{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:07:05Z","timestamp":1792782425538,"version":"4.5.0"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000273","name":"Diabetes Research and Wellness Foundation","doi-asserted-by":"publisher","award":["Professor David Matthews Non-Clinical Research Fel"],"award-info":[{"award-number":["Professor David Matthews Non-Clinical Research Fel"]}],"id":[{"id":"10.13039\/501100000273","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Steve Morgan Grand Chellenge\/JDRF","award":["2-SRA-2024-1473-M-N"],"award-info":[{"award-number":["2-SRA-2024-1473-M-N"]}]},{"name":"Steve Morgan Grand Chellenge\/JDRF","award":["2-SRA-2024-1474-M-N"],"award-info":[{"award-number":["2-SRA-2024-1474-M-N"]}]},{"name":"NIH","award":["P01 AI042288"],"award-info":[{"award-number":["P01 AI042288"]}]},{"name":"NIH","award":["DK108868"],"award-info":[{"award-number":["DK108868"]}]},{"name":"NIH","award":["DK131070 DK032083 DK099317"],"award-info":[{"award-number":["DK131070 DK032083 DK099317"]}]},{"name":"NIH","award":["U01-DK112217"],"award-info":[{"award-number":["U01-DK112217"]}]},{"name":"NIH","award":["U24 AI177622"],"award-info":[{"award-number":["U24 AI177622"]}]},{"DOI":"10.13039\/100007028","name":"Leona M. and Harry B. Helmsley Charitable Trust","doi-asserted-by":"publisher","award":["#2101-04969 #2301-06562"],"award-info":[{"award-number":["#2101-04969 #2301-06562"]}],"id":[{"id":"10.13039\/100007028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Diabetologia"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR\/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR\/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s00125-024-06298-y","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T23:02:26Z","timestamp":1730156546000},"page":"186-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium"],"prefix":"10.1007","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0821-4498","authenticated-orcid":false,"given":"Stephanie J.","family":"Hanna","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7575-6943","authenticated-orcid":false,"given":"Rachel H.","family":"Bonami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-6495","authenticated-orcid":false,"given":"Brian","family":"Corrie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2917-208X","authenticated-orcid":false,"given":"Monica","family":"Westley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9491-0958","authenticated-orcid":false,"given":"Amanda L.","family":"Posgai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9478-9211","authenticated-orcid":false,"given":"Eline T.","family":"Luning Prak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9762-6314","authenticated-orcid":false,"given":"Felix","family":"Breden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3766-5244","authenticated-orcid":false,"given":"Aaron W.","family":"Michels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2878-9296","authenticated-orcid":false,"given":"Todd M.","family":"Brusko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Type 1 Diabetes AIRR Consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin","family":"Baschal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Cerosaletti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorissa","family":"Corrie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iria","family":"Gomez-Tourino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Higdon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally C.","family":"Kent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Linsley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maki","family":"Nakayama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kira","family":"Neller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William E.","family":"Ruff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc","family":"Teyton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"issue":"5","key":"6298_CR1","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/ng.3822","volume":"49","author":"RO Emerson","year":"2017","unstructured":"Emerson RO, DeWitt WS, Vignali M et al (2017) Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat Genet 49(5):659\u2013665. https:\/\/doi.org\/10.1038\/ng.3822","journal-title":"Nat Genet"},{"key":"6298_CR2","doi-asserted-by":"publisher","unstructured":"Schulthei\u00df C, Paschold L, Simnica D, et al (2020) Next-generation sequencing of T and B cell receptor repertoires from COVID-19 patients showed signatures associated with severity of disease. Immunity S107476132030279X. https:\/\/doi.org\/10.1016\/j.immuni.2020.06.024","DOI":"10.1016\/j.immuni.2020.06.024"},{"issue":"1","key":"6298_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1111\/imr.12666","volume":"284","author":"BD Corrie","year":"2018","unstructured":"Corrie BD, Marthandan N, Zimonja B et al (2018) iReceptor: a platform for querying and analyzing antibody\/B-cell and T-cell receptor repertoire data across federated repositories. Immunol Rev 284(1):24\u201341. https:\/\/doi.org\/10.1111\/imr.12666","journal-title":"Immunol Rev"},{"key":"6298_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3389\/fdata.2020.00022","volume":"3","author":"S Christley","year":"2020","unstructured":"Christley S, Aguiar A, Blanck G et al (2020) The ADC API: A Web API for the programmatic query of the AIRR data commons. Front Big Data 3:22. https:\/\/doi.org\/10.3389\/fdata.2020.00022","journal-title":"Front Big Data"},{"issue":"23","key":"6298_CR5","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1001\/jama.2013.6285","volume":"309","author":"AG Ziegler","year":"2013","unstructured":"Ziegler AG, Rewers M, Simell O et al (2013) Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA 309(23):2473. https:\/\/doi.org\/10.1001\/jama.2013.6285","journal-title":"JAMA"},{"issue":"1","key":"6298_CR6","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s43856-024-00478-y","volume":"4","author":"JL