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Seo K, Choi JK. Comprehensive Analysis of TCR and BCR Repertoires: Insights into Methodologies, Challenges, and Applications. Genomics Inform 2025; 23:6. [PMID: 39994831 PMCID: PMC11853700 DOI: 10.1186/s44342-024-00034-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2024] [Accepted: 12/27/2024] [Indexed: 02/26/2025] Open
Abstract
The diversity of T-cell receptors (TCRs) and B-cell receptors (BCRs) underpins the adaptive immune system's ability to recognize and respond to a wide array of antigens. Recent advancements in RNA sequencing have expanded its application beyond transcriptomics to include the analysis of immune repertoires, enabling the exploration of TCR and BCR sequences across various physiological and pathological contexts. This review highlights key methodologies and considerations for TCR and BCR repertoire analysis, focusing on the technical aspects of receptor sequence extraction, data processing, and clonotype identification. We compare the use of bulk and single-cell sequencing, discuss computational tools and pipelines, and evaluate the implications of examining specific receptor regions such as CDR3. By integrating immunology, bioinformatics, and clinical research, immune repertoire analysis provides valuable insights into immune function, therapeutic responses, and precision medicine approaches, advancing our understanding of health and disease.
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Affiliation(s)
- Kayoung Seo
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
- SCL-KAIST Institute of Translational Research, Daejeon, Republic of Korea.
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Schrijver B, Kolijn PM, Hasib SH, Ten Berge JCEM, Putera I, Nagtzaam NMA, van Holten Neelen JCPA, Langerak AW, Schreurs MWJ, van Hagen PM, Dik WA. Anti-retinal immune response in sarcoid uveitis: A potential role for PCLO as an antigenic target. J Autoimmun 2025; 151:103375. [PMID: 39892202 DOI: 10.1016/j.jaut.2025.103375] [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: 11/02/2024] [Revised: 01/06/2025] [Accepted: 01/24/2025] [Indexed: 02/03/2025]
Abstract
PURPOSE To explore the autoimmune component of sarcoid uveitis (SU) by analyzing serum anti-retinal antibodies (ARAs), identifying targeted retinal proteins, T- and B-cell receptor repertoires and HLA genotype. METHODS Material from 45 sarcoidosis patients with no presenting uveitis (SNPU) and 46 with SU was analyzed. Serum ARAs and targeted retinal layers were assessed using indirect immunofluorescence staining. HuScan analysis identified autoantibody-targeted linear epitopes. Validation included a bead-based assay for anti-Piccolo Presynaptic Cytomatrix Protein (PCLO) antibodies and an ELISpot assay for PCLO-reactive T-lymphocytes. T cell receptor beta (TCRB) and B cell receptor heavy (BCRH) repertoire analyses were performed using next-generation sequencing and HLA class II genotypes were determined by sequence-specific primer analysis. RESULTS ARAs were more prevalent in SU patients than in SNPU patients (52 vs. 22 %, p = 0.003), with significant more reactivity against the nuclear retinal layer (32 vs. 7 %, p = 0.005). HuScan identified autoantibodies against three retinal proteins, including PCLO. Bead-based analysis showed higher anti-PCLO autoantibody levels in ARA-positive patients (median: 913.3 vs. 544.5, p = 0.035), and PCLO-directed T-lymphocytes were present in ARA-positive SU patients. Two TCRB clusters were identified in four unique ARA positive patients, while absent in ARA negative patients. No HLA allele association with ARA status could be detected. CONCLUSION Our findings reveal an association between serum ARA-positivity and SU, suggesting a link to autoimmune processes. An humoral and cellular response against the retinal protein PCLO was identified, highlighting PCLO as a potential autoimmune target in ARA-positive patients. Additionally, specific TCRB clusters were found to correlate with ARA status.
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Affiliation(s)
- Benjamin Schrijver
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - P Martijn Kolijn
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - Saad H Hasib
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | | | - Ikhwanuliman Putera
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands; Department of Ophthalmology, Erasmus MC University Medical Center Rotterdam, the Netherlands; Department of Internal Medicine, Section Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
| | - Nicole M A Nagtzaam
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - J Conny P A van Holten Neelen
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - Anton W Langerak
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - Marco W J Schreurs
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - P Martin van Hagen
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands; Department of Internal Medicine, Section Allergy & Clinical Immunology, Erasmus University Medical Center, Rotterdam, the Netherlands.
| | - Willem A Dik
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center Rotterdam, the Netherlands.
