Carrasco Yagüe Manuel, Zhang Xingjian, Volpatti Matthew, Wei Yiming, Lebedev Gor, Gamby Jean, Barakat Abdul I
LadHyX, CNRS, École Polytechnique, Institut Polytechnique de Paris, Palaiseau 91120, France.
Sensome, Massy 91300, France.
Sci Adv. 2025 Jul 18;11(29):eadx4919. doi: 10.1126/sciadv.adx4919. Epub 2025 Jul 16.
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.
监测细胞的时空动态对于理解复杂的生物过程（如器官发育和癌症进展）至关重要。使用活细胞荧光显微镜来追踪细胞动态通常受到染料诱导的细胞毒性和细胞光损伤的限制。在此，我们展示了一种结合微电极阵列、电阻抗光谱（EIS）和机器学习（ML）的替代方法，该方法能够以无创且无标记的方式实时监测细胞的时空动态。该平台应用于单培养或共培养的正常和癌性乳腺上皮细胞，将EIS测量结果与通过自动显微镜图像分析获得的细胞生长参数相关联。实施了一个ML模型，以准确预测细胞密度和大小的时空演变，并仅基于EIS记录对不同细胞类型进行分类。该技术还被证明能够追踪相关的生物过程，包括细胞增殖模式中的空间异质性和共培养中的细胞竞争。
