Identification of Antigen-Specific T Cell Receptors with Combinatorial Peptide Pooling

Supplementary repository to the paper: bioRxiv version.

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.

Figures

• Figures1-2-3.ipynb contains the code for figures in the main part of the manuscript. The data used in them is in "data" folder.
• Figures_Extended-data.ipynb contains the code for Extended data figures.
• Figures-Supplement.ipynb contains the code for figures from the supplementary materials.

In the code, we use codePUB and copepodTCR python packages. codePUB is installed automatically when you install copepodTCR:

pip install copepodTCR

or with conda:

conda install -c vasilisa.kovaleva copepodTCR

copepodTCR documentation: readthedocs.

In silico simulations

Code for in silico simulations is in separate repository: Peptide pooling simulation

Authors

Vasilisa A. Kovaleva, David J. Pattinson, Guanchen He, Carl Barton, Sarah R. Chapin, Anastasia A. Minervina, Qin Huang, Paul G. Thomas, Mikhail V. Pogorelyy, Hannah V. Meyer