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Teimouri H, Ghoreyshi ZS, Kolomeisky AB, George JT. Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.11.617901. [PMID: 39416168 PMCID: PMC11482946 DOI: 10.1101/2024.10.11.617901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.
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Affiliation(s)
- Hamid Teimouri
- Department of Chemistry, Rice University, Houston, TX, 77005, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, 77005, USA
| | - Zahra S Ghoreyshi
- Center for Theoretical Biological Physics, Rice University, Houston, TX, 77005, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Anatoly B Kolomeisky
- Department of Chemistry, Rice University, Houston, TX, 77005, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, 77005, USA
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, USA
| | - Jason T George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, 77005, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA
- Department of Hematopoietic Biology and Malignancy, MD Anderson Cancer Center, Houston, TX, 77030, USA
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2
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Ehrlich R, Glynn E, Singh M, Ghersi D. Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity. Annu Rev Biomed Data Sci 2024; 7:295-316. [PMID: 38748864 DOI: 10.1146/annurev-biodatasci-102423-122741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.
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Affiliation(s)
- Ryan Ehrlich
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
| | - Eric Glynn
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA;
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
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3
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Rathgeber AC, Ludwig LS, Penter L. Single-cell genomics-based immune and disease monitoring in blood malignancies. Clin Hematol Int 2024; 6:62-84. [PMID: 38884110 PMCID: PMC11180218 DOI: 10.46989/001c.117961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/25/2023] [Indexed: 06/18/2024] Open
Abstract
Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.
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Affiliation(s)
- Anja C. Rathgeber
- Berlin Institute for Medical Systems BiologyMax Delbrück Center for Molecular Medicine
- Department of Hematology, Oncology, and TumorimmunologyCharité - Universitätsmedizin Berlin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Leif S. Ludwig
- Berlin Institute for Medical Systems BiologyMax Delbrück Center for Molecular Medicine
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Livius Penter
- Department of Hematology, Oncology, and TumorimmunologyCharité - Universitätsmedizin Berlin
- BIH Biomedical Innovation AcademyBerlin Institute of Health at Charité - Universitätsmedizin Berlin
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4
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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5
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Leary AY, Scott D, Gupta NT, Waite JC, Skokos D, Atwal GS, Hawkins PG. Designing meaningful continuous representations of T cell receptor sequences with deep generative models. Nat Commun 2024; 15:4271. [PMID: 38769289 PMCID: PMC11106309 DOI: 10.1038/s41467-024-48198-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.
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Affiliation(s)
- Allen Y Leary
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
| | - Darius Scott
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Janelle C Waite
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Dimitris Skokos
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Gurinder S Atwal
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Peter G Hawkins
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
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6
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Wang A, Lin X, Chau KN, Onuchic JN, Levine H, George JT. RACER-m leverages structural features for sparse T cell specificity prediction. SCIENCE ADVANCES 2024; 10:eadl0161. [PMID: 38748791 PMCID: PMC11095454 DOI: 10.1126/sciadv.adl0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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Affiliation(s)
- Ailun Wang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Xingcheng Lin
- Department of Physics, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kevin Ng Chau
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - José N. Onuchic
- Departments of Physics and Astronomy, Chemistry, and Biosciences, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Jason T. George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, Houston, TX, USA
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7
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Croce G, Bobisse S, Moreno DL, Schmidt J, Guillame P, Harari A, Gfeller D. Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Nat Commun 2024; 15:3211. [PMID: 38615042 PMCID: PMC11016097 DOI: 10.1038/s41467-024-47461-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.
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Affiliation(s)
- Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Dana Léa Moreno
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Philippe Guillame
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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8
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Jensen MF, Nielsen M. Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration. eLife 2024; 12:RP93934. [PMID: 38437160 PMCID: PMC10942633 DOI: 10.7554/elife.93934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024] Open
Abstract
Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.
