All entries for joel voldman

Joel Voldman

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

Collaborators

T. Daniel, Univ. Washington
C. Love, MIT
C. Moritz, Univ. Washington
D. Irvine, MIT
H. Ploegh, MIT

Postdoctoral Associates

M. Castellarnau
A. Fendyur
T. Honegger
J. Prieto

Graduate Students

A. Dighe, Research Asst., ME
B. Dura, Research Asst., EECS
H.-W. Su, Research Asst., EECS
S. Varma, Research Asst., EECS
D. Wu, Research Asst., ME

Support Staff

C. Collins, Administrative Assistant

Publications

M. D. Vahey, L. Q. Pesudo, J. P. Svensson, L. D. Samson, and J. Voldman, “A genome-wide profile of intrinsic electrical properties in saccharomyces cerevisiae,” Lab on a Chip, in press, 2013.

L. Przybyla, T. Theunissen, R. Jaenisch, and J. Voldman, “Matrix remodelling maintains esc self-renewal by activating an endogenous stat3-mediated signalling pathway,” Stem Cells, in press, 2013.

J. R. Kovac, Y. Gerardin, and J. Voldman, “Image-predicated sorting of adherent cells using photopatterned hydrogels,” Advanced Healthcare Materials, vol. 2, pp. 552-556, April 2013.

T. Honegger, M. A. Scott, M. F. Yanik, and J. Voldman, “Electrokinetic confinement of axonal growth for dynamically configurable neural networks,” Lab on a Chip, vol. 13, pp. 589-98, 2013.

M. D. Vahey and J. Voldman, “Isodielectric separation and analysis of cells,” in Single-cell analysis: Methods and protocols, S. Lindström and H. Andersson-Svahn, Eds.: Springer Science+Business Media, 2012, pp. 53-63.

H.-W. Su, J. Prieto, and J. Voldman, “Automated high-throughput characterization of cells using multimodal electrical and optical cytometry (multimeoc),” in MicroTAS 2012 Okinawa, Japan, 2012.

A. Hinterwirth, B. Medina, J. Lockey, D. Otten, J. Voldman, J. Lang, J. Hildebrand, and T. Daniel, “Wireless stimulation of antennal muscles in freely flying hawkmoths leads to flight path changes,” PLoS ONE, vol. 7, p. e52725, 2012.

W. M. Tsang, A. L. Stone, D. Otten, Z. N. Aldworth, T. L. Daniel, J. G. Hildebrand, R. D. Levine, and J. Voldman, “Insect-machine interface: A carbon nanotube-enhanced flexible neural probe,” Journal of Neuroscience Methods, vol. 204, pp. 355-65, 2012.

L. M. Przybyla and J. Voldman, “Attenuation of extrinsic signaling reveals the importance of matrix remodeling on maintenance of embryonic stem cell self-renewal,” Proceedings of the National Academy of Sciences, vol. 109, pp. 835-840, January 17, 2012 2012.

L. Przybyla and J. Voldman, “Probing embryonic stem cell autocrine and paracrine signaling using microfluidics,” Annual Review of Analytical Chemistry, 2012.

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, vol. 11, pp. 2071-2080, 2011.

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.

Y.-C. Toh, K. Blagovic, H. Yu, and J. Voldman, “Differential environmental spatial patterning (δesp) recreates proximal-distal axial patterns in embryonic stem cell colonies,” in Micro Total Analysis Systems Seattle, WA USA, 2011, pp. 30-32.

Y.-C. Toh, K. Blagovic, H. Yu, and J. Voldman, “Spatially organized in vitro models instruct asymmetric stem cell differentiation,” Integrative Biology, vol. 3, pp. 1179-1187, 2011.

T. Sun and J. Voldman, “Image-based screening of high-performing clones using photoactivated cell sorting via dual photopoylmerized microwell arrays,” in Micro Total Analysis Systems Seattle, WA USA, 2011, pp. 693-5.

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.

