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Hey S, Whyte D, Hoang MC, Le N, Natvig J, Wingfield C, Onyeama C, Howrylak J, Toby IT. Analysis of CDR3 Sequences from T-Cell Receptor β in Acute Respiratory Distress Syndrome. Biomolecules 2023; 13:biom13050825. [PMID: 37238695 DOI: 10.3390/biom13050825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Acute Respiratory Distress Syndrome (ARDS) is an illness that typically develops in people who are significantly ill or have serious injuries. ARDS is characterized by fluid build-up that occurs in the alveoli. T-cells are implicated as playing a role in the modulation of the aberrant response leading to excessive tissue damage and, eventually, ARDS. Complementarity Determining Region 3 (CDR3) sequences derived from T-cells are key players in the adaptive immune response. This response is governed by an elaborate specificity for distinct molecules and the ability to recognize and vigorously respond to repeated exposures to the same molecules. Most of the diversity in T-cell receptors (TCRs) is contained in the CDR3 regions of the heterodimeric cell-surface receptors. For this study, we employed the novel technology of immune sequencing to assess lung edema fluid. Our goal was to explore the landscape of CDR3 clonal sequences found within these samples. We obtained more than 3615 CDR3 sequences across samples in the study. Our data demonstrate that: (1) CDR3 sequences from lung edema fluid exhibit distinct clonal populations, and (2) CDR3 sequences can be further characterized based on biochemical features. Analysis of these CDR3 sequences offers insight into the CDR3-driven T-cell repertoire of ARDS. These findings represent the first step towards applications of this technology with these types of biological samples in the context of ARDS.
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Affiliation(s)
- Sara Hey
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | - Dayjah Whyte
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | - Minh-Chau Hoang
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | - Nick Le
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | - Joseph Natvig
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | - Claire Wingfield
- Department of Biology, University of Dallas, Irving, TX 75062, USA
| | | | - Judie Howrylak
- Pulmonary, Allergy and Critical Care Division, Penn State Milton S. Hershey Medical Center, Hershey, PA 17033, USA
| | - Inimary T Toby
- Department of Biology, University of Dallas, Irving, TX 75062, USA
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2
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Christley S, Stervbo U, Cowell LG. Immune Repertoire Analysis on High-Performance Computing Using VDJServer V1: A Method by the AIRR Community. Methods Mol Biol 2022; 2453:439-446. [PMID: 35622338 PMCID: PMC9761903 DOI: 10.1007/978-1-0716-2115-8_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
AIRR-seq data sets are usually large and require specialized analysis methods and software tools. A typical Illumina MiSeq sequencing run generates 20-30 million 2 × 300 bp paired-end sequence reads, which roughly corresponds to 15 GB of sequence data to be processed. Other platforms like NextSeq, which is useful in projects where the full V gene is not needed, create about 400 million 2 × 150 bp paired-end reads. Because of the size of the data sets, the analysis can be computationally expensive, particularly the early analysis steps like preprocessing and gene annotation that process the majority of the sequence data. A standard desktop PC may take 3-5 days of constant processing for a single MiSeq run, so dedicated high-performance computational resources may be required.VDJServer provides free access to high-performance computing (HPC) at the Texas Advanced Computing Center (TACC) through a graphical user interface (Christley et al. Front Immunol 9:976, 2018). VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provides access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene assignment, repertoire characterization, and repertoire comparison. Furthermore, VDJServer has parallelized execution for tools such as IgBLAST, so more compute resources are utilized as the size of the input data grows. Analysis that takes days on a desktop PC might take only a few hours on VDJServer. VDJServer is a free, publicly available, and open-source licensed resource. Here, we describe the workflow for performing immune repertoire analysis on VDJServer's high-performance computing.
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Affiliation(s)
- Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA.
