) structures with a glass substrate. The cells are observed

using an optical microscope.

The experimental setup and the experimental procedure are optimized in order to make

proper image analyses. THP1 cells are used for the experiments. These are cells from a

human acute monocytic leukemia cell line. Movies made by using a high speed camera

were analyzed by a developed dedicated MATLAB code. The code enabled us to find the

local and the average velocities and sizes of the cells introduced into the microfluidic

device.

From the analysis, an expected trend is observed as a result of image analysis with the

developed Matlab code of the experiments with a device having the narrowest channels

of 16 µm, namely the velocity of the cell decreases when its size increases. In addition,

the measurements show a spread of the velocities of the cells with the same size, for

which there are several explanations. The position of the cell in the height and width

direction, on which the velocity of a cell depends, is ambiguous. A second reason of the

spread may be the change of the flow velocities due to the blockages in the other

channels. Finally, there may be a variation of the cell properties, since the cells are taken

from a cell population with mixed stages of development. To decrease this spread, the


24

cells may be restricted in moving both in height and in width direction in our future

research. Significant cell deformations are observed in the experiments with the

microfluidic channels with the narrowest width of 8 µm but they could not be

quantitatively analyzed because the movies were not made using the high speed camera

and the magnification was not enough to analyze cell deformations.

In conclusion, our developed setup and procedure enables us to analyze the mechanical

properties of cells. In the future, we will use the device to study the mechanical properties

of diseased and healthy cells, focusing on cardiovascular diseases.


25

9. RECOMMENDATIONS To decrease the spread of the velocities of the cells with the same size, the cells may be

restricted in moving both in height and in width direction. In other words, both the height

and the width of the narrowest channels should be smaller than the cell size. The cell is

taken always constricted by the walls so that its position is unambiguous.

As mentioned in the part 4, our microfluidic device consists of narrow channels which

are surrounded by larger channels to prevent any change of the pressure drop over the

narrow channels due to any possible blockage, shown Figure 2. This design has a

disadvantage that manifest itself during “filling of the device”. Firstly, the larger channels

were filled due to their low flow resistances. Then the smaller channels began to be filled.

This filling proceeds from both sides, during which the air still contained in the control

part and need to diffuse out of the device through the PDMS. However, it takes a long

time to fill the device due to pinning of the meniscus at the positions where the channels

split into two, i.e. the fluid proceeds gradually through the channels until it reaches the

sudden widening at the bifurcation point where it slows down since the meniscus is

pinned at the wall. The meniscus then very slowly bows out until it touches the edge in

front that breaks the meniscus. The next design of the device should have smoother edges

to decrease the time of filling.

Liquid , filled Air , unfilled Figure 14: Filling of one device


26

One manufacturing problem was that glass capillaries were not well aligned with the

holes in the glass substrates, see Figure 15, which may result in sticking of the cells at the

rough surfaces in the holes and this may lead to a blockage in the holes at the end.

The alignment of the glass capillaries with the holes in the glass substrate can also differ

for every microfluidic device, which may result in a variance of results, see Figure 15.

1) 2) 3)

Figure 15: The variance of alignment of the glass capillaries with the holes in the glass

substrate

Another problem was to clean the devices after experiments. FACS cleaner, which is

used for cleaning FACS machines, was used one time to clean a device. It was observed

that the device began to be delaminated, see Figure 16. Another solution was to use

trypsin which is an enzyme found in the digestive system, where it breaks down proteins.

It is clear that trypsin has helped to break down the cells. However, cell membranes

formed “net” structures that were sticking to the walls very rigidly, see Figure 17.


27

Figure 16: The result of using FACS cleaner to clean a device after an experiment which

results in delamination of PDMS from the glass.

Net structures formed

by cell membranes

Figure 17: The result of using trypsin to clean a device after an experiment Low concentrations solutions of NaOH and HCl may be a solution to clean the device

after experiments.


28

10. REFERENCES

[1] C. Dong, R. Skalak, Leukocyte deformability: finite element modeling of large

viscoelastic deformation. J. Theor. Biol.,158, 2, 173–193, 1992.

[2] W. Schmid-Schönbein, K. L .Sung, H. Tözeren, R. Skalak, and S. Chien, Passive

mechanical properties of human leukocytes, Biophysical Journal, 36, 243-256,1981.

[3] J.L.Wautier, G.W. Schmid-Schönbein, and G.B. Nash, Measurement of leukocyte

rheology in vascular disease: clinical rationale and methodology, Clin. Exp. Immunol.,

21, 1, 7-24, 1999.

