Optimized Cell Trapping and Multi-Perspective Imaging Using Microfluidic

Devices

Jeff Chamberlain, Matt Houston, Eric Kim

Advisors: Dr. Kevin Seale and Dr. John Wikswo

BME 273 – Design of Biomedical Engineering Devices and Systems

Instructor: Dr. Paul King

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ABSTRACT:

There was a need in the VIIBRE laboratories for characterization of the trapping

efficiency of various trap designs. In order to assist in the trap classification process,

computational fluid dynamics were assessed using Fluent©. Both single traps and arrays of traps

were analyzed for different trap designs. Dr. Seale’s technology, known as Mirrored Pyramidal

Wells (MPWs), provides this ability using inverted square pyramids with reflective walls, each

of which provides a different perspective of the specimen within the well that it creates. Our task

was to design an optimized microfluidic network which would interface with an array of wells

and passively guide the specimen into MPW’s. Different trap designs were tested on similar

devices. Media was continually flowed into the device, both to maintain cell viability and to

utilize trap design. Images were taken using a Carl Zeiss inverted microscope and Metamorph©

as the data acquisition program. Images were taken at known increments, and the images were

analyzed using ImageJ. MPW etching was performed in a multistep process: 1) SiO deposition,

2) photolithography, 3) HF etch, 4) photolith mask removal, 5) KOH etch, and 6) HF removal of

SiO. Results suggest that the gradient density, square, split-back array design is optimal for use

in the coupled device with MPW’s (90% traps filled, 20% traps with 1 cell). Images acquired

from Fluent© offer indications that directing flow into traps requires further modification.

MPW-PDMS coupled device prototypes have been produced (thick and thin) and images

acquired. Continuing development of this device and usage protocol is needed to eliminate

problems with bubbles in wells and cell clumping.

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INTRODUCTION:

The benefits of using microfluidics for cellular studies are well documented.

Microelectromechanical systems (MEMS) devices are constructed using batch fabrication

techniques, such as soft lithography and photolithography. These techniques allow rapid

fabrication of multiple devices by implementing relatively few specific steps. The devices used

in this project contain arrays of barriers which passively trap cells. These trapping arrays allow

the experimenter to perform high-throughput experimentation; each cell that is captured can be

analyzed individually. Using only microliters of reagent, complex biochemical analyses of

hundreds of individual cells can be accomplished in each experiment.

There was a need in the VIIBRE laboratories for characterization of the trapping

efficiency of various trap designs. There were many trap designs already in use, and no

classifications existed for the different designs. When maximizing efficiency, one must first

define what efficiency is for each experiment. For some experiments, efficiency is considered to

be capturing the greatest percentage of loaded cells possible, minimizing waste. For other

experiments, the experimenter may desire control over the number of cells captured by each trap.

For instance, in some experiments, it is preferable to have only 1 cell/trap, allowing the

experimenter to minimize the cell to cell interactions. In order to assist in the trap classification

process, computational fluid dynamics were assessed using Fluent©. Both single traps and arrays

of traps were analyzed for different trap designs.

In addition to analyzing and improving the cell trapping efficiency of microfluidic

devices in the VIIBRE laboratories, our group designed a microfluidic network to interface with

a novel microscopic imaging device developed by one of our advisors, Dr. Kevin Seale.

Conventional microscopy provides a two dimensional view of a specimen in the XY-plane, with

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adjustable focus along the Z-axis. While this method is sufficient for many biological studies,

imaging other phenomena on the cellular scale would benefit greatly from multiple, and

potentially simultaneous, viewpoints of the specimen. Dr. Seale’s technology, known as

Mirrored Pyramidal Wells (MPWs), provides this ability using inverted square pyramids with

reflective walls, each of which provides a different perspective of the specimen within the

well that it creates. Further, if the

specimen is centered within the

well, all four reflections reside in

the same focal plane, meaning

that four different viewpoints can

be visualized simultaneously.

MPWs can provide three-

dimensional spatial information

and increased signal-to-noise

ratios not possible with conventional microscopy, both of which are important in biological

events such as immune synapse formation, chemotaxis, and cytokinesis, just to name a few. The

set of images in figure 1 serves well to introduce the technology by showing the concept, the

topology of a single well, a complete array of wells, and lastly the impressive resolution

capabilities.

