dkNET-HIRN Webinar "T Cell Antigen Discovery: Experimental and Computational Approaches" 04/27/2022
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dkNET New Investigator Pilot Program in Bioinformatics Awardee Seminar Series Co-Hosted with Human Islet Research Network (HIRN) Presenter: Alok V. Joglekar, Ph.D. Assistant Professor, Center for Systems Immunology and Department of Immunology, University of Pittsburgh School of Medicine Abstract T cells are key players in many autoimmune diseases including Type 1 Diabetes. T cell responses are highly antigen specific by virtue of their T cell receptors (TCRs), that recognize epitopes on target cells. The enormous diversity of TCRs in an immune response poses a challenge in studying them, particularly regarding their antigenic specificity. Several experimental approaches have been developed to identify T cell specificities, with a recent surge in cell-based assays. More recently, computational approaches to predict T cell specificity are being developed and show great promise. This webinar will provide an overview of the experimental and computational approaches to identify T cell antigens. Furthermore, we will highlight the research performed in the Joglekar lab towards applying these approaches for auto-antigen discovery in Type 1 Diabetes. Finally, we will project what the future of these approaches may be, particularly for studying autoimmune diseases.
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dkNET-HIRN Webinar "T Cell Antigen Discovery: Experimental and Computational Approaches" 04/27/2022
- 1. T Cell Antigen Discovery: Experimental and Computational Approaches Alok V. Joglekar, Ph.D. Assistant Professor, Department of Immunology | Center for Systems Immunology University of Pittsburgh School of Medicine HIRN/dkNET Webinar 04.27.2022 https://www.joglekarimmunolab.org/
- 2. Outline • T cell antigen discovery: Scope of the challenge • Experimental approaches to identify T cell epitopes: • Antigen-directed • TCR-directed • Which method is right for you? • Computational approaches to predict T cell specificities • Integrating specificity data with multi-omics • Systems analyses of T cells in Type 1 Diabetes – research updates from the Joglekar lab
- 3. Bacteria, Viruses, Parasites Physical barriers e.g. Skin and mucosa Large scale non-specific responses e.g. Complement, Innate immunity Precise responses e.g. Adaptive immunity Immune System: Protector of the Realm Key mediators of immunity Defining Characteristics of adaptive immunity Inducible effector functions – rapid scale up ‘on demand’ Immunologic memory + recall response Multiple layers of regulation Antigen specificity: distinguish self vs non-self Enzo life sciences; Benioff, Weiss, Martin et al
- 4. Antigen Specificity is a Key Characteristic of T Cells T cells recognize peptide epitopes presented on MHC molecules using their surface TCR MHC complexes sample intra- and extra-cellular proteomes to present to T cells T Cell Repertoire ‘sees’ an enormous and highly diverse universe of antigens Joglekar and Li 2020; Kobayashi 2012
- 5. Antigen Specificity is a Key Characteristic of T Cells TCR ‘clonotype’ is defined by Vα/Jα/Vβ/Jβ/CDR3α/CDR3β V(D)J Recombination Generates enormous TCR diversity from a small number of gene fragments CDR1/2s bind MHC and CDR3s bind epitope and MHC >1020 Possible TCRs ~ 106-109 unique TCR clones 1011-1012 T cells Joglekar and Li 2020; Hennecke 2001; Laydon 2015 T Cell Repertoire itself is enormous and highly diverse
- 6. Facets of T Cell Function How to integrate T cell function at a single cell and repertoire level? Antigen Discovery scRNAseq Bulk Vβ seq Phosphoproteomics ChIP, ATAC-seq, CUT&RUN sc/Bulk RNAseq Flow cytometry, CyTOF, ELISA, Luminex, MSD
