All entries for Joel Voldman

MEMS@MIT

Leadership: Professor Martin A. Schmidt, Co-director, Associate Professor Joel Voldman, Co-director

The MEMS@MIT Center serves to unite the wide-ranging campus activities in micro/nano systems and MEMS with forward-looking industrial organizations. Currently, MEMS@MIT is composed of more than 150 faculty, students, and staff working on a broad research agenda and supported by more than $15 million/year in research sponsorship. The MEMS research efforts on campus focus on four overarching themes:

Materials, Processes, and Devices for MEMS – including work on piezoelectrics, magnetics, materials/package reliability, DRIE, wafer bonding, plastic fabrication, and printed MEMS
Biological and Chemical MEMS – includes cell manipulation, DNA and protein processing, biomolecule detection, medical sensors, microreactors, micro gas analyzers, and microfluidics
Actuators and Power MEMS – includeing switches, mirrors, pumps, turbines, fuel cells, thermophotovoltaics, chemical lasers, and energy harvesting
Sensors, Systems, and Modeling – includes wireless sensors, pressure sensing systems, and CAD for MEMS

Membership benefits include:

Insight into newest ideas in MEMS
Early access to research results
Early awareness of IP generated for licensing
Access to high-quality continuing education materials
Partnering for federal or other funding opportunities
Recruitment of leading MIT graduates

Joel Voldman

Title: Associate Professor
Department: Department of Electrical Engineering & Computer Science

Collaborators

T. Daniel, Univ. Washington
J.G. Hildebrand, Univ Arizona
K.M. Lim, NUS, Singapore
H. Ploegh, MIT
L. Samson, MIT
P. Sorger, Harvard Med. School
J. White, MIT

Postdoctoral Associates

K. Blagović
C. T. Lo
T. Sun
W.-M. Tsang
M. D. Vahey
M. Castellarnau

Graduate Students

M. Hoehl, Research Asst., HST
L. Pryzbyla, Res. Asst., Biology
P. Sampattavanich, Res. Asst., HST
H.-W. Su, Research Asst., EECS
S. Varma, Research Asst., EECS

Support Staff

C. Collins, Administrative Assistant

Publications

M. D. Vahey and J. Voldman, “Emergent behavior in particle-laden microfluidic systems informs strategies for improving cell and particle separations,” Lab on a Chip, in press.

Y.-C. Toh and J. Voldman, “Fluid shear stress primes mouse embryonic stem cells for differentiation in a self-renewing environment via heparan sulfate proteoglycans transduction,” Faseb Journal, vol. 25, pp. 1208-17, 2011.

N. Mittal and J. Voldman, “Non-Mitogenic Survival-Enhancing Autocrine Factors Including Cyclophilin A Contribute to Density-Dependent mESC Growth,” Stem Cell Research, vol. 6, pp. 168-76, 2011.

S. P. Desai and J. Voldman, “Cell-based sensors for quantifying the physiological impact of microsystems,” Integrative Biology, vol. 3, pp. 48-56, 2011.

M. D. Vahey, J. P. Svensson, L. Quiros-Pesudo, L. D. Samson, and J. Voldman, “Genome-wide Analysis of electrical phenotype using iso-dielectric separation,” in Micro Total Analysis Systems, Groningen, Netherlands, 2010.

W. M. Tsang, A. L. Stone, Z. Aldworth, J. G. Hildebrand, T. Daniel, A. I. Akinwande, and J. Voldman, “Flexible Split-Ring Electrode for Insect Flight Biasing using Multisite Neural Stimulation,” IEEE Transactions on Biomedical Engineering, vol. 57, pp. 1757-64, 2010 Jul 2010.

W. M. Tsang, A. Stone, Z. Aldworth, D. Otten, T. Akinwande, T. Daniel, J. G. Hildebrand, R. Levine, and J. Voldman, “Remote control of a cyborg moth using carbon nanotube-enhanced flexible neuroprosthetic probe,” in IEEE MEMS, Hong Kong, China, 2010.