Felton","year":"2024","unstructured":"Felton JL, Redondo MJ, Oram RA et al (2024) Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review. Commun Med 4(1):66. https:\/\/doi.org\/10.1038\/s43856-024-00478-y","journal-title":"Commun Med"},{"issue":"10","key":"6298_CR7","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.2337\/dc15-1419","volume":"38","author":"RA Insel","year":"2015","unstructured":"Insel RA, Dunne JL, Atkinson MA et al (2015) Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the endocrine society, and the American diabetes association. Diabetes Care 38(10):1964\u20131974. https:\/\/doi.org\/10.2337\/dc15-1419","journal-title":"Diabetes Care"},{"issue":"9","key":"6298_CR8","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1007\/s00125-009-1438-0","volume":"52","author":"P Achenbach","year":"2009","unstructured":"Achenbach P, Lampasona V, Landherr U et al (2009) Autoantibodies to zinc transporter 8 and SLC30A8 genotype stratify type 1 diabetes risk. Diabetologia 52(9):1881\u20131888. https:\/\/doi.org\/10.1007\/s00125-009-1438-0","journal-title":"Diabetologia"},{"issue":"9","key":"6298_CR9","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.2337\/dc17-0238","volume":"40","author":"JP Krischer","year":"2017","unstructured":"Krischer JP, Lynch KF, Lernmark \u00c5 et al (2017) Genetic and environmental interactions modify the risk of diabetes-related autoimmunity by 6 years of age: the TEDDY study. Diabetes Care 40(9):1194\u20131202. https:\/\/doi.org\/10.2337\/dc17-0238","journal-title":"Diabetes Care"},{"issue":"8","key":"6298_CR10","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1111\/pedi.13410","volume":"23","author":"REJ Besser","year":"2022","unstructured":"Besser REJ, Bell KJ, Couper JJ et al (2022) ISPAD clinical practice consensus guidelines 2022: stages of type 1 diabetes in children and adolescents. Pediatr Diabetes 23(8):1175\u20131187. https:\/\/doi.org\/10.1111\/pedi.13410","journal-title":"Pediatr Diabetes"},{"issue":"12","key":"6298_CR11","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1007\/s00125-023-06011-5","volume":"66","author":"A Galderisi","year":"2023","unstructured":"Galderisi A, Carr ALJ, Martino M, Taylor P, Senior P, Dayan C (2023) Quantifying beta cell function in the preclinical stages of type 1 diabetes. Diabetologia 66(12):2189\u20132199. https:\/\/doi.org\/10.1007\/s00125-023-06011-5","journal-title":"Diabetologia"},{"issue":"10","key":"6298_CR12","doi-asserted-by":"publisher","first-page":"2784","DOI":"10.1210\/clinem\/dgac440","volume":"107","author":"LM Jacobsen","year":"2022","unstructured":"Jacobsen LM, Bundy BN, Ismail HM et al (2022) Index60 is superior to HbA1c for identifying individuals at high risk for type 1 diabetes. J Clin Endocrinol Metab 107(10):2784\u20132792. https:\/\/doi.org\/10.1210\/clinem\/dgac440","journal-title":"J Clin Endocrinol Metab"},{"issue":"4","key":"6298_CR13","doi-asserted-by":"publisher","first-page":"913","DOI":"10.2337\/dc19-1731","volume":"43","author":"M So","year":"2020","unstructured":"So M, O\u2019Rourke C, Bahnson HT, Greenbaum CJ, Speake C (2020) Autoantibody reversion: changing risk categories in multiple-autoantibody\u2013positive individuals. Diabetes Care 43(4):913\u2013917. https:\/\/doi.org\/10.2337\/dc19-1731","journal-title":"Diabetes Care"},{"issue":"12","key":"6298_CR14","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/S2213-8587(23)00267-X","volume":"11","author":"PN Taylor","year":"2023","unstructured":"Taylor PN, Collins KS, Lam A et al (2023) C-peptide and metabolic outcomes in trials of disease modifying therapy in new-onset type 1 diabetes: an individual participant meta-analysis. Lancet Diabetes Endocrinol 11(12):915\u2013925. https:\/\/doi.org\/10.1016\/S2213-8587(23)00267-X","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"1","key":"6298_CR15","doi-asserted-by":"publisher","first-page":"7214","DOI":"10.1038\/s41467-023-42581-z","volume":"14","author":"A Ylescupidez","year":"2023","unstructured":"Ylescupidez A, Bahnson HT, O\u2019Rourke C, Lord S, Speake C, Greenbaum CJ (2023) A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes. Nat Commun 14(1):7214. https:\/\/doi.org\/10.1038\/s41467-023-42581-z","journal-title":"Nat Commun"},{"issue":"9","key":"6298_CR16","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s00125-023-05955-y","volume":"66","author":"AM Luckett","year":"2023","unstructured":"Luckett AM, Weedon MN, Hawkes G, Leslie RD, Oram RA, Grant SFA (2023) Utility of genetic risk scores in type 1 diabetes. Diabetologia 66(9):1589\u20131600. https:\/\/doi.org\/10.1007\/s00125-023-05955-y","journal-title":"Diabetologia"},{"issue":"8","key":"6298_CR17","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1038\/s41591-020-0930-4","volume":"26","author":"LA Ferrat","year":"2020","unstructured":"Ferrat LA, Vehik K, Sharp SA et al (2020) A combined risk score enhances prediction of type 1 diabetes among susceptible children. Nat Med 26(8):1247\u20131255. https:\/\/doi.org\/10.1038\/s41591-020-0930-4","journal-title":"Nat Med"},{"issue":"18","key":"6298_CR18","doi-asserted-by":"publisher","first-page":"e161885","DOI":"10.1172\/jci.insight.161885","volume":"7","author":"AM Mitchell","year":"2022","unstructured":"Mitchell AM, Baschal EE, McDaniel KA et al (2022) Temporal development of T cell receptor repertoires during childhood in health and disease. JCI Insight 7(18):e161885. https:\/\/doi.org\/10.1172\/jci.insight.161885","journal-title":"JCI Insight"},{"issue":"2","key":"6298_CR19","doi-asserted-by":"publisher","first-page":"100192","DOI":"10.1016\/j.xcrm.2021.100192","volume":"2","author":"MS Shoukat","year":"2021","unstructured":"Shoukat MS, Foers AD, Woodmansey S, Evans SC, Fowler A, Soilleux EJ (2021) Use of machine