[4] G.W. Schmid-Schönbein, Y.Y. Shih, S. Chien, Morphometry of human leukocytes,

Blood, 56,5, 866–875, 1980.

[5] M.I. Cybulsky, M.A. Jr Gimbrone, Endothelial expression of a mononuclear

leukocyte adhesion molecule during atherogenesis, Science, 251, 4995, 788-91, 1991.

[6] R. M. Bauersachs, G. Moessmer, C. Koch, F. J. Neumann, H. J. Meiselman, and C.

Pfafferott, Flow resistance of individual neutrophils in coronary artery disease-decreased

pore transit times in acute myocardial infarction, Heart, 77, 1, 18–23,1997.

[7] G. B. Nash, B. Christopher, A. J. Morris, and J. A. Dormandy, Changes in the flow

properties of white blood cells after acute myocardial infarction, Br Heart J., 62, 5, 329–

334, 1989.

[8] G. Ciuffetti, R. Balendra, S. E. Lennie, J. Anderson, and G. D. Lowe, Impaired

filterability of white cells in acute cerebral infarction, BMJ.; 298, 930–931, 1989.

[9] P.R.S Thomas, G.B.Nash and J.A Dormandy, White cell accumulation in dependent

legs of patients with venous hypertension- a possible mechanism for trophic changes in

the skin, Br. Med. Journal, 296, 89, 1988.

[10] S. Kado, N. Nagata, Circulating intercellular adhesion molecule-1, vascular cell

adhesion molecule-1, and E-selectin in patients with type 2 diabetes mellitus, Diab. Res.

Clin. Prac., 46, 2, 143-148, 1999.

[11] I. Vermes, E. T. Steinmetz, L. J. J. M. Zeyen and E. A. van der Veen, Rheological

properties of white blood cells are changed in diabetic patients with microvascular

complications, 30, 6, 434-436, 1987.


29

[12] K Miyamoto, Y Ogura, S Kenmochi , Y Honda, Role of Leukocytes in Diabetic

Microcirculatory Disturbances,Microvascular Reserach, 54, 43-48, 1997.

[13] M. J. Rosenbluth, W. A. Lam, and D. A. Fletcher, Biophysical flow cytometry for

hematologic diseases, MicroTAS, 2007.

[14] M. J Rosenbluth, W. A. Lam, and D. A Fletcher, Force Microscopy of Nonadherent

Cells- A Comparison of Leukemia, Biophysical Journal, 90, 2006.

[15] S.C. Jones, R.E. Banks, A. Haidar, A. J. Gearing, I. K. Hemingway, S. H. Ibbotson,

M. F. Dixon, and A. T.Axon, Adhesion molecules in inflammatory bowel disease, Gut.,

36, 5, 724–730, 1995.


11. APPENDIX A- MECHANICAL CELL EFFECTS CAUSED BY DISEASES

Diseases Test Responses Discussion

Atherosclerosis

In a rabbit model of atherosclerosis, an endothelial adhesion molecule selective

for mononuclear leukocytes is identified using an antibody strategy.

And it is homologous to human VCAM-1.[5]

• VCAM-1 is expressed by abnormal endothelial cells in the vicinity of an atherosclerotic plaque.[3]

• Monocyte recruitment as consequence of changes in the adhesive properties of the endothelial surface is a key event in the formation of the fatty streaks due to their differentiation in the subendothelium into foam cells containing lipids.[3][5]

• Ischemia (Tissue damage): The decrease of perfusion pressure may lead a vicious cycle of WBC: Trapping - activation of WBC - endothelial damage & release of activating factors - changes in circulating WBC - further trapping might lead progressive impairment of oxygen delivery and tissue damage.[3]

• Expectation: The deformability of WBC is different than those of the control group.

Vascular diseases

Coronory Disease / acute myocardial infarction

Filtration

• Higher white blood cell count, Higher percentage of granulocyte / lower percentage of lymphocyte / percentage of monocytes did not change, monocyte/lymphocyte ration is increased.

