1
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Nakamura M, Matsumoto M, Ito T, Hidaka I, Tatsuta H, Katsumoto Y. Microfluidic device for the high-throughput and selective encapsulation of single target cells. LAB ON A CHIP 2024; 24:2958-2967. [PMID: 38722067 DOI: 10.1039/d4lc00037d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Droplet-based microfluidic technologies for encapsulating single cells have rapidly evolved into powerful tools for single-cell analysis. In conventional passive single-cell encapsulation techniques, because cells arrive randomly at the droplet generation section, to encapsulate only a single cell with high precision, the average number of cells per droplet has to be decreased by reducing the average frequency at which cells arrive relative to the droplet generation rate. Therefore, the encapsulation efficiency for a given droplet generation rate is very low. Additionally, cell sorting operations are required prior to the encapsulation of target cells for specific cell type analysis. To address these challenges, we developed a cell encapsulation technology with a cell sorting function using a microfluidic chip. The microfluidic chip is equipped with an optical detection section to detect the optical information of cells and a sorting section to encapsulate cells into droplets by controlling a piezo element, enabling active encapsulation of only the single target cells. For a particle population including both targeted and non-targeted particles arriving at an average frequency of up to 6000 particles per s, with an average number of particles per droplet of 0.45, our device maintained a high purity above 97.9% for the single-target-particle droplets and achieved an outstanding throughput, encapsulating up to 2900 single target particles per s. The proposed encapsulation technology surpasses the encapsulation efficiency of conventional techniques, provides high efficiency and flexibility for single-cell research, and shows excellent potential for various applications in single-cell analysis.
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Affiliation(s)
- Masahiko Nakamura
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Masahiro Matsumoto
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Tatsumi Ito
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Isao Hidaka
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Hirokazu Tatsuta
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
| | - Yoichi Katsumoto
- Life Science Technology Research & Development Dept., Application Technology Research & Development Div., Technology Development Laboratories, Sony Corporation, Tokyo, Japan.
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2
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Gao Y, Dong K, Gao Y, Jin X, Yang J, Yan G, Liu Q. Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning. CELL GENOMICS 2024; 4:100553. [PMID: 38688285 PMCID: PMC11099349 DOI: 10.1016/j.xgen.2024.100553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/09/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
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Affiliation(s)
- Yicheng Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kejing Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuli Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingya Yang
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Gang Yan
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China; Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China.
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3
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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4
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Lee H, Shin K, Lee Y, Lee S, Lee S, Lee E, Kim SW, Shin HY, Kim JH, Chung J, Kwon S. Identification of B cell subsets based on antigen receptor sequences using deep learning. Front Immunol 2024; 15:1342285. [PMID: 38576618 PMCID: PMC10991714 DOI: 10.3389/fimmu.2024.1342285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024] Open
Abstract
B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.
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Affiliation(s)
- Hyunho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Yongju Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Soobin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Seungyoun Lee
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Eunjae Lee
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Woo Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ha Young Shin
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Hoon Kim
- Department of Dermatology and Cutaneous Biology Research Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Junho Chung
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea
- Bio-MAX Institute, Seoul National University, Seoul, Republic of Korea
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, Republic of Korea
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5
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Pothuri VS, Hogg GD, Conant L, Borcherding N, James CA, Mudd J, Williams G, Seo YD, Hawkins WG, Pillarisetty VG, DeNardo DG, Fields RC. Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma. Oncoimmunology 2024; 13:2320411. [PMID: 38504847 PMCID: PMC10950267 DOI: 10.1080/2162402x.2024.2320411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.
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Affiliation(s)
- Vikram S. Pothuri
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Graham D. Hogg
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Leah Conant
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas Borcherding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - C. Alston James
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline Mudd
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Greg Williams
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Yongwoo David Seo
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Department of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - William G. Hawkins
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Venu G. Pillarisetty
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WAUSA
| | - David G. DeNardo
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Ryan C. Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
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6
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Schäfer PSL, Dimitrov D, Villablanca EJ, Saez-Rodriguez J. Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 2024; 25:405-417. [PMID: 38413722 DOI: 10.1038/s41590-024-01768-2] [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: 05/31/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.
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Affiliation(s)
- Philipp Sven Lars Schäfer
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Daniel Dimitrov
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Eduardo J Villablanca
- Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
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7
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Zhu J, Wang Y, Chang WY, Malewska A, Napolitano F, Gahan JC, Unni N, Zhao M, Yuan R, Wu F, Yue L, Guo L, Zhao Z, Chen DZ, Hannan R, Zhang S, Xiao G, Mu P, Hanker AB, Strand D, Arteaga CL, Desai N, Wang X, Xie Y, Wang T. Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558298. [PMID: 37781617 PMCID: PMC10541142 DOI: 10.1101/2023.09.18.558298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between γδ T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.