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Affiliation(s)
- Mathias Fynbo Jensen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
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9
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Hudson D, Lubbock A, Basham M, Koohy H. A comparison of clustering models for inference of T cell receptor antigen specificity. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:None. [PMID: 38525047 PMCID: PMC10955519 DOI: 10.1016/j.immuno.2024.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/26/2024]
Abstract
The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | | | | | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Alan Turning Fellow in Health and Medicine, UK
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10
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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11
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Han Y, Yang Y, Tian Y, Fattah FJ, von Itzstein MS, Hu Y, Zhang M, Kang X, Yang DM, Liu J, Xue Y, Liang C, Raman I, Zhu C, Xiao O, Dowell JE, Homsi J, Rashdan S, Yang S, Gwin ME, Hsiehchen D, Gloria-McCutchen Y, Pan K, Wu F, Gibbons D, Wang X, Yee C, Huang J, Reuben A, Cheng C, Zhang J, Gerber DE, Wang T. pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569599. [PMID: 38105939 PMCID: PMC10723300 DOI: 10.1101/2023.12.01.569599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.
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12
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Korpela D, Jokinen E, Dumitrescu A, Huuhtanen J, Mustjoki S, Lähdesmäki H. EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. Bioinformatics 2023; 39:btad743. [PMID: 38070156 PMCID: PMC10963061 DOI: 10.1093/bioinformatics/btad743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
MOTIVATION T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION https://github.com/DaniTheOrange/EPIC-TRACE.
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Affiliation(s)
- Dani Korpela
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
| | - Emmi Jokinen
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | | | - Jani Huuhtanen
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
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13
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Montemurro A, Povlsen HR, Jessen LE, Nielsen M. Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data. Sci Rep 2023; 13:16147. [PMID: 37752190 PMCID: PMC10522655 DOI: 10.1038/s41598-023-43048-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Helle Rus Povlsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark.
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14
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Fast E, Dhar M, Chen B. TAPIR: a T-cell receptor language model for predicting rare and novel targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557285. [PMID: 37745475 PMCID: PMC10515850 DOI: 10.1101/2023.09.12.557285] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
T-cell receptors (TCRs) are involved in most human diseases, but linking their sequences with their targets remains an unsolved grand challenge in the field. In this study, we present TAPIR (T-cell receptor and Peptide Interaction Recognizer), a T-cell receptor (TCR) language model that predicts TCR-target interactions, with a focus on novel and rare targets. TAPIR employs deep convolutional neural network (CNN) encoders to process TCR and target sequences across flexible representations (e.g., beta-chain only, unknown MHC allele, etc.) and learns patterns of interactivity via several training tasks. This flexibility allows TAPIR to train on more than 50k either paired (alpha and beta chain) or unpaired TCRs (just alpha or beta chain) from public and proprietary databases against 1933 unique targets. TAPIR demonstrates state-of-the-art performance when predicting TCR interactivity against common benchmark targets and is the first method to demonstrate strong performance when predicting TCR interactivity against novel targets, where no examples are provided in training. TAPIR is also capable of predicting TCR interaction against MHC alleles in the absence of target information. Leveraging these capabilities, we apply TAPIR to cancer patient TCR repertoires and identify and validate a novel and potent anti-cancer T-cell receptor against a shared cancer neoantigen target (PIK3CA H1047L). We further show how TAPIR, when extended with a generative neural network, is capable of directly designing T-cell receptor sequences that interact with a target of interest.
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Affiliation(s)
- Ethan Fast
- Vcreate, Inc., Menlo Park, CA, 94025, USA
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15
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Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
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16
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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17
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Zhao Y, He B, Xu F, Li C, Xu Z, Su X, He H, Huang Y, Rossjohn J, Song J, Yao J. DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis. SCIENCE ADVANCES 2023; 9:eabo5128. [PMID: 37556545 PMCID: PMC10411891 DOI: 10.1126/sciadv.abo5128] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
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Affiliation(s)
- Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Bing He
- AI Lab, Tencent, Shenzhen, China
| | - Fan Xu
- AI Lab, Tencent, Shenzhen, China
| | - Chen Li
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | | | | | | | | | - Jamie Rossjohn
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
- Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Jiangning Song
- AI Lab, Tencent, Shenzhen, China
- Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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18
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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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19
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Myronov A, Mazzocco G, Król P, Plewczynski D. BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing. Bioinformatics 2023; 39:btad468. [PMID: 37535685 PMCID: PMC10444968 DOI: 10.1093/bioinformatics/btad468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/05/2023] Open
Abstract
MOTIVATION The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides. RESULTS We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training. AVAILABILITY AND IMPLEMENTATION The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.
