1
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Deguine J, Xavier RJ. B cell tolerance and autoimmunity: Lessons from repertoires. J Exp Med 2024; 221:e20231314. [PMID: 39093312 PMCID: PMC11296956 DOI: 10.1084/jem.20231314] [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: 04/25/2024] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Adaptive immune cell function is regulated by a highly diverse receptor recombined from variable germline-encoded segments that can recognize an almost unlimited array of epitopes. While this diversity enables the recognition of any pathogen, it also poses a risk of self-recognition, leading to autoimmunity. Many layers of regulation are present during both the generation and activation of B cells to prevent this phenomenon, although they are evidently imperfect. In recent years, our ability to analyze immune repertoires at scale has drastically increased, both through advances in sequencing and single-cell analyses. Here, we review the current knowledge on B cell repertoire analyses, focusing on their implication for autoimmunity. These studies demonstrate that a failure of tolerance occurs at multiple independent checkpoints in different autoimmune contexts, particularly during B cell maturation, plasmablast differentiation, and within germinal centers. These failures are marked by distinct repertoire features that may be used to identify disease- or patient-specific therapeutic approaches.
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Affiliation(s)
- Jacques Deguine
- Immunology Program, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, MA, USA
| | - Ramnik J Xavier
- Immunology Program, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School , Boston, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
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2
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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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3
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Lê Quý K, Chernigovskaya M, Stensland M, Singh S, Leem J, Revale S, Yadin DA, Nice FL, Povall C, Minns DH, Galson JD, Nyman TA, Snapkow I, Greiff V. Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling. NPJ Syst Biol Appl 2024; 10:73. [PMID: 38997321 PMCID: PMC11245537 DOI: 10.1038/s41540-024-00402-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: 11/02/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.
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Grants
- The Leona M. and Harry B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO World-Leading Research Community (to VG), UiO: LifeScience Convergence Environment Immunolingo (to VG), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Norwegian Cancer Society Grant (#215817, to VG), Research Council of Norway projects (#300740, (#311341, #331890 to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG). This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 101007799 (Inno4Vac). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA (to VG).
- Mass spectrometry-based proteomic analyses were performed by the Proteomics Core Facility, Department of Immunology, University of Oslo/Oslo University Hospital, which is supported by the Core Facilities program of the South-Eastern Norway Regional Health Authority. This core facility is also a member of the National Network of Advanced Proteomics Infrastructure (NAPI), which is funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910).
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Affiliation(s)
- Khang Lê Quý
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Stensland
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sachin Singh
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | | | | | | | | | | | | | - Tuula A Nyman
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Igor Snapkow
- Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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4
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Fert I, Douguet L, Vesin B, Moncoq F, Noirat A, Authié P, Ciret S, Le Chevalier F, Blanc C, Vitrenko Y, Charneau P, Majlessi L, Anna F. T-cell immunity induced and reshaped by an anti-HPV immuno-oncotherapeutic lentiviral vector. NPJ Vaccines 2024; 9:102. [PMID: 38858404 PMCID: PMC11164992 DOI: 10.1038/s41541-024-00894-0] [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: 11/30/2023] [Accepted: 05/23/2024] [Indexed: 06/12/2024] Open
Abstract
We recently developed an immuno-oncotherapy against human papillomavirus (HPV)-induced tumors based on a lentiviral vector encoding the Early E6 and E7 oncoproteins of HPV16 and HPV18 genotypes, namely "Lenti-HPV-07". The robust and long-lasting anti-tumor efficacy of Lenti-HPV-07 is dependent on CD8+ T-cell induction and remodeling of the tumor microenvironment. Here, we first established that anti-vector immunity induced by Lenti-HPV-07 prime has no impact on the efficacy of a homologous boost to amplify anti-HPV T-cell immunity. To longitudinally monitor the evolution of the T-cell repertoire generated after the prime, homologous or heterologous boost with Lenti-HPV-07, we tracked T-cell clonotypes by deep sequencing of T-Cell Receptor (TCR) variable β and α chain mRNA, applied to whole peripheral blood cells (PBL) and a T cell population specific of an immunodominant E7HPV16 epitope. We observed a hyper-expansion of clonotypes post prime, accompanied by increased frequencies of HPV-07-specific T cells. Additionally, there was a notable diversification of clonotypes post boost in whole PBL, but not in the E7HPV16-specific T cells. We then demonstrated that the effector functions of such Lenti-HPV-07-induced T cells synergize with anti-checkpoint inhibitory treatments by systemic administration of anti-TIM3 or anti-NKG2A monoclonal antibodies. While Lenti-HPV-07 is about to enter a Phase I/IIa clinical trial, these results will help better elucidate its mode of action in immunotherapy against established HPV-mediated malignancies.
