Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures

Abstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this...

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Main Authors: Alexander M. Sevy, Cinque Soto, Robin G. Bombardi, Jens Meiler, James E. Crowe
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Bioinformatics
Subjects:
Online Access:https://doi.org/10.1186/s12859-019-3281-8
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spelling doaj-5d7e85c571f04808b7755a6c515d3c5c2020-12-06T12:56:53ZengBMCBMC Bioinformatics1471-21052019-12-0120111010.1186/s12859-019-3281-8Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposuresAlexander M. Sevy0Cinque Soto1Robin G. Bombardi2Jens Meiler3James E. Crowe4Chemical & Physical Biology Program, Vanderbilt UniversityVanderbilt Vaccine Center, Vanderbilt University Medical CenterVanderbilt Vaccine Center, Vanderbilt University Medical CenterChemical & Physical Biology Program, Vanderbilt UniversityChemical & Physical Biology Program, Vanderbilt UniversityAbstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as “repertoire fingerprinting.” We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.https://doi.org/10.1186/s12859-019-3281-8Immune repertoire analysisPrincipal component analysisAntibody sequencingRepertoire dissimilarity index
collection DOAJ
language English
format Article
sources DOAJ
author Alexander M. Sevy
Cinque Soto
Robin G. Bombardi
Jens Meiler
James E. Crowe
spellingShingle Alexander M. Sevy
Cinque Soto
Robin G. Bombardi
Jens Meiler
James E. Crowe
Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
BMC Bioinformatics
Immune repertoire analysis
Principal component analysis
Antibody sequencing
Repertoire dissimilarity index
author_facet Alexander M. Sevy
Cinque Soto
Robin G. Bombardi
Jens Meiler
James E. Crowe
author_sort Alexander M. Sevy
title Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
title_short Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
title_full Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
title_fullStr Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
title_full_unstemmed Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
title_sort immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2019-12-01
description Abstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as “repertoire fingerprinting.” We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.
topic Immune repertoire analysis
Principal component analysis
Antibody sequencing
Repertoire dissimilarity index
url https://doi.org/10.1186/s12859-019-3281-8
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