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Ma X, Zhuo Y, Zhang Z, Yang Y, He P, Zeng Y, Huang Y, Wen X. Association of T-cell receptor repertoires and arterial stiffness in patients with essential hypertension. J Hypertens 2024; 42:1440-1448. [PMID: 38934191 DOI: 10.1097/hjh.0000000000003757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
BACKGROUND Abnormal immune responses, particularly T-cell activity, are linked to vascular complications in hypertension, but mechanisms remain unknown. Our study aims to explore the association between arterial stiffness, assessed by brachial-ankle pulse wave velocity (baPWV), and T-cell receptor (TCR) repertoires in essential hypertension patients, focusing on understanding the role of T cells in the development of arterial stiffness in this population. METHODS The study included 301 essential hypertension patients and 48 age-matched normotensive controls. Essential hypertension patients were stratified into high (baPWV ≥1400 cm/s, n = 213) and low (baPWV <1400 cm/s, n = 88) baPWV groups. High-throughput sequencing analyzed peripheral TCRβ repertoires. RESULTS Significant TCRβ repertoire differences were observed between essential hypertension and normotensive groups, as well as between high and low baPWV essential hypertension subgroups. Specifically, patients in the high baPWV group exhibited notable variations in the utilization of specific TCR beta joining (TRBJ) and variable (TRBV) genes compared to the low baPWV group. These alterations were accompanied by reduced TCRβ diversity (represented by diversity 50 s), increased percentages of the largest TCRβ clones, and a higher number of TCRβ clones exceeding 0.1%. The presence of specific TCRβ clones was detected in both groups. Furthermore, reduced diversity 50s and elevated percentages of the largest TCRβ clones were independently correlated with baPWV, emerging as potential risk factors for increased baPWV in essential hypertension patients. CONCLUSION TCR repertoires were independently associated with arterial stiffness in patients with essential hypertension, implicating a potential role for dysregulated T-cell responses in the pathogenesis of arterial stiffness in this patient population.Trial registration: ChiCTR2100054414.
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Affiliation(s)
- Xiaoxiang Ma
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China
| | - Yue Zhuo
- Department of Laboratory Research, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China
| | | | - Yanhua Yang
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China
| | - Pengming He
- Chengdu ExAb Biotechnology LTD, Chengdu, China
| | - Yi Zeng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China
| | - Yan Huang
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China
| | - Xueping Wen
- Chengdu ExAb Biotechnology LTD, Chengdu, China
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2
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Williams CG, Moreira ML, Asatsuma T, Lee HJ, Li S, Barrera I, Murray E, Soon MSF, Engel JA, Khoury DS, Le S, Wanrooy BJ, Schienstock D, Alexandre YO, Skinner OP, Joseph R, Beattie L, Mueller SN, Chen F, Haque A. Plasmodium infection induces phenotypic, clonal, and spatial diversity among differentiating CD4 + T cells. Cell Rep 2024; 43:114317. [PMID: 38848213 DOI: 10.1016/j.celrep.2024.114317] [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: 12/10/2023] [Revised: 04/21/2024] [Accepted: 05/20/2024] [Indexed: 06/09/2024] Open
Abstract
Naive CD4+ T cells must differentiate in order to orchestrate immunity to Plasmodium, yet understanding of their emerging phenotypes, clonality, spatial distributions, and cellular interactions remains incomplete. Here, we observe that splenic polyclonal CD4+ T cells differentiate toward T helper 1 (Th1) and T follicular helper (Tfh)-like states and exhibit rarer phenotypes not elicited among T cell receptor (TCR) transgenic counterparts. TCR clones present at higher frequencies exhibit Th1 skewing, suggesting that variation in major histocompatibility complex class II (MHC-II) interaction influences proliferation and Th1 differentiation. To characterize CD4+ T cell interactions, we map splenic microarchitecture, cellular locations, and molecular interactions using spatial transcriptomics at near single-cell resolution. Tfh-like cells co-locate with stromal cells in B cell follicles, while Th1 cells in red pulp co-locate with activated monocytes expressing multiple chemokines and MHC-II. Spatial mapping of individual transcriptomes suggests that proximity to chemokine-expressing monocytes correlates with stronger effector phenotypes in Th1 cells. Finally, CRISPR-Cas9 gene disruption reveals a role for CCR5 in promoting clonal expansion and Th1 differentiation. A database of cellular locations and interactions is presented: https://haquelab.mdhs.unimelb.edu.au/spatial_gui/.