The MPWs are anisotropically etched into silicon wafers at known angles specified by

the unit structure of silicon, with the ultimate width and depth of the wells customized for a

specific specimen by the fabricator. Standard photolithography techniques are applied for

Figure 1. MPWs allow simultaneous multi-perspective imaging using inverted square pyramids with reflective walls, with each wall providing a different view of the specimen. MPWs can provide three-dimensional spatial information and increased signal-to-noise ratios not possible with conventional microscopy. A) SEM micrograph of MPW. B) SolidWorks rendering illustrating MPW concept. C) Autofluorescent Helianthus Annuus pollen grain in MPW. D) Demonstration of scale. *Technology and images developed by Kevin Seale*

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creating arrays of many wells in a small area (scale: µm2-mm2), which makes them ideal for

integration with biomicroelectromechanical systems (BioMEMS).

There are several factors which limit the effectiveness of an array of MPWs. The first is

the loading efficiency of the specimen into the wells. Previous methods utilized a micro-

positioning tool to physically place the specimen into the well or a pipette to cover the array with

the specimen in solution, with the hope that a few cells would find their way into the wells. Our

task was to design a microfluidic network which would interface with an array of wells and

passively guide the specimen into them, thereby increasing the MPW loading efficiency and

increasing experimental throughput. Secondly, our device allows for long-term analyses because

of continuous and controllable perfusion of the specimen. Finally, this device couples the

experimental advantages of conducting studies using microfluidics and the novel imaging

abilities of the MPWs, increasing the experimental potential of both technologies.

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METHODOLOGY

When using microfluidic devices for

biochemical analysis, the experimental setup

is crucial. Firstly, the device must be

fabricated. Our devices were made of

polydimethylsiloxane (PDMS), a silicon

elastomer. The masters for the devices were

made using photolithography techniques,

with SU-8 photoresist. The PDMS devices

were made using soft lithography. The

PDMS was mixed at a 10:1 polymer to base

ratio, poured onto the SU-8 master, and

degassed. The PDMS was then baked at 60°C

for at least 2 hours. The devices were cut and peeled from the master, and finally plasma bonded

to glass slides (Figure 2). Fluid was pumped into the devices by connecting gas-tight syringes to

the devices via PEEK Polymer Tubing (ID 50 µm). Flow through the device was controlled by

Harvard Apparatus Syringe Pumps.

Figure 3 shows the general device layout. Using proper cell culture techniques, the

Jurkat Cells were placed into a microcentrifuge tube and spun at the lowest possible speed (1500

RPMs) for 1 minute. A syringe pump was run in reverse order to aspirate the cells into the

tubing, which could be observed using a microscope, making the process uncommonly

controllable. If drawn in too quickly, the loading of the cells is unsuccessful because clogging in

Figure 2. Basic steps to photolithography and soft lithography.

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the tubing causes the cells to lyse. When successful, the cells were loaded into inlet B, and

captured inside the trapping array. Different trap designs were tested on similar devices. Media

was continually flowed into the device, both to

maintain cell viability and to maintain force on the

cells to keep them inside the traps. Images were

taken using a Carl Zeiss inverted microscope and

Metamorph© as the data acquisition program.

Images were taken at known increments, and the

images were analyzed using ImageJ.

One of the prime advantages to

microfluidic bioanalysis systems is the low

quantity of biomaterial, cells in our case, required to perform an analysis. Optimizing this

property, therefore, strengthens one of the advantages of the system. Previous systems for

trapping cells in VIIBRE have proved effective. Despite this, it has often been felt that higher

trapping efficiency could be achieved and this advantage optimized. To this end, we have

developed two new "improved" trapping systems that we hope will optimize cell trapping

efficiency.

It has been frequently observed that a cell flowing through a trap array will weave

between traps and generally avoid being trapped until deep into the array, if it is trapped at all.

We have theorized that this was due to straight flow that simply pushes the cells axially along the

device, instead of adding a transverse component to the cells’ direction, which would offer a

much higher chance of trapping.

