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The cytosensor microphysiometer: biological applications of silicon technology

McConnell, H., Owicki, J., Parce, J., Miller, D., Baxter, G., Wada, H., Pitchford, S·1992·Science

Abstract

A silicon-based device, dubbed a microphysiometer, can be used to detect and monitor the response of cells to a variety of chemical substances, especially ligands for specific plasma membrane receptors. The microphysiometer measures the rate of proton excretion from 10(4) to 10(6) cells. This article gives an overview of experiments currently being carried out with this instrument with emphasis on receptors with seven transmembrane helices and tyrosine kinase receptors. As a scientific instrument, the microphysiometer can be thought of as serving two distinct functions. In terms of detecting specific molecules, selected biological cells in this instrument serve as detectors and amplifiers. The microphysiometer can also investigate cell function and biochemistry. A major application of this instrument may prove to be screening for new receptor ligands. In this respect, the microphysiometer appears to offer significant advantages over other techniques.

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Metadata

Authors
McConnell, H., Owicki, J., Parce, J., Miller, D., Baxter, G., Wada, H., & Pitchford, S.
Venue
Science
Published
1992-09-25
Volume
257
Issue
5078
Pages
1906-1912
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2036213823
Cited by
622
References
0
FWCI
7.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The cytosensor microphysiometer: biological applications of silicon technology. https://app.cochat.ai/papers/10.1126/science.1329199
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McConnell, H., Owicki, J., Parce, J., Miller, D., Baxter, G., Wada, H., & Pitchford, S. (1992). The cytosensor microphysiometer: biological applications of silicon technology. Science. https://doi.org/10.1126/science.1329199

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.