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Affiliation(s)
- Alexander Myronov
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Ardigen, Krakow, Poland
| | | | | | - Dariusz Plewczynski
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
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20
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Lagattuta KA, Nathan A, Rumker L, Birnbaum ME, Raychaudhuri S. The T cell receptor sequence influences the likelihood of T cell memory formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549939. [PMID: 37502994 PMCID: PMC10370203 DOI: 10.1101/2023.07.20.549939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.
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Affiliation(s)
- Kaitlyn A. Lagattuta
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael E. Birnbaum
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biomedical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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21
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Li Y, Qi J, Liu Y, Zheng Y, Zhu H, Zang Y, Guan X, Xie S, Zhao H, Fu Y, Xiang H, Zhang W, Chen H, Liu H, Zhao Y, Feng Y, Bu F, Liang Y, Li Y, Xu Q, He Y, Sun L, Liu L, Gu Y, Xu X, Hou Y, Dong X, Liu Y. High-Throughput Screening of Functional Neo-Antigens and Their Specific T-Cell Receptors via the Jurkat Reporter System Combined with Droplet Microfluidics. Anal Chem 2023. [PMID: 37300490 DOI: 10.1021/acs.analchem.3c01754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
T-cell receptor (TCR)-engineered T cells can precisely recognize a broad repertoire of targets derived from both intracellular and surface proteins of tumor cells. TCR-T adoptive cell therapy has shown safety and promising efficacy in solid tumor immunotherapy. However, antigen-specific functional TCR screening is time-consuming and expensive, which limits its application clinically. Here, we developed a novel integrated antigen-TCR screening platform based on droplet microfluidic technology, enabling high-throughput peptide-major histocompatibility complex (pMHC)-to-TCR paired screening with a high sensitivity and low background signal. We introduced DNA barcoding technology to label peptide antigen candidate-loaded antigen-presenting cells and Jurkat reporter cells to check the specificity of pMHC-TCR candidates. Coupled with the next-generation sequencing pipeline, interpretation of the DNA barcodes and the gene expression level of the Jurkat T-cell activation pathway provided a clear peptide-MHC-TCR recognition relationship. Our proof-of-principle study demonstrates that the platform could achieve pMHC-TCR paired high-throughput screening, which is expected to be used in the cross-reactivity and off-target high-throughput paired testing of candidate pMHC-TCRs in clinical applications.
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Affiliation(s)
- Yijian Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, Shenzhen 518083, China
| | - Jingyu Qi
- BGI-Shenzhen, Shenzhen 518083, China
- School of Basic Medicine, Qingdao Medical College, Qingdao University, Qingdao 266071, China
| | - Yang Liu
- Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518116, China
| | | | | | - Yupeng Zang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xiangyu Guan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | | | | | - Yunyun Fu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | - Haitao Xiang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | - Weicong Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | | | - Huan Liu
- BGI-Shenzhen, Shenzhen 518083, China
| | | | - Yu Feng
- BGI-Shenzhen, Shenzhen 518083, China
| | - Fanyu Bu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Yanling Liang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | - Yang Li
- BGI-Shenzhen, Shenzhen 518083, China
| | - Qumiao Xu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Ying He
- Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518060, China
| | - Li Sun
- Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518060, China
| | - Longqi Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China
| | - Ying Gu
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China
| | - Yong Hou
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xuan Dong
- BGI-Shenzhen, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, Shenzhen 518083, China
| | - Ya Liu
- BGI-Shenzhen, Shenzhen 518083, China
- Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, Shenzhen 518100, China
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22
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Deering RP, Blumenberg L, Li L, Dhanik A, Jeong S, Pourpe S, Song H, Boucher L, Ragunathan S, Li Y, Zhong M, Kuhnert J, Adler C, Hawkins P, Gupta NT, Moore M, Ni M, Hansen J, Wei Y, Thurston G. Rapid TCR:Epitope Ranker (RAPTER): a primary human T cell reactivity screening assay pairing epitope and TCR at single cell resolution. Sci Rep 2023; 13:8452. [PMID: 37231180 DOI: 10.1038/s41598-023-35710-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/22/2023] [Indexed: 05/27/2023] Open
Abstract
Identifying epitopes that T cells respond to is critical for understanding T cell-mediated immunity. Traditional multimer and other single cell assays often require large blood volumes and/or expensive HLA-specific reagents and provide limited phenotypic and functional information. Here, we present the Rapid TCR:Epitope Ranker (RAPTER) assay, a single cell RNA sequencing (scRNA-SEQ) method that uses primary human T cells and antigen presenting cells (APCs) to assess functional T cell reactivity. Using hash-tag oligonucleotide (HTO) coding and T cell activation-induced markers (AIM), RAPTER defines paired epitope specificity and TCR sequence and can include RNA- and protein-level T cell phenotype information. We demonstrate that RAPTER identified specific reactivities to viral and tumor antigens at sensitivities as low as 0.15% of total CD8+ T cells, and deconvoluted low-frequency circulating HPV16-specific T cell clones from a cervical cancer patient. The specificities of TCRs identified by RAPTER for MART1, EBV, and influenza epitopes were functionally confirmed in vitro. In summary, RAPTER identifies low-frequency T cell reactivities using primary cells from low blood volumes, and the resulting paired TCR:ligand information can directly enable immunogenic antigen selection from limited patient samples for vaccine epitope inclusion, antigen-specific TCR tracking, and TCR cloning for further therapeutic development.