Figure 3. CAD image of general cell trapping device. Inset shows actual image of trapped cells within a device.

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1) Curved-side arrays: To this end, we developed the Curved-side arrays with non-linear

boundaries to force diagonal flow.

2) Wing-Sided arrays: Optimization of trapping requires optimization of flow characteristics, as

it is clear that cell movement is strongly correlated with fluid flow in the device. The Wing-

Sided traps force fluid flow into downstream traps, theoretically optimizing cell trapping. In

addition to the fluid flow, the cells also have inertial characteristics which guide them into the

traps.

3) Gradient-Trap-Density arrays: This array was developed due to a clogging problem that

occurred due to cell clumping. It was noted that cells loaded into the device tended to clump,

and that high density trap arrays tended to catch a lot of these clumps and gather them into a

growing wall of cells that impeded flow. To prevent this, density of traps was varied from low in

front to high in back of the array. Low density traps helped to break up the clumps and avoid

cell walls, optimizing trapping for the downstream high density.

Figure 4. Schematics to elucidate reasons for changed trap and device designs. A) Curved-side arrays. B) Wingback traps. C) Trap density increases with axial position in device.

Optimization of trapping for design purposes required several layers of analysis:

1) Designs must be analyzed for their ability to fill themselves. To this end, analyses were

performed on each trap type for percentage of traps filled with cells vs. without cells. 2) Designs

must be analyzed for their ability to encourage single cell trapping for coupling will MPWs. To

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this end, analyses were performed on each trap type for percentage of traps with only one cell.

3) Finally, designs must be analyzed for their ability to trap cells uniformly. To this end,

analyses were performed on each trap type for uniformity of trapping percentages throughout the

devices. Analysis of each trap type is rated against a definition of success, the goals of which are

as follows: 1) at least 10% of traps must have one and only one cell (since results were good

enough, high-level success may be defined as above 20%); this parameter is a necessary MPW

operating parameter, 2) 90% of traps must be filled, and 3) a difference of more than 5% must be

identified to classify trap designs into different classes: a) optimized for cell trapping and b)

optimized for single cell trapping. Results were quantified in groups for statistical analysis, with

each column of traps in a device N=1. Cells were counted by hand for each trap in each column

with traps falling into 5 categories: 0 cells, 1 cell, 2 cells, 3 cells, and 4 cells. Percentages of

each category were calculated based of the totals for a single group (column). In addition to

these, rate loading experiments were performed for old arrays vs. Curve-Sided Arrays. Cell

solutions of constant density were flowed through the array and time-lapse pictures were taken.

Cells trapped per frame were counted and used to approximate a linear rate loading.

The computational fluid dynamics were generated for 2 different trap types: the wing

back traps and the triangular split back. For each of these types of traps, 2D meshes were made

for both an individual trap and an array of traps. Velocity magnitude was analyzed by looking at

velocity vector simulations. These vectors show the magnitude of flow both by color and by

length. The velocity contours were also analyzed for both trap designs.

The MPWs are fabricated using processes for etching silicon and metal deposition that

are well documented in the literature, and will be mostly omitted in this discussion. Briefly, the

array is defined using standard photolithographic techniques on a <100> silicon wafer with an

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oxide layer as a mask. The size and arrangement of the wells are determined by the photomask

used in the photolithography. The silicon is etched using KOH, with the amount of time spent in

the KOH determining the depth of the wells. Once the wells are etched and the oxide mask is

removed, the entire wafer is coated in a reflective metal such as platinum or aluminum using a

thin-film deposition technique known as electron beam evaporation. The result is an array of

MPWs with dimensions appropriate for the specimen.

The first design issue for the MPWs was creating an array of wells that were the correct

size for each of the following cell types: primary T cells, Jurkat T cells, and dendritic cells. KOH

etches silicon anisotropically, and with a <100> silicon wafer, the etching angle is fixed at 54.7o,

which provided our first design constraint. Our second design constraint required that the side

lengths of the bottom of the wells be at least 40% larger than the diameter of the cell they were

designed for. The value of 40% was chosen somewhat arbitrarily with our advisor, but with the

considerations that the wells should be able to accommodate some variance in cell size and leave

room for the cells to grow and possibly divide. The third design constraint is related to the first

two and requires that an incident light ray coming from the microscope should be able to reflect

off the very top of the well and still pass over the specimen. We developed equations on these

design constraints and found the required outer well dimensions and the minimum etch depths

for each type of cell as seen in Appendix A.