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Affiliation(s)
- Ingrid Fert
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Laëtitia Douguet
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Benjamin Vesin
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Fanny Moncoq
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Amandine Noirat
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Pierre Authié
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Sylvain Ciret
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Fabien Le Chevalier
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Catherine Blanc
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Yakov Vitrenko
- Institut Pasteur, Université Paris Cité, Biomics Technology Platform, F-75015, Paris, France
| | - Pierre Charneau
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France
| | - Laleh Majlessi
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France.
| | - François Anna
- Pasteur-TheraVectys Joint Lab, Institut Pasteur, Université de Paris, Virology Department, 28 Rue du Dr. Roux, F-75015, Paris, France.
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5
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Mhanna V, Barennes P, Vantomme H, Fourcade G, Coatnoan N, Six A, Klatzmann D, Mariotti-Ferrandiz E. Enhancing comparative T cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice. CELL REPORTS METHODS 2024; 4:100753. [PMID: 38614088 PMCID: PMC11045977 DOI: 10.1016/j.crmeth.2024.100753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/28/2024] [Accepted: 03/19/2024] [Indexed: 04/15/2024]
Abstract
Accurate characterization and comparison of T cell receptor (TCR) repertoires from small biological samples present significant challenges. The main challenge is the low material input, which compromises the quality of bulk sequencing and hinders the recovery of sufficient TCR sequences for robust analyses. We aimed to address this limitation by implementing a strategic approach to pool homologous biological samples. Our findings demonstrate that such pooling indeed enhances the TCR repertoire coverage, particularly for cell subsets of constrained sizes, and enables accurate comparisons of TCR repertoires at different levels of complexity across T cell subsets with different sizes. This methodology holds promise for advancing our understanding of T cell repertoires in scenarios where sample size constraints are a prevailing concern.
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Affiliation(s)
- Vanessa Mhanna
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France
| | - Pierre Barennes
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France
| | - Hélène Vantomme
- AP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France
| | - Gwladys Fourcade
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France
| | - Nicolas Coatnoan
- AP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France
| | - Adrien Six
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France
| | - David Klatzmann
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Clinical Investigation Center for Biotherapies (CIC-BTi) and Immunology-Inflammation-Infectiology and Dermatology Department (3iD), Paris, France
| | - Encarnita Mariotti-Ferrandiz
- Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), 75005 Paris, France; Institut Universitaire de France, France.
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6
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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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7
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Wang Q, Feng D, Jia S, Lu Q, Zhao M. B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases. Clin Rev Allergy Immunol 2024; 66:76-98. [PMID: 38459209 DOI: 10.1007/s12016-024-08984-6] [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] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
In the field of contemporary medicine, autoimmune diseases (AIDs) are a prevalent and debilitating group of illnesses. However, they present extensive and profound challenges in terms of etiology, pathogenesis, and treatment. A major reason for this is the elusive pathophysiological mechanisms driving disease onset. Increasing evidence suggests the indispensable role of B cells in the pathogenesis of autoimmune diseases. Interestingly, B-cell receptor (BCR) repertoires in autoimmune diseases display a distinct skewing that can provide insights into disease pathogenesis. Over the past few years, advances in high-throughput sequencing have provided powerful tools for analyzing B-cell repertoire to understand the mechanisms during the period of B-cell immune response. In this paper, we have provided an overview of the mechanisms and analytical methods for generating BCR repertoire diversity and summarize the latest research progress on BCR repertoire in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), multiple sclerosis (MS), and type 1 diabetes (T1D). Overall, B-cell repertoire analysis is a potent tool to understand the involvement of B cells in autoimmune diseases, facilitating the creation of innovative therapeutic strategies targeting specific B-cell clones or subsets.
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Affiliation(s)
- Qian Wang
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Delong Feng
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Sujie Jia
- Department of Pharmacy, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
| | - Ming Zhao
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China.
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
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8
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Peres A, Lees WD, Rodriguez OL, Lee NY, Polak P, Hope R, Kedmi M, Collins AM, Ohlin M, Kleinstein S, Watson C, Yaari G. IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data. Nucleic Acids Res 2023; 51:e86. [PMID: 37548401 PMCID: PMC10484671 DOI: 10.1093/nar/gkad603] [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: 01/03/2023] [Revised: 06/26/2023] [Accepted: 08/03/2023] [Indexed: 08/08/2023] Open
Abstract
In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).
