Noninvasive real-time monitoring of cellular spatiotemporal dynamics via machine learning-enhanced electrical impedance spectroscopy.

Abstract

Monitoring cellular spatiotemporal dynamics is essential for understanding complex biological processes such as organ development and cancer progression. Using live-cell fluorescence microscopy to track cellular dynamics is often limited by dye-induced cytotoxicity and cellular photodamage. Here, we demonstrate an alternative methodology combining microelectrode arrays, electrical impedance spectroscopy (EIS), and machine learning (ML) that enables real-time monitoring of cellular spatiotemporal dynamics in a noninvasive and label-free manner. The platform is applied to normal and cancerous breast epithelial cells in either mono- or coculture, correlating EIS measurements with cell growth parameters obtained from automated microscopy image analysis. An ML model is implemented to accurately predict the spatiotemporal evolution of cell density and size and to classify the different cell types based solely on EIS recordings. The technology is also shown to be capable of tracking pertinent biological processes including spatial heterogeneities in cell proliferation patterns and cell competition in coculture.