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

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.

Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity.

Background: Intracranial occlusion recanalization fails in 20% of endovascular thrombectomy procedures, and thrombus composition is likely to be an important factor. In this study, we demonstrate that the combination of electrical impedance spectroscopy (EIS) and machine learning constitutes a novel and highly accurate method for the identification of different human thrombus types.

Methods: 134 samples, subdivided into four categories, were analyzed by EIS: 29 'White', 26 'Mixed', 12 'Red' thrombi, and 67 liquid 'Blood' samples.