After designing the MPW arrays, we designed a microfluidic device to direct the cells

into the wells. For this we adapted an existing device used in our trapping efficiency experiments

and by others in the VIIBRE laboratory. However, we made a few changes to the traps to

improve trapping, prevent clogging, and most importantly, allow multi-perspective imaging

using the reflective walls of the wells. To improve trapping, we included an extra gap in the back

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of the trap to encourage more flow through it and added wings to the outside of the traps with the

hope of directing cells that passed one row of traps into the subsequent row. To prevent groups

of cells from clogging in the front of the device, we again implemented the strategy of increasing

trap density from the front to the back of the device. To ensure that multi-perspective imaging

was possible, no part of the trap could overlap with the wells because such features diffract light,

which significantly detracts from image quality. All of these considerations led to the general

device layout seen in Figure 6A. Figure 6B shows a schematic cross-section of a single well

with a passive microfluidic trap above it.

Figure 6. A) CAD drawing of microfluidic interface to be used by dendritic cells. Dendritic cells are much bigger than primary and Jurkat T cells, so the traps are larger and there are fewer of them. B) Schematic cross-section of a single trap and MPW. Cells are passively trapped above the wells, and then drop into them where they can be imaged.

Si Wafer

Cross Section of One Well

PDMS

Cell

Light Source and Imaging

A B

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With the design for the well and the traps established, several design constraints arose for

the coupling of the microfluidics to the MPW array. A common problem with microfluidic

devices is maintaining a tight seal between the microfluidic channels in the PDMS and the

substrate to prevent leakage. This problem is commonly overcome by irreversibly binding the

PDMS to the substrate, which is typically glass. However, this bonding process does not work

with a metal-coated silicon wafer. When bonding is not an option, it is often necessary to use a

clamp to maintain the fluidic integrity of the device. Before designing and fabricating a clamp,

we decided first to test the device with the PDMS passively bonded to the MPW array through

Van der Waals forces. To improve the possibility of this, we reduced the fluidic resistance by

using wide fluidic channels and eliminating any extraneous features. To test the unclamped

device, we pumped fluid starting with low flow rates of 50nl/min, gradually increasing the flow

if no leakage occurred, all the way up to 2000nl/min. Actual experiments in the device will rarely

use flow rates above 1000nl/min, so if the unbound device could withstand flows of 2000nl/min

without leakage, then our experimentation did not require a clamp.

In order to resolve the reflections of a cell in a single well, it is necessary to use an

objective with a magnification of at least 50x. This imposed a major design constraint because of

the limited working distance of the high power objectives. The 50x and 100x objectives on the

microscope which we used for imaging the MPW device have working distances of only 0.9mm

and 0.95mm, respectively. This means that the added distance between the specimen in the well

and the objective has to be less than these distances, requiring our PDMS microfluidic layer to fit

within this space. It is entirely possible to form PDMS devices which are less than 0.9mm, but

since the fluidic connection between the PEEK tubing and the PDMS relies on pressure to stay

sealed, thin devices present serious problems to fluidic delivery. Thin PDMS devices were

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fabricated by spinning small amount of PDMS onto the master. In order to improve the seal of

these thin devices, the PDMS was made with a higher cross-linking chemical concentration.

Using thin devices brought about two other major problems. As PDMS cures, it tends to

shrink by a small amount. However, this shrinkage is much more extreme with thin devices. In

order to overcome this problem, we started curing the devices at room temperature, as this seems

to lessen the effect significantly. Next, PDMS films are very difficult to work with as they are

very flexible and extremely electrostatic. Aligning a PDMS film within the necessary accuracy

of only a few microns generally requires a lubricating layer of methanol between the PDMS and

the object of alignment, which is the MPW array in our case. After alignment, the complete

device must be baked in the 60degC oven for a period of at least four hours in order for the

methanol to evaporate. Lastly, PDMS is extremely compliant when it is so thin, which

rejuvenated the possibility of having to use a clamp to prevent leaking. We discovered that

bonding a glass coverslip over the device everywhere except for the inlets and outlets reduced

the compliance to a point where leaking could be avoided.

