Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination

TL;DR

This paper presents a data-driven model that accurately predicts TCR sequence sharing across individuals by accounting for recombination biases and thymic selection, advancing understanding of immune repertoire publicness.

Contribution

The study introduces a model combining recombination biases and thymic selection to predict TCR sharing, and develops a classifier for public/private sequence determination.

Findings

Model accurately predicts TCR sharing across individuals.

Sharing depends on cohort size, sampling depth, and sequence features.

The PUBLIC classifier performs well even with small cohorts.

Abstract

Despite the extreme diversity of T cell repertoires, many identical T-cell receptor (TCR) sequences are found in a large number of individual mice and humans. These widely-shared sequences, often referred to as `public', have been suggested to be over-represented due to their potential immune functionality or their ease of generation by V(D)J recombination. Here we show that even for large cohorts the observed degree of sharing of TCR sequences between individuals is well predicted by a model accounting for by the known quantitative statistical biases in the generation process, together with a simple model of thymic selection. Whether a sequence is shared by many individuals is predicted to depend on the number of queried individuals and the sampling depth, as well as on the sequence itself, in agreement with the data. We introduce the degree of publicness conditional on the queried cohort size and the size of the sampled repertoires. Based on these observations we propose a public/private sequence classifier, `PUBLIC' (Public Universal Binary Likelihood Inference Classifier), based on the generation probability, which performs very well even for small cohort sizes.