Classifying contaminated cell cultures using time series features

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 easy visualization and interpretation, 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.

KEYWORDS:

• Time series classification
• feature-based classification
• contamination of cell cultures
• biophysics
• electric cell-substrate impedance sensing

Disclosure statement

Charles R. Keese is Chief Scientific Officer of Applied BioPhysics, Inc., which manufactures the ECIS $\mathrm{Z\theta }$ machine used to collect the data described in this paper.

Additional information

Funding

The authors gratefully acknowledge financial support from the Cornell University Institute of Biotechnology, the New York State Foundation of Science, Technology and Innovation (NYSTAR), a Xerox PARC Faculty Research Award, National Science Foundation Awards 1455172, 1934985, 1940124, and 1940276, USAID, and Cornell University Atkinson Center for a Sustainable Future.