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Affiliation(s)
- James Zhu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati, OH, 45221, USA
| | - Woo Yong Chang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Alicia Malewska
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fabiana Napolitano
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jeffrey C. Gahan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Nisha Unni
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Min Zhao
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Rongqing Yuan
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lauren Yue
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lei Guo
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zhuo Zhao
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Danny Z. Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Raquibul Hannan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Siyuan Zhang
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ping Mu
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
- Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ariella B. Hanker
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Douglas Strand
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Carlos L. Arteaga
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xinlei Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019, USA
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, TX, 76019, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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8
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Zhu B, Wang Y, Ku LT, van Dijk D, Zhang L, Hafler DA, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biol 2023; 24:292. [PMID: 38111007 PMCID: PMC10726524 DOI: 10.1186/s13059-023-03129-y] [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: 05/30/2023] [Accepted: 11/27/2023] [Indexed: 12/20/2023] Open
Abstract
Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.
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Affiliation(s)
- Biqing Zhu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, USA, MD , 20815
| | - Yuge Wang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA
| | - Li-Ting Ku
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA
| | - David van Dijk
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06511, USA
- Department of Computer Science, Yale University, New Haven, CT, 06511, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, USA, MD , 20815
| | - Le Zhang
- Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, 06511, USA
- Department of Immunobiology, School of Medicine, Yale University, New Haven, CT, 06511, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, USA, MD , 20815
| | - David A Hafler
- Department of Neurology, School of Medicine, Yale University, New Haven, CT, 06511, USA
- Department of Immunobiology, School of Medicine, Yale University, New Haven, CT, 06511, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, USA, MD , 20815
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA.
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA.
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9
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Ahmed J, Das B, Shin S, Chen A. Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies. Cancers (Basel) 2023; 15:5841. [PMID: 38136385 PMCID: PMC10741991 DOI: 10.3390/cancers15245841] [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: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.
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Affiliation(s)
- Jibran Ahmed
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Biswajit Das
- Molecular Characterization Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Sarah Shin
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
| | - Alice Chen
- Developmental Therapeutics Clinic (DTC), National Cancer Institute (NCI), National Institute of Health (NIH), Bethesda, MD 20892, USA
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10
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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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11
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Shah RK, Cygan E, Kozlik T, Colina A, Zamora AE. Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy. Front Immunol 2023; 14:1301100. [PMID: 38149253 PMCID: PMC10749952 DOI: 10.3389/fimmu.2023.1301100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.
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Affiliation(s)
- Ravi K. Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Erin Cygan
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alfredo Colina
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Anthony E. Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
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12
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Wang H, Ji Z. T-cell receptor sequences correlate with and predict gene expression levels in T cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568912. [PMID: 38076860 PMCID: PMC10705237 DOI: 10.1101/2023.11.27.568912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
T cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities. We analyzed fifteen single-cell immune profiling datasets to systematically investigate the association between T-cell receptor (TCR) sequences and the gene expression profiles of T cells. Our findings reveal that T cells sharing identical or similar TCR sequences tend to have highly similar gene expression profiles. Additionally, we developed a foundational model that leverages TCR information to predict gene expression levels in T cells.
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Affiliation(s)
- Hao Wang
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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13
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Chen DG, Xie J, Su Y, Heath JR. T cell receptor sequences are the dominant factor contributing to the phenotype of CD8 + T cells with specificities against immunogenic viral antigens. Cell Rep 2023; 42:113279. [PMID: 37883974 PMCID: PMC10729740 DOI: 10.1016/j.celrep.2023.113279] [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: 07/09/2023] [Revised: 08/23/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
Antigen-specific CD8+ T cells mediate pathogen clearance. T cell phenotype is influenced by T cell receptor (TCR) sequences and environmental signals. Quantitative comparisons of these factors in human disease, while challenging to obtain, can provide foundational insights into basic T cell biology. Here, we investigate the phenotype kinetics of 679 CD8+ T cell clonotypes, each with specificity against one of three immunogenic viral antigens. Data were collected from a longitudinal study of 68 COVID-19 patients with antigens from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cytomegalovirus (CMV), and influenza. Each antigen is associated with a different type of immune activation during COVID-19. We find TCR sequence to be by far the most important factor in shaping T cell phenotype and persistence for populations specific to any of these antigens. Our work demonstrates the important relationship between TCR sequence and T cell phenotype and persistence and helps explain why T cell phenotype often appears to be determined early in an infection.
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Affiliation(s)
- Daniel G Chen
- Institute of Systems Biology, Seattle, WA 98109, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Clinical Research Division, Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jingyi Xie
- Institute of Systems Biology, Seattle, WA 98109, USA; Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA 98105, USA
| | - Yapeng Su
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Clinical Research Division, Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - James R Heath
- Institute of Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
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14
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Yang Y, Wang K, Lu Z, Wang T, Wang X. Cytomulate: accurate and efficient simulation of CyTOF data. Genome Biol 2023; 24:262. [PMID: 37974276 PMCID: PMC10652542 DOI: 10.1186/s13059-023-03099-1] [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: 09/05/2022] [Accepted: 10/24/2023] [Indexed: 11/19/2023] Open
Abstract
Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.
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Affiliation(s)
- Yuqiu Yang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Kaiwen Wang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
| | - Zeyu Lu
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Xinlei Wang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA.
- Department of Mathematics, University of Texas at Arlington, Arlington, 76019, USA.
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, 76019, USA.
