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Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim
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Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Jan 19, 2016

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Page 1: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Optimization of T-Cell Trapping in a Microfluidic Device

Group #19

Jeff ChamberlainMatt Houston

Eric Kim

Page 2: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

MEMS- MicroElectroMechanical Systems

• Batch Fabrication Processes

• Cell Traps– High-throughput

experimentation– Complex biochemical

analysis– Single cell analysis– Reagent

conservation– Quick environmental

changes

Page 3: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Cell Trapping Basics

Reagent #1

Reagent #2

Cells / Media

Trap arrays:

Page 4: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Our Project

• Maximize trap efficiency by improving upon current trap designs.– Trap Efficiency : maximized number of

traps with 1 cell/trap

Page 5: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Cell Viability ResultsFlow Rate = 100 nl/min

Flow Rate = 250 nl/min

Page 6: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.
Page 7: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

SolidWorks® Rendering of a Single Well

Page 8: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Picture of Well Array

• Square or rectangular shaped well of any depth

• Thousands of mirrored wells in one etching

• Front Surface Mirrors with high reflectivity

• Nearly orthogonal views of specimen

200 um200 um

Page 9: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Background & Motivation

Three Dimensional Image Information May Be Important for Biological Studies

• Chemotaxis• Developmental Biology• Cellular Division• Pinocytic Loading • Volumetric Measurements

Page 10: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Our System is Constructed From Silicon Wafer <100>

Page 11: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Methods: Fabrication

Silicon Wafer Silicon Wafer <100><100>Grow SiOGrow SiO22

Spin Coat Mask Spin Coat Mask LayerLayerPattern with Pattern with PhotolithographyPhotolithography Etch with HFEtch with HF

Remove Remove PhotoresistPhotoresist

Etch with KOHEtch with KOH

Coat with Platinum Coat with Platinum or Aluminumor Aluminum

Cutaway ViewCutaway View

Page 12: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

SolidWorks® Rendering of a Single Well

Page 13: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Results: Dictyostilium Extrusion

AA

BB

CC

AA

BB CC

Primary ImagePrimary Image

ReflectionReflection

Primary ImagePrimary Image

ReflectionReflection

Reflection of Reflection of the Reflectionthe Reflection

ReflectionReflection

ObjectiveObjective

Page 14: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Coupling Microfluidics With the Pyramidal Wells

Si Wafer

Cross Section of One Well

PDMS

Flow

Flow

PDMSGlass

Page 15: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Pyramidal Well Dimensions

10 20 30 40 50

25

50

75

100

125

150

depth_min = 3.22728 cell_radius

outside_dim = 3.48504 cell_radius

Assuming:

0.5 base = 1.2 cell_radius

Page 16: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Micromirror Well Dimensions

Cell Type Cell Dimensions (um) Minimum Required Depth (um)

Outside Dimensions for Min Depth (um)

T-cells 5 diameter 8.0682 17.4252

Jurkats 10 diameter 16.1364 34.8504

Dendritic Cells 10-20 diameter 16.1364 - 32.2728 34.8504 - 69.7008

Dicti 10-20 diameter 16.1364 - 32.2728 34.8504 - 69.7008

Myocyte 15 by 100 24.6737 54.9399 by 37.9399

10 20 30 40 50

25

50

75

100

125

150

Page 17: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Future Directions

• Single Cell Design– Desire single cell per well for optimal imaging– Possibly controllably coupled with another

As opposed to…

Page 18: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Future Directions

?

• Single Cell Design– Desire single cell per well for optimal imaging– Possibly controllably coupled with another

Page 19: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Future Directions

• Flow Modeling– Design a more efficient trapping system– Use flow data to design trap design that will keep cell in well

?

Page 20: Optimization of T-Cell Trapping in a Microfluidic Device Group #19 Jeff Chamberlain Matt Houston Eric Kim.

Future Directions

• PDMS-mirror bonding– Questions to whether the PDMS will bond to the mirror array

– Use SU-80 and glass cover slip with a PDMS external flow system?