1
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Bardwell B, Bay J, Colburn Z. The clinical applications of immunosequencing. Curr Res Transl Med 2024; 72:103439. [PMID: 38447267 DOI: 10.1016/j.retram.2024.103439] [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] [Received: 11/23/2022] [Revised: 03/20/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024]
Abstract
Technological advances in high-throughput sequencing have opened the door for the interrogation of adaptive immune responses at unprecedented scale. It is now possible to determine the sequences of antibodies or T-cell receptors produced by individual B and T cells in a sample. This capability, termed immunosequencing, has transformed the study of both infectious and non-infectious diseases by allowing the tracking of dynamic changes in B and T cell clonal populations over time. This has improved our understanding of the pathology of cancers, autoimmune diseases, and infectious diseases. However, to date there has been only limited clinical adoption of the technology. Advances over the last decade and on the horizon that reduce costs and improve interpretability could enable widespread clinical use. Many clinical applications have been proposed and, while most are still undergoing research and development, some methods relying on immunosequencing data have been implemented, the most widespread of which is the detection of measurable residual disease. Here, we review the diagnostic, prognostic, and therapeutic applications of immunosequencing for both infectious and non-infectious diseases.
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Affiliation(s)
- B Bardwell
- Department of Clinical Investigation, Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA
| | - J Bay
- Department of Medicine, Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA
| | - Z Colburn
- Department of Clinical Investigation, Madigan Army Medical Center, 9040 Jackson Ave, Tacoma, WA 98431, USA.
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2
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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [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] [Received: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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3
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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4
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Peres A, Klein V, Frankel B, Lees W, Polak P, Meehan M, Rocha A, Correia Lopes J, Yaari G. Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data. Brief Bioinform 2024; 25:bbae221. [PMID: 38752856 PMCID: PMC11097599 DOI: 10.1093/bib/bbae221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/06/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.
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Affiliation(s)
- Ayelet Peres
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Vered Klein
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Boaz Frankel
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - William Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, United Kingdom
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Pazit Polak
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Mark Meehan
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Artur Rocha
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - João Correia Lopes
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
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5
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Aterido A, López-Lasanta M, Blanco F, Juan-Mas A, García-Vivar ML, Erra A, Pérez-García C, Sánchez-Fernández SÁ, Sanmartí R, Fernández-Nebro A, Alperi-López M, Tornero J, Ortiz AM, Fernández-Cid CM, Palau N, Pan W, Byrne-Steele M, Starenki D, Weber D, Rodriguez-Nunez I, Han J, Myers RM, Marsal S, Julià A. Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes. Genome Biol 2024; 25:68. [PMID: 38468286 PMCID: PMC10926600 DOI: 10.1186/s13059-024-03210-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA. RESULTS In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool. CONCLUSIONS Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.
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Affiliation(s)
- Adrià Aterido
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - María López-Lasanta
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - Francisco Blanco
- Rheumatology Department, Hospital Juan Canalejo, A Coruña, Spain
| | | | | | - Alba Erra
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
- Rheumatology Department, Hospital Sant Rafael, Barcelona, Spain
| | | | | | - Raimon Sanmartí
- Rheumatology Department, Hospital Clínic de Barcelona and IDIBAPS, Barcelona, Spain
| | | | | | - Jesús Tornero
- Rheumatology Department, Hospital Universitario Guadalajara, Guadalajara, Spain
| | - Ana María Ortiz
- Rheumatology Department, Hospital Universitario La Princesa, IIS La Princesa, Madrid, Spain
| | | | - Núria Palau
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | | | | | | | | | | | - Jian Han
- iRepertoire Inc, Huntsville, AL, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Sara Marsal
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain.
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6
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Zhu Y, Tang H, Xie W, Chen S, Zeng H, Lan C, Guan J, Ma C, Yang X, Wang Q, Wei L, Zhang Z, Yu X. The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing. SCIENCE ADVANCES 2024; 10:eadj5640. [PMID: 38266093 PMCID: PMC10807814 DOI: 10.1126/sciadv.adj5640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.
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Affiliation(s)
- Yan Zhu
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Haipei Tang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Wenxi Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sen Chen
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Huikun Zeng
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Division of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Chunhong Lan
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Junjie Guan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiujia Yang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Qilong Wang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhenhai Zhang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Division of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
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7
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Liu Y, Altreuter J, Bodapati S, Cristea S, Wong CJ, Wu CJ, Michor F. Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities. CELL GENOMICS 2024; 4:100444. [PMID: 38190106 PMCID: PMC10794784 DOI: 10.1016/j.xgen.2023.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/12/2023] [Accepted: 10/24/2023] [Indexed: 01/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.
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Affiliation(s)
- Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Jennifer Altreuter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Sudheshna Bodapati
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Simona Cristea
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Cheryl J Wong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA
| | - Catherine J Wu
- Harvard Medical School, Boston, MA 02115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02138, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA.
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8
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Suo C, Polanski K, Dann E, Lindeboom RGH, Vilarrasa-Blasi R, Vento-Tormo R, Haniffa M, Meyer KB, Dratva LM, Tuong ZK, Clatworthy MR, Teichmann SA. Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins. Nat Biotechnol 2024; 42:40-51. [PMID: 37055623 PMCID: PMC10791579 DOI: 10.1038/s41587-023-01734-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 03/07/2023] [Indexed: 04/15/2023]
Abstract
Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion .
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Affiliation(s)
- Chenqu Suo
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Department of Paediatrics, Cambridge University Hospitals, Cambridge, UK
| | | | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Lisa M Dratva
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Zewen Kelvin Tuong
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| | - Menna R Clatworthy
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Theory of Condensed Matter, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK.
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9
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Gervásio J, Ferreira A, Felicori LF. Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data. J Immunol Methods 2023; 523:113576. [PMID: 37966818 DOI: 10.1016/j.jim.2023.113576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023]
Abstract
MOTIVATION The next-generation sequencing technologies have transformed our understanding of immunoglobulin (Ig) profiles in various immune states. Clonotyping, which groups Ig sequences into B cell clones, is crucial in investigating the diversity of repertoires and changes in antigen exposure. Despite its importance, there is no widely accepted method for clonotyping, and existing methods are computationally intensive for large sequencing datasets. RESULTS To address this challenge, we introduce YClon, a fast and efficient approach for clonotyping Ig repertoire data. YClon uses a hierarchical clustering approach, similar to other methods, to group Ig sequences into B cell clones in a highly sensitive and specific manner. Notably, our approach outperforms other methods by being more than 30 to 5000 times faster in processing the repertoires analyzed. Astonishingly, YClon can effortlessly handle up to 2 million Ig sequences on a standard laptop computer. This enables in-depth analysis of large and numerous antibody repertoires. AVAILABILITY AND IMPLEMENTATION YClon was implemented in Python3 and is freely available on GitHub.