learning to identify a T cell response to SARS-CoV-2. Cell Rep Med 2(2):100192. https:\/\/doi.org\/10.1016\/j.xcrm.2021.100192","journal-title":"Cell Rep Med"},{"issue":"1","key":"6298_CR20","doi-asserted-by":"publisher","first-page":"14275","DOI":"10.1038\/s41598-021-93608-8","volume":"11","author":"J-W Sidhom","year":"2021","unstructured":"Sidhom J-W, Baras AS (2021) Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires. Sci Rep 11(1):14275. https:\/\/doi.org\/10.1038\/s41598-021-93608-8","journal-title":"Sci Rep"},{"issue":"10","key":"6298_CR21","doi-asserted-by":"publisher","first-page":"e151849","DOI":"10.1172\/jci.insight.151849","volume":"7","author":"RM Gittelman","year":"2022","unstructured":"Gittelman RM, Lavezzo E, Snyder TM et al (2022) Longitudinal analysis of T cell receptor repertoires reveals shared patterns of antigen-specific response to SARS-CoV-2 infection. JCI Insight 7(10):e151849. https:\/\/doi.org\/10.1172\/jci.insight.151849","journal-title":"JCI Insight"},{"issue":"17","key":"6298_CR22","doi-asserted-by":"publisher","first-page":"4540","DOI":"10.1158\/1078-0432.CCR-13-3231","volume":"20","author":"D Wu","year":"2014","unstructured":"Wu D, Emerson RO, Sherwood A et al (2014) Detection of minimal residual disease in B lymphoblastic leukemia by high-throughput sequencing of IGH. Clin Cancer Res Off J Am Assoc Cancer Res 20(17):4540\u20134548. https:\/\/doi.org\/10.1158\/1078-0432.CCR-13-3231","journal-title":"Clin Cancer Res Off J Am Assoc Cancer Res"},{"issue":"6","key":"6298_CR23","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1158\/1078-0432.CCR-19-3249","volume":"26","author":"H Zhang","year":"2020","unstructured":"Zhang H, Liu L, Zhang J et al (2020) Investigation of antigen-specific T-cell receptor clusters in human cancers. Clin Cancer Res 26(6):1359\u20131371. https:\/\/doi.org\/10.1158\/1078-0432.CCR-19-3249","journal-title":"Clin Cancer Res"},{"issue":"14","key":"6298_CR24","doi-asserted-by":"publisher","first-page":"e136471","DOI":"10.1172\/jci.insight.136471","volume":"5","author":"R Jiang","year":"2020","unstructured":"Jiang R, Fichtner ML, Hoehn KB et al (2020) Single-cell repertoire tracing identifies rituximab-resistant B cells during myasthenia gravis relapses. JCI Insight 5(14):e136471. https:\/\/doi.org\/10.1172\/jci.insight.136471","journal-title":"JCI Insight"},{"issue":"7","key":"6298_CR25","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1056\/NEJMoa1902226","volume":"381","author":"KC Herold","year":"2019","unstructured":"Herold KC, Bundy BN, Long SA et al (2019) An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes. N Engl J Med 381(7):603\u2013613. https:\/\/doi.org\/10.1056\/NEJMoa1902226","journal-title":"N Engl J Med"},{"issue":"10","key":"6298_CR26","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.2337\/dc23-0675","volume":"46","author":"KC Herold","year":"2023","unstructured":"Herold KC, Gitelman SE, Gottlieb PA, Knecht LA, Raymond R, Ramos EL (2023) Teplizumab: a disease-modifying therapy for type 1 diabetes that preserves \u03b2-cell function. Diabetes Care 46(10):1848\u20131856. https:\/\/doi.org\/10.2337\/dc23-0675","journal-title":"Diabetes Care"},{"issue":"9789","key":"6298_CR27","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/S0140-6736(11)60886-6","volume":"378","author":"T Orban","year":"2011","unstructured":"Orban T, Bundy B, Becker DJ et al (2011) Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet 378(9789):412\u2013419. https:\/\/doi.org\/10.1016\/S0140-6736(11)60886-6","journal-title":"Lancet"},{"issue":"6","key":"6298_CR28","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.2337\/db19-0057","volume":"68","author":"MJ Haller","year":"2019","unstructured":"Haller MJ, Long SA, Blanchfield JL et al (2019) Low-dose anti-thymocyte globulin preserves C-peptide, reduces HbA1c, and increases regulatory to conventional T-cell ratios in new-onset type 1 diabetes: two-year clinical trial data. Diabetes 68(6):1267\u20131276. https:\/\/doi.org\/10.2337\/db19-0057","journal-title":"Diabetes"},{"issue":"16","key":"6298_CR29","doi-asserted-by":"publisher","first-page":"e161812","DOI":"10.1172\/jci.insight.161812","volume":"8","author":"LM Jacobsen","year":"2023","unstructured":"Jacobsen LM, Diggins K, Blanchfield L et al (2023) Responders to low-dose ATG induce CD4+ T cell exhaustion in type 1 diabetes. JCI Insight 8(16):e161812. https:\/\/doi.org\/10.1172\/jci.insight.161812","journal-title":"JCI Insight"},{"issue":"22","key":"6298_CR30","doi-asserted-by":"publisher","first-page":"2143","DOI":"10.1056\/NEJMoa0904452","volume":"361","author":"MD Pescovitz","year":"2009","unstructured":"Pescovitz MD, Greenbaum CJ, Krause-Steinrauf H et al (2009) Rituximab, B-lymphocyte depletion, and preservation of beta-cell function. N Engl J Med 361(22):2143\u20132152. https:\/\/doi.org\/10.1056\/NEJMoa0904452","journal-title":"N Engl J Med"},{"issue":"21","key":"6298_CR31","doi-asserted-by":"publisher","first-page":"e167881","DOI":"10.1172\/jci.insight.167881","volume":"8","author":"E Balmas","year":"2023","unstructured":"Balmas E, Chen J, Hu AK et al (2023) Islet-autoreactive CD4+ T cells are linked with response to alefacept in type 1 diabetes. JCI Insight 8(21):e167881. https:\/\/doi.org\/10.1172\/jci.insight.167881","journal-title":"JCI Insight"},{"issue":"10","key":"6298_CR32","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1038\/s41590-020-0744-z","volume":"21","author":"NM Edner","year":"2020","unstructured":"Edner NM, Heuts F, Thomas N et al (2020) Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes. Nat Immunol 21(10):1244\u20131255. https:\/\/doi.org\/10.1038\/s41590-020-0744-z","journal-title":"Nat Immunol"},{"issue":"14","key":"6298_CR33","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1073\/pnas.1502967112","volume":"112","author":"M Nakayama","year":"2015","unstructured":"Nakayama M, McDaniel K, Fitzgerald-Miller L et al (2015) Regulatory vs. inflammatory cytokine T-cell responses to mutated insulin peptides in healthy and type 1 diabetic subjects. Proc Natl Acad Sci U S A 112(14):4429\u20134434. https:\/\/doi.org\/10.1073\/pnas.1502967112","journal-title":"Proc Natl Acad Sci U S A"},{"key":"6298_CR34","doi-asserted-by":"publisher","first-page":"751701","DOI":"10.3389\/fimmu.2021.751701","volume":"12","author":"SJ Hanna","year":"2021","unstructured":"Hanna SJ, Tatovic D, Thayer TC, Dayan CM (2021) Insights from single cell RNA sequencing into the immunology of type 1 diabetes- cell phenotypes and antigen specificity. Front Immunol 12:751701. https:\/\/doi.org\/10.3389\/fimmu.2021.751701","journal-title":"Front Immunol"},{"key":"6298_CR35","doi-asserted-by":"publisher","first-page":"1276255","DOI":"10.3389\/fimmu.2023.1276255","volume":"14","author":"SJ Hanna","year":"2023","unstructured":"Hanna SJ, Thayer TC, Robinson EJS et al (2023) Single-cell RNAseq identifies clonally expanded antigen-specific T-cells following intradermal injection of gold nanoparticles loaded with diabetes autoantigen in humans. Front Immunol 14:1276255. https:\/\/doi.org\/10.3389\/fimmu.2023.1276255","journal-title":"Front Immunol"},{"issue":"703","key":"6298_CR36","doi-asserted-by":"publisher","first-page":"eade3614","DOI":"10.1126\/scitranslmed.ade3614","volume":"15","author":"S Sharma","year":"2023","unstructured":"Sharma S, Tan X, Boyer J et al (2023) Measuring anti-islet autoimmunity in mouse and human by profiling peripheral blood antigen-specific CD4 T cells. Sci Transl Med 15(703):eade3614. https:\/\/doi.org\/10.1126\/scitranslmed.ade3614","journal-title":"Sci Transl Med"},{"issue":"3","key":"6298_CR37","doi-asserted-by":"publisher","first-page":"722","DOI":"10.2337\/db16-1025","volume":"66","author":"AW Michels","year":"2017","unstructured":"Michels AW, Landry LG, McDaniel KA et al (2017) Islet-derived CD4 T cells targeting proinsulin in human autoimmune diabetes. Diabetes 66(3):722\u2013734. https:\/\/doi.org\/10.2337\/db16-1025","journal-title":"Diabetes"},{"issue":"41","key":"6298_CR38","doi-asserted-by":"publisher","first-page":"e2107208118","DOI":"10.1073\/pnas.2107208118","volume":"118","author":"AM Anderson","year":"2021","unstructured":"Anderson AM, Landry LG, Alkanani AA et al (2021) Human islet T cells are highly reactive to preproinsulin in type 1 diabetes. Proc Natl Acad Sci 118(41):e2107208118. https:\/\/doi.org\/10.1073\/pnas.2107208118","journal-title":"Proc Natl Acad Sci"},{"issue":"22","key":"6298_CR39","doi-asserted-by":"publisher","first-page":"e151349","DOI":"10.1172\/jci.insight.151349","volume":"6","author":"PS Linsley","year":"2021","unstructured":"Linsley PS, Barahmand-pour-Whitman F, Balmas E et al (2021) Autoreactive T cell receptors with shared germline-like \u03b1 chains in type 1 diabetes. JCI Insight 6(22):e151349. https:\/\/doi.org\/10.1172\/jci.insight.151349","journal-title":"JCI Insight"},{"issue":"49","key":"6298_CR40","doi-asserted-by":"publisher","first-page":"eadj6975","DOI":"10.1126\/sciadv.adj6975","volume":"9","author":"AM Mitchell","year":"2023","unstructured":"Mitchell AM, Baschal EE, McDaniel KA et al (2023) Tracking DNA-based antigen-specific T cell receptors during progression to type 1 diabetes. Sci Adv 9(49):eadj6975. https:\/\/doi.org\/10.1126\/sciadv.adj6975","journal-title":"Sci Adv"},{"key":"6298_CR41","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3389\/fimmu.2019.00365","volume":"10","author":"M Nakayama","year":"2019","unstructured":"Nakayama M, Michels AW (2019) Determining antigen specificity of human islet infiltrating T cells in type 1 diabetes. Front Immunol 10:365. https:\/\/doi.org\/10.3389\/fimmu.2019.00365","journal-title":"Front Immunol"},{"key":"6298_CR42","doi-asserted-by":"publisher","first-page":"1183909","DOI":"10.3389\/fimmu.2023.1183909","volume":"14","author":"S Arif","year":"2023","unstructured":"Arif S, Domingo-Vila C, Pollock E, Christakou E, Williams E, Tree TIM (2023) Monitoring islet specific immune responses in type 1 diabetes clinical immunotherapy trials. Front Immunol 14:1183909. https:\/\/doi.org\/10.3389\/fimmu.2023.1183909","journal-title":"Front Immunol"},{"issue":"1","key":"6298_CR43","doi-asserted-by":"publisher","first-page":"323","DOI":"10.4049\/jimmunol.1700172","volume":"199","author":"K Cerosaletti","year":"2017","unstructured":"Cerosaletti K, Barahmand-pour-Whitman F, Yang J et al (2017) Single-cell RNA sequencing reveals expanded clones of islet antigen-reactive CD4 + T cells in peripheral blood of subjects with type 1 diabetes. J Immunol 199(1):323\u2013335. https:\/\/doi.org\/10.4049\/jimmunol.1700172","journal-title":"J Immunol"},{"key":"6298_CR44","doi-asserted-by":"publisher","first-page":"675746","DOI":"10.3389\/fimmu.2021.675746","volume":"12","author":"S Arif","year":"2021","unstructured":"Arif S, Pujol-Autonell I, Kamra Y et al (2021) Mapping T cell responses to native and neo-islet antigen epitopes in at risk and type 1 diabetes subjects. Front Immunol 12:675746. https:\/\/doi.org\/10.3389\/fimmu.2021.675746","journal-title":"Front Immunol"},{"issue":"6","key":"6298_CR45","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.4049\/jimmunol.1403031","volume":"194","author":"A Eugster","year":"2015","unstructured":"Eugster A, Lindner A, Catani M et al (2015) High diversity in the TCR repertoire of GAD65 autoantigen-specific human CD4 + T cells. J Immunol 194(6):2531\u20132538. https:\/\/doi.org\/10.4049\/jimmunol.1403031","journal-title":"J Immunol"},{"issue":"10","key":"6298_CR46","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.3390\/cells11101623","volume":"11","author":"T Okamura","year":"2022","unstructured":"Okamura T, Hamaguchi M, Tominaga H et al (2022) Characterization of peripheral blood TCR in patients with type 1 diabetes mellitus by BD rhapsodyTM