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dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons 01/27/2023-Final.pdf

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dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons 01/27/2023-Final.pdf

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Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.

The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.

The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.



The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:

1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?

2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?

3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?

Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor

Upcoming webinars schedule: https://dknet.org/about/webinar

Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.

The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.

The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.



The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:

1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?

2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?

3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?

Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor

Upcoming webinars schedule: https://dknet.org/about/webinar

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dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons 01/27/2023-Final.pdf

  1. 1. FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons Felix Breden, Brian Corrie, Kira Neller iReceptor dkNET Webinar January 27, 2023
  2. 2. Presentation Overview • Introduction to the AIRR (Adaptive Immune Receptor Repertoire) Community • AIRR Data Commons and iReceptor • iReceptor v4.0 – an Overview • Navigating the iReceptor Platform – T1D Use-Cases • Navigating iReceptor v4.0 Features – Clones, Cells, Analyses
  3. 3. Presentation Overview • Introduction to the AIRR (Adaptive Immune Receptor Repertoire) Community • AIRR Data Commons and iReceptor • iReceptor v4.0 – an Overview • Navigating the iReceptor Platform – T1D Use-Cases • Navigating iReceptor v4.0 Features – Clones, Cells, Analyses
  4. 4. Introduction to the AIRR Community: The Adaptive Immune System • Focus on antibody/B-cell receptors and T-cell receptors – AIRR-seq data (Adaptive Immune Receptor Repertoire) • Critical to development of vaccines, drugs suppressing autoimmune diseases, new cancer immunotherapies, etc. • Adaptive immune system evolves within the body in response to pathogens (bacteria, viruses, etc.) • Incredibly variable to recognize and remove bacteria and viruses (including new ones, e.g. novel coronavirus) • AIRR-seq repertoires are highly diverse: ~1013 potential human B-cell receptors • Systemic sclerosis (de Bourcy et al.) - 700M B-Cell receptors
  5. 5. • Clones are sets of B cells or T cells descended from ancestral cell produced by V(D)J recombination • Immunoglobulin and T-cell receptor genes are only genes in eukaryote genome that undergo this somatic recombination AIRR-seq data are difficult to share and compare: Somatic recombination demands unique database model & analysis tools
  6. 6. Yaari & Kleinstein 2015 Genome Medicine 7:121-135 Cell/sample prep Library prep AIRR-seq data are difficult to share and compare: Many ways for experiments to differ
  7. 7. B-cell Clonal Lineage Expansion in Health and Disease • Chronic Lymphocytic Leukemia (CLL) is characterized by the expansion of a few dominant clones in B-cell repertoire (Bashford-Rogers et al. 2019) • FDA approved Adaptive Biotechnologies clonoSEQ® test for Minimal Residual Disease (MRD) based on searching for these CLL- associated, expanded clones
  8. 8. Adaptive Immune Receptor Repertoire (AIRR) Community • The AIRR Community (2015) is a grass-roots group of immunologists, bioinformaticists, computer scientists, experts in legal, ethical and IP issues, who are developing guidelines and standards for the generation, annotation and storage of high-throughput AIRR-seq data to facilitate its use by the larger research community. • Ability to share AIRR-seq data greatly increases the value of any one data set: • Each researcher may have small N, large amount of data per sample • Increase sample sizes, statistical power • AI approaches demand huge sample sizes and number of data points • Facilitate comparisons between affected/controls/multiple disease states