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Affiliation(s)
- João Gervásio
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Alice Ferreira
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Liza F Felicori
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil.
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10
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Levi R, Dvorkin S, Louzoun Y. Shared bias in H chain V-J pairing in naive and memory B cells. Front Immunol 2023; 14:1166116. [PMID: 37790930 PMCID: PMC10543446 DOI: 10.3389/fimmu.2023.1166116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/23/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction H chain rearrangement in B cells is a two-step process where first DH binds JH, and only then VH is joined to the complex. As such, there is no direct rearrangement between VH and JH. Results Nevertheless, we here show that the VHJH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for DH genes or a preferred reading frame. Preferred VH JH combinations are shared between donors. Discussion These results suggest a common structural mechanism for these biases. Through development, thepreferred VH JH combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain VH JH usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific VH-JH pairs.
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Affiliation(s)
| | | | - Yoram Louzoun
- Department of Mathematics, Bar Ilan University, Ramat Gan, Israel
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11
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Lee CH, Huh J, Buckley PR, Jang M, Pinho MP, Fernandes RA, Antanaviciute A, Simmons A, Koohy H. A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Med 2023; 15:70. [PMID: 37705109 PMCID: PMC10498576 DOI: 10.1186/s13073-023-01225-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
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Affiliation(s)
- Chloe H Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Jaesung Huh
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, OX2 6NN, UK
| | - Paul R Buckley
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Myeongjun Jang
- Intelligent Systems Lab, Department of Computer Science, University of Oxford, Oxford, OX1 3QG, UK
| | - Mariana Pereira Pinho
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, OX3 7BN, UK
| | - Agne Antanaviciute
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- Alan Turning Fellow in Health and Medicine, The Alan Turing Institute, London, UK.
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12
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Olsen TH, Abanades B, Moal IH, Deane CM. KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies. Sci Rep 2023; 13:11612. [PMID: 37463925 DOI: 10.1038/s41598-023-38108-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .
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Affiliation(s)
- Tobias H Olsen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Iain H Moal
- GSK Medicines Research Centre, GlaxoSmithKline plc, Stevenage, SG1 2NY, UK
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
- Exscientia plc, Oxford, OX4 4GE, UK.
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13
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Shen Y, Voigt A, Leng X, Rodriguez AA, Nguyen CQ. A current and future perspective on T cell receptor repertoire profiling. Front Genet 2023; 14:1159109. [PMID: 37408774 PMCID: PMC10319011 DOI: 10.3389/fgene.2023.1159109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 06/12/2023] [Indexed: 07/07/2023] Open
Abstract
T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.
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Affiliation(s)
- Yiran Shen
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States
| | - Alexandria Voigt
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States
| | - Xuebing Leng
- Department of Microbiology and Immunology, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Amy A. Rodriguez
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States
| | - Cuong Q. Nguyen
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States
- Department of Oral Biology, College of Dentistry, University of Florida, Gainesville, FL, United States
- Center of Orphaned Autoimmune Diseases, University of Florida, Gainesville, FL, United States
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14
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Lees WD, Christley S, Peres A, Kos JT, Corrie B, Ralph D, Breden F, Cowell LG, Yaari G, Corcoran M, Karlsson Hedestam GB, Ohlin M, Collins AM, Watson CT, Busse CE. AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 10:100025. [PMID: 37388275 PMCID: PMC10310305 DOI: 10.1016/j.immuno.2023.100025] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.
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Affiliation(s)
- William D. Lees
- Institute of Structural and Molecular Biology, Birkbeck College, London, England
- Human-Centered Computing and Information Science, Institute for Systems and Computer Engineering Technology and Science, Porto, Portugal
| | - Scott Christley
- Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ayelet Peres
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, KY, USA
| | - Brian Corrie
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Duncan Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Lindsay G. Cowell
- Peter O’Donnell Jr. School of Public Health, Department of Immunology, School of Biomedical Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Gur Yaari
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Martin Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Swede
| | | | - Mats Ohlin
- Department of Immunotechnology and SciLifeLab, Lund University, Lund, Sweden
| | - Andrew M. Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, KY, USA
| | - Christian E. Busse
- Division of B Cell Immunology, German Cancer Research Center, Heidelberg, Germany
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15
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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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16
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Pelissier A, Luo S, Stratigopoulou M, Guikema JEJ, Rodríguez Martínez M. Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires. Front Immunol 2023; 14:1123968. [PMID: 37138881 PMCID: PMC10150052 DOI: 10.3389/fimmu.2023.1123968] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/13/2023] [Indexed: 05/05/2023] Open
Abstract
The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We observe that different methods lead to different clonal definitions, which affects the quantification of clonal diversity in repertoire data. Our analyses show that direct comparisons between clonal clusterings and clonal diversity of different repertoires should be avoided if different clone identification methods were used to define the clones. Despite this variability, the diversity indices inferred from the repertoires' clonal characterization across samples show similar patterns of variation regardless of the clonal identification method used. We find the Shannon entropy to be the most robust in terms of the variability of diversity rank across samples. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter sequencing read lengths. We make our implementation freely available as a Python library cdiversity.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Siyuan Luo
- IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Maria Stratigopoulou
- Department of Pathology, Amsterdam University Medical Centers, location AMC, Lymphoma and Myeloma Center Amsterdam (LYMMCARE), Amsterdam, Netherlands
| | - Jeroen E. J. Guikema
- Department of Pathology, Amsterdam University Medical Centers, location AMC, Lymphoma and Myeloma Center Amsterdam (LYMMCARE), Amsterdam, Netherlands
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17
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Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, Heymach JV, Zhang J, Reuben A. T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy. Clin Cancer Res 2023; 29:994-1008. [PMID: 36413126 PMCID: PMC10011887 DOI: 10.1158/1078-0432.ccr-22-2469] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.