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15
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Mudd P, Borcherding N, Kim W, Quinn M, Han F, Zhou J, Sturtz A, Schmitz A, Lei T, Schattgen S, Klebert M, Suessen T, Middleton W, Goss C, Liu C, Crawford J, Thomas P, Teefey S, Presti R, O'Halloran J, Turner J, Ellebedy A. Antigen-specific CD4 + T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans. RESEARCH SQUARE 2023:rs.3.rs-3304466. [PMID: 37790414 PMCID: PMC10543502 DOI: 10.21203/rs.3.rs-3304466/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Charles Goss
- Division of Biostatistics, Washington University in St.Louis
| | - Chang Liu
- Washington University School of Medicine
| | | | | | | | | | - Jane O'Halloran
- Department of Emergency Medicine, Washington University in St.Louis
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16
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Zhang B, Ren Z, Zhao J, Zhu Y, Huang B, Xiao C, Zhang Y, Deng J, Mao L, Tang L, Lan D, Gao L, Zhang H, Chen G, Luo OJ. Global analysis of HLA-A2 restricted MAGE-A3 tumor antigen epitopes and corresponding TCRs in non-small cell lung cancer. Theranostics 2023; 13:4449-4468. [PMID: 37649599 PMCID: PMC10465222 DOI: 10.7150/thno.84710] [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: 03/27/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
Background: Advanced non-small cell lung cancer (NSCLC) is the most common type of lung cancer with poor prognosis. Adoptive cell therapy using engineered T-cell receptors (TCRs) targeting cancer-testis antigens, such as Melanoma-associated antigen 3 (MAGE-A3), is a potential approach for the treatment of NSCLC. However, systematic analysis of T cell immune responses to MAGE-A3 antigen and corresponding antigen-specific TCR is still lacking. Methods: In this study, we comprehensively screened HLA-A2 restricted MAGE-A3 tumor epitopes and characterized the corresponding TCRs using in vitro artificial antigen presentation cells (APC) system, single-cell transcriptome and TCR V(D)J sequencing, and machine-learning. Furthermore, the tumor-reactive TCRs with killing potency was screened and verified. Results: We identified the HLA-A2 restricted T cell epitopes from MAGE-A3 that could effectively induce the activation and cytotoxicity of CD8+ T cells using artificial APC in vitro. A cohort of HLA-A2+ NSCLC donors demonstrated that the number of epitope specific CD8+ T cells increased in NSCLC than healthy controls when measured with tetramer derived from the candidate MAGE-A3 epitopes, especially epitope Mp4 (MAGE-A3: 160-169, LVFGIELMEV). Statistical and machine-learning based analyses demonstrated that the MAGE-A3-Mp4 epitope-specific CD8+ T cell clones were mostly in effector and proliferating state. Importantly, T cells artificially expressing the MAGE-A3-Mp4 specific TCRs exhibited strong MAGE-A3+ tumor cell recognition and killing effect. Cross-reactivity risk analysis of the candidates TCRs showed high binding stability to MAGE-A3-Mp4 epitope and low risk of cross-reaction. Conclusions: This work identified candidate TCRs potentially suitable for TCR-T design targeting HLA-A2 restricted MAGE-A3 tumor antigen.
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Affiliation(s)
- Bei Zhang
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Zhiyao Ren
- Guangzhou Geriatric Hospital, Guangzhou, China
- Collaborative Innovation Center for Civil Affairs of Guangzhou, Guangzhou, China
| | - Jianfu Zhao
- Department of Oncology, Research Center of Cancer Diagnosis and Therapy, the First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Yue Zhu
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Boya Huang
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Chanchan Xiao
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
| | - Yan Zhang
- Department of Oncology, Research Center of Cancer Diagnosis and Therapy, the First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jieping Deng
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Lipeng Mao
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Lei Tang
- School of Life Science & Technology, China Pharmaceutical University, Nanjing, China
| | - Dan Lan
- Department of Oncology, Research Center of Cancer Diagnosis and Therapy, the First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Lijuan Gao
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
| | - Hongyi Zhang
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
| | - Guobing Chen
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
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17
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 111] [Impact Index Per Article: 111.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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18
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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: 0] [Impact Index Per Article: 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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19
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Yang H, Cham J, Neal BP, Fan Z, He T, Zhang L. NAIR: Network Analysis of Immune Repertoire. Front Immunol 2023; 14:1181825. [PMID: 37614227 PMCID: PMC10443597 DOI: 10.3389/fimmu.2023.1181825] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/07/2023] [Indexed: 08/25/2023] Open
Abstract
T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.
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Affiliation(s)
- Hai Yang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
| | - Jason Cham
- Department of Medicine, Scripps Green Hospital, La Jolla, CA, United States
| | - Brian Patrick Neal
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Zenghua Fan
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Tao He
- Department of Mathematics, San Francisco State University, San Francisco, CA, United States
| | - Li Zhang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
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20
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Konstantinovsky T, Yaari G. A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression. Bioinformatics 2023; 39:btad426. [PMID: 37417959 PMCID: PMC10348835 DOI: 10.1093/bioinformatics/btad426] [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: 04/10/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023] Open
Abstract
MOTIVATION T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses. However, their high diversity and complexity present significant challenges in representation and analysis. The main motivation of this study is to develop a unified and compact representation of a TCRB repertoire that can efficiently capture its inherent complexity and diversity and allow for direct inference. RESULTS We introduce a novel approach to TCRB repertoire encoding and analysis, leveraging the Lempel-Ziv 76 algorithm. This approach allows us to create a graph-like model, identify-specific sequence features, and produce a new encoding approach for an individual's repertoire. The proposed representation enables various applications, including generation probability inference, informative feature vector derivation, sequence generation, a new measure for diversity estimation, and a new sequence centrality measure. The approach was applied to four large-scale public TCRB sequencing datasets, demonstrating its potential for a wide range of applications in big biological sequencing data. AVAILABILITY AND IMPLEMENTATION Python package for implementation is available https://github.com/MuteJester/LZGraphs.