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3
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Hui Z, Zhang J, Zheng Y, Yang L, Yu W, An Y, Wei F, Ren X. Single-Cell Sequencing Reveals the Transcriptome and TCR Characteristics of pTregs and in vitro Expanded iTregs. Front Immunol 2021; 12:619932. [PMID: 33868236 PMCID: PMC8044526 DOI: 10.3389/fimmu.2021.619932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/23/2021] [Indexed: 01/29/2023] Open
Abstract
Regulatory T cells (Tregs) play a critical role in the maintenance of immune tolerance and tumor evasion. However, the relative low proportion of these cells in peripheral blood and tissues has hindered many studies. We sought to establish a rapamycin-based in vitro Treg expansion procedure in patients diagnosed with colorectal cancer and perform single-cell sequencing to explore the characteristics of Treg cells. CD25+ cells enriched from peripheral blood mononuclear cells (PBMC) of colorectal tumor patients were cultured in X-VIVO15 medium, supplemented with 5% human AB serum, L-glutamine, rapamycin, interleukin-2 (IL-2), and Dynabeads human Treg expander for 21 days to expand Tregs. Treg cells with satisfactory phenotype and function were successfully expanded from CD4+CD25+ cells in patients with colorectal cancer. The median expansion fold was 75 (range, 20-105-fold), and >90.0% of the harvest cells were CD4+CD25+CD127dim/- cells. The ratio of CD4+CD25+Foxp3+ cells exceeded 60%. Functional assays showed that iTregs significantly inhibited CD8+T cell proliferation in vitro. Single-cell sequencing showed that the transcriptome of pTreg (CD4+CD25+CD127dim/- cells isolated from PBMC of colorectal cancer patients) and iTreg (CD4+CD25+CD127dim/- cells expanded in vitro according to the above regimen) cells were interlaced. pTregs exhibited enhanced suppressive function, whereas iTregs exhibited increased proliferative capacity. TCR repertoire analysis indicated minimal overlap between pTregs and iTregs. Pseudo-time trajectory analysis of Tregs revealed that pTregs were a continuum composed of three main branches: activated/effector, resting and proliferative Tregs. In contrast, in vitro expanded iTregs were a mixture of proliferating and activated/effector cells. The expression of trafficking receptors was also different in pTregs and iTregs. Various chemokine receptors were upregulated in pTregs. Activated effector pTregs overexpressed the chemokine receptor CCR10, which was not expressed in iTregs. The chemokine CCL28 was overexpressed in colorectal cancer and associated with poor prognosis. CCR10 interacted with CCL28 to mediate the recruitment of Treg into tumors and accelerated tumor progression. Depletion of CCR10+Treg cells from tumor microenvironment (TME) could be used as an effective treatment strategy for colorectal cancer patients. Our data distinguished the transcriptomic characteristics of different subsets of Treg cells and revealed the context-dependent functions of different populations of Treg cells, which was crucial to the development of alternative therapeutic strategies for Treg cells in autoimmune disease and cancer.
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Affiliation(s)
- Zhenzhen Hui
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jiali Zhang
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yu Zheng
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Lili Yang
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Wenwen Yu
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yang An
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Feng Wei
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiubao Ren
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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4
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Rubelt F, Busse CE, Bukhari SAC, Bürckert JP, Mariotti-Ferrandiz E, Cowell LG, Watson CT, Marthandan N, Faison WJ, Hershberg U, Laserson U, Corrie BD, Davis MM, Peters B, Lefranc MP, Scott JK, Breden F, Luning Prak ET, Kleinstein SH. Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data. Nat Immunol 2019; 18:1274-1278. [PMID: 29144493 DOI: 10.1038/ni.3873] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Florian Rubelt
- Department of Microbiology and Immunology and Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA
| | - Christian E Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Jean-Philippe Bürckert
- Department of Infection and Immunity, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Encarnita Mariotti-Ferrandiz
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, UMR_S 959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France
| | - Lindsay G Cowell
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, Kentucky, USA
| | - Nishanth Marthandan
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - William J Faison
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Uri Hershberg
- School of Biomedical Engineering, Science & Health Systems, and Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, Pennsylvania, USA
| | - Uri Laserson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Brian D Corrie
- iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Mark M Davis
- Department of Microbiology and Immunology and Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA.,Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, USA
| | - Marie-Paule Lefranc
- IMGT, the international ImMunoGeneTics information system, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Jamie K Scott
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada.,iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Felix Breden
- iReceptor, Simon Fraser University, Burnaby, British Columbia, Canada.,Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | | | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Immunobiology, Yale School of Medicine, and Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
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5
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Vander Heiden JA, Marquez S, Marthandan N, Bukhari SAC, Busse CE, Corrie B, Hershberg U, Kleinstein SH, Matsen IV FA, Ralph DK, Rosenfeld AM, Schramm CA, Christley S, Laserson U. AIRR Community Standardized Representations for Annotated Immune Repertoires. Front Immunol 2018; 9:2206. [PMID: 30323809 PMCID: PMC6173121 DOI: 10.3389/fimmu.2018.02206] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 09/05/2018] [Indexed: 01/21/2023] Open
Abstract
Increased interest in the immune system's involvement in pathophysiological phenomena coupled with decreased DNA sequencing costs have led to an explosion of antibody and T cell receptor sequencing data collectively termed "adaptive immune receptor repertoire sequencing" (AIRR-seq or Rep-Seq). The AIRR Community has been actively working to standardize protocols, metadata, formats, APIs, and other guidelines to promote open and reproducible studies of the immune repertoire. In this paper, we describe the work of the AIRR Community's Data Representation Working Group to develop standardized data representations for storing and sharing annotated antibody and T cell receptor data. Our file format emphasizes ease-of-use, accessibility, scalability to large data sets, and a commitment to open and transparent science. It is composed of a tab-delimited format with a specific schema. Several popular repertoire analysis tools and data repositories already utilize this AIRR-seq data format. We hope that others will follow suit in the interest of promoting interoperable standards.
