Inferring the immune response from repertoire sequencing

High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of e...

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Vydané v:PLoS computational biology Ročník 16; číslo 4; s. e1007873
Hlavní autori: Puelma Touzel, Maximilian, Walczak, Aleksandra M., Mora, Thierry
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 01.04.2020
PLOS
Public Library of Science (PLoS)
Predmet:
Adaptive immunology
B cells
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Chronic illnesses
Computational Biology
DNA sequencing
Frequency distribution
Genetic aspects
High-throughput screening (Biochemical assaying)
Humans
Immune response
Immune system
Immunology
Life Sciences
Lymphocytes T
Medicine and Health Sciences
Methods
Models, Immunological
Next-generation sequencing
Noise
Quantitative Methods
Receptors
Receptors, Antigen, B-Cell - genetics
Receptors, Antigen, B-Cell - immunology
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Research and Analysis Methods
RNA-Seq - methods
Stochasticity
T cells
Transcriptome - immunology
Vaccination
Vaccines
Vector-borne diseases
Yellow fever
Yellow Fever Vaccine - immunology
Canada
France
RNA sequencing
Immune response
Lymphocytes
Cloning
Mathematical models
Gene expression
Vaccination and immunization
Yellow fever
ISSN:1553-7358, 1553-734X, 1553-7358
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Abstract High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response.
AbstractList High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response.
High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response.High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response.
High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. Here, we present a general Bayesian approach to disentangle repertoire variations from these stochastic effects. Using replicate experiments, we first show how to learn the natural variability of read counts by inferring the distributions of clone sizes as well as an explicit noise model relating true frequencies of clones to their read count. We then use that null model as a baseline to infer a model of clonal expansion from two repertoire time points taken before and after an immune challenge. Applying our approach to yellow fever vaccination as a model of acute infection in humans, we identify candidate clones participating in the response. High-throughput immune repertoire sequencing (RepSeq) experiments are becoming a common way to study the diversity, structure and composition of lymphocyte repertoires, promising to yield unique insight into individuals’ past infection history. However, the analysis of these sequences remains challenging, especially when comparing two different temporal or tissue samples. Here we develop a new theoretical approach and methodology to extract the characteristics of the lymphocyte repertoire response from different samples. The method is specifically tailored to RepSeq experiments and accounts for the multiple sources of noise present in these experiments. Its output provides expansion parameters, as well as a list of potentially responding clonotypes. We apply the method to describe the response to yellow fever vaccine obtained from samples taken at different time points. We also use our results to estimate the diversity and clone size statistics from data.
Audience Academic
Author Puelma Touzel, Maximilian
Walczak, Aleksandra M.
Mora, Thierry
AuthorAffiliation 1 Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, Université de Paris, Paris, France
UNSW Australia, AUSTRALIA
2 Mila, Université de Montréal, Montreal, Canada
AuthorAffiliation_xml – name: 1 Laboratoire de physique de l’École normale supérieure (PSL University), CNRS, Sorbonne Université, Université de Paris, Paris, France
– name: 2 Mila, Université de Montréal, Montreal, Canada
– name: UNSW Australia, AUSTRALIA
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Distributed under a Creative Commons Attribution 4.0 International License
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Keywords RNA sequencing
Immune response
Lymphocytes
Cloning
Mathematical models
Gene expression
Vaccination and immunization
Yellow fever
Language English
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Snippet High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic...
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SubjectTerms Adaptive immunology
B cells
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Chronic illnesses
Computational Biology
DNA sequencing
Frequency distribution
Genetic aspects
High-throughput screening (Biochemical assaying)
Humans
Immune response
Immune system
Immunology
Life Sciences
Lymphocytes T
Medicine and Health Sciences
Methods
Models, Immunological
Next-generation sequencing
Noise
Quantitative Methods
Receptors
Receptors, Antigen, B-Cell - genetics
Receptors, Antigen, B-Cell - immunology
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Research and Analysis Methods
RNA-Seq - methods
Stochasticity
T cells
Transcriptome - immunology
Vaccination
Vaccines
Vector-borne diseases
Yellow fever
Yellow Fever Vaccine - immunology
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Title Inferring the immune response from repertoire sequencing
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