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Affiliation(s)
- Thomas Konstantinovsky
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
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21
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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22
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Wang K, Yang Y, Wu F, Song B, Wang X, Wang T. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. Nat Commun 2023; 14:1836. [PMID: 37005472 PMCID: PMC10067013 DOI: 10.1038/s41467-023-37478-w] [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: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.
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Affiliation(s)
- Kaiwen Wang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, 75275, USA
| | - Yuqiu Yang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, 75275, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Bing Song
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, 75275, USA.
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019, USA.
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, 76019, USA.
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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23
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Xiao C, Ren Z, Zhang B, Mao L, Zhu G, Gao L, Su J, Ye J, Long Z, Zhu Y, Chen P, Su X, Zhou T, Huang Y, Chen X, Xie C, Yuan J, Hu Y, Zheng J, Wang Z, Lou J, Yang X, Kuang Z, Zhang H, Wang P, Liang X, Luo OJ, Chen G. Insufficient epitope-specific T cell clones are responsible for impaired cellular immunity to inactivated SARS-CoV-2 vaccine in older adults. NATURE AGING 2023; 3:418-435. [PMID: 37117789 PMCID: PMC10154213 DOI: 10.1038/s43587-023-00379-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 02/03/2023] [Indexed: 04/30/2023]
Abstract
Aging is a critical risk factor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine efficacy. The immune responses to inactivated vaccine for older adults, and the underlying mechanisms of potential differences to young adults, are still unclear. Here we show that neutralizing antibody production by older adults took a longer time to reach similar levels in young adults after inactivated SARS-CoV-2 vaccination. We screened SARS-CoV-2 variant strains for epitopes that stimulate specific CD8 T cell response, and older adults exhibited weaker CD8 T-cell-mediated responses to these epitopes. Comparison of lymphocyte transcriptomes from pre-vaccinated and post-vaccinated donors suggested that the older adults had impaired antigen processing and presentation capability. Single-cell sequencing revealed that older adults had less T cell clone expansion specific to SARS-CoV-2, likely due to inadequate immune receptor repertoire size and diversity. Our study provides mechanistic insights for weaker response to inactivated vaccine by older adults and suggests the need for further vaccination optimization for the old population.
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Affiliation(s)
- Chanchan Xiao
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Guangzhou Laboratory, Guangzhou, China
| | - Zhiyao Ren
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
- Guangzhou Geriatric Hospital, Guangzhou, China
- NHC Key Laboratory of Male Reproduction and Genetics, Guangzhou, China
- Department of Central Laboratory, Guangdong Provincial Reproductive Science Institute (Guangdong Provincial Fertility Hospital), Guangzhou, China
| | - Bei Zhang
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
| | - Lipeng Mao
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
| | - Guodong Zhu
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Guangzhou Geriatric Hospital, Guangzhou, China
| | - Lijuan Gao
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Jun Su
- Affiliated Huaqiao Hospital, Jinan University, Guangzhou, China
| | - Jiezhou Ye
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Ze Long
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Yue Zhu
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China
| | - Pengfei Chen
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Xiangmeng Su
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Tong Zhou
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Yanhao Huang
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Xiongfei Chen
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Chaojun Xie
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Jun Yuan
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Yutian Hu
- Meng Yi Center Limited, Macau, China
| | - Jingshan Zheng
- Shenzhen Kangtai Biological Products Co. Ltd, Shenzhen, China
| | - Zhigang Wang
- Affiliated Huaqiao Hospital, Jinan University, Guangzhou, China
| | | | - Xiang Yang
- Leidebio Bioscience Co., Ltd., Guangzhou, China
| | - Zhiqiang Kuang
- Affiliated Huaqiao Hospital, Jinan University, Guangzhou, China
| | - Hongyi Zhang
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China
| | - Pengcheng Wang
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China.
| | - Xiaofeng Liang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
| | - Oscar Junhong Luo
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China.
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, China.
| | - Guobing Chen
- Department of Microbiology and Immunology; Institute of Geriatric Immunology; School of Medicine, Jinan University, Guangzhou, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, School of Medicine, Jinan University, Guangzhou, China.
- Guangzhou Laboratory, Guangzhou, China.
- Affiliated Huaqiao Hospital, Jinan University, Guangzhou, China.
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24
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Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, Heymach JV, Zhang J, Reuben A. T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy. Clin Cancer Res 2023; 29:994-1008. [PMID: 36413126 PMCID: PMC10011887 DOI: 10.1158/1078-0432.ccr-22-2469] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.