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Affiliation(s)
| | - Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, CT, United States
| | - Nishanth Marthandan
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | | | - Christian E. Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Brian Corrie
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, PA, United States
- Department of Human Biology, Faculty of Sciences, University of Haifa, Haifa, Israel
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT, United States
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
| | | | - Duncan K. Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Aaron M. Rosenfeld
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Chaim A. Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | | | - Scott Christley
- Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Uri Laserson
- Department of Genetics and Genomic Sciences and Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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6
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Christley S, Scarborough W, Salinas E, Rounds WH, Toby IT, Fonner JM, Levin MK, Kim M, Mock SA, Jordan C, Ostmeyer J, Buntzman A, Rubelt F, Davila ML, Monson NL, Scheuermann RH, Cowell LG. VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements. Front Immunol 2018; 9:976. [PMID: 29867956 PMCID: PMC5953328 DOI: 10.3389/fimmu.2018.00976] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/19/2018] [Indexed: 11/13/2022] Open
Abstract
Background Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation. Results VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity. Conclusion VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.
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Affiliation(s)
- Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Walter Scarborough
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Eddie Salinas
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - William H. Rounds
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Inimary T. Toby
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - John M. Fonner
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | | | - Min Kim
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Stephen A. Mock
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Christopher Jordan
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, United States
| | - Jared Ostmeyer
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Adam Buntzman
- Bio5 Institute, University of Arizona, Tucson, AZ, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Marco L. Davila
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Nancy L. Monson
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, TX, United States,Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Richard H. Scheuermann
- J. Craig Venter Institute, La Jolla, CA, United States,Department of Pathology, University of California, San Diego, San Diego, CA, United States,La Jolla Institute for Allergy & Immunology, La Jolla, CA, United States
| | - Lindsay G. Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States,*Correspondence: Lindsay G. Cowell,
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7
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Breden F, Luning Prak ET, Peters B, Rubelt F, Schramm CA, Busse CE, Vander Heiden JA, Christley S, Bukhari SAC, Thorogood A, Matsen Iv FA, Wine Y, Laserson U, Klatzmann D, Douek DC, Lefranc MP, Collins AM, Bubela T, Kleinstein SH, Watson CT, Cowell LG, Scott JK, Kepler TB. Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. Front Immunol 2017; 8:1418. [PMID: 29163494 PMCID: PMC5671925 DOI: 10.3389/fimmu.2017.01418] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 10/12/2017] [Indexed: 12/22/2022] Open
Abstract
High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1–3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community’s founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).
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Affiliation(s)
- Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Christian E Busse
- Division of B Cell Immunology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Jason A Vander Heiden
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Adrian Thorogood
- entre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Frederick A Matsen Iv
- Public Health Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Yariv Wine
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
| | - Uri Laserson
- Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Klatzmann
- Immunology-Immunopathology-Immunotherapy (i3 & i2B), Sorbonne Université, Paris, France
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Marie-Paule Lefranc
- IMGT, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Andrew M Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Tania Bubela
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jamie K Scott
- Faculty of Health Sciences, Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Thomas B Kepler
- Department of Microbiology, Boston University School of Medicine, Boston, MA, United States.,Department of Mathematics and Statistics, Boston University, Boston, MA, United States
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8
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Abstract
PURPOSE OF REVIEW The genetic susceptibility and dominant protection for type 1 diabetes (T1D) associated with human leukocyte antigen (HLA) haplotypes, along with minor risk variants, have long been thought to shape the T cell receptor (TCR) repertoire and eventual phenotype of autoreactive T cells that mediate β-cell destruction. While autoantibodies provide robust markers of disease progression, early studies tracking autoreactive T cells largely failed to achieve clinical utility. RECENT FINDINGS Advances in acquisition of pancreata and islets from T1D organ donors have facilitated studies of T cells isolated from the target tissues. Immunosequencing of TCR α/β-chain complementarity determining regions, along with transcriptional profiling, offers the potential to transform biomarker discovery. Herein, we review recent studies characterizing the autoreactive TCR signature in T1D, emerging technologies, and the challenges and opportunities associated with tracking TCR molecular profiles during the natural history of T1D.
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Affiliation(s)
- Laura M Jacobsen
- Department of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Amanda Posgai
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Howard R Seay
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Michael J Haller
- Department of Pediatrics, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA
| | - Todd M Brusko
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA.