• Decreased mean transit time of a single neutrophil with stable angina and with unstable angina and further decrease

• The increasing deformability of a single neutrophil before myocardial infarction may help to diagnose the expected acute myocardial infarction [6]

• The higher deformability at the early

stage of myocardial infarction offers


31

within 12 hours after the onset of myocardial infarction: Increase of Deformability of Neutrophils [6]

• On the first day after the onset of myocardial infarction the deformability of granulocytes did not change significantly. After first day, the deformability of granulocytes began to decrease, correlated with the activation of the cells, resulting myocardial tissue damage. After 3rd day the deformability began to increase again. [7]

• On the first day, the deformability of mononuclear cells is observed to be decreased as a result of a combination of monocyte activation & increased monocyte/lymphocyte ratio[7]

a window for treatment to prevent excessive activation of the cells resulting tissue damage.[6]

Cerebrovascular disease / acute cerebral infarciton

Filtration

• Decrease of filterability: %27.86 of granulocytes & %36.33 of mononuclear leukocytes. No difference in hematocrit & filterability of erythrocyte.[8]

• Plasma viscosity and fibrinogen concentration were increased with age and further increased with the patients with cerebral infarction. Decreased leukocyte filterability with age and further decrease with patients with cerebral infarction was observed. [8]

• The specific time of the experiments is not mentioned. (could be after one day )

• The reduced filterability, i.e. reduced deformability of leukocytes may promote malperfusion in microcirculation and tissue damage.[8]

• The risk of infarction increases with age.[8]

Ischemia Filtration

• Severe ischemia of the leg : Higher white blood cell count / Higher percentage of granulocyte (%95) / lower percentage of lymphocyte / percentage of monocytes did not change.[9]

• %49 decrease in flow rate of granulocytes, %79 decrease in flow rate of monocytes.[9]

• Changes in flow characteristics of

WBC linked to the presence of ischemia and may result from factors released ischemic tissue.[9]

• Activation of WBC decreases their filterability, i.e. decrease their deformability and is thus a likely


32

• More active cells were observed.[9] cause of the abnormalities in the flow detected. [9]

• Activated cells may cause endothelial damage by production of oxygen free radicals and release of lysosomal enzymes.[9]

• Changes in the flow properties of WBC parallel and contribute to the progress of tissue ischemia.[9]

Diabetes

• Enzyme-Linked ImmunoSorbent Assay, or ELISA[10]

• Filtration[11] • Microchannel Flow[12]

• NIDDM: Observations: Circulating ICAM-1 concentration of the diabetic patients was significantly higher than that of the control group, but no difference between the diabetic patients with and without macroangiopathy. Circulating VCAM-1 in the diabetic patients with macroangiopathy was significantly higher than those of the control group and those of the diabetic patients without macroangiopathy. No significant difference in VCAM-1 concentration between the control group and the diabetic patients without macroangiopathy. [10]

• DDM & NIDDM : A significantly higher number of clogging particles of white cell suspensions was observed with diabetic patients than with the control group.[11] A higher number of clogging particles of white cell suspensions was observed with diabetic patients with retinopathy than with patients without retinopathy.[11]

• NIDDM: Significant transit time difference of suspension of diabetic patients and control group containing erythrocyte & leukocyte, even though there was no difference between the transit times of their suspensions

• Increased adhesion molecules in diabetes lead activation of WBC which generate release of activating factors such as reactive oxygen radicals and lysosomal enzymes. They directly injure the endothelium and promote atherosclerosis.[10]

• Even elevated levels of glucose or

insulin are sufficient to activate neutrophils and monocytes.[3]


33

containing only erythrocytes, which proves lower deformability of leukocytes.[12]

Leukemia • Microchannel flow [13] • AFM [14]

• Transit times of AML and HL60 (subtype of AML) cells are significantly longer than ALL and Jurkat cells (ALL cell line to study acute T cell leukemia cell). Cell deformability affect transit time. Leukostasis is observed more common in patients with AML than those with ALL.[13]

• HL60 cells are observed to be 18 times stiffer than Jurkat cells and 6 times stiffer than human neutrophils. Their apparent stiffness are found as: E = 855 ± 670 Pa for HL60 cells,

E = 48 ± 35 Pa for Jurkat cells, E=156 ± 87 for neutrophils.[14]

• Different mechanical properties of WBC may help to diagnose different subclasses of leukemia and also the different phases of the disease.

• The WBC are not only activated but

also their morphology and cytochemistry are changed.

Inflammatory Disorders

Bowel Disease • Enzyme-Linked ImmunoSorbent Assay, ELISA

• Ulcerative colitis: Circulating VCAM-1 concentration is observed to be higher. Circulating ICAM-1 concentration is observed to be higher but lower than those with Crohn’s disease.[15]

• Crohn’s disease: Circulating ICAM-1 is observed to be higher. Circulating VCAM-1 concentration is observed to be higher but lower than in ulcerative colitis.[15]

• Both diseases show an increase of E-selectin expression.[15]

• Expectation: The deformability of WBC is different than those of the control group.


