0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-cell RNA sequencing reveals dysregulation of spinal cord cell types in a severe spinal muscular atrophy mouse model

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Although spinal muscular atrophy (SMA) is a motor neuron disease caused by the loss of survival of motor neuron (SMN) proteins, there is growing evidence that non-neuronal cells play important roles in SMA pathogenesis. However, transcriptome alterations occurring at the single-cell level in SMA spinal cord remain unknown, preventing us from fully comprehending the role of specific cells. Here, we performed single-cell RNA sequencing of the spinal cord of a severe SMA mouse model, and identified ten cell types as well as their differentially expressed genes. Using CellChat, we found that cellular communication between different cell types in the spinal cord of SMA mice was significantly reduced. A dimensionality reduction analysis revealed 29 cell subtypes and their differentially expressed gene. A subpopulation of vascular fibroblasts showed the most significant change in the SMA spinal cord at the single-cell level. This subpopulation was drastically reduced, possibly causing vascular defects and resulting in widespread protein synthesis and energy metabolism reductions in SMA mice. This study reveals for the first time a single-cell atlas of the spinal cord of mice with severe SMA, and sheds new light on the pathogenesis of SMA.

          Author summary

          Spinal muscular atrophy (SMA) is a neurodegenerative disease caused by functional loss of the SMN protein. Although SMA is characterized by degeneration of motor neurons in the anterior horn of the spinal cord, there is growing evidence that non-neuronal cells play important roles in SMA pathogenesis. It has long been unclear whether and to what extent individual cell types in the SMA spinal cord are affected by the deficiency of the SMN protein. Here, we sequenced the spinal cord of a mouse model of severe SMA using single-cell RNA sequencing. Over 20,000 cells, 10 cell types, 29 subtypes and their differentially expressed genes were identified. Overall, cell-cell communication in SMA is substantially reduced. A subpopulation of vasculature fibroblasts showed the most significant change in the SMA spinal cord at the single-cell level. This reduction may account for the vascular defects and reduced oxygen delivery capacity in SMA mice, as well as the widespread reduction of protein synthesis and energy metabolism in various spinal cell types. These findings not only create the first single-cell omics database for SMA, but also help reveal new pathological mechanisms associated with the disease.

          Related collections

          Most cited references106

          • Record: found
          • Abstract: found
          • Article: not found

          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                8 September 2022
                September 2022
                : 18
                : 9
                : e1010392
                Affiliations
                [1 ] Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-Innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Jiangsu Clinical Medicine Center of Tissue Engineering and Nerve Injury Repair, Nantong University, Nantong, China
                [2 ] Department of Prenatal Screening and Diagnosis Center, Affiliated Maternity and Child Health Care Hospital of Nantong University, Nantong, China
                [3 ] Laboratory Animal Center, Nantong University, Nantong, China
                [4 ] Biomedical Polymers Laboratory, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, China
                The Jackson Laboratory, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-8710-7318
                Article
                PGENETICS-D-22-00657
                10.1371/journal.pgen.1010392
                9488758
                36074806
                1910c012-3d68-4b4b-b420-a36fd3905b68
                © 2022 Sun et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 June 2022
                : 23 August 2022
                Page count
                Figures: 6, Tables: 0, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81701127
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 32000841
                Award Recipient :
                Funded by: Municipal Health Commission of Nantong
                Award ID: MA2020019
                Award Recipient :
                Funded by: Science and Technology Bureau of Nantong
                Award ID: JC2020101
                Award Recipient :
                This study was supported by the National Science Foundation of China (Grant No: 81701127 to X.L., Grant No: 32000841 to S.J.), the Municipal Health Commission of Nantong (Grant No: MA2020019 to Q.J.) and the Science and Technology Bureau of Nantong (Grant No: JC2020101 to Q.J.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Anatomy
                Nervous System
                Neuroanatomy
                Spinal Cord
                Medicine and Health Sciences
                Anatomy
                Nervous System
                Neuroanatomy
                Spinal Cord
                Biology and Life Sciences
                Neuroscience
                Neuroanatomy
                Spinal Cord
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Motor Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Motor Neurons
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Connective Tissue Cells
                Fibroblasts
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Glial Cells
                Macroglial Cells
                Astrocytes
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Glial Cells
                Microglial Cells
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Cell Biology
                Cell Physiology
                Cell Communication
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Marker Genes
                Research and Analysis Methods
                Molecular Biology Techniques
                Marker Genes
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-09-20
                All relevant data are within the manuscript and its Supporting Information files. Both raw and processed transcriptome sequencing data reported in this study have been submitted to the Gene Expression Omnibus (GEO) accession number with accession number GSE208629 (scRNA-seq) and GSE209926 (bulk-seq). Browsable data can be accessed via Single Cell Portal.

                Genetics
                Genetics

                Comments

                Comment on this article