  9. 9. AIRR Community Working Groups 1. Biological Resources – Biological calibrators and reagents for evaluation of AIRR-seq data 2. Common Repository – Data Commons for AIRR-seq data, following FAIR principles 3. Diagnostics – Facilitate development of diagnostics and markers for disease 4. Germline Database – Germline gene inference from AIRR-seq data 5. Legal and Ethics – Standards for human subjects 6. Software – Interoperability of analysis software 7. Standards – For publishing or depositing AIRR-seq data (MiAIRR)
  10. 10. AIRR Community Working Groups Develop Standards Minimal StandardsWG Data RepresentationWG Common RepositoryWG MiAIRR: Minimal metadata standard for depositing AIRR-seq data. Nature Immunology (2017) DataRep Standard: File format and specification for sharing AIRR-seq rearrangement data. Frontiers in Immunology (2018) ADC API:AIRR repository web API for data exploration. Frontiers in Big Data (2020) Standards (Publications) are ratified by full AIRR Community Work with us: www.airr-community.org
  11. 11. Presentation Overview • Introduction to the AIRR (Adaptive Immune Receptor Repertoire) Community • AIRR Data Commons and iReceptor • iReceptor v4.0 – an Overview • Navigating the iReceptor Platform – T1D Use-Cases • Navigating iReceptor v4.0 Features – Clones, Cells, Analyses
  12. 12. AIRR Data Commons • Philosophy: Distributed set of AIRR compliant repositories – AIRR Data Commons • AIRR Standards: Search across study (time points), subject (age) , sample (tissue, disease state) • Allows for scalable repositories (billions of sequences) 10s - 100s of repositories • Data curated at home institution under local data policy • Researcher needs: AIRR-seq data that is FAIR • Find data, federate data (Accessible and Interoperable) • Reuse data to derive new insights
  13. 13. The iReceptor Approach iReceptor Scientific Gateway Interactive web-based data discovery, exploration, and analytics Data Federation Data Query Hide complexity from the user: finding, federating, and analyzing data AIRR Data Commons Distributed AIRR-seq data repositories Based on standards developed by the international AIRR community
  14. 14. - 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Sequence Annotations (Millions) Year-Month AIRR Data Commons Growth T1D (Canada) Roche (Canada) Muenster (Germany) NICD (South Africa) sciReptor (Germany) Sorbonne (France) VDJBase (Israel) VDJServer (US) iReceptor COVID-19 (Canada) iReceptor Public Archive (Canada) COVID-19 5 new international repositories Small step increases Growth in the AIRR Data Commons (ADC) Large step increases NewT1D repository (Jan 2023)
  15. 15. International user base 24% new users from industry June 2020: COVID-19 data available iReceptor – Usage trends
  16. 16. COVID-19: Disease specific data sharing driving research COVID-19 data sharing (this is not normal): • Researchers reaching out to publish data • Researchers collaborating with us to publish data Schultheiß et al • Pre-published in ADC before pre-print • iReceptor cited as source for annotated data! • Incredibly rich data set • 46 subjects, IG+TR data, 15M annotations • Time series data – out to 55 days, up to 9 time points 2020-06 2020-12 2021-06 2021-12 Goel et al. Turner et al. Goel at al. Schmitz et al. Nielsen et al. Data Nielsen et al. Preprint Galson et al., Minvervina et al Schultheiß et al., Liao et al Shomuradova et al. Alsoussie et al. Kim et al. Kuri-Cervantes et al. Wen et al. Montague et al. Nolan et al Mor et al. Sokal et al. COVID-19 curation continues
  17. 17. Impact: iReceptor citations of data reuse – do the same for T1D?
  18. 18. Presentation Overview • Introduction to the AIRR (Adaptive Immune Receptor Repertoire) Community • AIRR Data Commons and iReceptor • iReceptor v4.0 – an Overview • Navigating the iReceptor Platform – T1D Use-Cases • Navigating iReceptor v4.0 Features – Clones, Cells, Analyses
  19. 19. iReceptor V4.0: Curate both AIRR-seq and Cell/GEX data in the ADC! Combining AIRR-seq and Single Cell Immune Profiling • Bulk AIRR-seq provides deep sampling across many cells (106 – 107) • Single cell provides paired chains, better clone resolution, gene expression (GEX) across a smaller number of cells (103) • Complementary - gain a better understanding of immune cell phenotype and immune system state Wen et al., DOI: 10.1038/s41421-020-0168-9 Early recovery COVID-19, ERS1-GEX: 5,107 cells; 9 clusters ADC: https://gateway.ireceptor.org/samples?query_id=51948 AIRR-seq Top clone (dark blue) 167/1392 (12%) What is the gene expression signature?
