Cell-based sensors for quantifying the physiological impact of microsystems
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voldman_Cell-based sensors.pdf
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2.32 MB
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Adobe PDF
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337556794d0616c71ee856513e8167cd
Author(s)
Desai, Salil P.
Voldman, Joel
Date Issued
October 2010
Journal
Integrative Biology
Publisher
Royal Society of Chemistry, The
Citation
Desai, Salil P., and Joel Voldman. “Cell-based Sensors for Quantifying the Physiological Impact of Microsystems.” Integrative Biology 3.1 (2011): 48.
Version
Author's final manuscript
Abstract
Microsystems are increasingly used in the manipulation, patterning and sorting of cells. Critical to the widespread adoption of these new technologies is development of an understanding of their impact on cellular physiology. Here we show the integration of a cell-based sensor, a microfabricated electrical screening platform, and quantitative imaging to enable the first large-scale physiological screens of the impact of microsystems on cells. To perform physiological screening, we developed a cell-based sensor that reports on stress-mediated transcription (via Heat Shock Factor 1 induced expression of GFP). This cell-based sensor was quantitatively characterized using automated imaging. The integration of this quantitative physiological sensor with a microfabricated system enabled the execution of multiplexed screens across electric field strength, frequency, and application duration. Voltage sweeps indicate increasing physiological stress with increasing voltage due to Joule heating, while frequency sweeps indicate increased stress at lower frequencies (<500 kHz) compared with higher frequencies (>1 MHz) due to generation of reactive species at lower frequencies. Combined voltage and frequency sweeps enable the generation of complex maps of physiological state.
Description
2012 January 1
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1039/c0ib00067a