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Affiliation(s)
- Meredith L. Frank
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Kaylene Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Can Erdogan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Rice University, Houston, Texas
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jian Hu
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
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18
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von Niederhäusern V, Ruder J, Ghraichy M, Jelcic I, Müller AM, Schanz U, Martin R, Trück J. B-Cell Reconstitution After Autologous Hematopoietic Stem Cell Transplantation in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/6/e200027. [PMID: 36229189 PMCID: PMC9562041 DOI: 10.1212/nxi.0000000000200027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/25/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND AND OBJECTIVES Autologous hematopoietic stem cell transplantation (aHSCT) is increasingly used to treat aggressive forms of multiple sclerosis (MS). This procedure is believed to result in an immune reset and restoration of a self-tolerant immune system. Immune reconstitution has been extensively studied for T cells, but only to a limited extent for B cells. As increasing evidence suggests an important role of B cells in MS pathogenesis, we sought here to better understand reconstitution and the extent of renewal of the B-cell system after aHSCT in MS. METHODS Using longitudinal multidimensional flow cytometry and immunoglobulin heavy chain (IgH) repertoire sequencing following aHSCT with BCNU + Etoposide + Ara-C + Melphalan anti-thymocyte globulin, we analyzed the B-cell compartment in a cohort of 20 patients with MS in defined intervals before and up to 1 year after aHSCT and compared these findings with data from healthy controls. RESULTS Total B-cell numbers recovered within 3 months and increased above normal levels 1 year after transplantation, successively shifting from a predominantly transitional to a naive immune phenotype. Memory subpopulations recovered slowly and remained below normal levels with reduced repertoire diversity 1 year after transplantation. Isotype subclass analysis revealed a proportional shift toward IgG1-expressing cells and a reduction in IgG2 cells. Mutation analysis of IgH sequences showed that highly mutated memory B cells and plasma cells may transiently survive conditioning while the analysis of sequence cluster overlap, variable (IGHV) and joining (IGHJ) gene usage and repertoire diversity suggested a renewal of the late posttransplant repertoire. In patients with early cytomegalovirus reactivation, reconstitution of naive and memory B cells was delayed. DISCUSSION Our detailed characterization of B-cell reconstitution after aHSCT in MS indicates a reduced reactivation potential of memory B cells up to 1 year after transplantation, which may leave patients susceptible to infection, but may also be an important aspect of its mechanism of action.
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Affiliation(s)
- Valentin von Niederhäusern
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Josefine Ruder
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Marie Ghraichy
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Ilijas Jelcic
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Antonia Maria Müller
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Urs Schanz
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Roland Martin
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich
| | - Johannes Trück
- From the Division of Immunology and Children's Research Center (V.N., M.G., J.T.), University Children's Hospital Zurich, University of Zurich; Neuroimmunology and MS Research Section (J.R., I.J., R.M.), Department of Neurology, University Hospital Zurich, University of Zurich; and Department of Medical Oncology and Hematology (A.M.M., U.S.), University Hospital Zurich.
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19
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Koraichi MB, Touzel MP, Mazzolini A, Mora T, Walczak AM. NoisET: Noise Learning and Expansion Detection of T-Cell Receptors. J Phys Chem A 2022; 126:7407-7414. [PMID: 36178325 DOI: 10.1021/acs.jpca.2c05002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and 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. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
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Affiliation(s)
- Meriem Bensouda Koraichi
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | | | - Andrea Mazzolini
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Thierry Mora
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
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20
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Waltari E, Nafees S, McCutcheon KM, Wong J, Pak JE. AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses. PLoS Comput Biol 2022; 18:e1010052. [PMID: 36126074 PMCID: PMC9524643 DOI: 10.1371/journal.pcbi.1010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/30/2022] [Accepted: 09/04/2022] [Indexed: 11/18/2022] Open
Abstract
The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.
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Affiliation(s)
- Eric Waltari
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (EW); (JEP)
| | - Saba Nafees
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
| | | | - Joan Wong
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
| | - John E. Pak
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (EW); (JEP)
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21
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Weber CR, Rubio T, Wang L, Zhang W, Robert PA, Akbar R, Snapkov I, Wu J, Kuijjer ML, Tarazona S, Conesa A, Sandve GK, Liu X, Reddy ST, Greiff V. Reference-based comparison of adaptive immune receptor repertoires. CELL REPORTS METHODS 2022; 2:100269. [PMID: 36046619 PMCID: PMC9421535 DOI: 10.1016/j.crmeth.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.
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Affiliation(s)
- Cédric R. Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Teresa Rubio
- Laboratory of Neurobiology, Centro Investigación Príncipe Felipe, Valencia, Spain
| | - Longlong Wang
- BGI-Shenzhen, Shenzhen, China
- BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Wei Zhang
- BGI-Shenzhen, Shenzhen, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Philippe A. Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | | | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sonia Tarazona
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Xiao Liu
- BGI-Shenzhen, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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22
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Song C, Pan W, Brown B, Tang C, Huang Y, Chen H, Peng N, Wang Z, Weber D, Byrne-Steele M, Wu H, Liu H, Deng Y, He N, Li S. Immune repertoire analysis of normal Chinese donors at different ages. Cell Prolif 2022; 55:e13311. [PMID: 35929064 DOI: 10.1111/cpr.13311] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES This study investigated the characteristics of the immune repertoire in normal Chinese individuals of different ages. MATERIALS AND METHODS In this study, all seven receptor chains from both B and T cells in peripheral blood of 16 normal Chinese individuals from two age groups were analyzed using high-throughput sequencing and dimer-avoided multiplex PCR amplification. Normal in this study is defined as no chronic, infectious or autoimmune disease within 6 months prior to blood draw. RESULTS We found that compared with the younger group, the clonal expression of T-cell receptor repertoire increased in the older group, while diversity decreased. In addition, we found that the T-cell receptor repertoire was more significantly affected by age than the B-cell receptor repertoire, including significant differences in the use of the unique TCR-alpha and TCR-beta V-J gene combinations, in the two groups of normal participants. We further analyzed the degree of complementarity determining region 3 sequence sharing between the two groups, and found shared TCR-alpha, TCR-gamma, immunoglobulin-kappa and immunoglobulin-lambda chain complementarity determining region 3 sequences in all subjects. CONCLUSION Taken together, our study gives us a better understanding of the immune repertoire of different normal Chinese people, and these results can be applied to the treatment of age-related diseases. Immune repertoire analysis also allows us to observe participant's wellness, aiding in early-stage diagnosis.
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Affiliation(s)
- Cailing Song
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Wenjing Pan
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China.,Nanjing ARP Biotechnology Co., Ltd., Nanjing, China
| | | | - Congli Tang
- Nanjing ARP Biotechnology Co., Ltd., Nanjing, China
| | - Yunqi Huang
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Houao Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Nan Peng
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Zhe Wang
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China.,Guangdong Provincial Hospital of Chinese Medicine & Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, China
| | | | | | - Haijing Wu
- Department of Dermatology, Second Xiangya Hospital, Hunan Key Laboratory of Medical Epigenomics, Central South University, Changsha, China
| | - Hongna Liu
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China.,Nanjing ARP Biotechnology Co., Ltd., Nanjing, China
| | - Yan Deng
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Nongyue He
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
| | - Song Li
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou, China
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23
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Lee H, Yoo DK, Han J, Kim KH, Noh J, Lee Y, Lee E, Kwon S, Chung J. Optimization of peripheral blood volume for in silico reconstitution of the human B cell receptor repertoire. FEBS Open Bio 2022; 12:1634-1643. [PMID: 35866358 PMCID: PMC9433817 DOI: 10.1002/2211-5463.13467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/08/2022] [Accepted: 07/20/2022] [Indexed: 11/30/2022] Open
Abstract
B cells recognize antigens via membrane‐expressed B‐cell receptors (BCR) and antibodies. Similar human BCR sequences are frequently found at a significantly higher frequency than that theoretically calculated. Patients infected with SARS‐CoV2 and HIV or with autoimmune diseases share very similar BCRs. Therefore, in silico reconstitution of BCR repertoires and identification of stereotypical BCR sequences related to human pathology have diagnostic potential. Furthermore, monitoring changes of clinically significant BCR sequences and isotype conversion has prognostic potential. For BCR repertoire analysis, peripheral blood (PB) is the most convenient source. However, the optimal human PB volume for in silico reconstitution of the BCR repertoire has not been studied in detail. Here, we sampled 5, 10, and 20 mL PB from the left arm and 40 mL PB from the right arm of two volunteers, reconstituted in silico PB BCR repertoires, and compared their composition. In both volunteers, PB sampling over 20 mL resulted in slight increases in functional unique sequences (FUSs) or almost no increase in repertoire diversity. All FUSs with a frequency above 0.08% or 0.03% in the 40 mL PB BCR repertoire were detected even in the 5 mL PB BCR repertoire from each volunteer. FUSs with a higher frequency were more likely to be found in BCR repertoires from reduced PB volume, and those coexisting in two repertoires showed a statistically significant correlation in frequency irrespective of sampled anatomical site. The correlation was more significant in higher‐frequency FUSs. These observations support the potential of BCR repertoire analysis for diagnosis.