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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
| | - William D Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, WC1E 7JE, UK
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, 40202, USA
| | - Noah Y Lee
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - 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
| | - Ronen Hope
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
| | - Meirav Kedmi
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06520, USA
- Division of Hematology and Bone Marrow Transplantation, Chaim Sheba Medical Center, Tel-Hashomer, 5262000, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Andrew M Collins
- School of Biotechnology and Biomedical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Mats Ohlin
- Department of Immunotechnology Lund University, Lund, 221 00, Sweden
| | - Steven H Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, 40202, USA
| | - 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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9
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Grimsholm O. CD27 on human memory B cells-more than just a surface marker. Clin Exp Immunol 2023; 213:164-172. [PMID: 36508329 PMCID: PMC10361737 DOI: 10.1093/cei/uxac114] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/23/2022] [Accepted: 12/07/2022] [Indexed: 07/23/2023] Open
Abstract
Immunological memory protects the human body from re-infection with an earlier recognized pathogen. This memory comprises the durable serum antibody titres provided by long-lived plasma cells and the memory T and B cells with help from other cells. Memory B cells are the main precursor cells for new plasma cells during a secondary infection. Their formation starts very early in life, and they continue to form and undergo refinements throughout our lifetime. While the heterogeneity of the human memory B-cell pool is still poorly understood, specific cellular surface markers define most of the cell subpopulations. CD27 is one of the most commonly used markers to define human memory B cells. In addition, there are molecular markers, such as somatic mutations in the immunoglobulin heavy and light chains and isotype switching to, for example, IgG. Although not every memory B cell undergoes somatic hypermutation or isotype switching, most of them express these molecular traits in adulthood. In this review, I will focus on the most recent knowledge regarding CD27+ human memory B cells in health and disease, and describe how Ig sequencing can be used as a tool to decipher the evolutionary pathways of these cells.
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Affiliation(s)
- Ola Grimsholm
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and Immunology, Medical University of Vienna, AT-1090 Vienna, Austria
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10
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Ford EE, Tieri D, Rodriguez OL, Francoeur NJ, Soto J, Kos JT, Peres A, Gibson WS, Silver CA, Deikus G, Hudson E, Woolley CR, Beckmann N, Charney A, Mitchell TC, Yaari G, Sebra RP, Watson CT, Smith ML. FLAIRR-Seq: A Method for Single-Molecule Resolution of Near Full-Length Antibody H Chain Repertoires. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:1607-1619. [PMID: 37027017 PMCID: PMC10152037 DOI: 10.4049/jimmunol.2200825] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/14/2023] [Indexed: 04/08/2023]
Abstract
Current Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using short-read sequencing strategies resolve expressed Ab transcripts with limited resolution of the C region. In this article, we present the near-full-length AIRR-seq (FLAIRR-seq) method that uses targeted amplification by 5' RACE, combined with single-molecule, real-time sequencing to generate highly accurate (99.99%) human Ab H chain transcripts. FLAIRR-seq was benchmarked by comparing H chain V (IGHV), D (IGHD), and J (IGHJ) gene usage, complementarity-determining region 3 length, and somatic hypermutation to matched datasets generated with standard 5' RACE AIRR-seq using short-read sequencing and full-length isoform sequencing. Together, these data demonstrate robust FLAIRR-seq performance using RNA samples derived from PBMCs, purified B cells, and whole blood, which recapitulated results generated by commonly used methods, while additionally resolving H chain gene features not documented in IMGT at the time of submission. FLAIRR-seq data provide, for the first time, to our knowledge, simultaneous single-molecule characterization of IGHV, IGHD, IGHJ, and IGHC region genes and alleles, allele-resolved subisotype definition, and high-resolution identification of class switch recombination within a clonal lineage. In conjunction with genomic sequencing and genotyping of IGHC genes, FLAIRR-seq of the IgM and IgG repertoires from 10 individuals resulted in the identification of 32 unique IGHC alleles, 28 (87%) of which were previously uncharacterized. Together, these data demonstrate the capabilities of FLAIRR-seq to characterize IGHV, IGHD, IGHJ, and IGHC gene diversity for the most comprehensive view of bulk-expressed Ab repertoires to date.