VDJ CDR3 assay. Cells 11(10):1623. https:\/\/doi.org\/10.3390\/cells11101623","journal-title":"Cells"},{"issue":"1","key":"6298_CR47","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1038\/s41467-017-01925-2","volume":"8","author":"I Gomez-Tourino","year":"2017","unstructured":"Gomez-Tourino I, Kamra Y, Baptista R, Lorenc A, Peakman M (2017) T cell receptor \u03b2-chains display abnormal shortening and repertoire sharing in type 1 diabetes. Nat Commun 8(1):1792. https:\/\/doi.org\/10.1038\/s41467-017-01925-2","journal-title":"Nat Commun"},{"issue":"8","key":"6298_CR48","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1038\/s41577-023-00835-3","volume":"23","author":"D Hudson","year":"2023","unstructured":"Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H (2023) Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 23(8):511\u2013521. https:\/\/doi.org\/10.1038\/s41577-023-00835-3","journal-title":"Nat Rev Immunol"},{"issue":"8","key":"6298_CR49","doi-asserted-by":"publisher","first-page":"e134525","DOI":"10.1172\/jci.insight.134525","volume":"5","author":"JK Panzer","year":"2020","unstructured":"Panzer JK, Hiller H, Cohrs CM et al (2020) Pancreas tissue slices from organ donors enable in situ analysis of type 1 diabetes pathogenesis. JCI Insight 5(8):e134525. https:\/\/doi.org\/10.1172\/jci.insight.134525","journal-title":"JCI Insight"},{"issue":"1","key":"6298_CR50","doi-asserted-by":"publisher","first-page":"4491","DOI":"10.1038\/s41467-024-49186-0","volume":"15","author":"G Dunlap","year":"2024","unstructured":"Dunlap G, Wagner A, Meednu N et al (2024) Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium. Nat Commun 15(1):4491. https:\/\/doi.org\/10.1038\/s41467-024-49186-0","journal-title":"Nat Commun"},{"issue":"7900","key":"6298_CR51","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/s41586-022-04432-7","volume":"603","author":"TV Lanz","year":"2022","unstructured":"Lanz TV, Brewer RC, Ho PP et al (2022) Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature 603(7900):321\u2013327. https:\/\/doi.org\/10.1038\/s41586-022-04432-7","journal-title":"Nature"},{"issue":"2","key":"6298_CR52","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/s00125-015-3792-4","volume":"59","author":"M Catani","year":"2016","unstructured":"Catani M, Walther D, Christie MR, McLaughlin KA, Bonifacio E, Eugster A (2016) Isolation of human monoclonal autoantibodies derived from pancreatic lymph node and peripheral blood B cells of islet autoantibody-positive patients. Diabetologia 59(2):294\u2013298. https:\/\/doi.org\/10.1007\/s00125-015-3792-4","journal-title":"Diabetologia"},{"issue":"5","key":"6298_CR53","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.2337\/db13-1798","volume":"64","author":"MJ Smith","year":"2015","unstructured":"Smith MJ, Packard TA, O\u2019Neill SK et al (2015) Loss of anergic B cells in prediabetic and new-onset type 1 diabetic patients. Diabetes 64(5):1703\u20131712. https:\/\/doi.org\/10.2337\/db13-1798","journal-title":"Diabetes"},{"issue":"8","key":"6298_CR54","doi-asserted-by":"publisher","first-page":"e20221604","DOI":"10.1084\/jem.20221604","volume":"220","author":"ZC Stensland","year":"2023","unstructured":"Stensland ZC, Magera CA, Broncucia H et al (2023) Identification of an anergic BND cell\u2013derived activated B cell population (BND2) in young-onset type 1 diabetes patients. J Exp Med 220(8):e20221604. https:\/\/doi.org\/10.1084\/jem.20221604","journal-title":"J Exp Med"},{"issue":"20","key":"6298_CR55","doi-asserted-by":"publisher","first-page":"e88242","DOI":"10.1172\/jci.insight.88242","volume":"1","author":"HR Seay","year":"2016","unstructured":"Seay HR, Yusko E, Rothweiler SJ et al (2016) Tissue distribution and clonal diversity of the T and B cell repertoire in type 1 diabetes. JCI Insight 1(20):e88242. https:\/\/doi.org\/10.1172\/jci.insight.88242","journal-title":"JCI Insight"},{"issue":"1","key":"6298_CR56","doi-asserted-by":"publisher","first-page":"e165299","DOI":"10.1172\/jci.insight.165299","volume":"8","author":"L Scharf","year":"2023","unstructured":"Scharf L, Axelsson H, Emmanouilidi A et al (2023) Longitudinal single-cell analysis of SARS-CoV-2\u2013reactive B cells uncovers persistence of early-formed, antigen-specific clones. JCI Insight 8(1):e165299. https:\/\/doi.org\/10.1172\/jci.insight.165299","journal-title":"JCI Insight"},{"issue":"7","key":"6298_CR57","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1016\/j.cell.2019.11.003","volume":"179","author":"I Setliff","year":"2019","unstructured":"Setliff I, Shiakolas AR, Pilewski KA et al (2019) High-throughput mapping of B cell receptor sequences to antigen specificity. Cell 179(7):1636-1646.e15. https:\/\/doi.org\/10.1016\/j.cell.2019.11.003","journal-title":"Cell"},{"key":"6298_CR58","doi-asserted-by":"publisher","first-page":"855772","DOI":"10.3389\/fimmu.2022.855772","volume":"13","author":"LM Walker","year":"2022","unstructured":"Walker LM, Shiakolas AR, Venkat R et al (2022) High-throughput B cell epitope determination by next-generation sequencing. Front Immunol 13:855772. https:\/\/doi.org\/10.3389\/fimmu.2022.855772","journal-title":"Front Immunol"},{"issue":"D1","key":"6298_CR59","doi-asserted-by":"publisher","first-page":"D1057","DOI":"10.1093\/nar\/gkz874","volume":"48","author":"DV Bagaev","year":"2020","unstructured":"Bagaev DV, Vroomans RMA, Samir J et al (2020) VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Res 48(D1):D1057\u2013D1062. https:\/\/doi.org\/10.1093\/nar\/gkz874","journal-title":"Nucleic Acids Res"},{"issue":"18","key":"6298_CR60","doi-asserted-by":"publisher","first-page":"2924","DOI":"10.1093\/bioinformatics\/btx286","volume":"33","author":"N Tickotsky","year":"2017","unstructured":"Tickotsky N, Sagiv T, Prilusky J, Shifrut E, Friedman N (2017) McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 