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Affiliation(s)
- Hyunho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Duck Kyun Yoo
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Jerome Han
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Ki Hyun Kim
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Jinsung Noh
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Yonghee Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Eunjae Lee
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.,Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Korea.,BK21+ Creative Research Engineer Development for IT, Seoul National University, Seoul, 08826, Korea.,Bio-MAX Institute, Seoul National University, Seoul, 08826, Korea
| | - Junho Chung
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Korea
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24
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Katayama Y, Yokota R, Akiyama T, Kobayashi TJ. Machine Learning Approaches to TCR Repertoire Analysis. Front Immunol 2022; 13:858057. [PMID: 35911778 PMCID: PMC9334875 DOI: 10.3389/fimmu.2022.858057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.
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Affiliation(s)
- Yotaro Katayama
- Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Ryo Yokota
- National Research Institute of Police Science, Kashiwa, Chiba, Japan
| | - Taishin Akiyama
- Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Tetsuya J. Kobayashi
- Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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25
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Abstract
High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.
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26
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Abondio P, De Intinis C, da Silva Gonçalves Vianez Júnior JL, Pace L. SINGLE CELL MULTIOMIC APPROACHES TO DISENTANGLE T CELL HETEROGENEITY. Immunol Lett 2022; 246:37-51. [DOI: 10.1016/j.imlet.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/16/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
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27
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Reis ALM, Deveson IW, Madala BS, Wong T, Barker C, Xu J, Lennon N, Tong W, Mercer TR. Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome. Genome Biol 2022; 23:19. [PMID: 35022065 PMCID: PMC8753822 DOI: 10.1186/s13059-021-02579-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Next-generation sequencing (NGS) can identify mutations in the human genome that cause disease and has been widely adopted in clinical diagnosis. However, the human genome contains many polymorphic, low-complexity, and repetitive regions that are difficult to sequence and analyze. Despite their difficulty, these regions include many clinically important sequences that can inform the treatment of human diseases and improve the diagnostic yield of NGS. RESULTS To evaluate the accuracy by which these difficult regions are analyzed with NGS, we built an in silico decoy chromosome, along with corresponding synthetic DNA reference controls, that encode difficult and clinically important human genome regions, including repeats, microsatellites, HLA genes, and immune receptors. These controls provide a known ground-truth reference against which to measure the performance of diverse sequencing technologies, reagents, and bioinformatic tools. Using this approach, we provide a comprehensive evaluation of short- and long-read sequencing instruments, library preparation methods, and software tools and identify the errors and systematic bias that confound our resolution of these remaining difficult regions. CONCLUSIONS This study provides an analytical validation of diagnosis using NGS in difficult regions of the human genome and highlights the challenges that remain to resolve these difficult regions.
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Affiliation(s)
- Andre L M Reis
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Ira W Deveson
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Bindu Swapna Madala
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Ted Wong
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Chris Barker
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA
| | - Tim R Mercer
- Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
- Australian Institute for Biotechnology and Nanoengineering, University of Queensland, Brisbane, QLD, Australia
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28
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Tian G, Li M, Lv G. Analysis of T-Cell Receptor Repertoire in Transplantation: Fingerprint of T Cell-mediated Alloresponse. Front Immunol 2022; 12:778559. [PMID: 35095851 PMCID: PMC8790170 DOI: 10.3389/fimmu.2021.778559] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
T cells play a key role in determining allograft function by mediating allogeneic immune responses to cause rejection, and recent work pointed their role in mediating tolerance in transplantation. The unique T-cell receptor (TCR) expressed on the surface of each T cell determines the antigen specificity of the cell and can be the specific fingerprint for identifying and monitoring. Next-generation sequencing (NGS) techniques provide powerful tools for deep and high-throughput TCR profiling, and facilitate to depict the entire T cell repertoire profile and trace antigen-specific T cells in circulation and local tissues. Tailing T cell transcriptomes and TCR sequences at the single cell level provides a full landscape of alloreactive T-cell clones development and biofunction in alloresponse. Here, we review the recent advances in TCR sequencing techniques and computational tools, as well as the recent discovery in overall TCR profile and antigen-specific T cells tracking in transplantation. We further discuss the challenges and potential of using TCR sequencing-based assays to profile alloreactive TCR repertoire as the fingerprint for immune monitoring and prediction of rejection and tolerance.
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Affiliation(s)
| | - Mingqian Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
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29
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Deszyński P, Młokosiewicz J, Volanakis A, Jaszczyszyn I, Castellana N, Bonissone S, Ganesan R, Krawczyk K. INDI-integrated nanobody database for immunoinformatics. Nucleic Acids Res 2022; 50:D1273-D1281. [PMID: 34747487 PMCID: PMC8728276 DOI: 10.1093/nar/gkab1021] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 12/11/2022] Open
Abstract
Nanobodies, a subclass of antibodies found in camelids, are versatile molecular binding scaffolds composed of a single polypeptide chain. The small size of nanobodies bestows multiple therapeutic advantages (stability, tumor penetration) with the first therapeutic approval in 2018 cementing the clinical viability of this format. Structured data and sequence information of nanobodies will enable the accelerated clinical development of nanobody-based therapeutics. Though the nanobody sequence and structure data are deposited in the public domain at an accelerating pace, the heterogeneity of sources and lack of standardization hampers reliable harvesting of nanobody information. We address this issue by creating the Integrated Database of Nanobodies for Immunoinformatics (INDI, http://naturalantibody.com/nanobodies). INDI collates nanobodies from all the major public outlets of biological sequences: patents, GenBank, next-generation sequencing repositories, structures and scientific publications. We equip INDI with powerful nanobody-specific sequence and text search facilitating access to >11 million nanobody sequences. INDI should facilitate development of novel nanobody-specific computational protocols helping to deliver on the therapeutic promise of this drug format.
