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

SUBMITTER: Carrasco Yague M 

PROVIDER: S-EPMC12266097 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature