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      Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

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          Abstract

          As single‐cell RNA sequencing enables the detailed clustering of T‐cell subpopulations and facilitates the analysis of T‐cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T‐cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour‐specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T‐cell subsets is key to obtaining field‐level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T‐cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

          Abstract

          Single T‐cell metabolism in tumour immunity

          1. Single‐cell RNA sequencing has gained prominence as the preferred tool for clarifying cell subtypes and metabolic heterogeneity.

          2. Various metabolic pathways within T cells in the tumour microenvironment are coordinated, leading to diverse T‐cell behaviours.

          3. Understanding the mechanisms underlying the interplay between T‐cell metabolism and immune functions will pave the way for precise immunotherapy approaches.

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          Most cited references238

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          Integrated analysis of multimodal single-cell data

          Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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            Elements of cancer immunity and the cancer–immune set point

            Immunotherapy is proving to be an effective therapeutic approach in a variety of cancers. But despite the clinical success of antibodies against the immune regulators CTLA4 and PD-L1/PD-1, only a subset of people exhibit durable responses, suggesting that a broader view of cancer immunity is
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              Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progression.

              Failure of T cells to protect against cancer is thought to result from lack of antigen recognition, chronic activation, and/or suppression by other cells. Using a mouse sarcoma model, we show that glucose consumption by tumors metabolically restricts T cells, leading to their dampened mTOR activity, glycolytic capacity, and IFN-γ production, thereby allowing tumor progression. We show that enhancing glycolysis in an antigenic "regressor" tumor is sufficient to override the protective ability of T cells to control tumor growth. We also show that checkpoint blockade antibodies against CTLA-4, PD-1, and PD-L1, which are used clinically, restore glucose in tumor microenvironment, permitting T cell glycolysis and IFN-γ production. Furthermore, we found that blocking PD-L1 directly on tumors dampens glycolysis by inhibiting mTOR activity and decreasing expression of glycolysis enzymes, reflecting a role for PD-L1 in tumor glucose utilization. Our results establish that tumor-imposed metabolic restrictions can mediate T cell hyporesponsiveness during cancer.
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                Author and article information

                Contributors
                zhangjian@i.smu.edu.cn
                luopeng@smu.edu.cn
                weitingyouyou@qq.com
                Journal
                Clin Transl Med
                Clin Transl Med
                10.1002/(ISSN)2001-1326
                CTM2
                Clinical and Translational Medicine
                John Wiley and Sons Inc. (Hoboken )
                2001-1326
                11 March 2024
                March 2024
                : 14
                : 3 ( doiID: 10.1002/ctm2.v14.3 )
                : e1620
                Affiliations
                [ 1 ] Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou China
                [ 2 ] The First Clinical Medical School Southern Medical University Guangzhou China
                [ 3 ] Department of Oncology Taishan People's Hospital Guangzhou China
                [ 4 ] Department of Pathogenic Microbiology and Immunology School of Basic Medical Sciences Xi'an Jiaotong University Xi'an Shaanxi China
                [ 5 ] Department of Neurosurgery Xiangya Hospital Central South University Changsha Hunan China
                [ 6 ] Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China
                [ 7 ] Key Laboratory of Medical Molecular Biology Chinese Academy of Medical Sciences Department of Pathophysiology Peking Union Medical College Institute of Basic Medical Sciences Beijing China
                Author notes
                [*] [* ] Correspondence

                Jian Zhang, Peng Luo and Ting Wei, Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

                Email: zhangjian@ 123456i.smu.edu.cn ; luopeng@ 123456smu.edu.cn and weitingyouyou@ 123456qq.com

                Author information
                https://orcid.org/0000-0003-3833-9117
                https://orcid.org/0000-0003-2401-5349
                https://orcid.org/0000-0002-0452-742X
                https://orcid.org/0000-0001-7217-0111
                https://orcid.org/0000-0002-8215-2045
                Article
                CTM21620
                10.1002/ctm2.1620
                10928360
                c04349e4-bc7e-4885-91ce-73428d196b6a
                © 2024 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 February 2024
                : 22 November 2023
                : 22 February 2024
                Page count
                Figures: 6, Tables: 1, Pages: 31, Words: 18800
                Funding
                Funded by: Natural Science Foundation of Guangdong Province , doi 10.13039/501100003453;
                Award ID: 2018A030313846
                Award ID: 2021A1515012593
                Funded by: Science and Technology Planning Project of Guangdong Province , doi 10.13039/501100012245;
                Award ID: 2019A030317020
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81802257
                Award ID: 81871859
                Award ID: 81772457
                Award ID: 82172750
                Award ID: 82172811
                Award ID: 82260546
                Funded by: Guangdong Basic and Applied Basic Research Foundation (Guangdong—Guangzhou Joint Funds)
                Award ID: 2022A1515111212
                Categories
                Review
                Reviews
                Custom metadata
                2.0
                March 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:12.03.2024

                Medicine
                immunotherapy,metabolic reprogramming,single‐cell rna sequencing,t‐cell metabolism,tumour immunity

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