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Affiliation(s)
| | | | - Adam Volanakis
- Harvard Medical School, 240 Longwood Ave, Boston, MA, USA
| | | | - Natalie Castellana
- Abterra Biosciences Inc. 3030 Bunker Hill Street Suite 218, San Diego, CA 92109, USA
| | - Stefano Bonissone
- Abterra Biosciences Inc. 3030 Bunker Hill Street Suite 218, San Diego, CA 92109, USA
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30
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Giudicelli V, Duroux P, Rollin M, Aouinti S, Folch G, Jabado-Michaloud J, Lefranc MP, Kossida S. IMGT ® Immunoinformatics Tools for Standardized V-DOMAIN Analysis. Methods Mol Biol 2022; 2453:477-531. [PMID: 35622340 PMCID: PMC9761511 DOI: 10.1007/978-1-0716-2115-8_24] [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
The variable domains (V-DOMAIN) of the antigen receptors, immunoglobulins (IG) or antibodies and T cell receptors (TR), which specifically recognize the antigens show a huge diversity in their sequences. This diversity results from the complex mechanisms involved in the synthesis of these domains at the DNA level (rearrangements of the variable (V), diversity (D), and joining (J) genes; N-diversity; and, for the IG, somatic hypermutations). The recognition of V, D, and J as "genes" and their entry in databases mark the creation of IMGT by Marie-Paule Lefranc, and the origin of immunoinformatics in 1989. For 30 years, IMGT®, the international ImMunoGeneTics information system® http://www.imgt.org , has implemented databases and developed tools for IG and TR immunoinformatics, based on the IMGT Scientific chart rules and IMGT-ONTOLOGY concepts and axioms, and more particularly, the princeps ones: IMGT genes and alleles (CLASSIFICATION axiom) and the IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION axiom). This chapter describes the online tools for the characterization and annotation of the expressed V-DOMAIN sequences: (a) IMGT/V-QUEST analyzes in detail IG and TR rearranged nucleotide sequences, (b) IMGT/HighV-QUEST is its high throughput version, which includes a module for the identification of IMGT clonotypes and generates immunoprofiles of expressed V, D, and J genes and alleles, (c) IMGT/StatClonotype performs the pairwise comparison of IMGT/HighV-QUEST immunoprofiles, (d) IMGT/DomainGapAlign analyzes amino acid sequences and is frequently used in antibody engineering and humanization, and (e) IMGT/Collier-de-Perles provides two-dimensional (2D) graphical representations of V-DOMAIN, bridging the gap between sequences and 3D structures. These IMGT® tools are widely used in repertoire analyses of the adaptive immune responses in normal and pathological situations and in the design of engineered IG and TR for therapeutic applications.
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Affiliation(s)
- Véronique Giudicelli
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France.
| | - Patrice Duroux
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
| | - Maël Rollin
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
| | - Safa Aouinti
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
- Clinical Research and Epidemiology Unit, CHU Montpellier, Univ Montpellier, Montpellier, France
| | - Géraldine Folch
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
| | - Joumana Jabado-Michaloud
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
| | - Marie-Paule Lefranc
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France.
| | - Sofia Kossida
- IMGT®, the international ImMunoGenetics information system®, Laboratoire d'ImmunoGénétique Moléculaire LIGM, Institut de Génétique Humaine, (IGH), Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Montpellier, France
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31
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Bulk gDNA Sequencing of Antibody Heavy-Chain Gene Rearrangements for Detection and Analysis of B-Cell Clone Distribution: A Method by the AIRR Community. Methods Mol Biol 2022; 2453:317-343. [PMID: 35622334 PMCID: PMC9374196 DOI: 10.1007/978-1-0716-2115-8_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
In this method we illustrate how to amplify, sequence, and analyze antibody/immunoglobulin (IG) heavy-chain gene rearrangements from genomic DNA that is derived from bulk populations of cells by next-generation sequencing (NGS). We focus on human source material and illustrate how bulk gDNA-based sequencing can be used to examine clonal architecture and networks in different samples that are sequenced from the same individual. Although bulk gDNA-based sequencing can be performed on both IG heavy (IGH) or kappa/lambda light (IGK/IGL) chains, we focus here on IGH gene rearrangements because IG heavy chains are more diverse, tend to harbor higher levels of somatic hypermutations (SHM), and are more reliable for clone identification and tracking. We also provide a procedure, including code, and detailed instructions for processing and annotation of the NGS data. From these data we show how to identify expanded clones, visualize the overall clonal landscape, and track clonal lineages in different samples from the same individual. This method has a broad range of applications, including the identification and monitoring of expanded clones, the analysis of blood and tissue-based clonal networks, and the study of immune responses including clonal evolution.
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Corrie BD, Christley S, Busse CE, Cowell LG, Neller KCM, Rubelt F, Schwab N. Data Sharing and Reuse: A Method by the AIRR Community. Methods Mol Biol 2022; 2453:447-476. [PMID: 35622339 PMCID: PMC9761493 DOI: 10.1007/978-1-0716-2115-8_23] [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: 11/28/2022]
Abstract
High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.
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Affiliation(s)
- Brian D Corrie
- Biological Sciences, Simon Fraser University, Burnaby, BC, Canada.
| | - Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
| | | | - 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
| | - Kira C M Neller
- Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | | | - Nicholas Schwab
- Department of Neurology with Institute of Translational Neurology, University of Muenster, Muenster, Germany
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AIRR Community Guide to Planning and Performing AIRR-Seq Experiments. Methods Mol Biol 2022; 2453:261-278. [PMID: 35622331 PMCID: PMC9761540 DOI: 10.1007/978-1-0716-2115-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The development of high-throughput sequencing of adaptive immune receptor repertoires (AIRR-seq of IG and TR rearrangements) has provided a new frontier for in-depth analysis of the immune system. The last decade has witnessed an explosion in protocols, experimental methodologies, and computational tools. In this chapter, we discuss the major considerations in planning a successful AIRR-seq experiment together with basic strategies for controlling and evaluating the outcome of the experiment. Members of the AIRR Community have authored several chapters in this edition, which cover step-by-step instructions to successfully conduct, analyze, and share an AIRR-seq project.