Y.-C. Toh and J. Voldman, “MULTIPLEX MICROFLUIDIC PERFUSION IDENTIFIES SHEAR STRESS MECHANOSENSING MEDIATORS IN MOUSE EMBRYONIC STEM CELLS,” in Micro Total Analysis Systems, Groningen, Netherlands, 2010.

Y.-C. Toh, K. Blagovic, and J. Voldman, “Advancing stem cell research with microtechnologies: opportunities and challenges,” Integrative Biology, vol. 2, pp. 305-25, 2010.

Vescle libraries – tools for dielectrophoresis metrology,” Langmuir, vol. 25, pp. 3867-75, 2009.

S. P. Desai, D. M. Freeman, and J. Voldman, “Plastic masters – rigid templates for soft lithography,” Lab on a Chip, vol. 9, pp. 1631-7, Jun 7 2009.

D. C. Daly, P. P. Mercier, M. Bhardwaj, A. L. Stone, J. Voldman, R. Levine, J. G. Hildebrand, and A. P. Chandrakasan, “A Pulsed UWB Receiver SoC for Insect Motion Control,” in ISSCC, San Francisco, 2009.

H.-H. Cui, J. Voldman, X.-F. He, and K.-M. Lim, “Separation of particles by pulsed dielectrophoresis,” Lab on a Chip, vol. 9, pp. 2306-2312, 2009

Flexible Multi-site Electrodes for Moth Flight Control

Authors: W. M. Tsang, J. Voldman
Sponsorship: DARPA

Significant interest exists in creating insect-based Micro-Air-Vehicles (MAVs)^[1]^[2]^[3] that would combine advantageous features of insects—small size, effective energy storage, navigation ability—with the benefits of MEMS and electronics—sensing, actuation and information processing. The key part of the insect-based MAVs is the multi-site electrode, which interfaces with the nervous system of the insect to bias the insect’s flight path by controlling insect’s abdominal motions.

In this work, we have developed a flexible multi-site electrode (FME) for insect flight control that directly interfaces with the animal’s central nervous system as shown in Figure 1b. The FMEs are made of two layers of polyimide with gold sandwiched in-between in a split-ring geometry using standard MEMS processing^[3]. The FMEs have a novel split-ring design that incorporates the anatomical bi-cylinder structure of the nerve cord of the Moth Manduca Sexta (Figure 1b) and allows for an efficient surgical process for implantation (Figure 1d). Additionally, we have integrated carbon nanotube (CNT)-Au nanocomposites into the FMEs to enhance the charge injection capability of the electrode.

To quantify the performance of the FME, we have developed a custom stimulation and measurement system that allows computer-controlled stimulation and automated image analysis of the resulting abdominal motion (Figure 2a). We measured the magnitude (r) and direction (q) of the abdominal movement by the position of the red dot on the abdomen tip of the moths (Figure 2 b) versus the stimulations signal delivered at various magnitude and across various site pair. Moreover, we measured the voltage and current across pairs of stimulation sites during stimulation signal delivery (Figure 2c); hence, we could estimate the power consumption and injection charge density for the FME stimulations. Finally, we have integrated the FMEs into a wireless system (Figure 1a). In the flight control experiment, we can force a freely flying animal to perform turning motions via the FME stimulations.

: Figure 1: (a) Illustration of i-MAVs. (b) Image of dissected moth showing the interface between the FME and nerve cord. (c) Image of a cross section of the nerve cord of the adult moth overlaid with scaled schematized drawings of the split-ring of the FME. (d) Schematic of the FME insertion process, showing the split-ring can be opened to fit around the nerve cord.

: Figure 2: (a) Schematic of the tethered stimulation system. (b) Image showing the abdominal movement of the moth following the FME stimulation. (c) The voltage and current across the site pair during stimulation signal delivery.

