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      Single-cell transcriptomic analysis uncovers the origin and intratumoral heterogeneity of parotid pleomorphic adenoma

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          Abstract

          Pleomorphic adenoma (PA) is the most common benign tumour in the salivary gland and has high morphological complexity. However, the origin and intratumoral heterogeneity of PA are largely unknown. Here, we constructed a comprehensive atlas of PA at single-cell resolution and showed that PA exhibited five tumour subpopulations, three recapitulating the epithelial states of the normal parotid gland, and two PA-specific epithelial cell (PASE) populations unique to tumours. Then, six subgroups of PASE cells were identified, which varied in epithelium, bone, immune, metabolism, stemness and cell cycle signatures. Moreover, we revealed that CD36 + myoepithelial cells were the tumour-initiating cells (TICs) in PA, and were dominated by the PI3K-AKT pathway. Targeting the PI3K-AKT pathway significantly inhibited CD36 + myoepithelial cell-derived tumour spheres and the growth of PA organoids. Our results provide new insights into the diversity and origin of PA, offering an important clinical implication for targeting the PI3K-AKT signalling pathway in PA treatment.

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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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            The Molecular Signatures Database (MSigDB) hallmark gene set collection.

            The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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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
                chendm29@mail.sysu.edu.cn
                wangch75@mail.sysu.edu.cn
                Journal
                Int J Oral Sci
                Int J Oral Sci
                International Journal of Oral Science
                Nature Publishing Group UK (London )
                1674-2818
                2049-3169
                7 September 2023
                7 September 2023
                2023
                : 15
                : 38
                Affiliations
                [1 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Hospital of Stomatology, , Sun Yat-sen University, ; Guangzhou, China
                [2 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Guangdong Provincial Key Laboratory of Stomatology, , Sun Yat-sen University, ; Guangzhou, China
                [3 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Guanghua School of Stomatology, , Sun Yat-sen University, ; Guangzhou, China
                [4 ]GRID grid.263488.3, ISNI 0000 0001 0472 9649, Institute for Advanced Study, , Shenzhen University, ; Shenzhen, China
                [5 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, Department of Oral and Maxillofacial Surgery, Nanfang Hospital, , Southern Medical University, ; Guangzhou, China
                [6 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Center for Translational Medicine, The First Affiliated Hospital, , Sun Yat-sen University, ; Guangzhou, China
                Author information
                http://orcid.org/0000-0002-0402-9818
                http://orcid.org/0000-0001-8155-5064
                Article
                243
                10.1038/s41368-023-00243-2
                10484943
                37679344
                271dd464-8bdf-484f-9bec-0a7f7cec51ac
                © West China School of Stomatology Sichuan University 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 February 2023
                : 16 August 2023
                : 16 August 2023
                Funding
                Funded by: Guangdong Financial Fund for High-Caliber Hospital Construction (174-2018-XMZC-0001-03-0125/D-14) to C.W.
                Categories
                Article
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                © West China School of Stomatology Sichuan University 2023

                Dentistry
                cancer,medical research
                Dentistry
                cancer, medical research

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