33(18):2924\u20132929. https:\/\/doi.org\/10.1093\/bioinformatics\/btx286","journal-title":"Bioinformatics"},{"issue":"D1","key":"6298_CR61","doi-asserted-by":"publisher","first-page":"D339","DOI":"10.1093\/nar\/gky1006","volume":"47","author":"R Vita","year":"2019","unstructured":"Vita R, Mahajan S, Overton JA et al (2019) The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47(D1):D339\u2013D343. https:\/\/doi.org\/10.1093\/nar\/gky1006","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6298_CR62","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s43586-023-00284-1","volume":"4","author":"V Mhanna","year":"2024","unstructured":"Mhanna V, Bashour H, L\u00ea Qu\u00fd K et al (2024) Adaptive immune receptor repertoire analysis. Nat Rev Methods Primer 4(1):6. https:\/\/doi.org\/10.1038\/s43586-023-00284-1","journal-title":"Nat Rev Methods Primer"},{"issue":"1","key":"6298_CR63","doi-asserted-by":"publisher","first-page":"160018","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson MD, Dumontier M, Aalbersberg IJ et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3(1):160018. https:\/\/doi.org\/10.1038\/sdata.2016.18","journal-title":"Sci Data"},{"issue":"12","key":"6298_CR64","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1038\/ni.3873","volume":"18","author":"F Rubelt","year":"2017","unstructured":"Rubelt F, Busse CE, Ahmad S et al (2017) Adaptive immune receptor repertoire community recommendations for sharing immune-repertoire sequencing data. Nat Immunol 18(12):1274\u20131278. https:\/\/doi.org\/10.1038\/ni.3873","journal-title":"Nat Immunol"},{"key":"6298_CR65","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.3389\/fimmu.2018.02206","volume":"9","author":"JA Vander Heiden","year":"2018","unstructured":"Vander Heiden JA, Marquez S, Marthandan N et al (2018) AIRR community standardized representations for annotated immune repertoires. Front Immunol 9:2206. https:\/\/doi.org\/10.3389\/fimmu.2018.02206","journal-title":"Front Immunol"},{"key":"6298_CR66","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.3389\/fimmu.2017.01418","volume":"8","author":"F Breden","year":"2017","unstructured":"Breden F, Luning Prak ET, Peters B et al (2017) Reproducibility and reuse of adaptive immune receptor repertoire data. Front Immunol 8:1418. https:\/\/doi.org\/10.3389\/fimmu.2017.01418","journal-title":"Front Immunol"},{"issue":"Database","key":"6298_CR67","doi-asserted-by":"publisher","first-page":"D747","DOI":"10.1093\/nar\/gkl995","volume":"35","author":"H Parkinson","year":"2007","unstructured":"Parkinson H, Kapushesky M, Shojatalab M et al (2007) ArrayExpress--a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 35(Database):D747\u2013D750. https:\/\/doi.org\/10.1093\/nar\/gkl995","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"6298_CR68","doi-asserted-by":"publisher","first-page":"D933","DOI":"10.1093\/nar\/gkac958","volume":"51","author":"FJ Martin","year":"2023","unstructured":"Martin FJ, Amode MR, Aneja A et al (2023) Ensembl 2023. Nucleic Acids Res 51(D1):D933\u2013D941. https:\/\/doi.org\/10.1093\/nar\/gkac958","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6298_CR69","doi-asserted-by":"publisher","first-page":"D1005","DOI":"10.1093\/nar\/gkq1184","volume":"39","author":"T Barrett","year":"2011","unstructured":"Barrett T, Troup DB, Wilhite SE et al (2011) NCBI GEO: archive for functional genomics data sets\u201310 years on. Nucleic Acids Res 39(1):D1005\u2013D1010. https:\/\/doi.org\/10.1093\/nar\/gkq1184","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"6298_CR70","doi-asserted-by":"publisher","first-page":"e1011664","DOI":"10.1371\/journal.pcbi.1011664","volume":"19","author":"S Liu","year":"2023","unstructured":"Liu S, Bradley P, Sun W (2023) Neural network models for sequence-based TCR and HLA association prediction. PLOS Comput Biol 19(11):e1011664. https:\/\/doi.org\/10.1371\/journal.pcbi.1011664","journal-title":"PLOS Comput Biol"},{"issue":"1","key":"6298_CR71","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41587-021-00989-2","volume":"40","author":"SA Schattgen","year":"2022","unstructured":"Schattgen SA, Guion K, Crawford JC et al (2022) Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA). Nat Biotechnol 40(1):54\u201363. https:\/\/doi.org\/10.1038\/s41587-021-00989-2","journal-title":"Nat Biotechnol"},{"issue":"6594","key":"6298_CR72","doi-asserted-by":"publisher","first-page":"eabl5197","DOI":"10.1126\/science.abl5197","volume":"376","author":"C Dom\u00ednguez Conde","year":"2022","unstructured":"Dom\u00ednguez Conde C, Xu C, Jarvis LB et al (2022) Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376(6594):eabl5197. https:\/\/doi.org\/10.1126\/science.abl5197","journal-title":"Science"},{"issue":"7","key":"6298_CR73","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.2337\/db19-0058","volume":"68","author":"KH Kaestner","year":"2019","unstructured":"Kaestner KH, Powers AC, Naji A, HPAP Consortium, Atkinson MA (2019) NIH initiative to improve understanding of the pancreas, islet, and autoimmunity in type 1 diabetes: the Human Pancreas Analysis Program (HPAP). Diabetes 68(7):1394\u20131402. https:\/\/doi.org\/10.2337\/db19-0058","journal-title":"Diabetes"},{"key":"6298_CR74","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/978-1-0716-2115-8_23","volume":"2453","author":"BD Corrie","year":"2022","unstructured":"Corrie BD, Christley S, Busse CE et al (2022) Data sharing and reuse: a method by the AIRR community. Methods Mol Biol 2453:447\u2013476. https:\/\/doi.org\/10.1007\/978-1-0716-2115-8_23","journal-title":"Methods Mol Biol"},{"issue":"D1","key":"6298_CR75","doi-asserted-by":"publisher","first-page":"D92","DOI":"10.1093\/nar\/gkad1067","volume":"52","author":"D Yuan","year":"2024","unstructured":"Yuan D, Ahamed A, Burgin J et al (2024) The European Nucleotide Archive in 2023. Nucleic Acids Res 