A. Bozkurt, F. Gilmour, and A. Lal, “Balloon-assisted flight of radio-controlled insect biobots,” IEEE Transactions on Biomedical Engineering, vol. 56, pp. 2304-2307, Sept. 2009. [↩]
H. Sato, C. W, Berry, Y. Peeri, E. Baghoomian, B. E. Casey, G. Lavella, J. M. VandenBrooks, J. F. Harrison, and M. M. Maharbiz, “Remote radio control of insect flight,” Frontiers in Integrative Neuroscience, vol 3, pp. 1-11, Oct. 2009. [↩]
W. M. Tsang, A. Stone, Z. Aldworth, D. Otten, T. Akinwande, T. Daniel, J. G. Hildebrand, R. Levine, and J. Voldman, “Remote control of a cyborg moth using carbon nanotube-enhanced flexible neuroprosthetic probe,” Proc. IEEE MEMS2010, pp. 39-42. [↩] [↩]

Image-based Sorting of Cells

Authors: J. R. Kovac, T. Sun, J. Voldman
Sponsorship: NDSEG Graduate Fellowship, NSF Graduate Fellowship

Microfabricated/microfluidic approaches to cell sorting, include purely dielectrophoretic (DEP) trap arrays^[1], passive hydrodynamic trap arrays with active DEP-based cell release^[2], and passive microwell arrays with optical cell release to permit sorting of non-adhered cells^[3]. As these proceeding technologies were best suited to operate with non-adherent cells, we are developing a solution for adherent cells. Our approach to sorting adherent cells based on the morphological features uses a photolithography-inspired method, illustrated in Figure 1 (a). We first plated adherent cells into a dish and imaged cells using a microscope. Machine learning algorithm-based software CellProfiler^[4] was used to quantitatively characterize the morphological features of the imaged cells, covering the cell area, shape, fluorescent intensity, and texture. As shown in Figure 1 (b), four classes were defined according to the fluorescent intensity difference in cell cellular compartments. A set of judging rules was generated by iteratively training the classifier based on hundreds of quantitative cell feature measurements to cluster the cells of similar phenotypes into a particular class. Desired cells were identified according to the classification. An alignment mark image was generated, with black features corresponding to locations of desired cells. Aligning the transparency mask to the back of the cell culture dish showed that opaque mask features resided beneath desired cells. We then mixed a prepolymer solutizon consisting of cell culture media, a UV-photoinitiator, and poly(ethylene glycol) diacrylate (PEGDA) monomer. We added the prepolymer to the cell culture dish and shined ultraviolet (UV) light from a standard fluorescence microscope fluorescence source through the transparency and into the dish. The prepolymer then crosslinked into a hydrogel in all unmasked locations, encapsulating undesired cells. The desired cells, which were not encapsulated, can be enzymatically released from the substrate and recovered, as shown in Figure 2. The overall technique requires standard equipment found in biological labs and inexpensive reagents (<$10 per experiment), encouraging widespread adoption.

: Figure 1: (a) Schematic of sorting method. (b) Image showing the automated cell classification using CellProfiler. Left: mixed cell phenotypes of four classes, Right: results of segmented individual cells and classified cell populations.

: Figure 2: (a) Image showing targeted cells (red) in the dish (b) Image showing the formation of photo-polymerized gel microwells (500-µm diameter) to isolate the targeted cells. (c) Image showing the remaining microwells after cell sorting.

A. Rosenthal and J. Voldman, “Dielectrophoretic traps for single-particle patterning,” Biophys. J., vol.88, pp. 2193-2205, 2005. [↩]
B. M. Taff, S. P. Desai, and J. Voldman, “Electroactive hydrodynamic weirs for micro-particle manipulation and patterning,” Applied Physics Letters, vol. 94, no. 8, p. 084102, 2009. [↩]
J. R. Kovac and J. Voldman, “ Intuitive, image-based cell sorting using opto-fluidic cell sorting,” Analytical Chemistry, vol. 79, pp. 9321-9330, 2007. [↩]
T. R. Jones, A. E. Carpenter, M. R. Lamprech, J. Moffat, S. J. Silver, J. K. Grenier, A. B. Castoreno, U. S. Eggert, D. E. Root, P. Golland, and D. M. Sabatini, “Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning,” PNAS, vol. 106, pp.1826-1831, 2009. [↩]

Iso-dielectric Separation of Cells and Particles

Authors: H.-W. Su, M. Sano, M. D. Vahey, J. Voldman
Sponsorship: NIH NIBIB, Singapore-MIT Alliance, NSF IDBR, DARPA, Hitachi

