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Bioinformatics. 2015 Aug 15;31(16):2705-12. doi: 10.1093/bioinformatics/btv216. Epub 2015 Apr 21.

Analysis of impedance-based cellular growth assays.

Author information

1
Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Institute of Theoretical Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.
2
BIMSB, Max-Delbrück-Centrum für Molekulare Medizin, 13092 Berlin and.
3
Institute for Pathology, Charité Universitätsmedizin Berlin, 10117 Berlin, Integrative Research Institute Life Sciences, Humboldt-Universität zu Berlin, 10099 Berlin, Germany.

Abstract

MOTIVATION:

Impedance-based technologies are advancing methods for measuring proliferation of adherent cell cultures non-invasively and in real time. The analysis of the resulting data has so far been hampered by inappropriate computational methods and the lack of systematic data to evaluate the characteristics of the assay.

RESULTS:

We used a commercially available system for impedance-based growth measurement (xCELLigence) and compared the reported cell index with data from microscopy. We found that the measured signal correlates linearly with the cell number throughout the time of an experiment with sufficient accuracy in subconfluent cell cultures. The resulting growth curves for various colon cancer cells could be well described with the empirical Richards growth model, which allows for extracting quantitative parameters (such as characteristic cycle times). We found that frequently used readouts like the cell index at a specific time or the area under the growth curve cannot be used to faithfully characterize growth inhibition. We propose to calculate the average growth rate of selected time intervals to accurately estimate time-dependent IC50 values of drugs from growth curves.

CONTACT:

nils.bluethgen@charite.de

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25900918
DOI:
10.1093/bioinformatics/btv216
[Indexed for MEDLINE]

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