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Affiliation(s)
- Cameron G Williams
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Marcela L Moreira
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Takahiro Asatsuma
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Hyun Jae Lee
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Shihan Li
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Irving Barrera
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Megan S F Soon
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
| | - Jessica A Engel
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
| | - David S Khoury
- Kirby Institute, University of New South Wales, Kensington, NSW 2052, Australia
| | - Shirley Le
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Brooke J Wanrooy
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Dominick Schienstock
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Yannick O Alexandre
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Oliver P Skinner
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Rainon Joseph
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Lynette Beattie
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Scott N Mueller
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Ashraful Haque
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3000, Australia.
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3
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Rathgeber AC, Ludwig LS, Penter L. Single-cell genomics-based immune and disease monitoring in blood malignancies. Clin Hematol Int 2024; 6:62-84. [PMID: 38884110 PMCID: PMC11180218 DOI: 10.46989/001c.117961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/25/2023] [Indexed: 06/18/2024] Open
Abstract
Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.
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Affiliation(s)
- Anja C Rathgeber
- Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine
- Department of Hematology, Oncology, and Tumorimmunology Charité - Universitätsmedizin Berlin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Leif S Ludwig
- Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Livius Penter
- Department of Hematology, Oncology, and Tumorimmunology Charité - Universitätsmedizin Berlin
- BIH Biomedical Innovation Academy Berlin Institute of Health at Charité - Universitätsmedizin Berlin
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4
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Weng KG, Lei HK, Shen DS, Wang Y, Zhu XD. Treatment-Related Lymphopenia is Possibly a Marker of Good Prognosis in Nasopharyngeal Carcinoma: a Propensity-Score Matching Analysis. Cancer Manag Res 2024; 16:603-616. [PMID: 38855327 PMCID: PMC11162643 DOI: 10.2147/cmar.s456717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/21/2024] [Indexed: 06/11/2024] Open
Abstract
Purpose The aims of the study were to monitor circulating lymphocyte subset counts before and after therapy for nasopharyngeal carcinoma (NPC), and investigate their relationships with patient outcomes. Patients and Methods Subjects comprised patients with TNM stage I-IVA NPC who underwent radiotherapy. Peripheral venous blood samples were collected before and after treatment. Lymphocyte subset counts were analyzed by flow cytometry. Differences between post-treatment and baseline counts were calculated to determine Δ values. Patients were divided into high and low groups, based on median lymphocyte subset counts; propensity score matching was applied to balance groups. Progression-free survival (PFS) and overall survival (OS) were plotted using Kaplan-Meier curves and compared using a Log rank test. Relationships between lymphocyte subset counts and patient survival were subjected to Cox regression analysis. Results Patients with NPC (n=746) were enrolled from 2012-2022. Higher CD8+ and total T cell baseline counts were associated with better 5-year PFS (73.7% vs 63.1%, P=0.002 and 73.8% vs 64.1%, P=0.005, respectively). Similarly, higher Δ values of CD4+ and total T cells were associated with higher 5-year PFS (76.2% vs 63.5%, P=0.001; 74.3% vs 65.4%, P=0.010) and OS (89.8% vs 81.6%, P=0.005; 88.6% vs 82.5%, P=0.009). Multivariate Cox regression revealed that CD8+ (hazard ratio (HR) 0.651, P=0.002) and total T (HR 0.600, P<0.001) cells were significantly associated with PFS. CD4+ (HR 0.708, P=0.038) and total T (HR 0.639, P=0.031) cells were independent prognostic factors for OS. Conclusion NPC patients with low total or CD8+ T cell counts before treatment had worse prognosis; however, those with more significant decreases in total or CD4+ T cells possibly had better outcomes. T cell counts can be reliable indicators to predict prognosis.
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Affiliation(s)
- Ke-gui Weng
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Hai-ke Lei
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - De-Song Shen
- Department of Oncology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, People’s Republic of China
| | - Ying Wang
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, People’s Republic of China
| | - Xiao-Dong Zhu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China
- Department of Oncology, Wuming Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
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5
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [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: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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6
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Shi W, Zhang J, Huang S, Fan Q, Cao J, Zeng J, Wu L, Yang C. Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry. JACS AU 2024; 4:1723-1743. [PMID: 38818076 PMCID: PMC11134576 DOI: 10.1021/jacsau.4c00118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.