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Mahdy AKH, Lokes E, Schöpfel V, Kriukova V, Britanova OV, Steiert TA, Franke A, ElAbd H. Bulk T cell repertoire sequencing (TCR-Seq) is a powerful technology for understanding inflammation-mediated diseases. J Autoimmun 2024; 149:103337. [PMID: 39571301 DOI: 10.1016/j.jaut.2024.103337] [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: 04/19/2024] [Revised: 10/12/2024] [Accepted: 11/09/2024] [Indexed: 12/15/2024]
Abstract
Multiple alterations in the T cell repertoire were identified in many chronic inflammatory diseases such as inflammatory bowel disease, multiple sclerosis, and rheumatoid arthritis, suggesting that T cells might, directly or indirectly, be implicated in these pathologies. This has sparked a deep interest in characterizing disease-associated T cell clonotypes as well as in identifying and quantifying their contribution to the pathophysiology of different autoimmune and inflammation-mediated diseases. Bulk T cell repertoire sequencing (TCR-Seq) has emerged as a powerful method to profile the T cell repertoire of a sample in a high throughput fashion. Given the increasing utilization of TCR-Seq, we aimed here to provide a comprehensive, up-to-date review of the method, its extensions, and its ability to investigate chronic and autoimmune diseases. Specifically, we started by introducing the immunological basis of TCR repertoire generation and features, followed by discussing different experimental approach to perform TCR-Seq, then we describe different methods and frameworks for analyzing the generated datasets. Subsequently, different experimental techniques for investigating the antigenicity of T cell clonotypes are described. Lastly, we discuss recent studies that utilized TCR-Seq to understand different inflammation-mediated diseases, discuss fallbacks of the technology and potential future directions to overcome current limitations.
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Affiliation(s)
- Aya K H Mahdy
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Evgeniya Lokes
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Valentina Schöpfel
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Valeriia Kriukova
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Olga V Britanova
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Tim A Steiert
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany.
| | - Hesham ElAbd
- Institute of Clinical Molecular Biology, Kiel University & University Medical Centre Schleswig-Holstein, Kiel, 24105, Germany.
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Martinez-Lopez J, Lopez-Muñoz N, Chari A, Dorado S, Barrio S, Arora S, Kumar A, Chung A, Martin T, Wolf J. Measurable residual disease (MRD) dynamics in multiple myeloma and the influence of clonal diversity analyzed by artificial intelligence. Blood Cancer J 2024; 14:131. [PMID: 39112458 PMCID: PMC11306767 DOI: 10.1038/s41408-024-01102-x] [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: 03/22/2024] [Revised: 07/08/2024] [Accepted: 07/11/2024] [Indexed: 08/10/2024] Open
Abstract
Minimal residual disease (MRD) assessment is a known surrogate marker for survival in multiple myeloma (MM). Here, we present a single institution's experience assessing MRD by NGS of Ig genes and the long-term impact of depth of response as well as clonal diversity on the clinical outcome of a large population of MM patients; 482 MM patients at the University of California, San Francisco (UCSF) diagnosed from 2008 to 2020 were analyzed retrospectively. MRD assessment was performed by NGS. PFS curves were plotted by the Kaplan-Meier method. In the newly diagnosed group, 119 of 304, achieved MRD negativity at the level of 10-6 at least once. These patients had a prolonged PFS versus patients who were persistently MRD positive at different levels (p > 0.0001). In the relapsed disease group, 64 of 178 achieved MRD negativity at 10-6, and PFS was prolonged versus patients who remained MRD positive (p = 0.03). Three categories of MRD dynamics were defined by artificial intelligence: (A) patients with ≥3 consistently MRD negative samples, (B) patients with continuously declining but detectable clones, and (C) patients with either increasing or a stable number of clones. Groups A and B had a more prolonged PFS than group C (p < 10-7). Patients who were MRD positive and had not yet relapsed had a higher clonal diversity than those patients who were MRD positive and had relapsed. MRD dynamics can accurately predict disease evolution and drive clinical decision-making. Clonal Diversity could complement MRD assessment in the prediction of outcomes in MM.