The development of new techniques to separate and characterize cells with high throughput has been essential to many of the advances in biology and biotechnology over the past few decades. Continuing or improving upon this trend – for example, by developing new avenues for performing genetic and phenotypic screens – requires continued advancements in cell sorting technologies. Towards this end, we are developing a novel method for the simultaneous separation and characterization of cells based upon their electrical properties. This method, iso-dielectric separation (IDS), uses dielectrophoresis (the force on a polarizable object^[1]) and a medium with spatially varying conductivity to sort electrically distinct cells while measuring their effective conductivity (Figure 1). It is similar to iso-electric focusing, except that it uses DEP instead of electrophoresis to concentrate cells and particles to the region in a conductivity gradient where their polarization charge vanishes^[2]^[3]^[4].

Previously, we have demonstrated the ability to perform continuous-flow, label-free, non-binary separations using IDS on a wide variety of cells and particles, while simultaneously extracting quantitative information from these samples as they are sorted^[4]. In order to make IDS discovery more unknown cell types, dynamically changing the conductivity gradient is crucial for increasing the efficiency of finding the optimal separation condition. Therefore, we are developing a tri-syringe pump system to dynamically control conductivity gradients. We have verified the stability of the tri-syringe pump system via quantitative fluorescence imaging. Combining this gradient control system with a computer-controlled function generator and automated microscope, we plan to fully automate IDS separation and electrical profile screening (Figure 2).

: Figure 1: Iso-dielectric separation. The spatial conductivity gradient makes cells with different electrical properties pass through the electrodes at different positions.

: Figure 2: Automated iso-dielectric separation system. We have developed a tri-syringe pump system that enables the real-time control of the electrical conductivity gradient with stable long term and short term response.

H. A. Pohl and J. S. Crane, “Dielectrophoresis of cells,” Biophysical Journal, vol. 11, pp. 711-727, 1971. [↩]
M. D. Vahey and J. Voldman, “An equilibrium method for continuous-flow cell sorting using dielectrophoresis,” Analytical Chemistry, vol. 80, no. 9, pp. 3135-3143, 2008. [↩]
M. D. Vahey and J. Voldman, “Iso-dielectric separation: A new method for the continuous-flow screening of cells,” Micro Total Analysis Systems ’06, vol. 2, pp. 1058-1060, 2006. [↩]
M. D. Vahey and J. Voldman, “High-throughput cell and particle characterization using isodielectric separation,” Analytical Chemistry, vol. 81, no. 7, pp. 2446-2455, 2009. [↩] [↩]

Micropatterning of Cells to Study Autocrine Signaling

Authors: S. Sampattavanich, J. Voldman
Sponsorship: NIH

Autocrine signaling is a mode of chemical signaling that occurs when cells can capture self-secreted diffusive factors. Apart from its major role in sustaining cancer growth, autocrine signaling is also involved in the positive-feedback regulation of various physiological processes. Due to the closed-loop nature and complex interplay of this signaling with other signaling cues, it is difficult to validate the presence and function of autocrine loops. Studying these loops typically requires the use of specific inhibitors to perturb the underlying ligand/receptor pairing, limiting investigation of poorly characterized autocrine loops.

To promote the examination of autocrine signaling in broader biological systems, we have developed a general method for modulating autocrine activity using cell patterning. In addition to capturing self-secreted ligands, cells with autocrine loops also acquire ligands from their neighbors. By modulating the relative positioning between cells, we are able to modulate capture of autocrine ligands without needing specific inhibitors. In particular, we use stencil cell patterning to organize cells as square-latticed arrays of circular patches of varying array spacing (Figures 1A & B). We found that the cell-patterning platform can maintain uniform local cell density at all array spacings, in contrast to randomly plated cells, which exhibit increasing local cell density (Figure 1C). By reducing the influence of these other environmental cues, we are able to more explicitly study the effect of autocrine signaling on cell phenotype.