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Affiliation(s)
- Meredith L Frank
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Kaylene Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Can Erdogan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Rice University, Houston, Texas
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jian Hu
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas.,Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
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25
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Porciello N, Franzese O, D’Ambrosio L, Palermo B, Nisticò P. T-cell repertoire diversity: friend or foe for protective antitumor response? JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2022; 41:356. [PMID: 36550555 PMCID: PMC9773533 DOI: 10.1186/s13046-022-02566-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.
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Affiliation(s)
- Nicla Porciello
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Ornella Franzese
- grid.6530.00000 0001 2300 0941Department of Systems Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Lorenzo D’Ambrosio
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Belinda Palermo
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Paola Nisticò
- grid.417520.50000 0004 1760 5276Tumor Immunology and Immunotherapy Unit, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
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26
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Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences. BMC Bioinformatics 2022; 23:469. [PMID: 36348271 PMCID: PMC9644450 DOI: 10.1186/s12859-022-05012-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022] Open
Abstract
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.
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27
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Morgan DM, Shreffler WG, Love JC. Revealing the heterogeneity of CD4+ T cells through single-cell transcriptomics. J Allergy Clin Immunol 2022; 150:748-755. [DOI: 10.1016/j.jaci.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/15/2022] [Accepted: 08/19/2022] [Indexed: 11/07/2022]
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28
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Ji F, Chen L, Chen Z, Luo B, Wang Y, Lan X. TCR repertoire and transcriptional signatures of circulating tumour-associated T cells facilitate effective non-invasive cancer detection. Clin Transl Med 2022; 12:e853. [PMID: 36134717 PMCID: PMC9494610 DOI: 10.1002/ctm2.853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/11/2022] [Accepted: 04/15/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Fansen Ji
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China.,School of Medicine, Tsinghua University, Beijing, China
| | - Lin Chen
- School of Medicine, Tsinghua University, Beijing, China.,General Surgery Department, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zhizhuo Chen
- School of Life Science, Tsinghua University, Beijing, China
| | - Bin Luo
- General Surgery Department, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yongwang Wang
- Department of Anesthesiology, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Xun Lan
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China.,School of Medicine, Tsinghua University, Beijing, China
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29
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Wang Y, Song B, Wang S, Chen M, Xie Y, Xiao G, Wang L, Wang T. Sprod for de-noising spatially resolved transcriptomics data based on position and image information. Nat Methods 2022; 19:950-958. [PMID: 35927477 DOI: 10.1038/s41592-022-01560-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/22/2022] [Indexed: 11/09/2022]
Abstract
Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.
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Affiliation(s)
- Yunguan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bing Song
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Mingyi Chen
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Li Wang
- Department of Mathematics and Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA.
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA. .,Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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30
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Zhang Z, Chang WY, Wang K, Yang Y, Wang X, Yao C, Wu T, Wang L, Wang T. Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00492-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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31
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Reitermaier R, Ayub T, Staller J, Kienzl P, Fortelny N, Vieyra-Garcia PA, Worda C, Fiala C, Staud C, Eppel W, Scharrer A, Krausgruber T, Elbe-Bürger A. The molecular and phenotypic makeup of fetal human skin T lymphocytes. Development 2022; 149:dev199781. [PMID: 34604909 PMCID: PMC8601710 DOI: 10.1242/dev.199781] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022]
Abstract
The adult human skin contains a vast number of T cells that are essential for skin homeostasis and pathogen defense. T cells are first observed in the skin at the early stages of gestation; however, our understanding of their contribution to early immunity has been limited by their low abundance and lack of comprehensive methodologies for their assessment. Here, we describe a new workflow for isolating and expanding significant amounts of T cells from fetal human skin. Using multiparametric flow cytometry and in situ immunofluorescence, we found a large population with a naive phenotype and small populations with a memory and regulatory phenotype. Their molecular state was characterized using single-cell transcriptomics and TCR repertoire profiling. Importantly, culture of total fetal skin biopsies facilitated T cell expansion without a substantial impact on their phenotype, a major prerequisite for subsequent functional assays. Collectively, our experimental approaches and data advance the understanding of fetal skin immunity and potential use in future therapeutic interventions.
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Affiliation(s)
- René Reitermaier
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
| | - Tanya Ayub
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
| | - Julia Staller
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
| | - Philip Kienzl
- Department of Dermatology, Medical University of Vienna, Vienna 1090, Austria
| | - Nikolaus Fortelny
- Department of Biosciences, University of Salzburg, Salzburg 5020, Austria
| | | | - Christof Worda
- Department of Obstetrics & Gynecology, Medical University of Vienna, Vienna 1090, Austria
| | - Christian Fiala
- Gynmed Clinic, Vienna 1150, Austria
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institute and Karolinska University Hospital, Stockholm 171 77, Sweden
| | - Clement Staud
- Department of Surgery, Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Vienna 1090, Austria
| | - Wolfgang Eppel
- Department of Obstetrics & Gynecology, Medical University of Vienna, Vienna 1090, Austria
| | - Anke Scharrer
- Department of Pathology, Medical University of Vienna, Vienna 1090, Austria
| | - Thomas Krausgruber
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
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Aran A, Garrigós L, Curigliano G, Cortés J, Martí M. Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality? Cancers (Basel) 2022; 14:cancers14071771. [PMID: 35406543 PMCID: PMC8996954 DOI: 10.3390/cancers14071771] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/22/2022] [Accepted: 03/29/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The TCR is the T cell antigen receptor, and it is responsible of the T cell activation, through the HLA-antigen complex recognition. Studying the TCR repertoire in patients with cancer can help to better understand the anti-tumoural responses and it has been suggested to have predictive and or/prognostic values, both for the disease and in response to treatments. The aim of this review is to summarize TCR repertoire studies performed in patients with cancer found in the literature, thoroughly analyse the different factors that can be involved in shaping the TCR repertoire, and draw the current conclusions in this field, especially focusing on whether the TCR diversity—or its opposite, the clonality—can be used as predictors or prognostic biomarkers of the disease. Abstract T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.
