8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Transcriptional profiling of mouse peripheral nerves to the single-cell level to build a sciatic nerve ATlas (SNAT)

      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

          Peripheral nerves are organ-like structures containing diverse cell types to optimize function. This interactive assembly includes mostly axon-associated Schwann cells, but also endothelial cells of supporting blood vessels, immune system-associated cells, barrier-forming cells of the perineurium surrounding and protecting nerve fascicles, and connective tissue-resident cells within the intra-fascicular endoneurium and inter-fascicular epineurium. We have established transcriptional profiles of mouse sciatic nerve-inhabitant cells to foster the fundamental understanding of peripheral nerves. To achieve this goal, we have combined bulk RNA sequencing of developing sciatic nerves up to the adult with focused bulk and single-cell RNA sequencing of Schwann cells throughout postnatal development, extended by single-cell transcriptome analysis of the full sciatic nerve both perinatally and in the adult. The results were merged in the transcriptome resource Sciatic Nerve ATlas (SNAT: https://www.snat.ethz.ch). We anticipate that insights gained from our multi-layered analysis will serve as valuable interactive reference point to guide future studies.

          Related collections

          Most cited references99

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

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Spatial reconstruction of single-cell gene expression

              Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
                Bookmark

                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                23 April 2021
                2021
                : 10
                : e58591
                Affiliations
                [1 ]Department of Biology, Institute of Molecular Health Sciences, Swiss Federal Institute of Technology, ETH Zurich ZurichSwitzerland
                [2 ]Functional Genomics Center Zurich, ETH Zurich/University of Zurich ZurichSwitzerland
                Johns Hopkins University School of Medicine United States
                California Institute of Technology United States
                Johns Hopkins University School of Medicine United States
                Johns Hopkins University United States
                John Hopkins United States
                Author information
                http://orcid.org/0000-0001-6613-654X
                https://orcid.org/0000-0002-0159-4133
                http://orcid.org/0000-0003-0026-8739
                https://orcid.org/0000-0002-9211-5184
                Article
                58591
                10.7554/eLife.58591
                8064760
                33890853
                92d9ebb1-142f-42be-a1f9-29330aa50184
                © 2021, Gerber et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 05 May 2020
                : 13 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003006, Eidgenössische Technische Hochschule Zürich;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Developmental Biology
                Neuroscience
                Custom metadata
                Establishing transcriptional profiles of nerve-inhabitant cells as resource to foster the fundamental understanding of peripheral nerves.

                Life sciences
                schwann cells,myelination,nerves,peripheral nervous system,mouse
                Life sciences
                schwann cells, myelination, nerves, peripheral nervous system, mouse

                Comments

                Comment on this article