Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity
- Fred Hutchinson Cancer Research Center, United States
Abstract
The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.
Data availability
Data and analysis scripts needed to reproduce the findings of this study have been deposited in the Zenodo database (doi:10.5281/zenodo.1248193).
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Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoirePublicly available in Adaptive Biotechnology's ImmuneAccess database.
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Dynamics of Individual T Cell Repertoires: From Cord Blood to CentenariansPublicly available in the NCBI's Short Read Archive with Bioproject accession PRJNA316572.
Article and author information
Author details
Frederick A Matsen
Funding
National Institutes of Health (CA015704)
- Anajane Smith
- Gary Schoch
- John A Hansen
- Frederick A Matsen
- Philip Bradley
Fred Hutchinson Cancer Research Center (Salary support)
- Philip Bradley
National Institutes of Health (R01-HL105914)
- Anajane Smith
- Gary Schoch
- John A Hansen
National Institutes of Health (R01-GM113246)
- Frederick A Matsen
National Institutes of Health (U19-AI117891)
- Frederick A Matsen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All samples were collected and analyzed, and informed consent and consent to publish were obtained, according to research protocols approved by the Fred Hutchinson Cancer Research Center (FHCRC) Institutional Review Board.
Reviewing Editor
- Aleksandra M Walczak, École Normale Supérieure, France
Version history
- Received: May 14, 2018
- Accepted: August 21, 2018
- Accepted Manuscript published: August 28, 2018 (version 1)
- Version of Record published: September 28, 2018 (version 2)
Copyright
© 2018, DeWitt et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity
eLife 7:e38358.
https://doi.org/10.7554/eLife.38358
Further reading
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Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.