52(D1):D92\u2013D97. https:\/\/doi.org\/10.1093\/nar\/gkad1067","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"6298_CR76","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1038\/s41590-024-01821-0","volume":"25","author":"S-J Sammut","year":"2024","unstructured":"Sammut S-J, Galson JD, Minter R et al (2024) Predictability of B cell clonal persistence and immunosurveillance in breast cancer. Nat Immunol 25(5):916\u2013924. https:\/\/doi.org\/10.1038\/s41590-024-01821-0","journal-title":"Nat Immunol"},{"issue":"20","key":"6298_CR77","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1126\/sciimmunol.aao4013","volume":"3","author":"S Culina","year":"2018","unstructured":"Culina S, Lalanne AI, Afonso G et al (2018) Islet-reactive CD8 + T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors. Sci Immunol 3(20):13. https:\/\/doi.org\/10.1126\/sciimmunol.aao4013","journal-title":"Sci Immunol"},{"issue":"1","key":"6298_CR78","doi-asserted-by":"publisher","first-page":"44661","DOI":"10.1038\/srep44661","volume":"7","author":"YF Fuchs","year":"2017","unstructured":"Fuchs YF, Eugster A, Dietz S et al (2017) CD8+ T cells specific for the islet autoantigen IGRP are restricted in their T cell receptor chain usage. Sci Rep 7(1):44661. https:\/\/doi.org\/10.1038\/srep44661","journal-title":"Sci Rep"},{"key":"6298_CR79","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.777788","volume":"12","author":"M Nakayama","year":"2021","unstructured":"Nakayama M, Michels AW (2021) Using the T cell receptor as a biomarker in type 1 diabetes. Front Immunol 12:777788. https:\/\/doi.org\/10.3389\/fimmu.2021.777788","journal-title":"Front Immunol"},{"issue":"20","key":"6298_CR80","doi-asserted-by":"publisher","first-page":"3356","DOI":"10.1093\/bioinformatics\/btv359","volume":"31","author":"NT Gupta","year":"2015","unstructured":"Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH (2015) Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data: Table 1. Bioinformatics 31(20):3356\u20133358. https:\/\/doi.org\/10.1093\/bioinformatics\/btv359","journal-title":"Bioinformatics"},{"issue":"5","key":"6298_CR81","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/nbt.3192","volume":"33","author":"R Satija","year":"2015","unstructured":"Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495\u2013502. https:\/\/doi.org\/10.1038\/nbt.3192","journal-title":"Nat Biotechnol"},{"issue":"W1","key":"6298_CR82","doi-asserted-by":"publisher","first-page":"W83","DOI":"10.1093\/nar\/gkw199","volume":"44","author":"J Reimand","year":"2016","unstructured":"Reimand J, Arak T, Adler P et al (2016) g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 44(W1):W83-9. https:\/\/doi.org\/10.1093\/nar\/gkw199","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"6298_CR83","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1038\/s41596-018-0103-9","volume":"14","author":"J Reimand","year":"2019","unstructured":"Reimand J, Isserlin R, Voisin V et al (2019) Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc 14(2):482\u2013517. https:\/\/doi.org\/10.1038\/s41596-018-0103-9","journal-title":"Nat Protoc"},{"key":"6298_CR84","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.coisb.2020.10.001","volume":"24","author":"JK Scott","year":"2020","unstructured":"Scott JK, Breden F (2020) The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data. Curr Opin Syst Biol 24:71\u201377. https:\/\/doi.org\/10.1016\/j.coisb.2020.10.001","journal-title":"Curr Opin Syst Biol"},{"issue":"4","key":"6298_CR85","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/j.chom.2020.09.002","volume":"28","author":"SCA Nielsen","year":"2020","unstructured":"Nielsen SCA, Yang F, Jackson KJL et al (2020) Human B cell clonal expansion and convergent antibody responses to SARS-CoV-2. Cell Host Microbe 28(4):516-525.e5. https:\/\/doi.org\/10.1016\/j.chom.2020.09.002","journal-title":"Cell Host Microbe"},{"key":"6298_CR86","doi-asserted-by":"publisher","unstructured":"Goel RR, Painter MM, Apostolidis SA, et al (2021) mRNA vaccines induce durable immune memory to SARS-CoV-2 and variants of concern. Science 374(6572):abm0829. https:\/\/doi.org\/10.1126\/science.abm0829","DOI":"10.1126\/science.abm0829"},{"issue":"1","key":"6298_CR87","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.immuni.2020.12.011","volume":"54","author":"M Heming","year":"2021","unstructured":"Heming M, Li X, R\u00e4uber S et al (2021) Neurological manifestations of COVID-19 feature T cell exhaustion and dedifferentiated monocytes in cerebrospinal fluid. Immunity 54(1):164-175.e6. https:\/\/doi.org\/10.1016\/j.immuni.2020.12.011","journal-title":"Immunity"},{"issue":"11","key":"6298_CR88","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1038\/s42256-021-00413-z","volume":"3","author":"M Pavlovi\u0107","year":"2021","unstructured":"Pavlovi\u0107 M, Scheffer L, Motwani K et al (2021) The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell 3(11):936\u2013944. https:\/\/doi.org\/10.1038\/s42256-021-00413-z","journal-title":"Nat Mach Intell"},{"issue":"1","key":"6298_CR89","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1038\/s42003-023-04447-4","volume":"6","author":"JJ Park","year":"2023","unstructured":"Park JJ, Lee KAV, Lam SZ, Moon KS, Fang Z, Chen S (2023) Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity. Commun Biol 6(1):76. https:\/\/doi.org\/10.1038\/s42003-023-04447-4","journal-title":"Commun Biol"},{"key":"6298_CR90","unstructured":"Nazarov V, Tsvetkov V, Fiadziushchanka S et al (2023) Immunarch: Bioinformatics analysis of T-cell and B-cell immune repertoires. Available from https:\/\/immunarch.com\/ and https:\/\/github.com\/immunomind\/immunarch, accessed 3 Oct 2024"},{"issue":"17","key":"6298_CR91","doi-asserted-by":"publisher","first-page":"2974","DOI":"10.1093\/bioinformatics\/btz035","volume":"35","author":"Z Sethna","year":"2019","unstructured":"Sethna