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      Dissecting human embryonic skeletal stem cell ontogeny by single-cell transcriptomic and functional analyses

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          Abstract

          Human skeletal stem cells (SSCs) have been discovered in fetal and adult long bones. However, the spatiotemporal ontogeny of human embryonic SSCs during early skeletogenesis remains elusive. Here we map the transcriptional landscape of human limb buds and embryonic long bones at single-cell resolution to address this fundamental question. We found remarkable heterogeneity within human limb bud mesenchyme and epithelium, and aligned them along the proximal–distal and anterior–posterior axes using known marker genes. Osteo-chondrogenic progenitors first appeared in the core limb bud mesenchyme, which give rise to multiple populations of stem/progenitor cells in embryonic long bones undergoing endochondral ossification. Importantly, a perichondrial embryonic skeletal stem/progenitor cell (eSSPC) subset was identified, which could self-renew and generate the osteochondral lineage cells, but not adipocytes or hematopoietic stroma. eSSPCs are marked by the adhesion molecule CADM1 and highly enriched with FOXP1/2 transcriptional network. Interestingly, neural crest-derived cells with similar phenotypic markers and transcriptional networks were also found in the sagittal suture of human embryonic calvaria. Taken together, this study revealed the cellular heterogeneity and lineage hierarchy during human embryonic skeletogenesis, and identified distinct skeletal stem/progenitor cells that orchestrate endochondral and intramembranous ossification.

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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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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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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
                zhudingdingabc@163.com
                bingliu17@yahoo.com
                ryue@tongji.edu.cn
                Journal
                Cell Res
                Cell Res
                Cell Research
                Springer Singapore (Singapore )
                1001-0602
                1748-7838
                20 January 2021
                20 January 2021
                July 2021
                : 31
                : 7
                : 742-757
                Affiliations
                [1 ]GRID grid.410740.6, ISNI 0000 0004 1803 4911, State Key Laboratory of Proteomics, Academy of Military Medical Sciences, , Academy of Military Sciences, ; Beijing, 100071 China
                [2 ]GRID grid.24516.34, ISNI 0000000123704535, Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, , Tongji University, ; Shanghai, 200092 China
                [3 ]GRID grid.73113.37, ISNI 0000 0004 0369 1660, Department of Orthopedics, Changzheng Hospital, , Naval Medical University, ; Shanghai, 200003 China
                [4 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, State Key Laboratory of Experimental Hematology, , Fifth Medical Center of Chinese PLA General Hospital, ; Beijing, 100071 China
                [5 ]GRID grid.24516.34, ISNI 0000000123704535, Department of Cardiology, Shanghai Tenth People’s Hospital, , Tongji University School of Medicine, ; Shanghai, 200072 China
                [6 ]GRID grid.414048.d, ISNI 0000 0004 1799 2720, Department of Transfusion, Daping Hospital, , Army Military Medical University, ; Chongqing, 400042 China
                [7 ]GRID grid.412633.1, Department of Hematology, , The First Affiliated Hospital of Zhengzhou University, ; Zhengzhou, Henan 450052 China
                [8 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, , Jinan University, ; Guangzhou, Guangdong 510632 China
                [9 ]GRID grid.508040.9, Guangzhou Regenerative Medicine and Health-Guangdong Laboratory (GRMH-GDL), ; Guangzhou, Guangdong 510530 China
                [10 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Department of Gynecology, , Fifth Medical Center of Chinese PLA General Hospital, ; Beijing, 100071 China
                [11 ]GRID grid.410740.6, ISNI 0000 0004 1803 4911, Beijing Institute of Radiation Medicine, ; Beijing, 100850 China
                Author information
                http://orcid.org/0000-0003-2231-1320
                http://orcid.org/0000-0002-5189-5268
                http://orcid.org/0000-0003-0680-4029
                http://orcid.org/0000-0002-4731-5945
                http://orcid.org/0000-0002-7313-6924
                http://orcid.org/0000-0002-3401-369X
                Article
                467
                10.1038/s41422-021-00467-z
                8249634
                33473154
                7dec81ca-e45f-4c0f-a13c-77af002c9661
                © Center for Excellence in Molecular Cell Science, CAS 2021

                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
                : 30 July 2020
                : 22 December 2020
                Funding
                Funded by: National Key R&D Program of China(2017YFA0106400)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81890991,31871173
                Award ID: 81871771
                Award ID: 31425012
                Award Recipient :
                Funded by: National Key R&D Program of China(2016YFA0100601)
                Funded by: Beijing Natural Sciences Foundation(7182123)
                Funded by: National Key R&D Program of China (2017YFA0103401, 2016YFA0100601, 2019YFA0110201); Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07S347); Beijing Municipal Science & Technology Commission (Z171100000417009); State Key Laboratory of Proteomics (SKLPK201502); Key Research and Development Program of Guangdong Province (2019B020234002)
                Categories
                Article
                Custom metadata
                © Center for Excellence in Molecular Cell Science, CAS 2021

                Cell biology
                mesenchymal stem cells,transcriptomics
                Cell biology
                mesenchymal stem cells, transcriptomics

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