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RESULTS AND DISCUSSION

Figure 7. The graphs above show percentage results for groups of different traps (see discussion below)

Different trap types represented here are graphed in figure 7 according to the percent of

traps filled and percent of traps filled with only one cell. Calculation of percentages are based

upon sums of all traps in each column, each column representing N=1. N values are as follows:

1) SFLD N=24, 2) TSLD N=16, 3) SSLD N=8, 4) TSHD N=30, 5) TFHD N=30, and 6) Curve-

Sided N = 84. The STD's represented in graph b show the relatively even trapping in the Curve-

Sided array. This is also elucidated by the front to back line graphs in c and d. These line graphs

show the % filled for each column in the array from front to back. c represents low density older

traps, and d represents the Curve-Sided traps. The following are specific results to note: 1)

Curve-Sided Arrays exceeds success parameters, reaching 99% of traps filled. It fails success

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parameters for single cell trapping, being below 8% single cell trapping. 2) Curve-Sided Arrays

offer more uniform trapping, as suggested by the lowest STD of all trap types tested and "front-

to-back" profile. 3) Square, split-back traps fit parameters for success both in traps filled (~90%)

and high-success parameters for single-cell trapping (20-21%). 4) High density traps have

substantially lower uniform loading, as evident from the higher STD’s in Figure 8.

Conclusions that may

be drawn from this data are

as follows: 1) Curve-Sided

Arrays are a perfect design

for high cell trapping

efficiency, 2) split-back, low

density trapping arrays are

best for single cell trapping,

3) gradient trap density is

unnecessary for Curve-Sided

Arrays due to uniformity of loading, and 4) a gradient, split-back, low density array is optimal

for MPW coupling due to its achievement of operational parameters needed for MPW coupling.

Low Density Vs High Density

0

20

40

60

80

100

120

140

160

180

200

% Traps Filled % Traps with 1 Cell

Perc

ent

Low DensityHigh Density

Figure 8. The graphs above show percentage results for high density and low density

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Figure 9. Velocity vector simulations of individual traps for wing back trap (left) and triangular split back trap (right). Scale is relative to input flow. The first computational fluid dynamics analyses were done on the single traps (Figure 9).

The flow was limited to only laminar flow, and for simplification purposes, the fluid analyzed

was water. The scale shows the velocity magnitude with respect to the input flow, and the values

should be considered arbitrary. From these flow profiles it was realized that there is nearly

stagnant flow inside the trap, and there are about 4 vortexes around each trap. It was hard to

decide if the flow profiles facilitated trapping, so further analysis was carried out.

Figure 10. Velocity vector simulations of trapping arrays for wing back trap (left) and triangular split back trap (right). Scale is relative to input flow.

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To further understanding of flow dynamics inside the devices, the different trap designs

were analyzed in arrays. Once again, the scale is relative to the input flow, and the values should

be considered arbitrary. Fortunately, the flow profiles throughout the triangular split back array

support the data, but for reasons that were unexpected. It appears that the extra turbulence in the

flow direct the velocity vectors closer to the insides of the traps. The wing back traps appear to

direct flow between traps, explaining the observed “slaloming” of the loaded cells. These images

and more can be seen in at a larger scale in Appendix B.

The integration of a microfluidic network with the MPWs was successfully

accomplished, as indicated by the absence of leaking. Early tests revealed that a clamp is not

necessary to prevent leaking if careful fabrication techniques are used and if the experimental

flows rates are kept at 2000nl/min. Despite later discovering that we would need to use

extremely thin PDMS devices, the coverslip successfully prevented the PDMS from bending and

no leaking was observed at flow rates below 2000nl/min.

Unfortunately, the farther reaching portion of this design project was not yet

accomplished, which is the ability of the microfluidic device to guide cells into the wells where

they can be analyzed while they are maintained and experimented on. However, a last-minute

trial suggested that success is not far off, and further trials are planned before we graduate.