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Affiliation(s)
- Weixiong Shi
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jing Zhang
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
| | - Shanqing Huang
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qian Fan
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiao Cao
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zeng
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lingling Wu
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Chaoyong Yang
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
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7
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [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: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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8
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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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9
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Shao W, Yao Y, Yang L, Li X, Ge T, Zheng Y, Zhu Q, Ge S, Gu X, Jia R, Song X, Zhuang A. Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy. Exp Hematol Oncol 2024; 13:37. [PMID: 38570883 PMCID: PMC10988985 DOI: 10.1186/s40164-024-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.
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Affiliation(s)
- Weihuan Shao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yiran Yao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiaoran Li
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Tongxin Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yue Zheng
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Qiuyi Zhu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Shengfang Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiang Gu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Renbing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Xin Song
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
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10
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Sud A, Parry EM, Wu CJ. The molecular map of CLL and Richter's syndrome. Semin Hematol 2024; 61:73-82. [PMID: 38368146 DOI: 10.1053/j.seminhematol.2024.01.009] [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: 10/31/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/19/2024]
Abstract
Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.
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Affiliation(s)
- Amit Sud
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Erin M Parry
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA
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11
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Jeffreys N, Brockman JM, Zhai Y, Ingber DE, Mooney DJ. Mechanical forces amplify TCR mechanotransduction in T cell activation and function. APPLIED PHYSICS REVIEWS 2024; 11:011304. [PMID: 38434676 PMCID: PMC10848667 DOI: 10.1063/5.0166848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/08/2023] [Indexed: 03/05/2024]
Abstract
Adoptive T cell immunotherapies, including engineered T cell receptor (eTCR) and chimeric antigen receptor (CAR) T cell immunotherapies, have shown efficacy in treating a subset of hematologic malignancies, exhibit promise in solid tumors, and have many other potential applications, such as in fibrosis, autoimmunity, and regenerative medicine. While immunoengineering has focused on designing biomaterials to present biochemical cues to manipulate T cells ex vivo and in vivo, mechanical cues that regulate their biology have been largely underappreciated. This review highlights the contributions of mechanical force to several receptor-ligand interactions critical to T cell function, with central focus on the TCR-peptide-loaded major histocompatibility complex (pMHC). We then emphasize the role of mechanical forces in (i) allosteric strengthening of the TCR-pMHC interaction in amplifying ligand discrimination during T cell antigen recognition prior to activation and (ii) T cell interactions with the extracellular matrix. We then describe approaches to design eTCRs, CARs, and biomaterials to exploit TCR mechanosensitivity in order to potentiate T cell manufacturing and function in adoptive T cell immunotherapy.
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Affiliation(s)
| | | | - Yunhao Zhai
- Wyss Institute for Biologically Inspired Engineering, Boston, Massachusetts 02115, USA
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12
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Maurer K, Park CY, Mani S, Borji M, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Farhi SL, Li S, Livak KJ, Ritz J, Soiffer RJ, Wu CJ, Azizi E. Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579677. [PMID: 38405900 PMCID: PMC10888840 DOI: 10.1101/2024.02.09.579677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.
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13
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Liu Y, Altreuter J, Bodapati S, Cristea S, Wong CJ, Wu CJ, Michor F. Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities. CELL GENOMICS 2024; 4:100444. [PMID: 38190106 PMCID: PMC10794784 DOI: 10.1016/j.xgen.2023.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/12/2023] [Accepted: 10/24/2023] [Indexed: 01/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.
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Affiliation(s)
- Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Jennifer Altreuter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Sudheshna Bodapati
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Simona Cristea
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Cheryl J Wong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA
| | - Catherine J Wu
- Harvard Medical School, Boston, MA 02115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02138, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA.
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14
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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15
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Saliby RM, Saad E, Kashima S, Schoenfeld DA, Braun DA. Update on Biomarkers in Renal Cell Carcinoma. Am Soc Clin Oncol Educ Book 2024; 44:e430734. [PMID: 38207251 DOI: 10.1200/edbk_430734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Immune checkpoint inhibitors have significantly transformed the treatment paradigm for metastatic renal cell carcinoma (RCC), offering prolonged overall survival and achieving remarkable deep and durable responses. However, given the multiple ICI-containing, standard-of-care regimens approved for RCC, identifying biomarkers that predict therapeutic response and resistance is of critical importance. Although tumor-intrinsic features such as pathological characteristics, genomic alterations, and transcriptional signatures have been extensively investigated, they have yet to provide definitive, robust predictive biomarkers. Current research is exploring host factors through in-depth characterization of the immune system. Additionally, innovative technological approaches are being developed to overcome challenges presented by existing techniques, such as tumor heterogeneity. Promising avenues in biomarker discovery include the study of the microbiome, radiomics, and spatial transcriptomics.