12. APPENDIX B- MATLAB CODES FOR IMAGE ANALYSIS Loading movie : clc clf clear all close all aviinfo('21-04-8-5ul-1-(5839-5989)') cellmot = aviread('21-04-8-5ul-1-(5839-5989)') ; [height,width,dim]=size([cellmot(1).cdata]); numframes = size(cellmot,2); gr_cellmot = uint8(zeros(height,width,numframes)); % allocate mem % loading each frame as a matrix for i = 1:1:numframes gr_cellmot(:,:,i) = (cellmot(i).cdata) ; end Calculating Pixel size: clear roi;clear p1;clear p2;clear f1;clear f11;clear f12;clear f2;clear f21;clear f22;clear x11;clear x12;clear x21;clear x22;clear firstx;clear firstxx;clear secondx;clear secondxx;clear firsty;clear secondy;clear smoothed;clear kernel;clear minp1;clear minp1x;clear minp1y;clear minp2;clear minp2x;clear minp2y;clear currentIm; clear intProfile;clear x;clear kernel;clear kWidth;clear firstbordercol; clear secondbordercol; clear co; clear ro; clear col; clear row;clear cellmot;clear sizeL;clear fromRow;clear toRow; clear pixellength;clear reallength;clear pixsize currentIm = gr_cellmot(:,:,1); figure(1); clf; imshow( currentIm ); figure(1); [co,ro]=ginput(2); col = round( mean(co) ); fromRow = round( ro(1) ); toRow = round( ro(2) ); intProfile = sum( currentIm( fromRow:toRow,col+[-5:5]),2 )/11; sizeL = length(intProfile); % illustrate selected region hold on; rectangle('Position',[col-5,fromRow,10,toRow-fromRow]) sigma=1.4; %kWidth = 9; tt2 = fspecial('gaussian',kWidth,sigma); kernel = tt2(floor(kWidth/2),:); %kernel = kernel/sum(kernel); x = floor(-3*sigma):ceil(3*sigma); kernel = (x.^2-sigma^2)./(2*pi*sigma^6) .* exp( -(x.^2)/(2*sigma^2) ); kWidth = length(kernel); smoothed = conv( double(intProfile),kernel ); smoothed = smoothed(floor(kWidth/2)+[1:sizeL]); xx=1:length(intProfile); %figure(2); clf; plot( xx,intProfile,'r',xx,smoothed,'b'); shg figure(2); clf; imagesc( currentIm( fromRow:toRow,col+[-10:10] )' ); colormap(gray); axis off;


35

hold on; plot(smoothed-min(smoothed(:))); hold on; %choose two points over the longest path [col row]=ginput(2); firstbordercol=round(col(1)); minp1=firstbordercol-3; for i=-2:1:3 if smoothed(firstbordercol+i)>smoothed(minp1) minp1=firstbordercol+i; end end %plot the first minima minp1y=smoothed(minp1); plot(minp1,minp1y-min(smoothed(:)),'o','LineWidth',2,'MarkerEdgeColor','r',... 'MarkerFaceColor','b',... 'MarkerSize',2); secondbordercol=round(col(2)); minp2=secondbordercol-3; for i=-2:1:3 if smoothed(secondbordercol+i)>smoothed(minp2) minp2=secondbordercol+i; end end minp2y=smoothed(minp2); plot(minp2,minp2y-min(smoothed(:)),'o','LineWidth',2,'MarkerEdgeColor','r',... 'MarkerFaceColor','b',... 'MarkerSize',2); %number of pixels over the path pixellength=abs(minp1-minp2); % get the real length reallength=input('Give the real length between the borders you have chosen : '); %pixel size pixsize= reallength/pixellength Choosing region of interest: clear clf; clear roi; clear p1;clear p2;clear f1;clear f11;clear f12;clear f2;clear f21; clear f22;clear x11;clear x12;clear x21;clear x22;clear firstx;clear firstxx;clear secondx;clear secondxx;clear firsty;clear secondy;clear smoothed;clear kernel;clear minp1;clear minp1x;clear minp1y;clear minp2;clear minp2x;clear minp2y;clear currentIm; clear intProfile;clear x;clear kernel;clear kWidth;clear firstbordercol; clear secondbordercol; clear co; clear ro; clear col; clear row;clear cellmot;clear sizeL;clear fromRow;clear toRow;clear roi;clear p1;clear p2;clear f1;clear f11;clear f12;clear f2;clear f21;clear f22;clear x11;clear x12;clear x21;clear x22;clear firstx;clear firstxx;clear secondx;clear secondxx;clear firsty;clear secondy;clear smoothed;clear kernel clear minp1;clear minp1x;clear minp1y;clear minp2;clear minp2x;clear minp2y;clear currentIm; clear intProfile;clear x;clear kernel;clear