- 7. TCR Sequencing: Bulk and Single Cell Methods Rosati 2017 How do we get from TCR sequence antigen specificity? Bulk: Adaptive biotechnologies immunoSEQ Takara SMARTeR-RACE Single cell: 10x Genomics 5’ with VDJ enrichment Takara SMARTeR-RACE
- 8. T Cell Antigen Discovery TCR Directed Antigen Directed Can we identify T cell recognizing antigens of interest? Start with a limited set of putative antigens Measure pMHC binding/functional output Can we identify antigens recognized by T cells de novo? Start with TCR sequences/T cell clones Screen libraries of epitopes Computational Can we predict antigens-specificity of T cells? Start with TCR sequences +/- putative antigen data Computational prediction of epitope presentation and recognition
- 9. T Cell Antigen Discovery Antigen Directed Can we identify T cell recognizing antigens of interest? Start with a limited set of putative antigens Measure pMHC binding/functional output
- 10. Antigen Directed Methods – Functional Readouts Advantages Highly sensitive Ease of implementation HLA typing not required Rapid Quantitative / semi-quantitative Disadvantages Small # of peptides/proteins at once No other information than reactivity Kawakami 1994; van der Bruggen 1991; McCutcheon 1997; Joglekar and Li 2020
- 11. Antigen Directed Methods – pMHC Multimers Advantages Cell isolation Multiplexing with immune phenotyping Direct quantification of cells Disadvantages Small # of peptides (up to 100s) Expensive High affinity HLA binders only Class II tetramers are often unstable Altman 1996; Klenerman 2002; Wooldrige 2009; Hadrup 2009; Newell 2009, 2012, 2013; Ma 2021; Peng 2021; Bentzen 2016; Zhang 2016
- 12. T Cell Antigen Discovery TCR Directed Can we identify antigens recognized by T cells de novo? Start with TCR sequences/T cell clones Screen libraries of epitopes
- 13. TCR Directed – Truly Degenerate/Combinatorial Advantages Truly degenerate starting libraries Very high scale (>108) of epitopes No a priori information needed Can start with TCR sequences Disadvantages Labor and cost intensive Yields mimotopes Soluble TCR production is non-robust Non-physiological pMHC presentation Less robust for class II Gavin 1994; Pinilla 2001; Wilson 1999; Hiemstra 1997; Ernst 1998; Wen 2008, 2011, 2013; Birnbaum 2012; Brophy 2013; Boder 1997;
- 14. TCR Directed – Cell-based Advantages Physiological pMHC presentation Flexible scale (102-106) of epitopes Actual epitopes identified Customizable design of libraries Class I and Class II Can start with TCR sequences Disadvantages Labor and cost intensive Some a priori information needed Affinity may impact sensitivity Joglekar 2019; Li 2019; Kisielow 2019; Kula 2019; Sharma 2019; Dobson 2022
- 15. T Cell Antigen Discovery Computational Can we predict antigens-specificity of T cells? Start with TCR sequences +/- putative antigen data Computational prediction of epitope presentation and recognition
- 16. Computational Approaches – Predicting Specificity TCRs sharing Ag-specificity often have similar CDR3 sequences TCRs with similar CDR3 sequences may share Ag-specificity Can NOT predict Ag-specificity de novo (i.e. in absence of known Ag-specific TCRs in the dataset) Glanville 2017; Dash 2017; Zhang 2020, 2021; Valkiers 2021
- 17. Computational Approaches – Predicting Repertoire Bias TCR repertoire features can distinguish disease states Can distinguish disease states even without the knowledge of Ag-specificity Xkcd.com; Zhang 2021; Davidsen 2019; Sidhom 2021; Dvorkin 2021
