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

Yuval Elhanati, Zachary Sethna, Curtis Gove Callan, Thierry Mora, Aleksandra M. Walczak

Research output: Contribution to journalReview article

23 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)167-179
Number of pages13
JournalImmunological Reviews
Volume284
Issue number1
DOIs
StatePublished - Jul 2018

All Science Journal Classification (ASJC) codes

  • Immunology and Allergy
  • Immunology

Keywords

  • TCR repertoires
  • TCR sharing
  • inference
  • probability of generation
  • public sequences

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