M. M. Hoehl, S. K. Dougan, H. L. Ploegh, and J. Voldman, “Massively parallel microfluidic cell-pairing platform for the statistical study of immunological cell-cell interactions,” in Micro Total Analysis Systems Seattle, WA USA, 2011, pp. 1508-10.

Medical Electronic Device Realization Center

Leadership: Charles Sodini, Co-Director
Brian Anthony, Co-Director
Joel Voldman, Co-Director

The vision of the Medical Electronic Device Research Center (MEDRC) is to transform the medical electronic device industry: to revolutionize medical diagnostics and treatments, bringing health care directly to the individual; and to create enabling technology for the future information-driven healthcare system. Specific areas that show promise are wearable or minimally invasive monitoring devices, medical imaging, laboratory instrumentation, and the data communication from these devices and instruments to healthcare providers and caregivers.

The MEDRC establishes a partnership between the microelectronics industry, the medical devices industry, medical professionals, and MIT to collaboratively achieve improvements in the cost and performance of medical electronic devices similar to those that have occurred in personal computers, communication devices and consumer electronics. The Medical Electronic Device Realization Center (MEDRC) was established to better connect industry, academia, and physicians. In the MEDRC, research activities are jointly defined by faculty, physicians and clinicians, and industrial partners. A visiting scientist from a project’s sponsoring company is present at MIT. Ultimately this individual is the champion that helps translate the technology back to the company for commercialization and provide the industrial viewpoint in the realization of the technology. We foster the creation of prototype devices and intellectual property in the field of medical electronic systems and serve as a catalyst for the successful deployment of innovative healthcare technology at an affordable price.

MEDRC projects have the advantage of insight from the technology arena, the medical arena, and the business arena, thus significantly increasing the chances that their devices will fulfill a real healthcare need as well as be profitable for companies to ensure a stable supply of the new devices. With a new trend toward increased healthcare quality, disease prevention, and cost-effectiveness, such a comprehensive perspective is crucial.

The collective aim of the MEDRC is to revolutionize medical diagnostics and treatments, bringing health care directly to the individual, and creating technology for the future of information-driven healthcare. We focus on areas of medical devices that are enabled and enhanced by electronics and computation – imaging, point-of-care devices, diagnostics and therapeutic instrumentation, ambulatory physiological monitors, etc.

The MEDRC serves as a focal point for large business, for venture-funded startups, and for the medical community. The Center fosters the creation of prototype devices and intellectual property and aims to serve as the catalyst for the deployment of innovative healthcare technology that will reduce the cost of healthcare in both the developed and developing world. Current members are Analog Devices, GE Global Research/Healthcare, and Maxim Integrated Products.

Cell Pairing for Studying Immunity

Authors: B. Dura, Y. Liu, J. Voldman
Sponsorship: Singapore-MIT Alliance, China Scholarship Council

Figure 1 a) Cell pairing device (scale 50 µm). b) 3-step cell pairing protocol. c) High-throughput cell pairing. d) Distinct Ca2+ dynamics from Trp1-hi and Trp1-lo cells paired with peptide loaded B cells.

Cell-cell interactions are crucial for proper functioning of the immune system because direct cell-cell contacts largely govern the successful progression of adaptive immune responses. The heterogeneity inherent in these interactions plays a critical role in the functional outcome produced. Assessing the heterogeneity in the initial activation and connecting it with the endpoint function would, therefore, clarify the signaling cascades involved in the observed outcomes. Current methods to study this heterogeneity are mainly limited by the control over pairing, and thus by initiation of activation and low throughput. To remedy this situation, we developed a high-throughput microfluidic cell-pairing platform for studying the activation kinetics of immune cells. We adapted the microfluidic device from a previously developed chip for studying cell reprogramming^[1] and altered device design and geometries to accommodate much smaller immune cells. The device comprises a dense array of weir-based hydrodynamic cell traps that contain a back-side single-cell trap and front-side two-cell trap (Figure 1a). Using a 3-step loading protocol (Figure 1b), we achieved 67 ± 12 % (n=18) pairing efficiencies with a range of 40-86 %, the highest ever reported for such smaller cells (Figure 1c). We used our microfluidic platform to dissect the activation kinetics of T cells from two lines of trans-nuclear mice, Trp1-hi and Trp1-lo, which recognize the identical peptide-MHC complex with markedly different affinities yet are equivalent in their ability to curtail the growth of B16 melanoma in vivo. We paired Trp1-hi/lo T cells with antigen-loaded B cells in a highly parallel and synchronous manner and measured the activation profiles through Ca²⁺ imaging. Our results show inherent heterogeneity within the clonal population of each cell type and indicate significant differences in the activation dynamics and activation percentages between the two lines (Figure 1d). These findings emphasize how immune cells dissociate the affinity of interaction and heterogeneity in the initial activation from a robust in vivo protective ability. Study of activation cascades in this well-defined system provides substantial insight into how variation leads to robust functional outcomes in immunity.