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Affiliation(s)
- Andrea Aran
- Immunology Unit, Department of Cell Biology, Physiology and Immunology, Institut de Biotecnologia I Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain;
| | - Laia Garrigós
- International Breast Cancer Center (IBCC), 08017 Barcelona, Spain; (L.G.); (J.C.)
| | - Giuseppe Curigliano
- Division of Early Drug Development, European Institute of Oncology, IRCCS, 20141 Milano, Italy;
- Department of Oncology and Hemato-Oncology, University of Milano, 20122 Milano, Italy
| | - Javier Cortés
- International Breast Cancer Center (IBCC), 08017 Barcelona, Spain; (L.G.); (J.C.)
- Medica Scientia Innovation Research (MedSIR), 08018 Barcelona, Spain
- Medica Scientia Innovation Research (MedSIR), Ridgewood, NJ 07450, USA
- Department of Medicine, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, 28670 Madrid, Spain
| | - Mercè Martí
- Immunology Unit, Department of Cell Biology, Physiology and Immunology, Institut de Biotecnologia I Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain;
- Correspondence: ; Tel.: +34-935812409
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Caprioli C, Nazari I, Milovanovic S, Pelicci PG. Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities. Front Oncol 2022; 11:796477. [PMID: 35186713 PMCID: PMC8847379 DOI: 10.3389/fonc.2021.796477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/31/2021] [Indexed: 11/26/2022] Open
Abstract
Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.
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Affiliation(s)
- Chiara Caprioli
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy.,Hematology and Bone Marrow Transplant Unit, Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Iman Nazari
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Sara Milovanovic
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, Istituto Europeo di Oncologia, Milan, Italy.,Scuola Europea di Medicina Molecolare (SEMM) European School of Molecular Medicine, Milan, Italy
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Fahad AS, Chung CY, Lopez Acevedo SN, Boyle N, Madan B, Gutiérrez-González MF, Matus-Nicodemos R, Laflin AD, Ladi RR, Zhou J, Wolfe J, Llewellyn-Lacey S, Koup RA, Douek DC, Balfour Jr HH, Price DA, DeKosky BJ. Immortalization and functional screening of natively paired human T cell receptor repertoires. Protein Eng Des Sel 2022; 35:gzab034. [PMID: 35174859 PMCID: PMC9005053 DOI: 10.1093/protein/gzab034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/16/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
Functional analyses of the T cell receptor (TCR) landscape can reveal critical information about protection from disease and molecular responses to vaccines. However, it has proven difficult to combine advanced next-generation sequencing technologies with methods to decode the peptide-major histocompatibility complex (pMHC) specificity of individual TCRs. We developed a new high-throughput approach to enable repertoire-scale functional evaluations of natively paired TCRs. In particular, we leveraged the immortalized nature of physically linked TCRα:β amplicon libraries to analyze binding against multiple recombinant pMHCs on a repertoire scale, and to exemplify the utility of this approach, we also performed affinity-based functional mapping in conjunction with quantitative next-generation sequencing to track antigen-specific TCRs. These data successfully validated a new immortalization and screening platform to facilitate detailed molecular analyses of disease-relevant antigen interactions with human TCRs.
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Affiliation(s)
- Ahmed S Fahad
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Cheng-Yu Chung
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Sheila N Lopez Acevedo
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Nicoleen Boyle
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Bharat Madan
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | | | - Rodrigo Matus-Nicodemos
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Amy D Laflin
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Rukmini R Ladi
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - John Zhou
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Jacy Wolfe
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
| | - Sian Llewellyn-Lacey
- Division of Infection and Immunity, Cardiff University School of Medicine, University Hospital of Wales, Cardiff CF14 4XN, UK
| | - Richard A Koup
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Henry H Balfour Jr
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - David A Price
- Division of Infection and Immunity, Cardiff University School of Medicine, University Hospital of Wales, Cardiff CF14 4XN, UK
- Systems Immunity Research Institute, Cardiff University School of Medicine, University Hospital of Wales, Cardiff CF14 4XN, UK
| | - Brandon J DeKosky
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS 66044, USA
- Department of Chemical Engineering, The University of Kansas, Lawrence, KS 66044, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- The Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA
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35
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Joshi K, Milighetti M, Chain BM. Application of T cell receptor (TCR) repertoire analysis for the advancement of cancer immunotherapy. Curr Opin Immunol 2022; 74:1-8. [PMID: 34454284 DOI: 10.1016/j.coi.2021.07.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022]
Abstract
T cell receptor (TCR) sequencing has emerged as a powerful new technology in analysis of the host-tumour interaction. The advances in NextGen sequencing technologies, coupled with powerful novel bioinformatic tools, allow quantitative and reproducible characterisation of repertoires from tumour and blood samples from an increasing number of patients with a variety of solid cancers. In this review, we consider how global metrics such as T cell clonality and diversity can be extracted from these repertoires and used to give insight into the mechanism of action of immune checkpoint blockade. Furthermore, we explore how the analysis of TCR overlap between repertories can help define spatial and temporal heterogeneity of the anti-tumoural immune response. Finally, we review how analysis of TCR sequence and structure, either of individual TCRs or from sets of related TCRs can be used to annotate the antigenic specificity, with important implications for the development of personalised adoptive cellular immunotherapies.
