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      YTHDF2 upregulation and subcellular localization dictate CD8 T cell polyfunctionality in anti-tumor immunity

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          Abstract

          RNA methylation is an important regulatory process to determine immune cell function but how it affects the anti-tumor activity of CD8 T cells is not fully understood. Here we show that the N 6-methyladenosine (m 6A) RNA reader YTHDF2 is highly expressed in early effector or effector-like CD8 T cells. We find that YTHDF2 facilitates nascent RNA synthesis, and m 6A recognition is fundamental for this distinctively nuclear function of the protein, which also reinforces its autoregulation at the RNA level. Loss of YTHDF2 in T cells exacerbates tumor progression and confers unresponsiveness to PD-1 blockade in mice and in humans. In addition to initiating RNA decay that is necessary for mitochondrial fitness, YTHDF2 orchestrates chromatin changes that promote T cell polyfunctionality. YTHDF2 interacts with IKZF1/3, which is important for sustained transcription of their target genes. Accordingly, immunotherapy-induced efficacy could be largely restored in YTHDF2-deficient T cells through combinational use of IKZF1/3 inhibitor lenalidomide in a mouse model. Thus, YTHDF2 coordinates epi-transcriptional and transcriptional networks to potentiate T cell immunity, which could inform therapeutic intervention.

          Abstract

          RNA methylation has recently identified as an important regulatory mechanism governing functional cellular states, but its effect on the antitumour activity of CD8  + T cells is not fully explored. Here authors assign an essential nuclear, m 6A-recognition-dependent function to YTHDF2, which, in conjunction with its regulatory role in IKZF1/3-mediated gene transcription, governs anti-tumor activity in CD8  + T cells.

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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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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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                xurh@sysucc.org.cn
                jiajiehou@um.edu.mo
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 November 2024
                5 November 2024
                2024
                : 15
                : 9559
                Affiliations
                [1 ]GRID grid.437123.0, ISNI 0000 0004 1794 8068, Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; MOE Frontier Science Center for Precision Oncology, , University of Macau, ; Macau, SAR China
                [2 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, , Sun Yat-sen University Cancer Center, ; Guangzhou, China
                [3 ]Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, ( https://ror.org/02drdmm93) Guangzhou, China
                [4 ]Translational Research Center, Zhuhai UM Science & Technology Research Institute, Zhuhai, China
                [5 ]Department of Systems Biology, The Beckman Research Institute of City of Hope, ( https://ror.org/05fazth07) Duarte, CA USA
                [6 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Liver Surgery, Renji Hospital, School of Medicine, , Shanghai Jiao Tong University, ; Shanghai, China
                [7 ]Peking-Tsinghua Center for Life Sciences, Peking University, ( https://ror.org/02v51f717) Beijing, China
                [8 ]GRID grid.89957.3a, ISNI 0000 0000 9255 8984, State Key Laboratory of Reproductive Medicine, , Nanjing Medical University, ; Nanjing, China
                [9 ]Department of Liver Surgery, Sun Yat-sen University Cancer Center, ( https://ror.org/0400g8r85) Guangzhou, China
                [10 ]Department of Medical Oncology, Sun Yat-sen University Cancer Center, ( https://ror.org/0400g8r85) Guangzhou, China
                Author information
                http://orcid.org/0009-0002-6401-7090
                http://orcid.org/0000-0003-3749-2902
                http://orcid.org/0000-0001-8033-3902
                http://orcid.org/0000-0001-9771-8534
                http://orcid.org/0000-0002-9011-6837
                Article
                53997
                10.1038/s41467-024-53997-6
                11538425
                39500904
                91a88045-d3d8-46fe-8fb8-2bac799d970d
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 19 December 2023
                : 28 October 2024
                Funding
                Funded by: This work was supported by Guangdong Provincial Science Fund for Distinguished Young Scholars (2021B1515020007, to J.H.), General Program of National Natural Science Foundation of China (8271881 & 81871970, to J.H.), Macau Science and Technology Development Fund (FDCT) (0071/2023/RIA2, to J.H.)
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                © Springer Nature Limited 2024

                Uncategorized
                lymphocyte activation,tumour immunology,cytotoxic t cells,methylation
                Uncategorized
                lymphocyte activation, tumour immunology, cytotoxic t cells, methylation

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