A. M. Skelley, O. Kirak, H. Suh, R. Jaenisch, and J. Voldman, “Microfluidic control of cell pairing and fusion,” Nature Methods vol. 6, pp. 147-152, 2009. [↩]

Electrokinetic Control of Axonal Growth

Authors: T. Honegger, M. Thielen, J. Voldman
Sponsorship: CNRS (FR), Centre National de la Rercherche Scientifique (France), MIT

Dynamic control of axonal outgrowth holds the potential to establishing a range of in-vivo and in-vitro applications including perpiheral nerve section injury recovery, neural computers, and neural interfaces. Although many axon guidance clues like surface topography^[1], biochemical^[2] or external forces^[3] have been investigated, those methods do not provide downscaling and dynamic control of axonal growth.

We have introduced the use of AC electrokinetics to dynamically control axonal growth in cultured rat hippocampal neurons. We find that the application of modest voltages at frequencies on the order of 10⁵ Hz can cause developing axons to be stopped adjacent to the electrodes while axons away from the electric fields exhibit uninhibited growth. By switching electrodes on or off, we can reversibly inhibit or permit axon passage across the electrodes. We make use of our dynamic control over axon elongation to create an axon-diode via an axon-lock system that consists of a pair of electrode “gates” that either permit or prevent axons from passing through as shown in Figure 1a-c. Finally, we developed a neural circuit consisting of three populations of neurons, separated by three axon-locks to demonstrate the assembly of a functional, engineered neural network as shown in Figure 1b. Action potential recordings demonstrate that the AC electrokinetic effect does not harm axons, and Ca2+ imaging demonstrated the unidirectional nature of the synaptic connections. AC electrokinetic confinement of axonal growth has potential for creating functional, configurable, and directional neural networks^[4].

We have extended this work to demonstrate the control of axonal growth in collagen scaffold. The scaffold is confined in a microfluidic channel of three different heights where axons can develop over electrodes. When the channel height was limited to the size range of the growth cone (~ 3 mm), axon repulsion in a 2D plane was observed. We find that developing axons in the microchannel are repelled from the electrodes and follow the field lines until lower field strength (Figure 2a). Axons that grow in the same channel but further away from the electrodes show uninhibited growth. When the channel height is in the range of the growth cone (~10 mm), axonal growth is slowed down by the AC field. Finally, when the channel height is significantly bigger than the growth cone (~ 50 mm), axons develop in 3D into the scaffold and are repelled from the electrodes in the depth of the scaffold itself, where the minimum plane height is linked to the magnitude of the electric field as presented in Figure 2a-b.

This new technology provides a powerful tool to confine axonal growth and leads the way to dynamic configurable neuronal networks.

: Figure 1: (a) Side-view schematic of axon diode, (b) Fluorescent false-colored image of a 12-DIV neuronal network configured by the axon-lock system. The plain arrows represent the direction of the diode. (c) Images of axon diode chip showing three microchambers and electrodes.

: Figure 2: (a) Picture of developing axons in a 3-μm-high channel filled with collagen after 4 DIV. The axons are repelled and guided by the activated electrodes. (b) Confocal microscope reconstruction of developing axons in a 50-μm-high channel. The axons develop in a parallel plane above the activated electrodes. (c) Quantification of the minimum height of this plane for several voltages.