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Affiliation(s)
- Renée M Saliby
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
- Yale Center of Cellular and Molecular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT
| | - Eddy Saad
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Soki Kashima
- Yale Center of Cellular and Molecular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT
| | - David A Schoenfeld
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - David A Braun
- Yale Center of Cellular and Molecular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
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16
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Iorgulescu JB, Yoon CH, Wu CJ, Macosko EZ, Chen F. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 2024; 625:101-109. [PMID: 38093010 PMCID: PMC10764288 DOI: 10.1038/s41586-023-06837-4] [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: 03/26/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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Affiliation(s)
- Andrew J C Russell
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Jackson A Weir
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | - Naeem M Nadaf
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Vipin Kumar
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sandeep Kambhampati
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard University, Boston, MA, USA
| | - Ruth Raichur
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Sophia Liu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biophysics Program, Harvard University, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Vignesh Shanmugam
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Guangzhou Laboratory, Guangdong, China
| | - J Bryan Iorgulescu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
- Molecular Diagnostics Laboratory, Department of Hematopathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles H Yoon
- Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine J Wu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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17
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Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [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: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
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Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
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18
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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19
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Xiao H, Rosen A, Chhibbar P, Moise L, Das J. From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches. Hum Vaccin Immunother 2023; 19:2282803. [PMID: 38100557 PMCID: PMC10730168 DOI: 10.1080/21645515.2023.2282803] [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: 07/15/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023] Open
Abstract
A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.
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Affiliation(s)
- Hanxi Xiao
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron Rosen
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Prabal Chhibbar
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jishnu Das
- Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
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20
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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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21
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Engblom C, Thrane K, Lin Q, Andersson A, Toosi H, Chen X, Steiner E, Lu C, Mantovani G, Hagemann-Jensen M, Saarenpää S, Jangard M, Saez-Rodriguez J, Michaëlsson J, Hartman J, Lagergren J, Mold JE, Lundeberg J, Frisén J. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 2023; 382:eadf8486. [PMID: 38060664 DOI: 10.1126/science.adf8486] [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: 11/22/2022] [Accepted: 10/23/2023] [Indexed: 12/18/2023]
Abstract
The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.
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Affiliation(s)
- Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kim Thrane
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Qirong Lin
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Alma Andersson
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Hosein Toosi
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xinsong Chen
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Embla Steiner
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Chang Lu
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Giulia Mantovani
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mattias Jangard
- ENT Unit, Sophiahemmet University Research Laboratory and Sophiahemmet Hospital, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
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22
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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23
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Cross AR, Gartner L, Hester J, Issa F. Opportunities for High-plex Spatial Transcriptomics in Solid Organ Transplantation. Transplantation 2023; 107:2464-2472. [PMID: 36944604 DOI: 10.1097/tp.0000000000004587] [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] [Indexed: 03/23/2023]
Abstract
The last 5 y have seen the development and widespread adoption of high-plex spatial transcriptomic technology. This technique detects and quantifies mRNA transcripts in situ, meaning that transcriptomic signatures can be sampled from specific cells, structures, lesions, or anatomical regions while conserving the physical relationships that exist within complex tissues. These methods now frequently implement next-generation sequencing, enabling the simultaneous measurement of many targets, up to and including the whole mRNA transcriptome. To date, spatial transcriptomics has been foremost used in the fields of neuroscience and oncology, but there is potential for its use in transplantation sciences. Transplantation has a clear dependence on biopsies for diagnosis, monitoring, and research. Transplant patients represent a unique cohort with multiple organs of interest, clinical courses, demographics, and immunosuppressive regimens. Obtaining high complexity data on the disease processes underlying rejection, tolerance, infection, malignancy, and injury could identify new opportunities for therapeutic intervention and biomarker identification. In this review, we discuss currently available spatial transcriptomic technologies and how they can be applied to transplantation.