In addition to studying the direct role of autocrine signaling, the cell-patterning platform can also be used to investigate the interplay of autocrine signaling with other signaling cues and to evaluate its contribution towards cell-to-cell variability. To determine the concurrent role of cell-cell contacts, we can compare cell responses between single patches and multiple patches where the cell number of both designs is equal (Figures 2A & B). To evaluate the contribution of autocrine loops in causing cell-to-cell variability, we can determine how the inclusion of a large cell patch will perturb the response of an array of small patches (Figure 2C). These innovative cell-patterning designs provide us novel tools for characterizing the impact of autocrine signaling without prior knowledge of the underlying ligand/receptor interactions.

: Figure 1: Investigation of autocrine signaling using cell patterning. A) Stencil patterning was used to organize the square-latticed arrays of circular patches. B) Resulting cell patterns of the A431 epidermoid carcinoma cells of varying array spacing. Scale bar, 500 µm. C) Changes of local density (e.g., neighbor number) with alteration of global cell density in randomly plated and patterned cell arrays.

: Figure 2: Creative cell-patterning designs. A) Calculated fractions of ligands with autocrine trajectories for varying total patch areas and patch radii. B) Examples of single-patch designs with varying radii, the composite-patch designs with the same total area, and the actual resulting patterns. C) Designs of cell arrays for studying autocrine signaling contribution on cell-to-cell variability. The right-hand plot illustrates predicted ligand concentration for both designs.

Microfluidic Perfusion for Modulating Stem Cell Diffusible Signaling

Authors: K. Blagović, L. Przybyla, Y.-C. Toh, J. Voldman
Sponsorship: NIH NIBIB, NSF Graduate Research Fellowship, A*STAR International Fellowship

Stem cell phenotype and function are influenced by microenvironmental cues that include cell-cell, cell-extracellular matrix (ECM), and cell-media interactions, as well as mechanical forces. Our research focuses on developing microscale systems for controlling the cellular microenvironment of mouse embryonic stem cells (mESCs), in particular mechanical forces (i.e., shear stress) and cell-media interactions (i.e., diffusible signaling).

Many emerging technologies used for ESC expansion or differentiation require perfusion culture, an example being pluripotent stem cell expansion in bioreactors for clinical applications^[1]. We employ a multiplex microfluidic perfusion array to study the effects of shear stress on mESCs across a wide range of flow rates in a graded, quantitative manner. Using this device, we are able to show that perfusion elicits phenotypic changes and that the specific shear-responsive phenotype is due to mechanosensing by heparan sulfate proteoglycans (HSPGs, Figure 1A-C). This is the first study describing the ESC machinery capable of responding to shear stress, thus providing a foundation for further shear mechanotransduction studies^[2].

Cells are constantly secreting and responding to soluble signals, the removal of which can be mediated by modulating flow properties at the microscale. To assess the contribution of cell-secreted factors to mESC differentiation and self-renewal, we utilized a two-layer microfluidic perfusion device allowing for parallel comparison of different cell culture conditions (Figure 2A)^[3]. Our results demonstrate that mESCs do not grow in differentiation conditions with minimal autocrine signaling, even with supplementation by Fgf4, a signal that has been shown to be a crucial factor in differentiation toward a neuronal stem cell fate (Figure 2B). Conversely, under self-renewal conditions, mESCs proliferate but lose self-renewal markers and upregulate differentiation markers (Figure 2C). These results, together with signaling and downstream differentiation assays, indicate that a differentiation towards an epiblast-like early differentiation state under conditions that had previously been shown as sufficient for self-renewal. Together, these results indicate the importance of cell-secreted signals for mESC growth and self-renewal.

: Figure 1: (A) Microfluidic perfusion array for probing shear stress effects on mESCs. (B) Range of shear stress used in this study and corresponding Peclet number (Pe). (C) Gene profile for a range of applied shear values under conditions of saturated soluble environment (CM) in the presence or absence of HSPG disruption.

: Figure 2: (A) Schematic of microfluidic perfusion device and mESCs growing in a single chamber in self-renewing medium. (B) Growth analysis of perfusion cultures in differentiation medium (N2B27) supplemented with Fgf4, and soluble factors saturated medium (N2B27+CM). (C) Expression levels of self-renewal (left) and differentiation (right) markers of mESCs growing in self-renewal medium in the device shown in (A) versus mESCs grown in a standard culture dish.