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Babrak L, Marquez S, Busse CE, Lees WD, Miho E, Ohlin M, Rosenfeld AM, Stervbo U, Watson CT, Schramm CA. Adaptive Immune Receptor Repertoire (AIRR) Community Guide to TR and IG Gene Annotation. Methods Mol Biol 2022; 2453:279-296. [PMID: 35622332 PMCID: PMC9761530 DOI: 10.1007/978-1-0716-2115-8_16] [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: 10/18/2022]
Abstract
High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to carry out large-scale experiments to study the adaptive immune response. Since the method was first introduced in 2009, AIRR sequencing (AIRR-Seq) has been applied to survey the immune state of individuals, identify antigen-specific or immune-state-associated signatures of immune responses, study the development of the antibody immune response, and guide the development of vaccines and antibody therapies. Recent advancements in the technology include sequencing at the single-cell level and in parallel with gene expression, which allows the introduction of multi-omics approaches to understand in detail the adaptive immune response. Analyzing AIRR-seq data can prove challenging even with high-quality sequencing, in part due to the many steps involved and the need to parameterize each step. In this chapter, we outline key factors to consider when preprocessing raw AIRR-Seq data and annotating the genetic origins of the rearranged receptors. We also highlight a number of common difficulties with common AIRR-seq data processing and provide strategies to address them.
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Affiliation(s)
- Lmar Babrak
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Christian E Busse
- Division of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - William D Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, UK
| | - Enkelejda Miho
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- aiNET GmbH, Basel, Switzerland
| | - Mats Ohlin
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Aaron M Rosenfeld
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
- Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
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Wang Q, Zeng H, Zhu Y, Wang M, Zhang Y, Yang X, Tang H, Li H, Chen Y, Ma C, Lan C, Liu B, Yang W, Yu X, Zhang Z. Dual UMIs and Dual Barcodes With Minimal PCR Amplification Removes Artifacts and Acquires Accurate Antibody Repertoire. Front Immunol 2021; 12:778298. [PMID: 35003093 PMCID: PMC8727365 DOI: 10.3389/fimmu.2021.778298] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/25/2021] [Indexed: 12/03/2022] Open
Abstract
Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.
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Affiliation(s)
- Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Huikun Zeng
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Zhu
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Minhui Wang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanfang Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiujia Yang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hongliang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Chunhong Lan
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- *Correspondence: Wei Yang, ; Xueqing Yu, ; Zhenhai Zhang, ;
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Division of Nephrology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Wei Yang, ; Xueqing Yu, ; Zhenhai Zhang, ;
| | - Zhenhai Zhang
- Center for Precision Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China
- *Correspondence: Wei Yang, ; Xueqing Yu, ; Zhenhai Zhang, ;
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Robert C. Nurturing the egg: the essential connection between cumulus cells and the oocyte. Reprod Fertil Dev 2021; 34:149-159. [PMID: 35231386 DOI: 10.1071/rd21282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The determinants of oocyte quality remain uncertain. Under suitable conditions, which have yet to be defined, the gamete grows and acquires the competence to resume meiosis, be fertilised and undergo embryonic development at least beyond genome activation, after which the blastomere is autonomous enough to adapt to the specificity of its environment. This review describes the central role played by the oocyte in reproductive success and how communication between cumulus cells and the oocyte are essential to proper oogenesis and the quality of the resulting gamete. While most attempts to improve oocyte quality have been directed at gonadotrophin-based systemic endocrine signalling, it is proposed that parallel control of fertility may act locally within ovarian follicles through intimate cooperation between somatic cells and the oocyte via the network of transzonal projections. This intercellular communication may prove to be more sensitive to environmental conditions than systemic endocrine signalling, which is essential for many non-reproductive tissues.
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Affiliation(s)
- Claude Robert
- Département des sciences animales, Faculté des sciences de l'agriculture et de l'alimentation, Université Laval, Québec, QC, Canada
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37
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The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00413-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Olsen TH, Boyles F, Deane CM. Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci 2021; 31:141-146. [PMID: 34655133 PMCID: PMC8740823 DOI: 10.1002/pro.4205] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/09/2021] [Accepted: 10/12/2021] [Indexed: 11/17/2022]
Abstract
The antibody repertoires of individuals and groups have been used to explore disease states, understand vaccine responses, and drive therapeutic development. The arrival of B‐cell receptor repertoire sequencing has enabled researchers to get a snapshot of these antibody repertoires, and as more data are generated, increasingly in‐depth studies are possible. However, most publicly available data only exist as raw FASTQ files, making the data hard to access, process, and compare. The Observed Antibody Space (OAS) database was created in 2018 to offer clean, annotated, and translated repertoire data. In this paper, we describe an update to OAS that has been driven by the increasing volume of data and the appearance of paired (VH/VL) sequence data. OAS is now accessible via a new web server, with standardized search parameters and a new sequence‐based search option. The new database provides both nucleotides and amino acids for every sequence, with additional sequence annotations to make the data Minimal Information about Adaptive Immune Receptor Repertoire compliant, and comments on potential problems with the sequence. OAS now contains 25 new studies, including severe acute respiratory syndrome coronavirus 2 data and paired sequencing data. The new database is accessible at http://opig.stats.ox.ac.uk/webapps/oas/, and all data are freely available for download.
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Affiliation(s)
- Tobias H Olsen
- Department of Statistics, University of Oxford, Oxford, UK
| | - Fergus Boyles
- Department of Statistics, University of Oxford, Oxford, UK
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Access to ultra-long IgG CDRH3 bovine antibody sequences using short read sequencing technology. Mol Immunol 2021; 139:97-105. [PMID: 34464839 PMCID: PMC8508064 DOI: 10.1016/j.molimm.2021.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/23/2022]
Abstract
The advances in high-throughput DNA sequencing and recombinant antibody technologies has presented new methods for characterizing antibody repertoires and significantly increased our understanding on the functional role of antibodies in immunity and their use in diagnostics, vaccine antigen design and as biological therapeutics. A subset of Bos taurus antibodies possesses unique ultra-long third complementary-determining region of the heavy chain (CDRH3) and are of special interest because they are thought to have unique functional abilities of broadly neutralizing properties - a functional role that has not been fully explored in vaccine development. Next generation sequencing technologies that are widely used to profile immunoglobulin (Ig) repertoires are based on short-read methods such as the Illumina technology. Although this technology has worked well in sequencing Ig V-D-J regions of most jawed vertebrates, it has faced serious technical challenges with sequencing regions in bovine Ig bearing ultra-long CDRH3 sequences, which are longer than 120 bp. To overcome this limitation, we have developed a sequencing strategy based on nested PCR products that allows sequence assembly of full-length bovine Ig heavy-chain (IgH) V-D-J regions. We have used this strategy to sequence IgH V-D-J regions of two Bos indicus breeds, Ankole and Boran. We confirm the presence of ultra-long CDRH3 sequences in IgG transcripts in both African cattle breeds, and provide preliminary evidence for differences and preferences in germline VH, DH and JH allele gene usage as well as differences in the length of the VH region in the two bovine breeds. Our method provides tools that should allow more robust analyses of ultra-long CDRH3 sequences aiding antibody and epitope discovery in different cattle breeds and their role in mediating immunity.
