{"doi":"10.1038/s41598-021-04286-5","title":"Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions","abstract":"Abstract Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selecting antigen-specific T cells are time-consuming and limited to pre-defined antigenic peptide sequences. Here, we demonstrate the ability to use deep learning to rapidly classify videos of antigen-specific CD8 + T cells. The trained model distinguishes distinct interaction dynamics (in motility and morphology) between cognate and non-cognate T cells and DCs over 20 to 80 min. The model classified high affinity antigen-specific CD8 + T cells from OT-I mice with an area under the curve (AUC) of 0.91, and generalized well to other types of high and low affinity CD8 + T cells. The classification accuracy achieved by the model was consistently higher than simple image analysis techniques, and conventional metrics used to differentiate between cognate and non-cognate T cells, such as speed. Also, we demonstrated that experimental addition of anti-CD40 antibodies improved model prediction. Overall, this method demonstrates the potential of video-based deep learning to rapidly classify cognate T cell-DC interactions, which may also be potentially integrated into high-throughput methods for selecting antigen-specific T cells in the future.","journal":"Scientific Reports","year":2022,"id":289073,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9477,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":848227,"name":"Abigail G. Ayers","orcid":"0000-0001-7145-303X","position":1,"is_corresponding":false},{"id":401573,"name":"Pawel Muranski","orcid":"0000-0002-4446-6385","position":2,"is_corresponding":false},{"id":369807,"name":"Rémi J. Creusot","orcid":"0000-0002-8328-8155","position":3,"is_corresponding":false},{"id":363669,"name":"Samuel K. Sia","orcid":"0000-0003-0230-3542","position":4,"is_corresponding":false},{"id":477768,"name":"Priya N. Anandakumaran","orcid":"0000-0001-6201-5804","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-19T00:30:18.766838Z","pmid":"35017558","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":[]}