Classifying Contaminated Cell Cultures using Time Series Features

TL;DR

This study uses time series features from ECIS data to accurately classify contaminated cell cultures, emphasizing interpretability and robustness across experimental variations.

Contribution

First comprehensive analysis of ECIS time course features for contamination detection, combining diverse features for accurate, interpretable classification, and addressing plate-to-plate variation.

Findings

High classification accuracy with only two features

Existence of experimental variation between plates

Identification of features more robust to variation

Abstract

We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of interpretable results, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.