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40
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Japp AS, Meng W, Rosenfeld AM, Perry DJ, Thirawatananond P, Bacher RL, Liu C, Gardner JS, Atkinson MA, Kaestner KH, Brusko TM, Naji A, Luning Prak ET, Betts MR. TCR +/BCR + dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes. Cell 2021; 184:827-839.e14. [PMID: 33545036 DOI: 10.1016/j.cell.2020.11.035] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 06/30/2020] [Accepted: 11/19/2020] [Indexed: 12/20/2022]
Abstract
Ahmed and colleagues recently described a novel hybrid lymphocyte expressing both a B and T cell receptor, termed double expresser (DE) cells. DE cells in blood of type 1 diabetes (T1D) subjects were present at increased numbers and enriched for a public B cell clonotype. Here, we attempted to reproduce these findings. While we could identify DE cells by flow cytometry, we found no association between DE cell frequency and T1D status. We were unable to identify the reported public B cell clone, or any similar clone, in bulk B cells or sorted DE cells from T1D subjects or controls. We also did not observe increased usage of the public clone VH or DH genes in B cells or in sorted DE cells. Taken together, our findings suggest that DE cells and their alleged public clonotype are not enriched in T1D. This Matters Arising paper is in response to Ahmed et al. (2019), published in Cell. See also the response by Ahmed et al. (2021), published in this issue.
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Affiliation(s)
- Alberto Sada Japp
- Department of Microbiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Wenzhao Meng
- Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Aaron M Rosenfeld
- Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel J Perry
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL 32610, USA
| | - Puchong Thirawatananond
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL 32610, USA
| | - Rhonda L Bacher
- Department of Biostatistics, University of Florida, College of Medicine, Gainesville, FL 32610, USA
| | - Chengyang Liu
- Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jay S Gardner
- Department of Microbiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | -
- The Human Pancreas Analysis Program, Perelman School of Medicine, Philadelphia, PA 19104
| | - Mark A Atkinson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL 32610, USA
| | - Klaus H Kaestner
- Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104
| | - Todd M Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, College of Medicine, Gainesville, FL 32610, USA
| | - Ali Naji
- Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Eline T Luning Prak
- Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
| | - Michael R Betts
- Department of Microbiology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
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Ricker E, Manni M, Flores-Castro D, Jenkins D, Gupta S, Rivera-Correa J, Meng W, Rosenfeld AM, Pannellini T, Bachu M, Chinenov Y, Sculco PK, Jessberger R, Prak ETL, Pernis AB. Altered function and differentiation of age-associated B cells contribute to the female bias in lupus mice. Nat Commun 2021; 12:4813. [PMID: 34376664 PMCID: PMC8355159 DOI: 10.1038/s41467-021-25102-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 07/15/2021] [Indexed: 12/11/2022] Open
Abstract
Differences in immune responses to viruses and autoimmune diseases such as systemic lupus erythematosus (SLE) can show sexual dimorphism. Age-associated B cells (ABC) are a population of CD11c+T-bet+ B cells critical for antiviral responses and autoimmune disorders. Absence of DEF6 and SWAP-70, two homologous guanine exchange factors, in double-knock-out (DKO) mice leads to a lupus-like syndrome in females marked by accumulation of ABCs. Here we demonstrate that DKO ABCs show sex-specific differences in cell number, upregulation of an ISG signature, and further differentiation. DKO ABCs undergo oligoclonal expansion and differentiate into both CD11c+ and CD11c- effector B cell populations with pathogenic and pro-inflammatory function as demonstrated by BCR sequencing and fate-mapping experiments. Tlr7 duplication in DKO males overrides the sex-bias and further augments the dissemination and pathogenicity of ABCs, resulting in severe pulmonary inflammation and early mortality. Thus, sexual dimorphism shapes the expansion, function and differentiation of ABCs that accompanies TLR7-driven immunopathogenesis.
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Affiliation(s)
- Edd Ricker
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY, USA
| | - Michela Manni
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
| | - Danny Flores-Castro
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
| | - Daniel Jenkins
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
| | - Sanjay Gupta
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
| | - Juan Rivera-Correa
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY, USA
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, USA
| | - Aaron M Rosenfeld
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, USA
| | - Tania Pannellini
- Research Division and Precision Medicine Laboratory, Hospital for Special Surgery, New York, NY, USA
| | - Mahesh Bachu
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY, USA
| | - Yurii Chinenov
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA
| | - Peter K Sculco
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | - Rolf Jessberger
- Institute of Physiological Chemistry, Technische Universitat, Dresden, Germany
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, USA
| | - Alessandra B Pernis
- Autoimmunity and Inflammation Program, Hospital for Special Surgery, New York, NY, USA.
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY, USA.
- David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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Abstract
A mouse model supports the hypothesis that latent Epstein-Barr virus exacerbates the symptoms of rheumatoid arthritis.
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Affiliation(s)
- David Taussig
- The Shmunis School of Biomedicine and Cancer Research, the George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yariv Wine
- The Shmunis School of Biomedicine and Cancer Research, the George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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43
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Pai JA, Satpathy AT. High-throughput and single-cell T cell receptor sequencing technologies. Nat Methods 2021; 18:881-892. [PMID: 34282327 PMCID: PMC9345561 DOI: 10.1038/s41592-021-01201-8] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
T cells express T cell receptors (TCRs) composed of somatically recombined TCRα and TCRβ chains, which mediate recognition of major histocompatibility complex (MHC)-antigen complexes and drive the antigen-specific adaptive immune response to pathogens and cancer. The TCR repertoire in each individual is highly diverse, which allows for recognition of a wide array of foreign antigens, but also presents a challenge in analyzing this response using conventional methods. Recent studies have developed high-throughput sequencing technologies to identify TCR sequences, analyze their antigen specificities using experimental and computational tools, and pair TCRs with transcriptional and epigenetic cell state phenotypes in single cells. In this Review, we highlight these technological advances and describe how they have been applied to discover fundamental insights into T cell-mediated immunity.
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Affiliation(s)
- Joy A Pai
- Program in Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ansuman T Satpathy
- Program in Immunology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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Trück J, Eugster A, Barennes P, Tipton CM, Luning Prak ET, Bagnara D, Soto C, Sherkow JS, Payne AS, Lefranc MP, Farmer A, Bostick M, Mariotti-Ferrandiz E. Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling. eLife 2021; 10:e66274. [PMID: 34037521 PMCID: PMC8154019 DOI: 10.7554/elife.66274] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/15/2021] [Indexed: 12/15/2022] Open
Abstract
Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community's Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.