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CONCLUSIONS

The overall goal of this project was to design and construct the internal system of a cell-

based microfluidic lab-on-a-chip that optimized high-throughput and incorporated MPW micro-

cellular imaging. It has been determined that, due to the size of the MPW's, a low density array

must be used. Several parameters were established as definitions for success:

Goals Successes

The cell trapping system used must fill at least

90% of traps; 10% of those traps, at least, must

hold single cells only (20% for high-success).

This is necessary for MPW operation

Gradient density, straight, square, split-back

traps achieved a total trapping of ~90% and a

total single cell trapping of ~20%. This makes

them optimal for MPW integration.

Imaging phenomena on the cellular scale from

multiple, and potentially simultaneous,

viewpoints of the specimen with MPW's.

Microfluidics have been successfully coupled

with MPW arrays, and images of cells from

multiple perspectives in the wells have been

acquired. Prototypes with both thick and thin

PDMS have been produced.

Accurately generate computer fluid dynamics

profiles for our traps for use to support theory

of cell loading

Vector velocity approximation of single and

array of traps for two different trap types

obtained. Also velocity contours for same.

Cell trapping experiments could be further elucidated by rate-loading studies. However,

sufficient data has been collected to suggest the conclusions here conceived. MPW's are a

cutting-edge technology for imaging recently developed in the VIIBRE laboratories, and as such

has design complications and limitations that have yet to be worked out. As the MPW

technology changes, improvements and modifications to this design must be made to maintain

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this successful coupling. Maintaining proper operational parameters for both the fluidics and the

MPW's, as MPW technology is modified, require the flow analyses that we have generated.

Together, these design elements form the system that was intended and a compilation of data that

will allow modification of the device in future research and development.

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RECOMMENDATIONS

Some problems encountered include:

• Bubbles adhere preferentially to the small wells during etching, causing rough or

misshapen wells.

• Bubbles adhere preferentially to the small wells during cell loading, causing flow and

imaging issues

• Cells clump in large MPW coupled traps, causing imaging problems

Continuing development of this device and usage protocol is needed to eliminate these problems

and optimize the device for use. Cell-based microfluidics offers great potential for high-

throughput analysis. The new design proposed and tested by this report integrates fluidics

designed to minimize necessary biomaterial for analysis with a promising new imaging system

just starting its development in the field. This optimization of old advantages in combination

with cutting edge imaging analysis alone makes this design something worth continued research

and development. The successes of this design project are as follows:

• Optimized and customizable microfluidic systems that minimize necessary quantities of

biomaterial

• Optimized microfluidic systems that create sufficient operating parameters for coupling

and proper operation of MPW's.

• Thick-layer PDMS coupled with an MPW array, our first concept prototype for the

device proposed.

• Thin-layer PDMS coupled with a MPW array, our first thin-layer prototype for the device

proposed.

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The final two of these points have been achieved, but not quantified, nor tested. Operational

parameters, limitations, and quantified success of this device are things recommended for the

future research and development. Additionally, further development of the relatively new MPW

imaging technology should also be coupled with new designs of this device.

Economic, Social, and Ethical Discussion: There are no major ethical issues involved with our

device, as it has been developed solely with the goal of data acquisition in biological research.

The field of cell-based microfluidics has the potential for clinical use. If it passes this point,

standard ethical questions involved with new device use in patients will be present. Socially, this

device is one step on the way towards lab-on-a-chip, an area that will, in itself, have major social

consequences. Most significantly, public availability of high-throughput scientific analysis

equipment may personalize medical care and diagnosis. Again, this is out of the scope of our

device. Economically, this device will become the intellectual property of VIIBRE. If this

system becomes part of a popularly used lab-on-a-chip system, VIIBRE has the potential to

make money. All research and development costs were paid by VIIBRE.

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APPENDIX A – Calculation of MPW dimensions

Well Bottom Size

b = w – 2*h / tan(54.7o)

Minimum Etch Depth

h = d + w/2 * tan(19.4o)

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APPENDIX B – Fluent Image

Asymmetric velocity contour of single wing back trap. Asymmetry due to meshing.

