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

Summary 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 immun...

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Published in:Immunological reviews Vol. 284; no. 1; pp. 167 - 179
Main Authors: Elhanati, Yuval, Sethna, Zachary, Callan, Curtis G., Mora, Thierry, Walczak, Aleksandra M.
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 01.07.2018
John Wiley and Sons Inc
Subjects:
Algorithms
Animals
Classifiers
Humans
inference
Invited Review
Invited Reviews
Mathematical models
Mice
Probabilistic inference
probability of generation
public sequences
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Sequences
Statistical analysis
T cell receptors
T-Lymphocytes - immunology
TCR repertoires
TCR sharing
Thymus
V(D)J recombination
V(D)J Recombination - genetics
V(D)J Recombination - immunology
probability of generation
TCR repertoires
TCR sharing
public sequences
inference
ISSN:0105-2896, 1600-065X, 1600-065X
Online Access:Get full text
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Summary:Summary 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.
Bibliography:Elhanati and Sethna contributed equally.
Mora and Walczak contributed equally.
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This article is part of a series of reviews covering Characterization of the Immunologic Repertoire appearing in Volume 284 of Immunological Reviews.
ISSN:0105-2896
1600-065X
1600-065X
DOI:10.1111/imr.12665