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Affiliation(s)
- Easton E. Ford
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Oscar L. Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Nancy J. Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Juan Soto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Ayelet Peres
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - William S. Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Catherine A. Silver
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Elizabeth Hudson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Cassandra R. Woolley
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Noam Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Thomas C. Mitchell
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Robert P. Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Melissa L. Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
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11
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Al Hajj GS, Pensar J, Sandve GK. DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation. PLoS One 2023; 18:e0284443. [PMID: 37058511 PMCID: PMC10104342 DOI: 10.1371/journal.pone.0284443] [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: 11/30/2022] [Accepted: 03/30/2023] [Indexed: 04/15/2023] Open
Abstract
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences. DagSim is available as a Python package at PyPI. Source code and documentation are available at: https://github.com/uio-bmi/dagsim.
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Affiliation(s)
| | - Johan Pensar
- Department of Mathematics, University of Oslo, Oslo, Norway
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, Oslo, Norway
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12
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T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer. Int J Mol Sci 2022; 23:ijms23158590. [PMID: 35955721 PMCID: PMC9369427 DOI: 10.3390/ijms23158590] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/17/2022] Open
Abstract
The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.
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13
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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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14
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Janjic A, Wange LE, Bagnoli JW, Geuder J, Nguyen P, Richter D, Vieth B, Vick B, Jeremias I, Ziegenhain C, Hellmann I, Enard W. Prime-seq, efficient and powerful bulk RNA sequencing. Genome Biol 2022; 23:88. [PMID: 35361256 PMCID: PMC8969310 DOI: 10.1186/s13059-022-02660-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/23/2022] [Indexed: 12/21/2022] Open
Abstract
Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude that prime-seq is currently one of the best options to set up an early barcoding bulk RNA-seq protocol from which many labs would profit.
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Affiliation(s)
- Aleksandar Janjic
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
- Graduate School of Systemic Neurosciences, Faculty of Biology, Ludwig-Maximilians University, Martinsried, Germany
| | - Lucas E Wange
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Johannes W Bagnoli
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Johanna Geuder
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Phong Nguyen
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Daniel Richter
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Beate Vieth
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Binje Vick
- Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Irmela Jeremias
- Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
- Department of Pediatrics, Dr. von Hauner Children's Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Ines Hellmann
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany
| | - Wolfgang Enard
- Anthropology & Human Genomics, Faculty of Biology, Ludwig-Maximilians University, Großhaderner Str. 2, 82152, Martinsried, Germany.
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15
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Dahal-Koirala S, Balaban G, Neumann RS, Scheffer L, Lundin KEA, Greiff V, Sollid LM, Qiao SW, Sandve GK. TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Brief Bioinform 2022; 23:bbab566. [PMID: 35062022 PMCID: PMC8921636 DOI: 10.1093/bib/bbab566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 01/19/2023] Open
Abstract
T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5' RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.
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Affiliation(s)
- Shiva Dahal-Koirala
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Gabriel Balaban
- Biomedical Informatics, Department of Informatics, University of Oslo, 0373, Oslo, Norway
- Department of Computational Physiology, Simula Research Laboratory, 1364, Fornebu, Norway
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, 0373, Oslo, Norway
| | - Ralf Stefan Neumann
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
| | - Lonneke Scheffer
- Biomedical Informatics, Department of Informatics, University of Oslo, 0373, Oslo, Norway
| | - Knut Erik Aslaksen Lundin
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Gastroenterology, Oslo University Hospital-Rikshospitalet, 0372, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Ludvig Magne Sollid
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Shuo-Wang Qiao
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Geir Kjetil Sandve
- Biomedical Informatics, Department of Informatics, University of Oslo, 0373, Oslo, Norway
- PharmaTox Strategic Research Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, 0373, Oslo, Norway
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16
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Slabodkin A, Chernigovskaya M, Mikocziova I, Akbar R, Scheffer L, Pavlović M, Bashour H, Snapkov I, Mehta BB, Weber CR, Gutierrez-Marcos J, Sollid LM, Haff IH, Sandve GK, Robert PA, Greiff V. Individualized VDJ recombination predisposes the available Ig sequence space. Genome Res 2021; 31:2209-2224. [PMID: 34815307 PMCID: PMC8647828 DOI: 10.1101/gr.275373.121] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 10/20/2021] [Indexed: 11/25/2022]
Abstract
The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.
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Affiliation(s)
- Andrei Slabodkin
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Ivana Mikocziova
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Lonneke Scheffer
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Milena Pavlović
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Cédric R Weber
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | | | - Ludvig M Sollid
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | | | | | - Philippe A Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
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