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Vector velocity simulation of single wing back trap. Vectors show velocity relative to length and

color.

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Velocity contour of array of wing back traps. No symmetry used.

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Velocity vector simulation of wing back trapping array. No symmetry used.

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Velocity vector simulation of single triangular split back trap. Symmetry used.

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Velocity contour analysis of triangular split back trapping array. No symmetry used.

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Velocity vector approximation of triangular split back trapping array. No symmetry used.

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APPENDIX C - Innovation Workbench

Ideation Process

Project Initiation

Project name:Optimization of T cell trapping in a microfluidic device

Project timeline:September 2006 - May 2007

Project team:

Matt Houston

Jeff Chamberlain

Eric Kim

Innovation Situation Questionnaire

Brief description of the situation

Improve functional efficiency

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Current devices for cell trapping can be improved upon in regard to controlling the total number of cells trapped as well as the number of cells per trap, which will require a new design for the individual traps as well as the overall device.

Detailed description of the situation

Supersystem - System - Subsystems

System name

Microfluidic device for trapping cells. For the traps, the problem is that there are many cells that flow past the traps in the device, and avoid the traps. There is also a problem with traps containing too many cells, when only 1 cell per trap is desired.

System structure

The system structure consists of the following elements:

•pump

•tubing

•microfluidic device

•traps

•cells

•media

Supersystems and environment

Other systems located nearby:

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•Zeiss inverted microscope

•Computer (PC)

•Multiple moniters

Systems with similar problems

Similar problems exist in many microfluidic applications. This is because the flow dynamics are not well understood. For this reason, a characterization of fluidic flow will accompany the optimization of the traps.

Input - Process - Output

Functioning of the system

The cells will be pumped through the microfluidic device, and will be transported in media. The device will have an array of traps. The primary useful function of the traps is to capture cells as they flow in the environment surrounding the trap. For the trap to be optimized, minimal cells must avoid the trap, and cells should not be able to escape the trap once captured.

System inputs

Microfluidic device inputs

•cells (many)

•media

•chemical reagants

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System outputs

•cells (as few as possible)

•media

•chemical reagants

•data

Cause - Problem - Effect

Problem to be resolved

Improve the trapping efficiency of the cell-trapping array, so that there are more cells trapped and fewer cells lost. A large number of cells must be loaded into the device, and the percentage of these cells that become trapped is too small.

Mechanism causing the problem

The flow characteristics throughout the array of traps causes the cells to slalom between traps, and not become captured. Changing the design of the cell traps will alter the flow characteristics, ideally causing more cells to be trapped.

Undesirable consequences if the problem is not resolved

If the trapping efficiency problem is not resolved, cells will continue to be wasted, and the process will lack optimization.

Other problems to be solved

Control the number of cells captured and held by each trap.

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Create an understanding of the fluid dynamics and molecular diffusion in the device.

Past - Present - Future

History of the problem

MEMS have been around since the late 1960's, and the first device was a gold resonating MOS gate structure. BioMEMS is the biological application of MEMS, and is a surging field of research. There are many opportunities for improvements in MEMS, one of these being trapping efficiency and techniques. The dynamics of flow are currently not well understood at this scale, and an understanding of this will improve the success of the project.

Pre-process time

Time before pump is turned on.

Post-process time

Time after pump is turned off.

Resources, constraints and limitations

Available resources

Substance resources:

•Polydimethylsiloxane

•Cells

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•Media

•PEEK tubing

•Connectors

•Pumps

•Glass slides

Space resources:

•Microscope lab room

•Space inside microfluidic device

Efficiency resources:

•Cells trapped by device

•Cells escaping device

Allowable changes to the system

•System must contain an array of traps, but drastic changes are allowed for trap design.

•Any reduction in efficiency is unacceptable.

•Any drastic increase in difficulty of fabrication is unacceptable.

Constraints and limitations

•Deadline: April 2007

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•Fabrication techniques must remain simple.

•Cost of computational fluid dynamics program must not be extreme.

Criteria for selecting solution concepts

•Noticeable increase in trapping efficiency.