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Affiliation(s)
- J Martinez-Lopez
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain.
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
| | - N Lopez-Muñoz
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - A Chari
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - S Dorado
- Medical Department, Altum Sequencing, Madrid, Spain
| | - S Barrio
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - S Arora
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - A Kumar
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - A Chung
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - T Martin
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - J Wolf
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Front Immunol 2024; 15:1428773. [PMID: 39161769 PMCID: PMC11330812 DOI: 10.3389/fimmu.2024.1428773] [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: 05/07/2024] [Accepted: 06/24/2024] [Indexed: 08/21/2024] Open
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- Institute of Computational Life Sciences, Zürich University of Applied Sciences (ZHAW), Wädenswil, Switzerland
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - María Rodríguez Martínez
- AI for Scientific Discovery, IBM Research Europe, Rüschlikon, Switzerland
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, United States
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Fu J, Hsiao T, Waffarn E, Meng W, Long KD, Frangaj K, Jones R, Gorur A, Shtewe A, Li M, Muntnich CB, Rogers K, Jiao W, Velasco M, Matsumoto R, Kubota M, Wells S, Danzl N, Ravella S, Iuga A, Vasilescu ER, Griesemer A, Weiner J, Farber DL, Luning Prak ET, Martinez M, Kato T, Hershberg U, Sykes M. Dynamic establishment and maintenance of the human intestinal B cell population and repertoire following transplantation in a pediatric-dominated cohort. Front Immunol 2024; 15:1375486. [PMID: 39007142 PMCID: PMC11239347 DOI: 10.3389/fimmu.2024.1375486] [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: 01/23/2024] [Accepted: 06/17/2024] [Indexed: 07/16/2024] Open
Abstract
Introduction It is unknown how intestinal B cell populations and B cell receptor (BCR) repertoires are established and maintained over time in humans. Following intestinal transplantation (ITx), surveillance ileal mucosal biopsies provide a unique opportunity to map the dynamic establishment of recipient gut lymphocyte populations in immunosuppressed conditions. Methods Using polychromatic flow cytometry that includes HLA allele group-specific antibodies distinguishing donor from recipient cells along with high throughput BCR sequencing, we tracked the establishment of recipient B cell populations and BCR repertoire in the allograft mucosa of ITx recipients. Results We confirm the early presence of naïve donor B cells in the circulation (donor age range: 1-14 years, median: 3 years) and, for the first time, document the establishment of recipient B cell populations, including B resident memory cells, in the intestinal allograft mucosa (recipient age range at the time of transplant: 1-44 years, median: 3 years). Recipient B cell repopulation of the allograft was most rapid in infant (<1 year old)-derived allografts and, unlike T cell repopulation, did not correlate with rejection rates. While recipient memory B cell populations were increased in graft mucosa compared to circulation, naïve recipient B cells remained detectable in the graft mucosa for years. Comparisons of peripheral and intra-mucosal B cell repertoires in the absence of rejection (recipient age range at the time of transplant: 1-9 years, median: 2 years) revealed increased BCR mutation rates and clonal expansion in graft mucosa compared to circulating B cells, but these parameters did not increase markedly after the first year post-transplant. Furthermore, clonal mixing between the allograft mucosa and the circulation was significantly greater in ITx recipients, even years after transplantation, than in deceased adult donors. In available pan-scope biopsies from pediatric recipients, we observed higher percentages of naïve recipient B cells in colon allograft compared to small bowel allograft and increased BCR overlap between native colon vs colon allograft compared to that between native colon vs ileum allograft in most cases, suggesting differential clonal distribution in large intestine vs small intestine. Discussion Collectively, our data demonstrate intestinal mucosal B cell repertoire establishment from a circulating pool, a process that continues for years without evidence of stabilization of the mucosal B cell repertoire in pediatric ITx patients.