D. Steiner, H. Khaner, M. Cohen, S. Even-Ram, Y. Gil, P. Itsykson, T. Turetsky, M. Idelson, E. Aizenman, R. Ram, Y. Berman-Zaken, and B. Reubinoff, “Derivation, propagation and controlled differentiation of human embryonic stem cells in suspension,” Nature Biotechnology, vol. 28, pp. 361-364, Mar. 2010. [↩]
Y.-C. Toh and J. Voldman, “Fluid shear stress primes mouse embryonic stem cells for differentiation in a self-renewing environment via heparan sulfate proteoglycans transduction,” The FASEB Journal, vol. 25, pp. 1208-1217, 2011. [↩]
K. Blagović, L.Y. Kim, A. M. Skelley, and J. Voldman, “Microfluidic control of stem cell diffusible signaling,” in Proc. Twelfth International Conference on Miniaturized Systems for Chemistry and Life Sciences, San Diego, USA, pp. 677-679, Oct. 2008. [↩]

Cell-based Sensors for Measuring Impact of Microsystems on Cell Physiology

Authors: C. T. Lo, S. Varma, J. Voldman
Sponsorship: NIH

The use of microsystems to manipulate and study cells in microenvironments is continually increasing. However, along with such increase in usage is also a growing concern regarding the impact of these microsystems on cell physiology. In this project, we are developing a set of cell-based fluorescent sensors to measure the impact of common stresses experienced in microsystems on cell physiology. We are including stress agents commonly found in microsystems (e.g., UV exposure, heat shock, fluid flow, etc.). Each sensor is designed to respond to one particular stress agent but can also be combined for multiplexed analysis of multiple stresses at once, as might be experienced in a typical microsystem. Designed to ease multiplexed analysis, each sensor will use different colors to both indicate the type of sensor and the strength of the signal.

One sensor in the system will be a heat shock sensor that responds to activation of the heat shock pathway, which is a generalized stress pathway in cells. We are adapting a version of this sensor that we previously reported^[1]^[2], which coupled fluorescent protein expression to activation of heat shock factor 1, from green fluorescent protein (EGFP) to a red fluorescent protein (RFP) and from red (DsRed) to yellow (YPet) for the constitutive color. Figure 1 shows the heat shock sensor response to 15 min heating at 42 ºC. Alongside this effort, we are using a multi-flow microfluidic device that can simultaneously apply different flows to cells across a 1000× range to understand the behavior of cells in flow^[3]. Figure 2 is an image of the multi-flow device used to test NIH3T3 mouse fibroblast cells. Cells are seeded in 6 chambers concurrently and exposed to flow for 24 hrs, after which we can extract PCR from each chamber to study the cell response.

: Figure 1: Heat shock reporter sensor. Images of NIH3T3 cells (A) before and (B) after heat shock at 42 °C for 15 minutes, after recovering for 16 hrs. The left images are of the constitutive yellow reporter, while the right images are of the red fluorescent heat shock reporter.

: Figure 2: Multi-flow microfluidic device. Shown is a schematic of the multi-flowrate microfluidic device, showing the fluidic layer (blue) where the cells are grown and the pneumatic layer (red) containing microvalves.

S. P. Desai and J. Voldman, “Cell-based sensors for quantifying the physiological impact of microsystems,” Integrative Biology, vol. 3, pp. 48-56, 2011. [↩]
S. P. Desai and J. Voldman, “Measuring the impact of dielectrophoresis on cell physiology using a high-content screening platform,” Micro Total Analysis Systems ’08, San Diego, CA, USA, 2008, pp. 1308-10. [↩]
Y.-C. Toh and J. Voldman, “Fluid shear stress primes mouse embryonic stem cells for differentiation in a self-renewing environment via heparan sulfate proteoglycans transduction,” Faseb Journal, vol. 25, pp. 1208-17, 2011. [↩]

Massively Parallel Microfluidic Cell-pairing Platform for the Statistical Study of Immunological Cell-cell Interactions