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Affiliation(s)
- Raquel P Deering
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA.
| | - Lili Blumenberg
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Lianjie Li
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Ankur Dhanik
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Se Jeong
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Stephane Pourpe
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Hang Song
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Lauren Boucher
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Shoba Ragunathan
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Yanxia Li
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Maggie Zhong
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Jessica Kuhnert
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Christina Adler
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Peter Hawkins
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Michael Moore
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Min Ni
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Johanna Hansen
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Yi Wei
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
| | - Gavin Thurston
- Regeneron Pharmaceuticals, Inc., 777 Old Saw Mill River Road, Tarrytown, NY, USA
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23
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Pham MDN, Nguyen TN, Tran LS, Nguyen QTB, Nguyen TPH, Pham TMQ, Nguyen HN, Giang H, Phan MD, Nguyen V. epiTCR: a highly sensitive predictor for TCR-peptide binding. Bioinformatics 2023; 39:btad284. [PMID: 37094220 PMCID: PMC10159657 DOI: 10.1093/bioinformatics/btad284] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 02/28/2023] [Accepted: 04/22/2023] [Indexed: 04/26/2023] Open
Abstract
MOTIVATION Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. RESULTS We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).
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Affiliation(s)
| | | | - Le Son Tran
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | | | | | | | - Hoai-Nghia Nguyen
- NexCalibur Therapeutics, Wilmington, DE, United States
- University of Medicine & Pharmacy, Ho Chi Minh City, Vietnam
| | - Hoa Giang
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | - Minh-Duy Phan
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
- NexCalibur Therapeutics, Wilmington, DE, United States
| | - Vy Nguyen
- Medical Genetics Institute, Ho Chi Minh City, Vietnam
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24
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Povlsen HR, Bentzen AK, Kadivar M, Jessen LE, Hadrup SR, Nielsen M. Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells. eLife 2023; 12:e81810. [PMID: 37133356 PMCID: PMC10156162 DOI: 10.7554/elife.81810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 03/13/2023] [Indexed: 05/04/2023] Open
Abstract
Novel single-cell-based technologies hold the promise of matching T cell receptor (TCR) sequences with their cognate peptide-MHC recognition motif in a high-throughput manner. Parallel capture of TCR transcripts and peptide-MHC is enabled through the use of reagents labeled with DNA barcodes. However, analysis and annotation of such single-cell sequencing (SCseq) data are challenged by dropout, random noise, and other technical artifacts that must be carefully handled in the downstream processing steps. We here propose a rational, data-driven method termed ITRAP (improved T cell Receptor Antigen Paring) to deal with these challenges, filtering away likely artifacts, and enable the generation of large sets of TCR-pMHC sequence data with a high degree of specificity and sensitivity, thus outputting the most likely pMHC target per T cell. We have validated this approach across 10 different virus-specific T cell responses in 16 healthy donors. Across these samples, we have identified up to 1494 high-confident TCR-pMHC pairs derived from 4135 single cells.
