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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Vydané v:	Immunological reviews Ročník 284; číslo 1; s. 167 - 179
Hlavní autori:	Elhanati, Yuval, Sethna, Zachary, Callan, Curtis G., Mora, Thierry, Walczak, Aleksandra M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Wiley Subscription Services, Inc 01.07.2018 John Wiley and Sons Inc
Predmet:	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
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Abstract	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.
AbstractList	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. 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. 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. 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.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.
Author	Callan, Curtis G. Walczak, Aleksandra M. Mora, Thierry Sethna, Zachary Elhanati, Yuval
AuthorAffiliation	2 Laboratoire de physique statistique CNRS Sorbonne Université Université Paris‐Diderot, and École Normale Supérieure (PSL University) Paris France 1 Joseph Henry Laboratories Princeton University Princeton NJ USA 3 Laboratoire de physique théorique CNRS Sorbonne Université, and École Normale Supérieure (PSL University) Paris France
AuthorAffiliation_xml	– name: 3 Laboratoire de physique théorique CNRS Sorbonne Université, and École Normale Supérieure (PSL University) Paris France – name: 2 Laboratoire de physique statistique CNRS Sorbonne Université Université Paris‐Diderot, and École Normale Supérieure (PSL University) Paris France – name: 1 Joseph Henry Laboratories Princeton University Princeton NJ USA
Author_xml	– sequence: 1 givenname: Yuval surname: Elhanati fullname: Elhanati, Yuval organization: Princeton University – sequence: 2 givenname: Zachary surname: Sethna fullname: Sethna, Zachary organization: Princeton University – sequence: 3 givenname: Curtis G. surname: Callan fullname: Callan, Curtis G. organization: Princeton University – sequence: 4 givenname: Thierry surname: Mora fullname: Mora, Thierry email: tmora@lps.ens.fr organization: Université Paris‐Diderot, and École Normale Supérieure (PSL University) – sequence: 5 givenname: Aleksandra M. surname: Walczak fullname: Walczak, Aleksandra M. email: awalczak@lpt.ens.fr organization: Sorbonne Université, and École Normale Supérieure (PSL University)
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Keywords	probability of generation TCR repertoires TCR sharing public sequences inference
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Notes	Elhanati and Sethna contributed equally. Mora and Walczak contributed equally. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 This article is part of a series of reviews covering Characterization of the Immunologic Repertoire appearing in Volume 284 of Immunological Reviews.
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Snippet	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... 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.... 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....
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SubjectTerms	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
Title	Predicting the spectrum of TCR repertoire sharing with a data‐driven model of recombination
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