Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS)

TL;DR

This paper demonstrates that electric cell-substrate impedance sensing (ECIS) data from multiple cell lines can be effectively used to classify cell types, improving reproducibility and accuracy in biological research.

Contribution

The study introduces a novel approach to classify multiple cell lines using multivariate ECIS data, including multi-frequency analysis, with high accuracy.

Findings

High out-of-sample accuracy in classifying 15 cell lines

Development of a 29-feature dictionary for ECIS data

Potential to improve reproducibility in cell line identification

Abstract

We consider cell line classification using multivariate time series data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS device, which monitors the attachment and spreading of mammalian cells in real time through the collection of electrical impedance data, has historically been used to study one cell line at a time. However, we show that if applied to data from multiple cell lines, ECIS can be used to classify unknown or potentially mislabeled cells, which may help to mitigate the current crisis of reproducibility in the biological literature. We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Our analysis also makes use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.