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Affiliation(s)
- Helle Rus Povlsen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Amalie Kai Bentzen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Mohammad Kadivar
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Leon Eyrich Jessen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Sine Reker Hadrup
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology at Technical University of DenmarkKongens LyngbyDenmark
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25
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Xu AM, Chour W, DeLucia DC, Su Y, Pavlovitch-Bedzyk AJ, Ng R, Rasheed Y, Davis MM, Lee JK, Heath JR. Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities. Cell Syst 2023; 14:273-284.e5. [PMID: 37001518 PMCID: PMC10355346 DOI: 10.1016/j.cels.2023.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 09/01/2022] [Accepted: 03/01/2023] [Indexed: 04/22/2023]
Abstract
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
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Affiliation(s)
- Alexander M Xu
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA
| | - Diana C DeLucia
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Rachel Ng
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yusuf Rasheed
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Mark M Davis
- Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA.
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26
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Tippalagama R, Chihab LY, Kearns K, Lewis S, Panda S, Willemsen L, Burel JG, Lindestam Arlehamn CS. Antigen-specificity measurements are the key to understanding T cell responses. Front Immunol 2023; 14:1127470. [PMID: 37122719 PMCID: PMC10140422 DOI: 10.3389/fimmu.2023.1127470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 05/02/2023] Open
Abstract
Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.
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27
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Mayer A, Callan CG. Measures of epitope binding degeneracy from T cell receptor repertoires. Proc Natl Acad Sci U S A 2023; 120:e2213264120. [PMID: 36649423 PMCID: PMC9942805 DOI: 10.1073/pnas.2213264120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/13/2022] [Indexed: 01/19/2023] Open
Abstract
Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.
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Affiliation(s)
- Andreas Mayer
- Division of Infection and Immunity, University College London, LondonWC1E 6BT, UK
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton08544, NJ
- Institute for the Physics of Living Systems, University College London, LondonWC1E 6BT, UK
| | - Curtis G. Callan
- Department of Physics, Princeton University, Princeton08544, NJ
- Institute for Advanced Study, Princeton08540, NJ
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28
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Inferring the T cell repertoire dynamics of healthy individuals. Proc Natl Acad Sci U S A 2023; 120:e2207516120. [PMID: 36669107 PMCID: PMC9942919 DOI: 10.1073/pnas.2207516120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The adaptive immune system is a diverse ecosystem that responds to pathogens by selecting cells with specific receptors. While clonal expansion in response to particular immune challenges has been extensively studied, we do not know the neutral dynamics that drive the immune system in the absence of strong stimuli. Here, we learn the parameters that underlie the clonal dynamics of the T cell repertoire in healthy individuals of different ages, by applying Bayesian inference to longitudinal immune repertoire sequencing (RepSeq) data. Quantifying the experimental noise accurately for a given RepSeq technique allows us to disentangle real changes in clonal frequencies from noise. We find that the data are consistent with clone sizes following a geometric Brownian motion and show that its predicted steady state is in quantitative agreement with the observed power-law behavior of the clone-size distribution. The inferred turnover time scale of the repertoire increases with patient age and depends on the clone size in some individuals.
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29
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Montemurro A, Jessen LE, Nielsen M. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Front Immunol 2022; 13:1055151. [PMID: 36561755 PMCID: PMC9763291 DOI: 10.3389/fimmu.2022.1055151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina,*Correspondence: Morten Nielsen,
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30
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Hong CH, Pyo HS, Baek IC, Kim TG. Rapid identification of CMV-specific TCRs via reverse TCR cloning system based on bulk TCR repertoire data. Front Immunol 2022; 13:1021067. [PMID: 36466875 PMCID: PMC9716090 DOI: 10.3389/fimmu.2022.1021067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/31/2022] [Indexed: 08/11/2023] Open
Abstract
Advances in next-generation sequencing (NGS) have improved the resolution of T-cell receptor (TCR) repertoire analysis, and recent single-cell sequencing has made it possible to obtain information about TCR pairs. In our previous study, cytomegalovirus (CMV) pp65-specific T-cell response restricted by a single human leukocyte antigen (HLA) class I allotype was observed in an individual. Therefore, to effectively clone an antigen-specific TCR from these T cells, we developed a TCR cloning system that does not require a single cell level. First, we established the improved Jurkat reporter cell line, which was TCRαβ double knock-out and expressed CD8αβ molecules. Furthermore, functional TCRs were directly obtained by reverse TCR cloning using unique CDR3-specific PCR primers after bulk TCR sequencing of activation marker-positive CD8 T cells by NGS. A total of 15 TCRα and 14 TCRβ strands were successfully amplified by PCR from cDNA of 4-1BB-positive CD8 T cells restricted by HLA-A*02:01, HLA-A*02:06, HLA-B*07:02, and HLA-B*40:06. The panels with combinations of TCRα and TCRβ genes were investigated using Jurkat reporter cell line and artificial antigen-presenting cells (APCs). In two TCR pairs restricted by HLA-A*02:01, one TCR pair by HLA-A*02:06, four TCR pairs by HLA-B*07:02, and one TCR pair by HLA-B*40:06, their specificity and affinity were confirmed. The TCR pair of A*02:01/1-1 showed alloreactivity to HLA-A*02:06. The one TCR pair showed a higher response to the naturally processed antigen than that of the peptide pool. This reverse TCR cloning system will not only provide functional information to TCR repertoire analysis by NGS but also help in the development of TCR-T therapy.