- 18. T Cell Antigen Discovery TCR Directed Antigen Directed Can we identify T cell recognizing antigens of interest? Functional readouts pMHC multimers Can we identify antigens recognized by T cells de novo? Combinatorial/degenerate Cell-based Computational Can we predict antigens-specificity of T cells? Predict specificity TCR feature based classfiers
- 19. Which Antigen Discovery Method is Right for You? Absolutely no ab initio idea e.g. auto/allo-reactive TCRs Some idea e.g. TCRs with known tissue targets Narrow range of potential Ag e.g. pathogen-specific TCRs What is known about potential Ag? Source of TCRs TCR sequences and/or T cells Tissues/PBMCs #TCRs or T cells under investigation Very small <10 e.g. clonally expanded TCRs known to be of importance Small Large 10s 100,000s e.g. polyclonal T cell populations Small <100 e.g. clonally expanded TCRs Joglekar and Li 2020
- 20. Integrating Antigen-Specificity with Transcriptome Data Schattgen 2021; Ma 2021
- 21. Summary – Part 1 • T cell antigen discovery: Scope of the challenge • Fast growing universe of TCR sequence data • Need to incorporate Ag-specificity with multi-omic measurements • Experimental approaches to identify T cell epitopes (VERY ROUGH guide) • Antigen-directed • Narrow range of potential Ags + Large numbers of T cells under study • TCR-directed • Wide range of potential Ags + Small numbers of T cells under study • Computational approaches to predict T cell specificities • Nascent, but fast growing with the eye on the prize • Integrating specificity data with multi-omics • Nascent, but with growing capabilities + augment experimental approaches
- 22. T Cell Repertoires and Antigen Discovery for Type 1 Diabetes NIDDK New Investigator Gateway Award 1R03DK127447 dkNET New investigator Pilot Program in Bioinformatics
- 23. Type 1 Diabetes Pugliese, 2017; Bettini, 2011; Katsarou 2017; Ilonen 2019 Islet infiltration by T cells is central to pathogenesis of T1D NOD/ShiLtJ NOD mice are a robust model of T1D Glucagon Insulin CD45 CD3 CD4 CD8
- 24. Autoantigens in Type 1 Diabetes Shared autoantigens between NOD mice and humans The epitope specificities of a significant fraction of islet-infiltrating T cells are not defined Pugliese, 2017; Amdare 2021; Katsarou 2017; Ilonen 2019
- 25. TCR Directed Antigen Discovery in NOD Mice Antigen discovery in mice Identifying novel autoantigens Testing for reactivity in humans
- 26. Summary – Part 2 • Cell based epitope discovery method: Signaling and Antigen-presenting Bifunctional Receptors • Construction and validation of SABR screens for islet-specific CD4+ T cells • T cell repertoire analysis in islets of NOD mice • De novo identification of epitopes recognized by several islet-infiltrating CD4+ TCRs • Correlation of Ag-specificity with transcriptomes • In progress • Continuing discovery and validation of CD4+ T cell epitopes in NOD mice • Ag discovery of islet infiltrating CD8+ T cells in NOD mice • SABR libraries for Ag discovery of human CD4+ and CD8+ T cells • Computational framework to identify gene signatures associated with Ag-specific T cells
- 27. Core services Novogene Fulgent Genetics Flow core DLAR Single cell core Joglekar lab Paul Zdinak Rashi Ranjan Louise Hicks Salome Martinez Luba Kublo Jessica Torrey Eduardo Zarate-Martinez Acknowledgements Collaborators and Advisors Dario Vignali Stephanie Grebinoski Tony Cillo Mark Anderson Jennifer Bridge HIRN-CMAI HIRN-NewInv dkNET-NewInv Jishnu Das Hanxi Xiao Mark Shlomchik Jeremy Tilstra Alex Rowe Harinder Singh Anita Bansal Paul Thomas Shin-heng Chiou $$$$ NIDDK New Investigator Gateway Award 1R03DK127447 dkNET New investigator Pilot Program in Bioinformatics PACER Innovative Discovery Award JDRF Innovative Immunotherapies Award UPSOM start up funds UPMC-ITTC Mitsubishi-Tanabe Pharma WE ARE HIRING!! joglekar@pitt.edu Lab Alumni Kelsey Ford Kobie Rankin Sanjay Rathod