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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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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