{"doi":"10.1101/2023.11.28.569052","title":"Identification of Antigen-Specific T Cell Receptors with Combinatorial Peptide Pooling","abstract":"Abstract T cell receptor (TCR) repertoire diversity enables the antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR-antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR , an open-access tool to design and interpret high-throughput experimental TCR specificity assays. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, that can be used to identify the specificity of (or “deorphanize”) TCRs. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased interpretation, identifies the response-eliciting peptide sequence for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. Using in silico simulations, we demonstrate the varied experimental settings in which copepodTCR yields efficient and interpretable TCR specificity results. We validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein, split across 12 pools. A single stimulation with combinatorial pools identified the correct epitope of two TCRs with known specificity and then deorphanized two SARS-CoV-2 associated TCRs shared among a large cohort of COVID-19 patients. We provide experimental guides to efficiently design larger screens covering thousands of peptides which will be crucial to identify antigen-specific T cells and their targets from limited clinical material.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":401742,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":456513,"name":"David Pattinson","orcid":"0000-0003-0001-8203","position":1,"is_corresponding":false},{"id":1178695,"name":"Guanchen He","orcid":"0000-0003-1373-7098","position":2,"is_corresponding":false},{"id":805095,"name":"Carl Barton","orcid":"0000-0001-6938-9446","position":3,"is_corresponding":false},{"id":870976,"name":"Sarah R. Chapin","orcid":"0000-0002-7775-3380","position":4,"is_corresponding":false},{"id":618932,"name":"Anastasia A. Minervina","orcid":"0000-0001-9884-6351","position":5,"is_corresponding":false},{"id":1178696,"name":"Qin Huang","orcid":"0000-0003-1621-6984","position":6,"is_corresponding":false},{"id":225737,"name":"Paul G. Thomas","orcid":"0000-0001-7955-0256","position":7,"is_corresponding":false},{"id":618933,"name":"Mikhail V. Pogorelyy","orcid":"0000-0003-0773-1204","position":8,"is_corresponding":false},{"id":64945,"name":"Hannah V. Meyer","orcid":"0000-0003-4564-0899","position":9,"is_corresponding":false},{"id":1178694,"name":"Vasilisa A. Kovaleva","orcid":"0000-0003-0215-4938","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T01:20:12.316631Z","pmid":"38077028","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":[]}