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. 2025 Jan;68(1):186-202.
doi: 10.1007/s00125-024-06298-y. Epub 2024 Oct 29.

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

Collaborators, Affiliations

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

Stephanie J Hanna et al. Diabetologia. 2025 Jan.

Abstract

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.

Keywords: AIRR; AIRR Data Commons; Autoantibodies; B cell receptors; FAIR data; Next-generation sequencing; Single-cell RNA-seq; T cell receptors; Type 1 diabetes.

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Conflict of interest statement

Acknowledgements: 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] Data availability: All data in Table 2 are available from https://gateway.ireceptor.org/login Data from Fig. 7 are available at https://app.litmaps.com/shared/12ead8ba-35d9-4232-91a0-26da9e3c3f59 Funding: 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 “Steve Morgan Foundation Type 1 Diabetes Grand Challenge” by Breakthrough 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. Authors’ relationships and activities: 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. Contribution statement: 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

Figures

Fig. 1
Fig. 1
Overview of how the AIRR type 1 diabetes repository sits within the AIRR Data Commons and is accessed through the iReceptor Gateway. T1D, type 1 diabetes
Fig. 2
Fig. 2
iReceptor Gateway overview page of the data in the ADC. Summary statistics of the annotated sequence, clone and single-cell data available in the ADC are provided. Users can select a ‘Repertoire Metadata Search’ or a ‘Sequence Quick Search’ workflow. The metadata search workflow can be done from the perspective of annotated sequences, clones or single-cell data. This is the user interface for Step 1 in the iReceptor Gateway workflow from Fig. 1. Reproduced with the permission of the iReceptor project
Fig. 3
Fig. 3
iReceptor Gateway ‘Repertoire Metadata Search’ page for annotated sequence data. Repertoires are filtered so that the data are limited to TCRβ sequences from individuals with type 1 diabetes. The data are further stratified to select only data from individuals between 0 and 5 years of age. Over 10 million annotated sequences from 61 repertoires and two studies in the ADC meet this search criteria. All of these type 1 diabetes data are retrieved from the AIRR T1D TCR/BCR Repository. This is the user interface for Step 2 in the iReceptor Gateway workflow from Fig. 1. Reproduced with the permission of the iReceptor project. TRB, alternative name for TCRβ
Fig. 4
Fig. 4
iReceptor Gateway ‘Sequence Search’ page. A search for a CDR3 amino acid sequence of interest (CASSLQSSYNSPLHF) is performed on the TCRβ data from an individual with type 1 diabetes between 0 and 5 years of age in the ADC. The iReceptor Gateway reports that it found 36 such sequences across 26 repertoires and 20 subjects, suggesting that this is a ‘public’ CDR3. It also reports that this CDR3 has a known antigen/epitope specificity and provides a link to external resources (e.g. IEDB) to find out further information about the antigen/epitope. At this stage, the user can either download the data for offline analysis or perform internally supported analyses of these data. This is the user interface for Step 3 in the iReceptor Gateway workflow from Fig. 1. Reproduced with the permission of the iReceptor project. TRB, alternative name for TCRβ
Fig. 5
Fig. 5
iReceptor internal statistical analysis of the selected data. The left frame is a view of a subregion of the ‘Sequence Search’ page, showing the set of analysis tools that are available for the selected data. The top right frame is the output of the ‘Statistics’ analysis tool. The user can either download the analysis results or view a summary of the analysis output. The search parameters for the repertoire metadata as well as the sequence features that were used to select the analysed data are displayed. The bottom right frame is a view of a subset of the analysis results, in this case a V/J gene heatmap for the CDR3 of interest. The results of this analysis provide the frequency for all TCRβ receptors (V/J gene combinations) for the CDR3 sequence CASSLQSSYNSPLHF from individuals with type 1 diabetes between 0 and 5 years of age in the ADC. This is the user interface for Step 4 in the iReceptor Gateway workflow from Fig. 1. Reproduced with the permission of the iReceptor project. TRB, alternative name for TCRβ
Fig. 6
Fig. 6
An overview of the six MiARR [64] standard classes of data and associated data fields. More details on the standard are available at the AIRR Community website: https://docs.airr-community.org/en/stable/standards/overview.html. Copyright AIRR Community, reproduced under a CC BY 4.0 licence. QC, quality control
Fig. 7
Fig. 7
Citations as a metric of iReceptor manuscript influence, by disease. Citations of the iReceptor paper (Corrie et al 2018 [3], top left corner), coloured by high level disease classification. Grey citations are not disease specific and either represent standards (e.g. AIRR Standards), tools (e.g. ML), or techniques (e.g. repertoire classification) papers. Grey connecting lines indicate manuscripts that were cited in subsequent publications. Interactive visualisation of citation data is available at https://app.litmaps.com/shared/12ead8ba-35d9-4232-91a0-26da9e3c3f59; colour coding in the interactive visualisation is different from the figure above. T1D, type 1 diabetes

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