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Affiliation(s)
- Johannes Trück
- University Children’s Hospital and the Children’s Research Center, University of ZurichZurichSwitzerland
| | - Anne Eugster
- CRTD Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität DresdenDresdenGermany
| | - Pierre Barennes
- Sorbonne Université U959, Immunology-Immunopathology-Immunotherapy (i3)ParisFrance
- AP-HP Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi)ParisFrance
| | - Christopher M Tipton
- Lowance Center for Human Immunology, Emory University School of MedicineAtlantaUnited States
| | - Eline T Luning Prak
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Davide Bagnara
- University of Genoa, Department of Experimental MedicineGenoaItaly
| | - Cinque Soto
- The Vanderbilt Vaccine Center, Vanderbilt University Medical CenterNashvilleUnited States
- Department of Pediatrics, Vanderbilt University Medical CenterNashvilleUnited States
| | - Jacob S Sherkow
- College of Law, University of IllinoisChampaignUnited States
- Center for Advanced Studies in Biomedical Innovation Law, University of Copenhagen Faculty of LawCopenhagenDenmark
- Carl R. Woese Institute for Genomic Biology, University of IllinoisUrbana, IllinoisUnited States
| | - Aimee S Payne
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Marie-Paule Lefranc
- IMGT, The International ImMunoGeneTics Information System (IMGT), Laboratoire d'ImmunoGénétique Moléculaire (LIGM), Institut de Génétique Humaine (IGH), CNRS, University of MontpellierMontpellierFrance
- Laboratoire d'ImmunoGénétique Moléculaire (LIGM) CNRS, University of MontpellierMontpellierFrance
- Institut de Génétique Humaine (IGH), CNRS, University of MontpellierMontpellierFrance
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45
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Nelson ND, Meng W, Rosenfeld AM, Bullman S, Sekhar Pedamallu C, Nomburg JL, Wertheim GB, Paessler ME, Pinkus G, Hornick JL, Meyerson M, Luning Prak ET, Pillai V. Characterization of Plasmacytoid Dendritic Cells, Microbial Sequences, and Identification of a Candidate Public T-Cell Clone in Kikuchi-Fujimoto Disease. Pediatr Dev Pathol 2021; 24:193-205. [PMID: 33530869 DOI: 10.1177/1093526620987961] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVES Kikuchi-Fujimoto disease (KFD) is a self-limited lymphadenitis of unclear etiology. We aimed to further characterize this disease in pediatric patients, including evaluation of the CD123 immunohistochemical (IHC) staining and investigation of potential immunologic and infectious causes. METHODS Seventeen KFD cases and 12 controls were retrospectively identified, and the histologic and clinical features were evaluated. CD123 IHC staining was quantified by digital image analysis. Next generation sequencing was employed for comparative microbial analysis via RNAseq (5 KFD cases) and to evaluate the immune repertoire (9 KFD cases). RESULTS In cases of lymphadenitis with necrosis, >0.85% CD123+ cells by IHC was found to be six times more likely in cases with a final diagnosis of KFD (sensitivity 75%, specificity 87.5%). RNAseq based comparative microbial analysis did not detect novel or known pathogen sequences in KFD. A shared complementarity determining region 3 (CDR3) sequence and use of the same T-cell receptor beta variable region family was identified in KFD LNs but not controls, and was not identified in available databases. CONCLUSIONS Digital quantification of CD123 IHC can distinguish KFD from other necrotizing lymphadenitides. The presence of a unique shared CDR3 sequence suggests that a shared antigen underlies KFD pathogenesis.
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Affiliation(s)
- Nya D Nelson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aaron M Rosenfeld
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Susan Bullman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts.,Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Chandra Sekhar Pedamallu
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts.,Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jason L Nomburg
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts.,Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gerald B Wertheim
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Michele E Paessler
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Geraldine Pinkus
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jason L Hornick
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Matthew Meyerson
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts.,Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Vinodh Pillai
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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46
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Klie A, Tsui BY, Mollah S, Skola D, Dow M, Hsu CN, Carter H. Increasing metadata coverage of SRA BioSample entries using deep learning-based named entity recognition. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6259052. [PMID: 33914028 PMCID: PMC8083811 DOI: 10.1093/database/baab021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 03/11/2021] [Accepted: 04/16/2021] [Indexed: 11/14/2022]
Abstract
High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE.
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Affiliation(s)
- Adam Klie
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Brian Y Tsui
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Shamim Mollah
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA.,Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.,Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Dylan Skola
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Chun-Nan Hsu
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
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47
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Bhattacharya S, Hu Z, Butte AJ. Opportunities and Challenges in Democratizing Immunology Datasets. Front Immunol 2021; 12:647536. [PMID: 33936065 PMCID: PMC8086961 DOI: 10.3389/fimmu.2021.647536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/04/2021] [Indexed: 11/26/2022] Open
Abstract
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.
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Affiliation(s)
- Sanchita Bhattacharya
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Zicheng Hu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
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48
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Pertseva M, Gao B, Neumeier D, Yermanos A, Reddy ST. Applications of Machine and Deep Learning in Adaptive Immunity. Annu Rev Chem Biomol Eng 2021; 12:39-62. [PMID: 33852352 DOI: 10.1146/annurev-chembioeng-101420-125021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.
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Affiliation(s)
- Margarita Pertseva
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8006 Zurich, Switzerland
| | - Beichen Gao
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Daniel Neumeier
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Department of Pathology and Immunology, University of Geneva, 1205 Geneva, Switzerland.,Department of Biology, Institute of Microbiology and Immunology, ETH Zurich, 8093 Zurich, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
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49
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Christley S, Ostmeyer J, Quirk L, Zhang W, Sirak B, Giuliano AR, Zhang S, Monson N, Tiro J, Lucas E, Cowell LG. T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates. Front Immunol 2021; 12:624230. [PMID: 33868241 PMCID: PMC8050337 DOI: 10.3389/fimmu.2021.624230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/09/2021] [Indexed: 12/24/2022] Open
Abstract
Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor β chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.
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Affiliation(s)
- Scott Christley
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Jared Ostmeyer
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Lisa Quirk
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Wei Zhang
- Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Bradley Sirak
- Center for Immunization and Infection Research, Moffitt Cancer Center, Tampa, FL, United States
| | - Anna R Giuliano
- Center for Immunization and Infection Research, Moffitt Cancer Center, Tampa, FL, United States
| | - Song Zhang
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Nancy Monson
- Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Jasmin Tiro
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Elena Lucas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, United States.,Department of Pathology, Parkland Health and Hospital System, Dallas, TX, United States
| | - Lindsay G Cowell
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States.,Department of Neurology and Neurotherapeutics, Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States
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50
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Laustsen AH, Greiff V, Karatt-Vellatt A, Muyldermans S, Jenkins TP. Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery. Trends Biotechnol 2021; 39:1263-1273. [PMID: 33775449 DOI: 10.1016/j.tibtech.2021.03.003] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from non-animal-derived recombinant repertoires using display technologies. While it has been argued that using recombinant display libraries can reduce animal consumption, we hold that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies. Thus, improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires present unique advantages for discovering antibodies that are fit for purpose. Furthermore, we anticipate that machine learning will play a significant role within discovery workflows, refining current antibody discovery practices.
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Affiliation(s)
- Andreas H Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | | | - Serge Muyldermans
- Department of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Timothy P Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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