J. W. Lee, K. S. Lee, N. Cho, B. K. Ju, K. B. Lee, and S.-H. Lee, “Topographical guidance of mouse neuronal cell on SiO2 microtracks,” Sensors and Actuators B 128, 252-257, 2007. [↩]
A. M. McCormick and N. D. Leipzig, “Neural Regenerative Strategies Incorporating Biomolecular Axon Guidance Signals,” An. Biomed Eng, 2012. [↩]
T. Wu, T. A. Nieminen, S. Mohanty, J. Miotke, R. L. Meyer, H. Rubinsztein-Dunlop, and M. W. Berns, “A photon-driven micromotor can direct nerve fibre growth”. Nat Photon, pp. 62-67, 2012. [↩]
T. Honegger, M. A. Scott, and J. Voldman, Lab on a Chip, 13, 4, pp. 589-598, 2013 [↩]

Iso-dielectric Separation of Cells and Particles

Authors: J. L. Prieto, H. W. Su, J. Voldman
Sponsorship: NSF IDBR, DARPA

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. 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].

Sepsis is a clinical condition caused by infection and, despite state-of-the-art facilities and treatments, it has a mortality rate of ~30%. Sepsis induces inflammation and organ failure; a possible treatment would require removing inflammatory agents, such as activated neutrophils, from whole blood. We used a CLP mouse model of sepsis (Figure 2a) and PMA-activated human granulocytes (Figure 2b) to monitor electrical differences between septic blood and leukocytes. With human granulocytes we saw a shift in their average isodielectric position (IDP) at high frequencies. Based on these results we did an IDP profile of leukocytes in healthy mouse blood and established a gate for the activated leukocytes. Applying the same gate under the same conditions with blood from CLP mice (n=4), we saw an increase in the number of activated leukocytes (Figure 2c). Finally we took aliquots of the same samples from healthy and CLP mice and measured common activation biomarkers with flow cytometry. Comparing both results, we see good correlation between our estimation of activated cells and the number of activated granulocytes measured in flow cytometry.

: Figure 1: Iso-dielectric separation (IDS) microfluidic device used to measure electrical properties of the cells. The spatial conductivity gradient makes cells with different electrical properties pass through the electrodes at different positions (isodielectric point – IDP).

: Figure 2: a) CLP intervention was used as a septic model in mice. b) At high frequencies, activated and non-activated human granulocytes show different IDP. c) A gate was defined in the IDP profile of healthy mouse blood and used to quantify the number of activated leukocytes in CLP mice. d) A good correlation of IDP results exists when compared to flow cytometry results.

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. [↩]

Microfluidic Electronic Detection of Protein Biomarkers

Authors: D. Wu, J. Voldman
Sponsorship: Maxim Integrated

Immunoassays use antibodies to detect protein biomarkers, with a substantial global market and significant importance for clinical practice. However, traditional immunoassays are performed in centralized laboratories using optical detection methods, which means that results take days and cannot be highly multiplexed, in turn increasing patient visits, healthcare costs, and decreasing healthcare outcomes. In our project, we are developing an all-electronic immunoassay. All-electronic immunoassays have three major benefits: 1) we can achieve high-throughput immunoassays, allowing us to detect over 100 proteins in a single sample and potentially even to measure all approved protein biomarkers; 2) we can reduce cost by taking advantage of decreasing costs of silicon electronics; and 3) patients can get results before meeting with their physicians, since impedance detection is much faster than optical readout.

The basic idea of an all-electronic biosensor is that by looking into the impedance change caused by association of antigens to antibodies, we can determine if certain antigens are present in the blood. Our biosensor is illustrated in Figure 1. To facilitate the sensor design, we determined three parameters (detection sensitivity, response time and sensor dimension) to characterize the performance of the sensor. We built two models to gain insight into the relationship between the sensor design and performance. The transport model of molecular binding indicated that the response time is determined mainly by the height of the channel, and sensors with higher channels need more time to reach equilibrium but can have better signal-to-noise ratio (Figure 2a). The electric model of impedance change shows that the interfacial impedance determines the electric readout and that sensors with wide electrode can offer good signal-to-noise ratio but end up with large size (Figure 2b). Therefore, the three key performance parameters cannot be achieved simultaneously, and the sensor design needs to be optimized.