36

kWidth;clear firstbordercol; clear secondbordercol; clear co; clear ro; clear col; clear row; figure(1), imshow(gr_cellmot(:,:,1)) msgbox('choose your region of interest','help','help'); maskposition=[]; h=imrect(gca,maskposition); roimask= input('copy position of rectangle on the command window(right click your mouse) : '); maskx= roimask(1); masky=roimask(2); masklength=roimask (3); maskwidth=roimask(4); maskybegin=masky; numberroi= input(' how many regions of interest in total do you have : '); y=2; for i=1:1:numberroi-1 h=imrect(gca,[2 y masklength maskwidth]); y=y+maskwidth; end for i=1:1:numberroi roi(i,:)= input(['copy position of ',num2str(i),' region of interest on the command window: ']); end Background subtraction and detection of moving cells in the region of interest: clear sum_ints;clear Endminpoint;clear Maxpoint;clear Minpoint;clear roichannel;clear background;clear roichannel1; clear background1;clear backdiff;clear imgsqnc;clear begin;clear Med;clear pixel;clear sc;clear a;clear b;clear c;clear d;clear e;clear f;clear g;clear i;clear j;clear k; clear r;clear regions1 %flip movie %for i= 1:1:numframes % regions1(:,:,i)= fliplr(regions(:,:,i)); %end reply=input('which region of interest do you want to analyse:') rect=(roi(reply,:)); roichannel = (zeros(round(rect(4))+1,round(rect(3))+1,numframes)); % allocate mem for i= 1:1:numframes % roichannel(:,:,i)= imcrop(regions(:,:,i),rect); roichannel(:,:,i)= imcrop(gr_cellmot(:,:,i),rect); end % region of interest : which channel? [a b c]=size(roichannel); pixel=(zeros(c,a*b)); y=1; z=1; for j= 1:1:a for k= 1:1:b for i= 1:1:c pixel(y,z)= roichannel(j,k,i);


37

y=y+1; end y=1; z=z+1; end y=1; end % median of each pixel: background background=(zeros(a,b)); Med=median(pixel); y=1; for i= 1:1:a for j= 1:1:b background(i,j)= Med(1,y); y=y+1; end end backdiff=(zeros(a,b,c)); %substraction of your background for i=1:c for j= 1:1:a for k= 1:1:b roichannel1=fft(double(roichannel(j,k,i))) ; background1=fft(double(background(j,k))); backdiff(j,k,i)= roichannel1- background1; end end end %integration over the width sum_ints= (zeros(1,b,c)); sum=0; for i=1:c for k= 1:1:b for j=1:1:a sumc= sum+ backdiff(j,k,i); sum=sumc; end sum_ints(1,k,i)=sum; sum=0; end end %display imgsqnc= (zeros(c,b)); for sc=1:1:c imgsqnc(sc,:)=sum_ints(:,:,sc); end figure; [a b]=size(imgsqnc); imagesc(imgsqnc(1:a,1:b)), figure(gcf) colormap(gray);hold on


38

Calculating Local/average velocities and sizes of the cells: %close all clear imgsqnc1;clear threshold;clear maxthreshhold2;clear r ; clear c; clear Maxpoint; clear Localpoints; clear Points;clear MinLocalpoints;clear MinLocalpointsnum;clear MaxLocalpoint;clear Sizeofcell;clear m; clear n;clear l; clear s;clear LocalCelldata;clear tempcelldata;clear AverageCelldata Points=imgsqnc; [r c]= size(Points); %find local minima of the intensities in the image representing the boundaries %of the cell MinLocalpoints=zeros(r,c); z=1; for i=1:1:r for j=1:1:c %it does not count j=1 and c! if Points(i,j) <-150 if j+1<=c && j-1>=1 if Points(i,j+1)>Points(i,j) && Points(i,j-1)> Points(i,j)&& Points(i,j-1)<0 && Points(i,j+1)<0 MinLocalpoints(i,j)= Points(i,j); elseif Points(i,j+1)==Points(i,j) && Points(i,j-1)> Points(i,j)&& Points(i,j+2)> Points(i,j+1) && Points(i,j-1)~=0 && Points(i,j+2)~=0 MinLocalpoints(i,j)= Points(i,j); else MinLocalpoints(i,j)=0; end end else MinLocalpoints(i,j)=0; end end end MinLocalpointsnum = zeros(r,2); k=1; for i=1:1:r for j=1:1:c if MinLocalpoints(i,j)< 0 MinLocalpointsnum(i,k)=j; k=k+1; MinLocalpointsnum(i,k)=MinLocalpoints(i,j); k=k+1; end end k=1; end %put the data of the cell borders into another matrix [m n]= size(MinLocalpointsnum); Cellborderpoints=zeros(i,4); for i=1:1:m for j=2:2:n