  20. 20. • AIRR Community developed Single Cell/GEX extension (collaboration with 10x Genomics) • Load Cell/GEX data into ADC repositories (matrix/features/barcodes files) • Query studies based on both VDJ and Cell/GEX data • AIRR standard released August 2022 • iReceptor v4.0 • iReceptor Turnkey repositories can store and query Cell & GEX data • iReceptor Gateway extended to support Single Cell immunology workflows • In production Dec 2022 – curated Single Cell data & Single Cell user workflows Single Cell Immune Profiling in the AIRR Data Commons
  21. 21. iReceptor v3.0… A platform for finding AIRR-seq data in the AIRR Data Commons AIRR v1.4: Clones, Cells/GEX iReceptor v4.0 – an integrative approach (data mashup) iReceptor Scientific Gateway Interactive web-based data discovery, exploration, and analytics Data Federation Data Query AIRR Data Commons Distributed AIRR-seq data repositories Antigen specificity: IEDB Cells: Human Cell Atlas Ontologies: EBI OLS Clones, Cells/GEX Analysis Workflows Job Management Analysis Results Complex analysis tools Analyze rearrangements, clones, cells across entire ADC
  22. 22. Presentation Overview • Introduction to the AIRR (Adaptive Immune Receptor Repertoire) Community • AIRR Data Commons and iReceptor • iReceptor v4.0 – an Overview • Navigating the iReceptor Platform – T1D Use-Cases • Navigating iReceptor v4.0 Features – Clones, Cells, Analyses
  23. 23. Searches the AIRR Data Commons 8 repositories, 86 studies, 9346 repertoires, >5 billion sequence annotations Two workflows Searching study metadata, searching sequence annotations The iReceptor Gateway – How Does it Work?
  24. 24. Finding Data – Repertoire Metadata Search
  25. 25. Search over 80 Study/Subject/Sample AIRR Standard metadata fields! Finding Data – Repertoire Metadata Search New!
  26. 26. Finding Data – Healthy Controls
  27. 27. Finding Data – Interactive Repertoire Statistics
  28. 28. Finding Data – Interactive Repertoire Statistics
  29. 29. Finding Data – Healthy Controls
  30. 30. Downloading Sequences
  31. 31. Sequence “Quick Search” – T1D Use Case #1
  32. 32. Sequence Quick Search – T1D Use Case #1: Islet-specific TCRs are present in non-T1D individuals??
  33. 33. Islet-specific TCRs ARE present in non-T1D individuals – Agreement with previous findings
  34. 34. Antigen Specificity (IEDB Integration) – T1D Use Case #2: Identifying insulin-binding TCRs
  35. 35. IEDB – 206 insulin-binding TCRs are associated with T1D
  36. 36. CDR3 search in gateway simultaneously queries IEDB for known binding interactions! iReceptor Gateway Sequence Search Links Out to IEDB – Discovery of novel antigen/epitope specificity Associated with previous infection?
  37. 37. Reproducing the Findings in IEDB
  38. 38. iReceptor v4.0 – New Clone, Cell, and Analysis Capabilities
  39. 39. iReceptor v4.0: New Workflows for Finding Clone and Cell Data
  40. 40. iReceptor v4.0: Browsing Clones
  41. 41. iReceptor v4.0: Browsing Cells
  42. 42. iReceptor v3.0: Select data of interest and download! Run the analysis on the data directly User and data never leave the platform! iReceptor v4.0: Choose the Analysis to run on the data! iReceptor v4.0: Analysis Applications
  43. 43. Choose an Analysis App CellTypist Gateway does all the work - Downloads data from ADC - Stages data to computation - Stages app to computation - Runs job - Tabulates results - Presents results to the user Submit a Job iReceptor v4.0: Analysis Applications
  44. 44. Cell Classification per Repertoire (CellTypist) iReceptor v4.0: Analysis Applications
  45. 45. iReceptor v4.0: Apps for Sequences/Clones (Example Output)
  46. 46. iReceptor v4.0: Apps for Cells (Example Output)
  47. 47. Wrapping up…
  48. 48. Opportunities for T1D & AIRR Data Commons • Achieve “Network Effect” • More repositories/more data to the AIRR Data Commons • AIRR Data Commons “go-to” place for combined AIRR-seq and Single Cell Immune Profile study data • Extend COVID-19 sharing culture to T1D • All disease areas would benefit from the same spirit of sharing – can we do it for T1D • We are working with T1D colleagues to curate a critical mass of T1D AIRR-seq studies • Systems Immunology – AIRR-seq data doesn’t stand alone! • Bringing multi-omics data together (AIRR-seq, Epitope, GEX, …) To contribute to or explore the AIRR Data Commons: support@ireceptor.org
  49. 49. Acknowledgements • Colleagues in the AIRR Community • Collaborators: Partners in CIHR/EU Horizon 2020 iReceptor Plus project • Funders • CANARIE • Canada Foundation for Innovation • CIHR • BC Knowledge Development Fund • EU Horizon 2020 Research and Innovation Programme • Simon Fraser University

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