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      Large-Scale Integration of Single-Cell RNA-Seq Data Reveals Astrocyte Diversity and Transcriptomic Modules across Six Central Nervous System Disorders

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      Biomolecules
      MDPI AG

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          Abstract

          The dysfunction of astrocytes in response to environmental factors contributes to many neurological diseases by impacting neuroinflammation responses, glutamate and ion homeostasis, and cholesterol and sphingolipid metabolism, which calls for comprehensive and high-resolution analysis. However, single-cell transcriptome analyses of astrocytes have been hampered by the sparseness of human brain specimens. Here, we demonstrate how large-scale integration of multi-omics data, including single-cell and spatial transcriptomic and proteomic data, overcomes these limitations. We created a single-cell transcriptomic dataset of human brains by integration, consensus annotation, and analyzing 302 publicly available single-cell RNA-sequencing (scRNA-seq) datasets, highlighting the power to resolve previously unidentifiable astrocyte subpopulations. The resulting dataset includes nearly one million cells that span a wide variety of diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), multiple sclerosis (MS), epilepsy (Epi), and chronic traumatic encephalopathy (CTE). We profiled the astrocytes at three levels, subtype compositions, regulatory modules, and cell–cell communications, and comprehensively depicted the heterogeneity of pathological astrocytes. We constructed seven transcriptomic modules that are involved in the onset and progress of disease development, such as the M2 ECM and M4 stress modules. We validated that the M2 ECM module could furnish potential markers for AD early diagnosis at both the transcriptome and protein levels. In order to accomplish a high-resolution, local identification of astrocyte subtypes, we also carried out a spatial transcriptome analysis of mouse brains using the integrated dataset as a reference. We found that astrocyte subtypes are regionally heterogeneous. We identified dynamic cell–cell interactions in different disorders and found that astrocytes participate in key signaling pathways, such as NRG3-ERBB4, in epilepsy. Our work supports the utility of large-scale integration of single-cell transcriptomic data, which offers new insights into underlying multiple CNS disease mechanisms where astrocytes are involved.

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          Most cited references85

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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                Author and article information

                Contributors
                Journal
                BIOMHC
                Biomolecules
                Biomolecules
                MDPI AG
                2218-273X
                April 2023
                April 19 2023
                : 13
                : 4
                : 692
                Article
                10.3390/biom13040692
                37189441
                57de91fb-4637-44fe-80c9-02cc10d30e60
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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