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      Single-cell transcriptome profiling of the vaginal wall in women with severe anterior vaginal prolapse

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          Abstract

          Anterior vaginal prolapse (AVP) is the most common form of pelvic organ prolapse (POP) and has deleterious effects on women’s health. Despite recent advances in AVP diagnosis and treatment, a cell atlas of the vaginal wall in AVP has not been constructed. Here, we employ single-cell RNA-seq to construct a transcriptomic atlas of 81,026 individual cells in the vaginal wall from AVP and control samples and identify 11 cell types. We reveal aberrant gene expression in diverse cell types in AVP. Extracellular matrix (ECM) dysregulation and immune reactions involvement are identified in both non-immune and immune cell types. In addition, we find that several transcription factors associated with ECM and immune regulation are activated in AVP. Furthermore, we reveal dysregulated cell–cell communication patterns in AVP. Taken together, this work provides a valuable resource for deciphering the cellular heterogeneity and the molecular mechanisms underlying severe AVP.

          Abstract

          Anterior vaginal prolapse (AVP), the most common form of pelvic organ prolapse, has deleterious effects on women’s health. Here the authors employ single-cell RNA-seq to construct a transcriptomic atlas of vaginal wall cells from AVP patients, and find that extracellular matrix dysregulation and immune reaction are associated with AVP.

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          Most cited references54

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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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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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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                Author and article information

                Contributors
                ygyang@big.ac.cn
                zhulan@pumch.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 January 2021
                4 January 2021
                2021
                : 12
                : 87
                Affiliations
                [1 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Medical Science Research Center, Peking Union Medical College Hospital, , Chinese Academy of Medical Science and Peking Union Medical College, ; 100730 Beijing, China
                [2 ]GRID grid.464209.d, ISNI 0000 0004 0644 6935, CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, , Beijing Institute of Genomics, Chinese Academy of Sciences, ; 100101 Beijing, China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Sino-Danish College, , University of Chinese Academy of Sciences, ; 101408 Beijing, China
                [4 ]GRID grid.464209.d, ISNI 0000 0004 0644 6935, China National Center for Bioinformation, ; 100101 Beijing, China
                [5 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; 100049 Beijing, China
                [6 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, ; 100101 Beijing, China
                [7 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Departments of Obstetrics and Gynecology, Peking Union Medical College Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; 100730 Beijing, China
                [8 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; 100730 Beijing, China
                Author information
                http://orcid.org/0000-0002-6032-6032
                http://orcid.org/0000-0002-7965-1927
                http://orcid.org/0000-0002-2821-8541
                http://orcid.org/0000-0001-5753-5426
                Article
                20358
                10.1038/s41467-020-20358-y
                7782707
                33397933
                a139280b-732a-45bb-a93f-b5e358cfa40e
                © The Author(s) 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
                : 7 May 2020
                : 30 November 2020
                Funding
                Funded by: Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SMC027, ZDBS-LY-SM013)
                Funded by: the National Key R&D Program of China, Stem Cell and Translational Research (2019YFA0110901);the K. C. Wong Education Foundation, Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)
                Funded by: FundRef https://doi.org/10.13039/501100005150, Chinese Academy of Medical Sciences (CAMS);
                Award ID: 2017-I2M-1-002
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                transcriptomics,reproductive disorders,urogenital reproductive disorders,reproductive signs and symptoms

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