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Convolution: a method for data analysis in perifusion systems

R M Brand1, A R Midgley, W J Williams

  • 1Department of Electrical Engineering and Computer Sciences, University of Michigan, Ann Arbor 48109-0404.

The American Journal of Physiology
|November 1, 1994
PubMed
Summary
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This study introduces a novel perifusion system to rapidly detect cellular metabolic changes. Convolutional analysis corrects distortions, revealing rapid cellular responses and adaptation to metabolic uncouplers.

Area of Science:

  • Cellular Metabolism
  • Biophysics

Background:

  • Understanding dynamic changes in cellular metabolism in response to stimuli is crucial.
  • Existing methods for measuring metabolic changes can be slow.

Purpose of the Study:

  • To develop and validate a miniature perifusion system for rapid detection of extracellular proton changes.
  • To apply convolutional analysis to correct for distortions in perifusion data.
  • To investigate cellular responses to metabolic perturbants using this system.

Main Methods:

  • Development of a miniature perifusion system with two pH sensors.
  • Application of convolutional analysis to correct for signal distortion.
  • Analysis of HeLa cells exposed to carbonyl cyanide m-chlorophenyl hydrazone (a metabolic uncoupler).

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Main Results:

  • The perifusion system detects extracellular proton alterations within seconds.
  • Convolutional analysis successfully corrects for system-induced distortions.
  • Analysis of HeLa cells shows a rapid uncoupling of oxidative phosphorylation followed by adaptation.

Conclusions:

  • The developed perifusion system enables rapid assessment of cellular metabolic dynamics.
  • Convolutional analysis is a valuable tool for interpreting perifusion data.
  • This approach provides new insights into cellular responsiveness and metabolic adaptation.