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Affiliation(s)
- Cheol-Hwa Hong
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Catholic Hematopoietic Stem Cell Bank, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hong-Seon Pyo
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - In-Cheol Baek
- Catholic Hematopoietic Stem Cell Bank, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Tai-Gyu Kim
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Catholic Hematopoietic Stem Cell Bank, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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31
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Abstract
The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.
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32
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Abstract
The human liver is a complex organ made up of multiple specialized cell types that carry out key physiological functions. An incomplete understanding of liver biology limits our ability to develop therapeutics to prevent chronic liver diseases, liver cancers, and death as a result of organ failure. Recently, single-cell modalities have expanded our understanding of the cellular phenotypic heterogeneity and intercellular cross-talk in liver health and disease. This review summarizes these findings and looks forward to highlighting new avenues for the application of single-cell genomics to unravel unknown pathogenic pathways and disease mechanisms for the development of new therapeutics targeting liver pathology. As these technologies mature, their integration into clinical data analysis will aid in patient stratification and in developing treatment plans for patients suffering from liver disease.
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Affiliation(s)
- Jawairia Atif
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Immunology, University of Toronto, Medical Sciences Building, Toronto, Ontario, Canada
| | - Cornelia Thoeni
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ian D. McGilvray
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Sonya A. MacParland
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Immunology, University of Toronto, Medical Sciences Building, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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33
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Designing Personalized Antigen-Specific Immunotherapies for Autoimmune Diseases-The Case for Using Ignored Target Cell Antigen Determinants. Cells 2022; 11:cells11071081. [PMID: 35406645 PMCID: PMC8997884 DOI: 10.3390/cells11071081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 12/15/2022] Open
Abstract
We have proposed that antigen-specific immunotherapies (ASIs) for autoimmune diseases could be enhanced by administering target cell antigen epitopes (determinants) that are immunogenic but ignored by autoreactive T cells because these determinants may have large pools of naïve cognate T cells available for priming towards regulatory responses. Here, we identified an immunogenic preproinsulin determinant (PPIL4-20) that was ignored by autoimmune responses in type 1 diabetes (T1D)-prone NOD mice. The size of the PPIL4-20-specific splenic naive T cell pool gradually increased from 2–12 weeks in age and remained stable thereafter, while that of the major target determinant insulin B-chain9-23 decreased greatly after 12 weeks in age, presumably due to recruitment into the autoimmune response. In 15–16 week old mice, insulin B-chain9-23/alum immunization induced modest-low level of splenic T cell IL-10 and IL-4 responses, little or no spreading of these responses, and boosted IFNγ responses to itself and other autoantigens. In contrast, PPIL4-20/alum treatment induced robust IL-10 and IL-4 responses, which spread to other autoantigens and increased the frequency of splenic IL-10-secreting Treg and Tr-1-like cells, without boosting IFNγ responses to ß-cell autoantigens. In newly diabetic NOD mice, PPIL4-20, but not insulin B-chain9-23 administered intraperitoneally (with alum) or intradermally (as soluble antigen) supplemented with oral GABA induced long-term disease remission. We discuss the potential of personalized ASIs that are based on an individual’s naïve autoantigen-reactive T cell pools and the use of HLA-appropriate ignored autoantigen determinants to safely enhance the efficacy of ASIs.
