Single-image inference of clathrin-mediated endocytosis dynamics via deep learning

Clathrin-mediated endocytosis (CME) is a vital cellular process that exhibits spatial and temporal heterogeneity in its dynamics, traditionally studied through labor-intensive time-lapse microscopy and single particle tracking. To overcome the limitations posed by phototoxicity, temporal undersampling, and computational complexity, we introduce a deep learning framework that infers CME dynamics from single fluorescence images. Using a modified U-Net architecture, our model predicts spatial maps of the standard deviation (SD) of clathrin coat growth rates—an established metric of CME activity—directly from static frames of AP2-eGFP–labeled cells. The network was trained on paired image data and SD maps derived from experimentally tracked endocytic events. The model accurately recapitulates dynamic features such as front–rear asymmetry in migrating cells and responses to membrane tension alterations, demonstrating strong agreement with traditional time-lapse-derived metrics. This approach eliminates the need for trajectory reconstruction or prolonged imaging, enabling real-time, non-invasive assessment of endocytic dynamics across diverse biological contexts. Our results highlight the potential of deep learning to extract dynamic biophysical information from static imaging data and establish a scalable methodology for probing CME and related subcellular processes.