Advancing Cellular Spatiotemporal Monitoring: Machine Learning-Enhanced Multi-Electrode Impedance Spectroscopy
Résumé
Understanding how cells organize in space and time is crucial for organ development, tissue regeneration, and cancer progression [1]. Conventional methods, such as live-cell fluorescence microscopy imaging, face challenges such as dye toxicity and photo-induced cell damage. To overcome these hurdles, we propose a costeffective, label-free solution using a micro-electrode array (MEA) and impedance spectroscopy (IS). This approach allows us to monitor different cell types and is ideal for long-term experiments, co-culture studies, and organ-on-chip applications.
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