{"doi":"10.1063/5.0288422","title":"Single-image inference of clathrin-mediated endocytosis dynamics via deep learning","abstract":"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.","journal":"The Journal of Chemical Physics","year":2025,"id":527185,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9522,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":647532,"name":"Cömert Kural","orcid":"0000-0002-9065-6542","position":1,"is_corresponding":false},{"id":1007204,"name":"Tianyao Wu","orcid":null,"position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-19T02:50:39.280101Z","pmid":"41090577","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}