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34
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Tian G, Li M, Lv G. Analysis of T-Cell Receptor Repertoire in Transplantation: Fingerprint of T Cell-mediated Alloresponse. Front Immunol 2022; 12:778559. [PMID: 35095851 PMCID: PMC8790170 DOI: 10.3389/fimmu.2021.778559] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
T cells play a key role in determining allograft function by mediating allogeneic immune responses to cause rejection, and recent work pointed their role in mediating tolerance in transplantation. The unique T-cell receptor (TCR) expressed on the surface of each T cell determines the antigen specificity of the cell and can be the specific fingerprint for identifying and monitoring. Next-generation sequencing (NGS) techniques provide powerful tools for deep and high-throughput TCR profiling, and facilitate to depict the entire T cell repertoire profile and trace antigen-specific T cells in circulation and local tissues. Tailing T cell transcriptomes and TCR sequences at the single cell level provides a full landscape of alloreactive T-cell clones development and biofunction in alloresponse. Here, we review the recent advances in TCR sequencing techniques and computational tools, as well as the recent discovery in overall TCR profile and antigen-specific T cells tracking in transplantation. We further discuss the challenges and potential of using TCR sequencing-based assays to profile alloreactive TCR repertoire as the fingerprint for immune monitoring and prediction of rejection and tolerance.
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Affiliation(s)
| | - Mingqian Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
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35
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Clonotype pattern in T-cell lymphomas map the cell of origin to immature lymphoid precursors. Blood Adv 2022; 6:2334-2345. [PMID: 35015812 PMCID: PMC9006294 DOI: 10.1182/bloodadvances.2021005884] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/07/2021] [Indexed: 11/20/2022] Open
Abstract
Mature T-cell lymphomas (TCLs) are rare, clinically heterogeneous hematologic cancers of high medical need. TCLs have inferior prognosis which is attributed to poor understanding of their pathogenesis. Based on phenotypic similarities between normal and neoplastic lymphocytes it has been assumed that TCLs develop in the periphery, directly from various subtypes of normal T-cells. To address the debated question of the cell of origin in TCLs we analyzed to identify the highly variable complementarity determining regions (CDR3) regions of T-cell receptor (TCR) to trace the clonal history of the T-cells. We have collected previously published whole genome -exome, and -transcriptome sequencing data from 574 TCL patients. TCR clonotypes were identified by de novo assembly of CDR3 regions of TCR γ, β and α. We have found that the vast majority of TCLs are clonotypically oligoclonal, although the pattern oligoclonality varied. Anaplastic large cell lymphoma was most diverse comprising multiple clonotypes of TCRγ, β and α whereas adult T-cell lymphoma/leukemia and peripheral T-cell lymphomas often showed monoclonality for TCRγ and β but had diverse TCRα clonotypes. These patterns of rearrangements indicated that TCLs are initiated at the level of the lymphoid precursor. In keeping with this hypothesis, TCR rearrangements in TCLs resembled the pattern seen in the human thymus showing biased usage of V and J segments of high combinatorial probability resulting in recurrent, "public" CDR3 sequences shared across unrelated patients and different clinical TCL entities. Clonotypically diverse initiating cells may seed target tissues being responsible for disease relapses after therapy.
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36
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Nakayama M, Michels AW. Using the T Cell Receptor as a Biomarker in Type 1 Diabetes. Front Immunol 2021; 12:777788. [PMID: 34868047 PMCID: PMC8635517 DOI: 10.3389/fimmu.2021.777788] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 12/20/2022] Open
Abstract
T cell receptors (TCRs) are unique markers that define antigen specificity for a given T cell. With the evolution of sequencing and computational analysis technologies, TCRs are now prime candidates for the development of next-generation non-cell based T cell biomarkers, which provide a surrogate measure to assess the presence of antigen-specific T cells. Type 1 diabetes (T1D), the immune-mediated form of diabetes, is a prototypical organ specific autoimmune disease in which T cells play a pivotal role in targeting pancreatic insulin-producing beta cells. While the disease is now predictable by measuring autoantibodies in the peripheral blood directed to beta cell proteins, there is an urgent need to develop T cell markers that recapitulate T cell activity in the pancreas and can be a measure of disease activity. This review focuses on the potential and challenges of developing TCR biomarkers for T1D. We summarize current knowledge about TCR repertoires and clonotypes specific for T1D and discuss challenges that are unique for autoimmune diabetes. Ultimately, the integration of large TCR datasets produced from individuals with and without T1D along with computational 'big data' analysis will facilitate the development of TCRs as potentially powerful biomarkers in the development of T1D.
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Affiliation(s)
- Maki Nakayama
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Aaron W Michels
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States.,Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
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