Another critical issue is chemical modification of the electrode’s surface; the goal is to eventually immobilize probe proteins on the electrodes. To this end, we built alkanethiol self-assembled monolayers (SAMs)as the links between gold electrodes and probe proteins. Since creating SAMs is the first step of surface modification, it is critically important to have successful SAMs. We used different surface characterization techniques, including Fourier transform infrared spectroscopy and impedance spectroscopy. Both methods showed that the self-assembled monolayers have been successfully built (Figures 2c and 2d).

: Figure 1: (a) The illustration of multiplexed all-electronic biosensors; (b) The illustration of one element of the sensor; and (c) The cross-section of one element.

: Figure 2: (a) The relationship between channel height and response time predicted by the transport model of molecule binding; (b) The relationship between electrode width and signal-to-noise ratio predicted by the electric model of impedance change; (c) Characterization of SAMs by FTIR. The two peaks corresponding to CO and COH bond are from the COOH group of SAMs; and (d) Characterization of SAMs by impedance spectroscopy. The increase in impedance is consistent with the fact that SAMs increase the thickness of interfacial capacitor.

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

Authors: A. Fendyur, 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 comes 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 by cells in microsystems.

The shear-stress sensor is the most relevant sensor to the microfluidic and bio-MEMS community and the most challenging sensor to create, since relevant molecular pathways have not been entirely elucidated. To address this quastion, we characterized the gene expression profile of NIH3T3 cells using a multi-flow microfluidic device (Figure 1a) that can simultaneously apply a logarithmic (1000×) range of shear stress conditions^[1]. Using qRT-PCR we identified the genetic node sensitive to shear and designed a red fluorescence protein (RFP) reporter plasmid. We transfected and cloned this plasmid into NIH3T3s to create our shear stress response sensor. We seeded this sensor in the same device, exposed it to flow for 0.1-3 hours, and assayed for fluorescence expression changes using flow cytometry (Figure 1b). Using our sensor, we were able to successfully correlate device shear conditions to population level inductions (Figure 1c).

In parallel we are developing a sensor that responds to activation of the p53 protein pathway, for generalized DNA damage analysis. The DNA damage sensor couples RFP expression to activation of p53. The new sensor will be identified by constitutive cyan (mTurquoise) color (Figure 2a-b).

Each cell-based fluorescent sensor will respond to one particular stress agent. The number of sensors can be combined for multiplexed analysis of multiple stresses at once, as might occur in a typical microsystem. Each sensor will use different constitutive colors to indicate the type of sensor and RFP as the activation color. For this purpose we re-engineered our existing heat shock sensor^[2]^[3] to express constitutive yellow (YPET) and red (RFP) as the activation color (Figure 2c-d).

: Figure 1: (A) Multi-flow microfluidic device, showing the fluidic layer (blue) where the cells are grown and the pneumatic layer (red) containing microvalves. (B) Approach to quantify shear stress using our sensor. (C) Correlation of fluorescence induction to shear stress intensity.

: Figure 2: NIH3T3 cells. (A) DNA damage sensor before and (B) 24 hrs after DNA damage with methyl methanesulfonate – DNA alkylating agent. The left images – constitutive cyan; right – red fluorescent DNA damage reporter. (C) Heat shock sensor before and (D) 16 hrs after a heat shock ( 43oC) for 60 min. Left images are constitutive YPET reporter; right images are red fluorescent heat shock reporter. Scale: 100 μm.

Y.-C. Toh and J. Voldman, “Fluid shear stress primes mouse embryonic stem cells for differentiation in a self-renewing environment via heparan sulfate [↩]
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,” in Micro Total Analysis Systems ’08, San Diego, CA, USA, 2008, pp. 1308-1310. [↩]

MTL Annual Research Report 2013