Authors: M. M. Hoehl, J. Voldman

Many immune responses are mediated by cell-cell interactions. In particular, cytotoxic T cells form conjugates with pathogenic and cancer cells in order to fight disease. Moreover, T cell maturation and activation is governed by direct cell interactions with antigen-presenting cells (APCs). Errors in these processes can lead to the progression of severe diseases, such as multiple sclerosis (MS) and type 1 diabetes. The study of these intricate cell-cell interactions at the molecular scale is therefore crucial for understanding the dynamics and specificity of the immune response. One important feature of these interactions is the variability of response across populations. Cell-to-cell variability in presumably homogeneous populations exposed to the same environmental conditions is ubiquitous, yet has long been neglected in immunology due to the limitations of conventional assay methods^[1]^[2]. Traditional methods to study cell-cell interactions, such as bulk measurements^[3] or immobilization of cell pairs on a dish^[4]^[5], suffer from both the inability to control cell-pairing at the single cell level and the inability to study dynamic cell-cell interaction processes with high spatial and temporal resolution. We have overcome these limitations by developing a platform that can control cell pairing across thousands of individual immune cell pairs simultaneously while allowing visualization of the resulting responses. This approach enables us to quantify and understand variations in cell-cell interactions within large cell populations at the resolution of individual cell pairs. Previously, we developed a microfluidic device with the capability to create thousands of such single cell pairs for the study of stem cell reprogramming (Figure 1,^[6]). To adapt the approach to work with smaller primary immune cells, we performed hydrodynamic modeling to guide redesign of the trap geometry (Figure 2). The modeling was used to determine how to adjust the trap geometry to maximize flow through the center of the cups, which is crucial to the loading process. We determined that altering the cup-to-cup spacing transverse to the flow had the greatest impact on flow through the cups. We fabricated redesigned traps and are in the process of testing their pairing efficiency with primary immune cells.

: Figure 1:^[6] (a) Schematic of the microfluidic device operation and structure. The flow passes under the cell trap, directing the cells into the capture cup. The support pillars maintain the proper vertical gap. (b) Scanning electron micrograph image of the 2 mm x 2 mm device, which contains 1,166 cell traps. (c) Magnified image of the device, showing the densely packed structures. (d) Detail of the trap structure, including the larger front-side and smaller back-side capture cups (14 µm tall, 18µ m wide, 25–40 µm deep, and 10 µm wide by 5 µm deep, respectively), along with support pillars (7.5µ m wide x 35–50 µm long x 6–8 µm tall). (e) Different arrays that can pair and capture thousands of cells in parallel.

: Figure 2: To redesign the cell-pairing device for immune cells, we have established a finite element fluids model. The plot shows the predicted effect of reducing cup spacing (normalized by the downscaled immune cell device) on the relative flow rate through the capture cups. The blue line represents the original cell fusion device. The green line represents simple downscaling of the original device, yielding low flow and, presumably, pairing efficiency. The physical limit (black line) indicates the smallest cup spacing possible that still permits cells to pass between two cups. The model shows that reduc¬ing the horizontal cup spacing can increase flow through the cups and, ultimately, pairing efficiencies for immune cells.

S. L. Spencer, et al., “Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis,” Nature, vol. 459, no. 7245, p. 428, 2009. [↩]
A. Colman-Lerner, et al., “Regulated cell-to-cell variation in a cell-fate decision system,” Nature, vol. 437, no. 7059, p. 699, 2005. [↩]
S. Burgdorf, et al., “Distinct pathways of antigen uptake and intracellular routing in CD4 and CD8 T cell activation,” Science, vol. 316, no. 5824, p. 612, 2007. [↩]
M. S. Fassett, et al., “Signaling at the inhibitory natural killer cell immune synapse regulates lipid raft polarization but not class I MHC clustering,” Proc. National Academy of Sciences, vol. 98, no. 25, p. 14547, 2001. [↩]
X. Chen, et al., “Many NK cell receptors activate ERK2 and JNK1 to trigger microtubule organizing center and granule polarization and cytotoxicity, “Proc. National Academy of Sciences, vol. 104, no. 15, p. 6329, 2007. [↩]
A. M. Skelley, et al., “Microfluidic control of cell pairing and fusion,” Nature Methods, vol. 6, no. 2, p. 147, 2009. [↩] [↩]

MTL Annual Research Report 2011