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Problem Formulation and Brainstorming

Cell Trap Diagram

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Develop Concepts

Potential Trap Designs:

Original Design

-------------------------------------------------------------------------

Alternate Design #1

Add curve to center of original

--------------------------------------------------------------------------

Alternate Design #2

Place 2 traps side by side

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Design #3

Add opening in middle of original trap

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Design #4

Add angles to large section of trap

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Alternate Design #5

Add angle to large section of trap and opening in middle of trap

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Alternate Design #6

39

Add opening down middle of trap, and round off sides of slit

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Alternate Design #7

Add opening in arms of trap on Design #6

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Alternate Design #8

Shorten arms of trap on Design #6

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Alternate Design #9

40

Add directional bumpers to guide cells into traps.

(Image on right shows placement of traps in an array)

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Evaluate Results

Current results for trap design efficiency show that the original trap design lacks efficiency. Further testing results for other designs will be posted soon.

41

REFERENCES

1. Alper Joe. "Massively parallel single-cell analyses." Analytical Chemistry, 5249-5250, 1

August 2006.

2. Andersson, Helene, and Albert van den Berg. "Microfluidic devices for cellomics: a review." Sensors and Actuators B 92 (2003) 315–325.

3. Brittain,S, Paul,K, Zhao,XM, Whitesides,G. Soft Lithography and Microfabrication.

Physics World, 11, 31-36, 1998

4. David N. Breslauer, Philip J. Lee, and Luke P. Lee, "Microfluidics-Based Systems Biology," Molecular Biosystems, 2, 97-112 (2006).

5. Dino Di Carlo, Liz Y. Wu, Luke P. Lee, "Dynamic single cell culture array," Lab on a

Chip (2006).

6. Dino Di Carlo, Nima Aghdam, Luke P. Lee, "Single-cell enzyme concentrations, kinetics, and inhibition analysis using high density hydrodynamic cell isolation arrays," Analytical Chemistry 78(14), 4925-4930 (2006).

7. Herman, Peter R , Andrew Yick, Jianzhao Li, Nigel Munce, Lothar Lilge, Eric Jervis, and

Sergey Krylov. "FJaser micromachining of microfluidic channels and vias for biophotonic chip applications." Optical Society of America (2002).

8. J. R. Anderson, D. T. Chiu, R. J. Jackman, O. Cherniavskaya, J. C. McDonald, H. K. Wu,

S. H. Whitesides, and G. M. Whitesides. Fabrication of topologically complex three-dimensional microfluidic systems in PDMS by rapid prototyping. Anal.Chem. 72 (14):3158-3164, 2000.

9. McDonald,JC and Whitesides,GM. Poly(Dimethylsiloxane) As a Material for Fabricating

Microfluidic Devices. Acc.Chem.Res., 35, 491-499, 2002

10. Paul Hung, Philip Lee, and Luke P. Lee, "A Continuous Perfusion Microfluidic Cell Culture Array for High Throughput Cell-based Assays," Biotechnology & Bioengineering, 89, 1-8 (2005)

11. Paul J. Hung, Philip J. Lee, Poorya Sabounchi, Nima Aghdam, Robert Lin, and Luke P.

Lee, "A Novel High Aspect Ratio Microfluidic Design to Provide a Stable and Uniform Microenvironment for Cell Growth in High Throughput Mammalian Cell Culture Chip Array," Lab on a Chip, 5 (1), 44-48 (2005)

12. Quake,SR and Scherer,A. From Micro- to Nanofabrication With Soft Materials. Science,

290, 1536-1540, 2000

42

13. Shawgo, R. S., Grayson A. Richards, Y Li, and M. J. Cima. "BioMEMS for Drug

Delivery." Elsevier 6 (2002). 22 Apr. 2007 <http://www.ingentaconnect.com>qw2c4z

14. Wikswo, Dr. John P. and Franz J. Baudenbacher. Third-Year Review of Academic Venture Capital Fund Initiatives. December 2004.

15. Yi, Changqing, Cheuk-Wing Li, Shenglin Ji, and Mengsu Yang. "Microfluidics

technology for manipulation and analysis of biological cells." Analytica Chimica Acta 560 (2006) 1–23.