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9
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Bolen CR, Rubelt F, Vander Heiden JA, Davis MM. The Repertoire Dissimilarity Index as a method to compare lymphocyte receptor repertoires. BMC Bioinformatics 2017; 18:155. [PMID: 28264647 PMCID: PMC5340033 DOI: 10.1186/s12859-017-1556-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 02/21/2017] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The B and T cells of the human adaptive immune system leverage a highly diverse repertoire of antigen-specific receptors to protect the human body from pathogens. The sequencing and analysis of immune repertoires is emerging as an important tool to understand immune responses, whether beneficial or harmful (in the case of autoimmunity). However, methods for studying these repertoires, and for directly comparing different immune repertoires, are lacking. RESULTS In this paper, we present a non-parametric method for directly comparing sequencing repertoires, with the goal of rigorously quantifying differences in V, D, and J gene segment utilization. This method, referred to as the Repertoire Dissimilarity Index (RDI), uses a bootstrapped subsampling approach to account for variance in sequencing depth, and, coupled with a data simulation approach, allows for direct quantification of the average variation between repertoires. We use the RDI method to recapitulate known differences in the formation of the CD4+ and CD8+ T cell repertoires, and further show that antigen-driven activation of naïve CD8+ T cells is more selective than in the CD4+ repertoire, resulting in a more specialized CD8+ memory repertoire. CONCLUSIONS We prove that the RDI method is an accurate and versatile method for comparisons of immune repertoires. The RDI method has been implemented as an R package, and is available for download through Bitbucket.
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Affiliation(s)
- Christopher R. Bolen
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305 CA USA
- Genentech, Inc., 1 DNA Way, MS 93, South San Francisco, 94080 CA USA
| | - Florian Rubelt
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305 CA USA
| | - Jason A. Vander Heiden
- Interdepartmental Program in Computational Biology and Bioinformatics, Department of Computational Biology & Bioinformatics, Yale University, New Haven, 06520 CT USA
| | - Mark M. Davis
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, 94305 CA USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, 94305 CA USA
- Institute of Immunity, Department of Microbiology and Immunology, Transplantation and Infection, Stanford University School of Medicine, Stanford, 94305 CA USA
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10
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Breden F, Luning Prak ET, Peters B, Rubelt F, Schramm CA, Busse CE, Vander Heiden JA, Christley S, Bukhari SAC, Thorogood A, Matsen Iv FA, Wine Y, Laserson U, Klatzmann D, Douek DC, Lefranc MP, Collins AM, Bubela T, Kleinstein SH, Watson CT, Cowell LG, Scott JK, Kepler TB. Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data. Front Immunol 2017. [PMID: 29163494 DOI: 10.3389/fimmu.2017.01418/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1-3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community's founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).
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Affiliation(s)
- Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Florian Rubelt
- Department of Microbiology and Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, United States
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Christian E Busse
- Division of B Cell Immunology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Jason A Vander Heiden
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Scott Christley
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Adrian Thorogood
- entre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Frederick A Matsen Iv
- Public Health Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Yariv Wine
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
| | - Uri Laserson
- Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Klatzmann
- Immunology-Immunopathology-Immunotherapy (i3 & i2B), Sorbonne Université, Paris, France
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Marie-Paule Lefranc
- IMGT, LIGM, Institut de Génétique Humaine IGH, CNRS, University of Montpellier, Montpellier, France
| | - Andrew M Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Kensington, NSW, Australia
| | - Tania Bubela
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
| | - Lindsay G Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jamie K Scott
- Faculty of Health Sciences, Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Thomas B Kepler
- Department of Microbiology, Boston University School of Medicine, Boston, MA, United States
- Department of Mathematics and Statistics, Boston University, Boston, MA, United States
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11
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Wren JD, Toby I, Hong H, Nanduri B, Kaundal R, Dozmorov MG, Thakkar S. Proceedings of the 2016 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference. BMC Bioinformatics 2016; 17:356. [PMID: 27766933 PMCID: PMC5073803 DOI: 10.1186/s12859-016-1213-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Jonathan D Wren
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104-5005, USA. .,Biochemistry and Molecular Biology Department, University of Oklahoma Health Sciences Center, Oklahoma City, USA. .,Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, USA. .,Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, USA.
| | - Inimary Toby
- Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA
| | - Huxiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Bindu Nanduri
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi, MS, USA
| | - Rakesh Kaundal
- Bioinformatics Facility, Institute for Integrative Genome Biology, University of California, Riverside, California, USA
| | - Mikhail G Dozmorov
- Department of Biostatistics, Richmond Academy of Medicine, Virginia Commonwealth University, Virginia, USA
| | - Shraddha Thakkar
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
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