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Affiliation(s)
- Kroopa Joshi
- Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Benjamin M Chain
- Division of Infection and Immunity, University College London, London, United Kingdom; Department of Computer Science, University College London, London, United Kingdom.
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36
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Lu T, Zhang Z, Zhu J, Wang Y, Jiang P, Xiao X, Bernatchez C, Heymach JV, Gibbons DL, Wang J, Xu L, Reuben A, Wang T. Deep learning-based prediction of the T cell receptor-antigen binding specificity. NAT MACH INTELL 2021; 3:864-875. [PMID: 36003885 PMCID: PMC9396750 DOI: 10.1038/s42256-021-00383-2] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3β), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool.
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Affiliation(s)
- Tianshi Lu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - Ze Zhang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - James Zhu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - Peixin Jiang
- Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX USA, 77030
| | - Xue Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - Chantale Bernatchez
- Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX USA, 77030
| | - John V. Heymach
- Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX USA, 77030
| | - Don L. Gibbons
- Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX USA, 77030
| | - Jun Wang
- Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016
| | - Lin Xu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390
| | - Alexandre Reuben
- Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX USA, 77030.,Corresponding authors: (1) Tao Wang, Ph.D., Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA; (lead contact). (2) Alexandre Reuben, Ph.D., Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, 77030, USA;
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390.,Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390.,Corresponding authors: (1) Tao Wang, Ph.D., Quantitative Biomedical Research Center, Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA; (lead contact). (2) Alexandre Reuben, Ph.D., Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, 77030, USA;
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37
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GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation. Nat Commun 2021; 12:4699. [PMID: 34349111 PMCID: PMC8339063 DOI: 10.1038/s41467-021-25006-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/19/2021] [Indexed: 01/18/2023] Open
Abstract
Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform. Grouping T-cell receptors (TCRs) by sequence similarity could lead to new immunological insights. Here, the authors propose a tool that allows the rapid clustering of millions of TCR sequences, identifying TCRs potentially associated with the response to cancer, infectious and autoimmune diseases.
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38
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Serr I, Drost F, Schubert B, Daniel C. Antigen-Specific Treg Therapy in Type 1 Diabetes - Challenges and Opportunities. Front Immunol 2021; 12:712870. [PMID: 34367177 PMCID: PMC8341764 DOI: 10.3389/fimmu.2021.712870] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/06/2021] [Indexed: 01/16/2023] Open
Abstract
Regulatory T cells (Tregs) are key mediators of peripheral self-tolerance and alterations in their frequencies, stability, and function have been linked to autoimmunity. The antigen-specific induction of Tregs is a long-envisioned goal for the treatment of autoimmune diseases given reduced side effects compared to general immunosuppressive therapies. However, the translation of antigen-specific Treg inducing therapies for the treatment or prevention of autoimmune diseases into the clinic remains challenging. In this mini review, we will discuss promising results for antigen-specific Treg therapies in allergy and specific challenges for such therapies in autoimmune diseases, with a focus on type 1 diabetes (T1D). We will furthermore discuss opportunities for antigen-specific Treg therapies in T1D, including combinatorial strategies and tissue-specific Treg targeting. Specifically, we will highlight recent advances in miRNA-targeting as a means to foster Tregs in autoimmunity. Additionally, we will discuss advances and perspectives of computational strategies for the detailed analysis of tissue-specific Tregs on the single-cell level.