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Affiliation(s)
- Amy R Cross
- Translational Research and Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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24
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Norberg SM, Bai K, Sievers C, Robbins Y, Friedman J, Yang X, Kenyon M, Ward E, Schlom J, Gulley J, Lankford A, Semnani R, Sabzevari H, Brough DE, Allen CT. The tumor microenvironment state associates with response to HPV therapeutic vaccination in patients with respiratory papillomatosis. Sci Transl Med 2023; 15:eadj0740. [PMID: 37878675 DOI: 10.1126/scitranslmed.adj0740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/03/2023] [Indexed: 10/27/2023]
Abstract
Recurrent respiratory papillomatosis (RRP) is a rare, debilitating neoplastic disorder caused by chronic infection with human papillomavirus (HPV) type 6 or 11 and characterized by growth of papillomas in the upper aerodigestive tract. There is no approved medical therapy, and patients require repeated debulking procedures to maintain voice and airway function. PRGN-2012 is a gorilla adenovirus immune-therapeutic capable of enhancing HPV 6/11-specific T cell immunity. This first-in-human, phase 1 study (NCT04724980) of adjuvant PRGN-2012 treatment in adult patients with severe, aggressive RRP demonstrates the overall safety and clinically meaningful benefit observed with PRGN-2012, with a 50% complete response rate in patients treated at the highest dose. Responders demonstrate greater expansion of peripheral HPV-specific T cells compared with nonresponders. Additional correlative studies identify an association between reduced baseline papilloma HPV gene expression, greater interferon responses and expression of CXCL9 and CXCL10, and greater papilloma T cell infiltration in responders. Conversely, nonresponders were characterized by greater HPV and CXCL8 gene expression, increased neutrophilic cell infiltration, and reduced T cell papilloma infiltration. These results suggest that papilloma HPV gene expression may regulate interferon signaling and chemokine expression profiles within the tumor microenvironment that cooperate to govern clinical response to therapeutic HPV vaccination in patients with respiratory papillomatosis.
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Affiliation(s)
- Scott M Norberg
- Center for Immune-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ke Bai
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Cem Sievers
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yvette Robbins
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jay Friedman
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xinping Yang
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Meg Kenyon
- Center for Immune-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Jeffrey Schlom
- Center for Immune-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - James Gulley
- Center for Immune-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | | | | | | | | | - Clint T Allen
- Center for Immune-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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25
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 92] [Impact Index Per Article: 92.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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26
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Cheng M, Jiang Y, Xu J, Mentis AFA, Wang S, Zheng H, Sahu SK, Liu L, Xu X. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023; 50:625-640. [PMID: 36990426 DOI: 10.1016/j.jgg.2023.03.011] [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: 10/05/2022] [Revised: 03/11/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
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Affiliation(s)
| | - Yujia Jiang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
| | | | | | - Shuai Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Sunil Kumar Sahu
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xun Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; BGI-Shenzhen, Shenzhen, Guangdong 518103, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China.
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27
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Goyette MA, Lipsyc-Sharf M, Polyak K. Clinical and translational relevance of intratumor heterogeneity. Trends Cancer 2023; 9:726-737. [PMID: 37248149 PMCID: PMC10524913 DOI: 10.1016/j.trecan.2023.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/03/2023] [Accepted: 05/05/2023] [Indexed: 05/31/2023]
Abstract
Intratumor heterogeneity (ITH) is a driver of tumor evolution and a main cause of therapeutic resistance. Despite its importance, measures of ITH are still not incorporated into clinical practice. Consequently, standard treatment is frequently ineffective for patients with heterogeneous tumors as changes to treatment regimens are made only after recurrence and disease progression. More effective combination therapies require a mechanistic understanding of ITH and ways to assess it in clinical samples. The growth of technologies enabling the spatially intact analysis of tumors at the single-cell level and the development of sophisticated preclinical models give us hope that ITH will not simply be used as a predictor of a poor outcome but will guide treatment decisions from diagnosis through treatment.