Z, Elhanati Y, Callan CG, Walczak AM, Mora T (2019) OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs. Bioinformatics 35(17):2974\u20132981. https:\/\/doi.org\/10.1093\/bioinformatics\/btz035","journal-title":"Bioinformatics"},{"issue":"10","key":"6298_CR92","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1038\/s41587-020-0505-4","volume":"38","author":"H Huang","year":"2020","unstructured":"Huang H, Wang C, Rubelt F, Scriba TJ, Davis MM (2020) Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat Biotechnol 38(10):1194\u20131202. https:\/\/doi.org\/10.1038\/s41587-020-0505-4","journal-title":"Nat Biotechnol"},{"issue":"2","key":"6298_CR93","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1093\/nar\/gkad1128","volume":"52","author":"M Wang","year":"2024","unstructured":"Wang M, Patsenker J, Li H, Kluger Y, Kleinstein SH (2024) Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity. Nucleic Acids Res 52(2):548\u2013557. https:\/\/doi.org\/10.1093\/nar\/gkad1128","journal-title":"Nucleic Acids Res"},{"key":"6298_CR94","doi-asserted-by":"publisher","DOI":"10.1101\/2022.04.26.489314","author":"ME Zaslavsky","year":"2024","unstructured":"Zaslavsky ME, Craig E, Michuda JK et al (2024) Disease diagnostics using machine learning of immune receptors. BioR\u03c7iv. https:\/\/doi.org\/10.1101\/2022.04.26.489314","journal-title":"BioR\u03c7iv"},{"issue":"9","key":"6298_CR95","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1010052","volume":"18","author":"E Waltari","year":"2022","unstructured":"Waltari E, Nafees S, McCutcheon KM, Wong J, Pak JE (2022) AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses. PLOS Comput Biol 18(9):e1010052. https:\/\/doi.org\/10.1371\/journal.pcbi.1010052","journal-title":"PLOS Comput Biol"}],"container-title":["Diabetologia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00125-024-06298-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00125-024-06298-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00125-024-06298-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T02:02:17Z","timestamp":1734746537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00125-024-06298-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":95,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6298"],"URL":"https:\/\/doi.org\/10.1007\/s00125-024-06298-y","relation":{},"ISSN":["0012-186X","1432-0428"],"issn-type":[{"value":"0012-186X","type":"print"},{"value":"1432-0428","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"26 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors would like to acknowledge the contributions of the(sugar)science, a type 1 diabetes research advocacy organisation, for initiating, organising and hosting the consortium activities. All screen grabs of the iReceptor Gateway are provided with the permission of the iReceptor project.Type 1 Diabetes AIRR Consortium members: Erin Baschal [Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO 80045, USA], Karen Cerosaletti [Center for Translational Immunology, Benaroya Research Institute at Virginia Mason, Seattle, WA], Lorissa Corrie [iReceptor Genomic Services, Summerland, BC, Canada], Iria Gomez-Tourino [Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela, Santiago de Compostela, Spain], Lauren Higdon [Diabetes Center, University of California San Francisco, San Francisco, CA, USA], Sally C. Kent [Diabetes Center of Excellence, Department of Medicine, University of Massachusetts Medical Chan School, Worcester, MA, USA], Peter Linsley [Benaroya Research Institute, Seattle, WA 98101, USA], Maki Nakayama [Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO 80045, USA], Kira Neller [iReceptor Genomic Services, Summerland, BC, Canada], William E. Ruff [Repertoire Immune Medicines, Cambridge, MA 02139, USA], Luc Teyton [Department of Immunology and Microbiology, Scripps Research, La Jolla, CA 92037, USA]","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Acknowledgements"}},{"value":"All data in Table\n                      \n                      are available from\n                      \n                      Data from Fig.\n                      \n                      are available at","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}},{"value":"SJH is funded by the Diabetes Research and Wellness Foundation Professor David Matthews Non-Clinical Research Fellowship, The Leona M. and Harry B. Helmsley Charitable Trust by grant #2101-04969, and this work was supported on behalf of the \u201cSteve Morgan Foundation Type 1 Diabetes Grand Challenge\u201d by\u00a0Breakthrough T1D UK, formerly JDRF and SMF (grant numbers 2-SRA-2024-1474-M-N and 2-SRA-2024-1473-M-N). This work was also funded by NIH grants R01 DK131070 (RHB), DK108868 (AWM), DK032083 (AWM), DK099317 (AWM), U24 AI177622 (FB, BC) and U01-DK112217 (ETLP). TMB is funded by NIH P01 AI042288 and AWM and TMB by The Leona M. and Harry B. Helmsley Charitable Trust by grant #2301-06562.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"TMB has consulted for Repertoire Immune Medicines and both TMB and AWM have received in-kind sequencing support by Adaptive Biotech. BC and FB are both researchers on the iReceptor team at Simon Fraser University and are partners in iReceptor Genomic Services. The other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Authors\u2019 relationships and activities"}},{"value":"SJH, RHB, ALP, ETLP, MW, FB, AWM and TMB were responsible for conceptualisation and design of the work. BC was responsible for data curation: MW provided project administration for the Type 1 Diabetes AIRR Consortium. MW, SJH, RHB and BC wrote the original draft. All authors reviewed and edited the original draft and approved the final manuscript for publication. TMB and AWM are guarantors of this work","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Contribution statement"}}]}}