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Automated Quantification of the Capacitance of Epithelial Cell Layers from an Impedance Spectrum

Authors:
Thomas Schmid
Dorothee Günzel
Martin Bogdan

Keywords: Physiology, Epithelia, Impedance Spectroscopy, Artificial Neural Networks, Clustering

Abstract:
Quantifying the intestinal surface area of epithelia is crucial to assess changes in protein expression during disease. A convenient alternative to microscopic evaluation of serial sections is capacitance measurement by impedance spectroscopy. While the underlying theoretical relations are well-known, in practice data scatter considerably decreases precision of estimations. Estimations are even less precise if obtained impedance spectra cannot be approximated by a semicircle. Here, we demonstrate that using machine learning techniques together with detailed modeling of cell layers allows reliable predictions in the presence of data scatter. Our results show that estimates for modeled impedance spectra can be obtained with less than 20 percent relative deviation from the target value. In particular, this is shown for spectra that deviate from a semicircular shape.

Pages: 27 to 32

Copyright: Copyright (c) IARIA, 2015

Publication date: May 24, 2015

Published in: conference

ISSN: 2308-4383

ISBN: 978-1-61208-409-1

Location: Rome, Italy

Dates: from May 24, 2015 to May 29, 2015