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      Deciphering the tumor immune microenvironment of imatinib-resistance in advanced gastrointestinal stromal tumors at single-cell resolution

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          Abstract

          The heterogeneous nature of tumors presents a considerable obstacle in addressing imatinib resistance in advanced cases of gastrointestinal stromal tumors (GIST). To address this issue, we conducted single-cell RNA-sequencing in primary tumors as well as peritoneal and liver metastases from patients diagnosed with locally advanced or advanced GIST. Single-cell transcriptomic signatures of tumor microenvironment (TME) were analyzed. Immunohistochemistry and multiplex immunofluorescence staining were used to further validate it. This analysis revealed unique tumor evolutionary patterns, transcriptome features, dynamic cell-state changes, and different metabolic reprogramming. The findings indicate that in imatinib-resistant TME, tumor cells with activated immune and cytokine-mediated immune responses interacted with a higher proportion of Treg cells via the TIGIT-NECTIN2 axis. Future immunotherapeutic strategies targeting Treg may provide new directions for the treatment of imatinib-resistant patients. In addition, IDO1+ dendritic cells (DC) were highly enriched in imatinib-resistant TME, interacting with various myeloid cells via the BTLA-TNFRSF14 axis, while the interaction was not significant in imatinib-sensitive TME. Our study highlights the transcriptional heterogeneity and distinct immunosuppressive microenvironment of advanced GIST, which provides novel therapeutic strategies and innovative immunotherapeutic agents for imatinib resistance.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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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
                21716123@zju.edu.cn
                qdfyniuzhaojian@163.com
                zhouyanbing@qduhospital.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                5 March 2024
                5 March 2024
                March 2024
                : 15
                : 3
                : 190
                Affiliations
                [1 ]Department of General Surgery, Affiliated Hospital of Qingdao University, ( https://ror.org/026e9yy16) 16# Jiangsu Road, Qingdao, Shandong China
                [2 ]School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, ( https://ror.org/0220qvk04) Shanghai, 200240 China
                [3 ]Pathology Department, Affiliated Hospital of Qingdao University, ( https://ror.org/026e9yy16) 16# Jiangsu Road, Qingdao, Shandong China
                [4 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, , Zhejiang University, ; Hangzhou, 310058 Zhejiang China
                Author information
                http://orcid.org/0009-0007-5572-2288
                http://orcid.org/0000-0003-3287-6798
                http://orcid.org/0000-0002-0620-7999
                http://orcid.org/0000-0002-3683-1565
                Article
                6571
                10.1038/s41419-024-06571-3
                10914684
                38443340
                b88202f2-f328-455b-b694-3204f13d753e
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.

                History
                : 30 October 2023
                : 16 February 2024
                : 21 February 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82002528
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Associazione Differenziamento e Morte Cellulare ADMC 2024

                Cell biology
                cancer microenvironment,mechanisms of disease
                Cell biology
                cancer microenvironment, mechanisms of disease

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