copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling
Kovaleva, V. A.; Pattinson, D. J.; Barton, C.; Chapin, S. R.; Minervina, A. A.; Richards, K. A.; Sant, A. J.; Thomas, P. G.; Pogorelyy, M. V.; Meyer, H. V.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 97%
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 97%
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes 96%
Similar papers in this journal
- Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions 96%
- Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions 96%
- TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing 95%
Similar papers in this journal
- Modular DNA Barcoding of Nanobodies Enables Multiplexed in situ Protein Imaging and High-throughput Biomolecule Detection 95%
- Human thymopoiesis produces polyspecific CD8+ alfa/beta T cells responding to multiple viral antigens 95%
- Chromatin conformation dynamics during CD4+ T cell activation implicates autoimmune disease-associated genes and regulatory elements 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.