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Affiliation(s)
- Jianing Fu
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Thomas Hsiao
- Department of Human Biology, University of Haifa, Haifa, Israel
| | - Elizabeth Waffarn
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katherine D. Long
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Kristjana Frangaj
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Rebecca Jones
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Alaka Gorur
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Areen Shtewe
- Department of Human Biology, University of Haifa, Haifa, Israel
| | - Muyang Li
- Department of Pathology, Columbia University, New York, NY, United States
| | - Constanza Bay Muntnich
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Kortney Rogers
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Wenyu Jiao
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Monica Velasco
- Department of Pediatrics, Columbia University, New York, NY, United States
| | - Rei Matsumoto
- Department of Microbiology and Immunology, Columbia University, New York, NY, United States
| | - Masaru Kubota
- Department of Microbiology and Immunology, Columbia University, New York, NY, United States
| | - Steven Wells
- Department of Microbiology and Immunology, Columbia University, New York, NY, United States
| | - Nichole Danzl
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
| | - Shilpa Ravella
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, New York, NY, United States
| | - Alina Iuga
- Department of Pathology, Columbia University, New York, NY, United States
| | | | - Adam Griesemer
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
- Department of Surgery, Columbia University, New York, NY, United States
| | - Joshua Weiner
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
- Department of Surgery, Columbia University, New York, NY, United States
| | - Donna L. Farber
- Department of Microbiology and Immunology, Columbia University, New York, NY, United States
- Department of Surgery, Columbia University, New York, NY, United States
| | - Eline T. Luning Prak
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mercedes Martinez
- Department of Pediatrics, Columbia University, New York, NY, United States
| | - Tomoaki Kato
- Department of Surgery, Columbia University, New York, NY, United States
| | - Uri Hershberg
- Department of Human Biology, University of Haifa, Haifa, Israel
| | - Megan Sykes
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University, New York, NY, United States
- Department of Microbiology and Immunology, Columbia University, New York, NY, United States
- Department of Surgery, Columbia University, New York, NY, United States
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Pelissier A, Laragione T, Gulko PS, Rodríguez Martínez M. Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.28.573505. [PMID: 38234732 PMCID: PMC10793435 DOI: 10.1101/2023.12.28.573505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
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Affiliation(s)
- Aurelien Pelissier
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
- Currently at Institute of Computational Life Sciences, ZHAW, 8400 Winterthur, Switzerland
| | - Teresina Laragione
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - Percio S. Gulko
- Division of Rheumatology, Icahn School of Medicine at Mount Sinai, 10029 New York, United States
| | - María Rodríguez Martínez
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Currently at Yale School of Medicine, 06510 New Haven, United States
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Pelissier A, Stratigopoulou M, Donner N, Dimitriadis E, Bende RJ, Guikema JE, Rodriguez Martinez M, van Noesel CJ. Convergent evolution and B-cell recirculation in germinal centers in a human lymph node. Life Sci Alliance 2023; 6:e202301959. [PMID: 37640448 PMCID: PMC10462906 DOI: 10.26508/lsa.202301959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Germinal centers (GCs) play a central role in generating an effective immune response against infectious pathogens, and failures in their regulating mechanisms can lead to the development of autoimmune diseases and cancer. Although previous works study experimental systems of the immune response with mouse models that are immunized with specific antigens, our study focused on a real-life situation, with an ongoing GC response in a human lymph node (LN) involving multiple asynchronized GCs reacting simultaneously to unknown antigens. We combined laser capture microdissection of individual GCs from human LN with next-generation repertoire sequencing to characterize individual GCs as distinct evolutionary spaces. In line with well-characterized GC responses in mice, elicited by immunization with model antigens, we observe a heterogeneous clonal diversity across individual GCs from the same human LN. Still, we identify shared clones in several individual GCs, and phylogenetic tree analysis combined with paratope modeling suggest the re-engagement and rediversification of B-cell clones across GCs and expanded clones exhibiting shared antigen responses across distinct GCs, indicating convergent evolution of the GCs.
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Affiliation(s)
- Aurelien Pelissier
- 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, Amsterdam, Netherlands
| | - Naomi Donner
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | | | - Richard J Bende
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | - Jeroen E Guikema
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
| | | | - Carel Jm van Noesel
- Department of Pathology, Amsterdam University Medical Centers, Location AMC, Lymphoma and Myeloma Center Amsterdam, Amsterdam, Netherlands
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