Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling 2259233

Abstract Introduction T cell receptor (TCR) repertoire diversity enables the orchestration of antigen-specific immune responses against the vast space of possible pathogenic peptides. Identifying TCR/antigen specificity from the large TCR repertoire and antigen space is crucial for biomedical research. Methods We introduce copepodTCR, an open-access tool for the design and interpretation of high-throughput experimental assays to determine TCR/antigen specificity. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, useful for “deorphaning” TCRs of unknown specificity. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased results interpretation, identifies the response-eliciting peptide for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. Results Using in silico simulation, we demonstrated the applicability of our design scheme and the sensitivity of our results evaluation across varied experimental layouts and range of TCR-peptide activation signals. We experimentally 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. Conclusion In conclusion, copepodTCR enables efficient and accurate mapping of TCR—peptide specificities through optimized combinatorial peptide pooling coupled with Bayesian inference. Beyond deorphanizing TCRs from established cell lines, we anticipate that copepodTCR can facilitate primary T cells deorphanization using single-sequencing as a read out, due to optimization of the pooling scheme, rational assignment of peptides and robust error-correction. Funding Source Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory; Starr Centennial Scholarship; US National Institutes of Health Grants U01AI150747, R01AI136514, S10OD028632-01, 1R01AI167862; Simons Pivot Fellowship; National Natural Science Foundation of China Grant 62331002, National Science Foundation grant PHY-2210452. Topic Categories Computational and Systems Immunology (COMP)