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Affiliation(s)
- Isabelle Serr
- Group Immune Tolerance in Type 1 Diabetes, Helmholtz Diabetes Center at Helmholtz Zentrum München, Institute of Diabetes Research, Munich, Germany
- Deutsches Zentrum für Diabetesforschung (DZD), Neuherberg, Germany
| | - Felix Drost
- School of Life Sciences Weihenstephan, Technische Universität München, Garching bei München, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Garching bei München, Germany
| | - Carolin Daniel
- Group Immune Tolerance in Type 1 Diabetes, Helmholtz Diabetes Center at Helmholtz Zentrum München, Institute of Diabetes Research, Munich, Germany
- Deutsches Zentrum für Diabetesforschung (DZD), Neuherberg, Germany
- Division of Clinical Pharmacology, Department of Medicine IV, Ludwig-Maximilians-Universität München, Munich, Germany
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39
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Xiong D, Zhang Z, Wang T, Wang X. A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences. Comput Struct Biotechnol J 2021; 19:3255-3268. [PMID: 34141144 PMCID: PMC8192570 DOI: 10.1016/j.csbj.2021.05.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/12/2021] [Accepted: 05/20/2021] [Indexed: 11/02/2022] Open
Abstract
As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In biomedical research, the use of MIL has been focused on medical image analysis and molecule activity prediction. We review and apply 16 methods to investigate the applicability of MIL to a novel biomedical application, cancer detection using T-cell receptor (TCR) sequences. This important application can be a viable approach for large-scale cancer screening, as TCRs can be easily profiled from a subject's peripheral blood. We consider two feasible data-generating mechanisms, and for the purpose of performance evaluation, we simulate data under each mechanism, where we vary potentially important factors to mimic realistic situations. We also apply the methods to sequencing data of ten cancer types from The Cancer Genome Atlas, as an early proof of concept for distinguishing tumor patients from healthy individuals via TCR sequencing of peripheral blood. We find that given an appropriate MIL method is used, satisfactory performance with Area Under the Receiver Operating Characteristic Curve above 80% can be achieved for five in the ten cancers. Based on our numerical results, we make suggestions about selection of a proper method and avoidance of any method with poor performance. We further point out directions of future research as well as identify a pressing need of new MIL methodologies for improved performance (for some cancer types) and more explainable outcomes.
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Affiliation(s)
- Danyi Xiong
- Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, Dallas 75275, TX, USA
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas 75390, TX, USA
| | - Ze Zhang
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas 75390, TX, USA
| | - Tao Wang
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas 75390, TX, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, Dallas 75275, TX, USA
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40
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Systemic Inflammation and Tumour-Infiltrating T-Cell Receptor Repertoire Diversity Are Predictive of Clinical Outcome in High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements. Cancers (Basel) 2021; 13:cancers13040887. [PMID: 33672644 PMCID: PMC7924187 DOI: 10.3390/cancers13040887] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 01/07/2023] Open
Abstract
Simple Summary The current version of the World-Health-Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues acknowledges the provisional entity of high-grade B-cell lymphoma, with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/TH) which is associated with dire prognosis compared to triple-negative diffuse-large-B-cell-lymphoma (tnDLBCL). There is growing evidence for the essential prognostic role of the tumor-microenvironment (TME) and especially the extent of tumor-infiltration by the adaptive immune-system through tumor-infiltrating-lymphocytes (TIL) across a variety of cancers. More precisely, the clonal-architecture of the tumor-infiltrating T-cell-receptor (TCR)-repertoire has recently emerged as a key determinant of risk-stratification in patients with hematological malignancies. Moreover, inflammation-based prognostic-scores, such as the Glasgow-prognostic-score (GPS) were shown to reflect the TME. We therefore performed a large scale next-generation-sequencing (NGS) and clinicopathological study of the TCR-β-chain-repertoire in HGBL-DH/TH revealing several entity-exclusive clonotypes distinct from tnDLBCL, suggestive of tumor-neoantigen-selection and correlate our findings with the GPS in context of clinical outcome in HGBL-DH/TH. Abstract High-grade B-cell lymphoma, with MYC and BCL2 and/or BCL6 rearrangements (double/triple-hit high grade B-cell lymphoma, HGBL-DH/TH) constitutes a provisional entity among B-cell malignancies with an aggressive behavior and dire prognosis. While evidence for the essential prognostic role of the composition of the tumor-microenvironment (TME) in hematologic malignancies is growing, its prognostic impact in HGBL-DH/TH remains unknown. In this study, we outline the adaptive immune response in a cohort of 47 HGBL-DH/TH and 27 triple-negative diffuse large B-cell lymphoma (tnDLBCL) patients in a large-scale, next-generation sequencing (NGS) investigation of the T-cell receptor (TCR) β-chain repertoire and supplement our findings with data on the Glasgow-Prognostic Score (GPS) at diagnosis, as a score-derived measure of systemic inflammation. We supplement these studies with an immunophenotypic investigation of the TME. Our findings demonstrate that the clonal architecture of the TCR repertoire of HGBL-DH/TH differs significantly from tnDLBCL. Moreover, several entity-exclusive clonotypes, suggestive of tumor-neoantigen selection are identified. Additionally, both productive clonality and percentage of maximum frequency clone as measures of TCR repertoire diversity and tumor-directed activity of the adaptive immune system had significant impact on overall survival (OS; productive clonality: p = 0.0273; HR: 2.839; CI: 1.124–7.169; maximum productive frequency: p = 0.0307; HR: 2.167; CI: 1.074–4.370) but not PFS (productive clonality: p = 0.4459; maximum productive frequency: p = 0.5567) in HGBL-DH/TH patients, while GPS was a significant predictor of both OS and PFS (OS: p < 0.0001; PFS: p = 0.0002). Subsequent multivariate analysis revealed GPS and the revised international prognostic index (R-IPI) to be the only prognosticators holding significant impact for OS (GPS: p = 0.038; R-IPI: p = 0.006) and PFS (GPS: p = 0.029; R-IPI: p = 0.006) in HGBL-DH/TH. Through the identification of expanded, recurrent and entity-exclusive TCR-clonotypes we provide indications for a distinct subset of tumor-neoantigenic elements exclusively shared among HGBL-DH/TH. Further, we demonstrate an adverse prognostic role for both systemic inflammation and uniform adaptive immune response.
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