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Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves

1
Animal Physiology & Immunology, School of Life Sciences Weihenstephan, Technical University of Munich, Weihenstephaner Berg 3, D-85354 Freising, Germany
2
Center for Cardiology, Genomics and System Biology, UKE, D-20246 Hamburg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Shimshon Belkin, Gérald Thouand and Anna Chiara De Luca
Sensors 2021, 21(16), 5286; https://doi.org/10.3390/s21165286
Received: 31 May 2021 / Revised: 26 July 2021 / Accepted: 2 August 2021 / Published: 5 August 2021
(This article belongs to the Section Biosensors)
Measurement of cell surface coverage has become a common technique for the assessment of growth behavior of cells. As an indirect measurement method, this can be accomplished by monitoring changes in electrode impedance, which constitutes the basis of electric cell-substrate impedance sensing (ECIS). ECIS typically yields growth curves where impedance is plotted against time, and changes in single cell growth behavior or cell proliferation can be displayed without significantly impacting cell physiology. To provide better comparability of ECIS curves in different experimental settings, we developed a large toolset of R scripts for their transformation and quantification. They allow importing growth curves generated by ECIS systems, edit, transform, graph and analyze them while delivering quantitative data extracted from reference points on the curve. Quantification is implemented through three different curve fit algorithms (smoothing spline, logistic model, segmented regression). From the obtained models, curve reference points such as the first derivative maximum, segmentation knots and area under the curve are then extracted. The scripts were tested for general applicability in real-life cell culture experiments on partly anonymized cell lines, a calibration setup with a cell dilution series of impedance versus seeded cell number and finally IPEC-J2 cells treated with 1% and 5% ethanol. View Full-Text
Keywords: ECIS (impedance vs. time); IPEC-J2 (adherent cells); segmented regression; four-parameter logistic; smoothing spline; area under the curve (AUC) ECIS (impedance vs. time); IPEC-J2 (adherent cells); segmented regression; four-parameter logistic; smoothing spline; area under the curve (AUC)
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MDPI and ACS Style

Binder, A.R.D.; Spiess, A.-N.; Pfaffl, M.W. Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves. Sensors 2021, 21, 5286. https://doi.org/10.3390/s21165286

AMA Style

Binder ARD, Spiess A-N, Pfaffl MW. Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves. Sensors. 2021; 21(16):5286. https://doi.org/10.3390/s21165286

Chicago/Turabian Style

Binder, Anna Ronja Dorothea, Andrej-Nikolai Spiess, and Michael W. Pfaffl. 2021. "Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves" Sensors 21, no. 16: 5286. https://doi.org/10.3390/s21165286

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