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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Bibliographic Details
Published in:PLoS computational biology Vol. 16; no. 4; p. e1007873
Main Authors: Puelma Touzel, Maximilian, Walczak, Aleksandra M., Mora, Thierry
Format: Journal Article
Language:English
Published: United States Public Library of Science 01.04.2020
PLOS
Public Library of Science (PLoS)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007873