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Affiliation(s)
- Marie-Anne Goyette
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marla Lipsyc-Sharf
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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28
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Playoust E, Remark R, Vivier E, Milpied P. Germinal center-dependent and -independent immune responses of tumor-infiltrating B cells in human cancers. Cell Mol Immunol 2023; 20:1040-1050. [PMID: 37419983 PMCID: PMC10468534 DOI: 10.1038/s41423-023-01060-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023] Open
Abstract
B cells play essential roles in immunity, mainly through the production of high affinity plasma cells (PCs) and memory B (Bmem) cells. The affinity maturation and differentiation of B cells rely on the integration of B-cell receptor (BCR) intrinsic and extrinsic signals provided by antigen binding and the microenvironment, respectively. In recent years, tumor infiltrating B (TIL-B) cells and PCs (TIL-PCs) have been revealed as important players in antitumor responses in human cancers, but their interplay and dynamics remain largely unknown. In lymphoid organs, B-cell responses involve both germinal center (GC)-dependent and GC-independent pathways for Bmem cell and PC production. Affinity maturation of BCR repertoires occurs in GC reactions with specific spatiotemporal dynamics of signal integration by B cells. In general, the reactivation of high-affinity Bmem cells by antigens triggers GC-independent production of large numbers of PC without BCR rediversification. Understanding B-cell dynamics in immune responses requires the integration of multiple tools and readouts such as single-cell phenotyping and RNA-seq, in situ analyses, BCR repertoire analysis, BCR specificity and affinity assays, and functional tests. Here, we review how those tools have recently been applied to study TIL-B cells and TIL-PC in different types of solid tumors. We assessed the published evidence for different models of TIL-B-cell dynamics involving GC-dependent or GC-independent local responses and the resulting production of antigen-specific PCs. Altogether, we highlight the need for more integrative B-cell immunology studies to rationally investigate TIL-B cells as a leverage for antitumor therapies.
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Affiliation(s)
- Eve Playoust
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | | | - Eric Vivier
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
- Innate Pharma, Marseille, France
| | - Pierre Milpied
- Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France.
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29
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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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30
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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: 137] [Impact Index Per Article: 137.0] [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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31
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Luo Z, Wang Y, Bi X, Ismtula D, Wang H, Guo C. Cytokine-induced apoptosis inhibitor 1: a comprehensive analysis of potential diagnostic, prognosis, and immune biomarkers in invasive breast cancer. Transl Cancer Res 2023; 12:1765-1786. [PMID: 37588751 PMCID: PMC10425657 DOI: 10.21037/tcr-23-34] [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: 01/07/2023] [Accepted: 06/07/2023] [Indexed: 08/18/2023]
Abstract
Background Cytokine-induced apoptosis inhibitor 1 (CIAPIN1) is strictly associated with the incidence and progress of several malignant tumors, but its effect on invasive breast cancer (IBC) remains unclear. We directed to research the potential diagnostic and prognostic significance of CIAPIN1 in IBC. Methods The Cancer Genome Atlas (TCGA) database and Tumor Immune Estimation Resource (TIMER) database were utilized to examine CIAPIN1 expression level in IBC and its relationship with clinicopathological features. The diagnostic value and prognostic importance of CIAPIN1 in IBC were assessed by Kaplan-Meier analysis, Cox regression analysis, receiver operating characteristic (ROC) curve and nomogram model. The STRING database and enrichment analysis were utilized to discover the interacting proteins, biological roles and possible cellular mechanisms related to CIAPIN1. The methylation status of CIAPIN1 was analyzed using MethSurv database and the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN). By using Spearman correlation assessment, how the expression of CIAPIN1 was related to TP53, immune checkpoint genes and immune cell infiltration was determined. Results CIAPIN1 mRNA and protein levels were overexpressed in IBC, and significantly correlated with T stage, histological type, age, ER status, PR status and PAM50 (P<0.001). CIAPIN1 overexpression significantly decreased overall survival, distant metastasis free survival (DMFS) and relapse free survival in IBC patients (P<0.001). Similarly, hypermethylation of CIAPIN1 was associated with adverse outcomes in IBC patients. Multivariate Cox analysis identified CIAPIN1 as a potential risk factor for disease specific survival (DSS) and progression free survival (PFS) in individuals with IBC. The outcomes of the ROC curve showed that CIAPIN1 had a better accuracy in predicting ER(-), PR(-) and Asian breast cancer subtypes. Furthermore, there was a substantial correlation between the CIAPIN1 expression level in IBC and immune cell infiltration, TP53, and immune checkpoint genes. Conclusions The high expression of CIAPIN1 in IBC is significantly related to the infiltration status of various tumor immune cells and the poor prognosis of IBC patients. According to this current study, CIAPIN1 is a promising diagnostic and prognostic marker for IBC.