39

if MinLocalpointsnum(i,j)< Cellborderpoints(i,2) Cellborderpoints(i,1)=MinLocalpointsnum(i,j-1); Cellborderpoints(i,2)=MinLocalpointsnum(i,j); end end for j=2:2:n if MinLocalpointsnum(i,j)< Cellborderpoints(i,4)&& MinLocalpointsnum(i,j-1)~=Cellborderpoints(i,1)&& abs(MinLocalpointsnum(i,j-1)-Cellborderpoints(i,1))>=10 Cellborderpoints(i,3)=MinLocalpointsnum(i,j-1); Cellborderpoints(i,4)=MinLocalpointsnum(i,j); end end end % Get rid of minimas near the real minima! [t y]= size(Cellborderpoints); for i=1:1:t if abs(Cellborderpoints(i,3)-Cellborderpoints(i,1))<=5 if Cellborderpoints(i,2)<Cellborderpoints(i,4) Cellborderpoints(i,3)=0; Cellborderpoints(i,4)=0; else Cellborderpoints(i,1)=0; Cellborderpoints(i,2)=0; end end end for i=1:1:t for j=1:1:y plot(Cellborderpoints(i,j),i,'.','LineWidth',1,'MarkerEdgeColor','g', 'MarkerSize',4) end end % find max between borders - if there are 2 borders then it calculates. j=1; for i=1:1:t if Cellborderpoints(i,j)~= 0 && Cellborderpoints(i,j+2)~= 0 MaxLocalpoint(i,1)=(Cellborderpoints(i,j)+Cellborderpoints(i,j+2))/2; Sizeofcell(i,1)=abs(Cellborderpoints(i,j)-Cellborderpoints(i,j+2)); else MaxLocalpoint(i,1)=0; Sizeofcell(i,1)=0; end end %plot max between borders, the first col is the place of max for i=1:1:m if MaxLocalpoint(i,1)~= 0 plot(MaxLocalpoint(i,1),i,'.','LineWidth',1,'MarkerEdgeColor','r', 'MarkerSize',8) end end


40

%tempcell data= 1.col = frame 2.col= position of the middle of the boundaries 4.col= size of the %cell in that frame h=1; f=1; g=1; [m n]=size(MaxLocalpoint); for i=1:1:m if MaxLocalpoint(i,1)~= 0 tempcelldata(f,g,h)=i; tempcelldata(f,g+1,h)=MaxLocalpoint(i,1); tempcelldata(f,g+3,h)=Sizeofcell(i,1); f=f+1; if MaxLocalpoint(i+1,1)==0 && MaxLocalpoint(i,1)~= 0 h=h+1; f=1; g=1; end end end [f g h]= size(tempcelldata); for k=1:1:h for j=1:1:g for i=1:1:f LocalCelldata(i,j,k)=tempcelldata(i,j,k); if tempcelldata(i,j,k)==0 LocalCelldata(i,j,k)=NaN; end end end end %cell data= 1.col = frame 2.col= position of the middle of the boundaries 3.col=local velocity (pixel per frame) 4.col= size of the %cell in that frame for k=1:1:h for i=2:1:f LocalCelldata(i,3,k)=(LocalCelldata(i,2,k)-LocalCelldata(i-1,2,k)); end end for k=1:1:h AverageCelldata(1,1,k)= nanmean(LocalCelldata(:,3,k))/0.001*(floor(pixsize*100)/100); AverageCelldata(1,2,k)= nanmean(LocalCelldata(:,4,k))*(floor(pixsize*100)/100); end

development of a microfluidic device and experimental ... · health and disease states. white blood...

Documents