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      Single-cell transcriptomic profile of satellite glial cells in trigeminal ganglion

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          Abstract

          Satellite glial cells (SGCs) play an important role in regulating the function of trigeminal ganglion (TG) neurons. Multiple mediators are involved in the bidirectional communication between SGCs and neurons in different physiological and pathological states. However, molecular insights into the transcript characteristics of SGCs are limited. Moreover, little is known about the heterogeneity of SGCs in TG, and a more in-depth understanding of the interactions between SGCs and neuron subtypes is needed. Here we show the single-cell RNA sequencing (scRNA-seq) profile of SGCs in TG under physiological conditions. Our results demonstrate TG includes nine types of cell clusters, such as neurons, SGCs, myeloid Schwann cells (mSCs), non-myeloid Schwann cells (nmSCs), immune cells, etc., and the corresponding markers are also presented. We reveal the signature gene expression of SGCs, mSCs and nmSCs in the TG, and analyze the ligand-receptor pairs between neuron subtypes and SGCs in the TG. In the heterogeneity analysis of SGCs, four SGCs subtypes are identified, including subtypes enriched for genes associated with extracellular matrix organization, immediate early genes, interferon beta, and cell adhesion molecules, respectively. Our data suggest the molecular characteristics, heterogeneity of SGCs, and bidirectional interactions between SGCs and neurons, providing a valuable resource for studying SGCs in the TG.

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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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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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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
                Journal
                Front Mol Neurosci
                Front Mol Neurosci
                Front. Mol. Neurosci.
                Frontiers in Molecular Neuroscience
                Frontiers Media S.A.
                1662-5099
                02 February 2023
                2023
                : 16
                : 1117065
                Affiliations
                [1] 1Hospital of Stomatology, Sun Yat-sen University , Guangzhou, China
                [2] 2Guangdong Provincial Key Laboratory of Stomatology , Guangzhou, China
                [3] 3Guanghua School of Stomatology, Sun Yat-sen University , Guangzhou, China
                [4] 4Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong , Pokfulam, Hong Kong SAR, China
                Author notes

                Edited by: Robert Blum, University Hospital Würzburg, Germany

                Reviewed by: Manuela Simonetti, Heidelberg University, Germany; Sara Jager, University of Copenhagen, Denmark

                *Correspondence: Wenguo Fan, fanweng@ 123456mail.sysu.edu.cn ; Fang Huang, hfang@ 123456mail.sysu.edu.cn

                This article was submitted to Molecular Signalling and Pathways, a section of the journal Frontiers in Molecular Neuroscience

                Article
                10.3389/fnmol.2023.1117065
                9932514
                36818656
                da2886b3-e356-4d5d-ab9a-213a07f2f3bc
                Copyright © 2023 Chu, Jia, Xu, Liu, Mai, Liu, Fan and Huang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 December 2022
                : 16 January 2023
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 108, Pages: 16, Words: 12695
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Categories
                Molecular Neuroscience
                Original Research

                Neurosciences
                single-cell rna sequencing,trigeminal ganglion,satellite glial cell,cell heterogeneity,neuron-glia communication

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