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Affiliation(s)
- Zhiwen Luo
- Department of Breast Surgery, Center of Digestive and Vascular, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Yiyang Wang
- Department of Breast Surgery, Center of Digestive and Vascular, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaojuan Bi
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Dilimulati Ismtula
- Department of Breast Surgery, Center of Digestive and Vascular, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Haiyan Wang
- Department of Breast Surgery, Center of Digestive and Vascular, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Chenming Guo
- Department of Breast Surgery, Center of Digestive and Vascular, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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32
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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33
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Abstract
Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.
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Affiliation(s)
- Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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34
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Nakandakari-Higa S, Canesso MCC, Walker S, Chudnovskiy A, Jacobsen JT, Bilanovic J, Parigi SM, Fiedorczuk K, Fuchs E, Bilate AM, Pasqual G, Mucida D, Pritykin Y, Victora GD. Universal recording of cell-cell contacts in vivo for interaction-based transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.533003. [PMID: 36993443 PMCID: PMC10055214 DOI: 10.1101/2023.03.16.533003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Cellular interactions are essential for tissue organization and functionality. In particular, immune cells rely on direct and usually transient interactions with other immune and non-immune populations to specify and regulate their function. To study these "kiss-and-run" interactions directly in vivo, we previously developed LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts), an approach that uses enzymatic transfer of a labeled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ helper T cells and antigen presenting cells, however. Here, we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the cellular partners of regulatory T cells in steady state, and identify germinal center (GC)-resident T follicular helper (Tfh) cells based on their ability to interact cognately with GC B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalog of the immune populations that physically interact with intestinal epithelial cells (IECs) and find evidence of stepwise acquisition of the ability to interact with IECs as CD4+ T cells adapt to residence in the intestinal tissue. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell-cell interactions across multiple biological systems.
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Affiliation(s)
| | - Maria C C Canesso
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
- Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA
| | - Sarah Walker
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Quantitative and Computational Biology Graduate Program, Princeton University, Princeton, NJ, USA
| | - Aleksey Chudnovskiy
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - Johanne T Jacobsen
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - Jana Bilanovic
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - S Martina Parigi
- Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA
| | - Karol Fiedorczuk
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY, USA
| | - Elaine Fuchs
- Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Angelina M Bilate
- Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA
| | - Giulia Pasqual
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Daniel Mucida
- Laboratory of Mucosal Immunology, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Yuri Pritykin
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Gabriel D Victora
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
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35
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Wu CJ, Yoon CH, Macosko EZ, Chen F. Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.01.535228. [PMID: 37066158 PMCID: PMC10103946 DOI: 10.1101/2023.04.01.535228] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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36
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Montagne JM, Jaffee EM, Fertig EJ. Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:859-868. [PMID: 36947820 PMCID: PMC10236355 DOI: 10.4049/jimmunol.2200660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/23/2022] [Indexed: 03/24/2023]
Abstract
Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.
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Affiliation(s)
- Janelle M Montagne
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
- Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
- Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
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37
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Laumont CM, Nelson BH. B cells in the tumor microenvironment: Multi-faceted organizers, regulators, and effectors of anti-tumor immunity. Cancer Cell 2023; 41:466-489. [PMID: 36917951 DOI: 10.1016/j.ccell.2023.02.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 03/14/2023]
Abstract
Our understanding of tumor-infiltrating lymphocytes (TILs) is rapidly expanding beyond T cell-centric perspectives to include B cells and plasma cells, collectively referred to as TIL-Bs. In many cancers, TIL-Bs carry strong prognostic significance and are emerging as key predictors of response to immune checkpoint inhibitors. TIL-Bs can perform multiple functions, including antigen presentation and antibody production, which allow them to focus immune responses on cognate antigen to support both T cell responses and innate mechanisms involving complement, macrophages, and natural killer cells. In the stroma of the most immunologically "hot" tumors, TIL-Bs are prominent components of tertiary lymphoid structures, which resemble lymph nodes structurally and functionally. Additionally, TIL-Bs participate in a variety of other lympho-myeloid aggregates and engage in dynamic interactions with the tumor stroma. Here, we summarize our current understanding of TIL-Bs in human cancer, highlighting the compelling therapeutic opportunities offered by their unique tumor recognition and effector mechanisms.
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Affiliation(s)
- Céline M Laumont
- Deeley Research Centre, BC Cancer, Victoria, BC V8R 6V5, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Brad H Nelson
- Deeley Research Centre, BC Cancer, Victoria, BC V8R 6V5, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 3E6, Canada.
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38
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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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