6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      An optimized quantitative proteomics method establishes the cell type‐resolved mouse brain secretome

      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

          To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the “high‐performance secretome protein enrichment with click sugars” (hi SPECS) method. To demonstrate its broad utility, hi SPECS was used to identify the secretory response of brain slices upon LPS‐induced neuroinflammation and to establish the cell type‐resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer‐linked protease BACE1. hi SPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type‐specific biomarkers for CNS diseases.

          Abstract

          hi SPECS, a miniaturized proteomics protocol based on pull‐down of glycosylated secretory proteins from smaller numbers of cells, defines the specific secretomes of astrocytes, microglia, neurons and oligodendrocytes from primary cells, as well as secretion changes in LPS‐induced inflammatory conditions.

          Related collections

          Most cited references61

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

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

            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
                Bookmark

                Author and article information

                Contributors
                stefan.lichtenthaler@dzne.de
                Journal
                EMBO J
                EMBO J
                10.1002/(ISSN)1460-2075
                EMBJ
                embojnl
                The EMBO Journal
                John Wiley and Sons Inc. (Hoboken )
                0261-4189
                1460-2075
                21 September 2020
                15 October 2020
                21 September 2020
                : 39
                : 20 ( doiID: 10.1002/embj.v39.20 )
                : e105693
                Affiliations
                [ 1 ] German Center for Neurodegenerative Diseases (DZNE) Munich Germany
                [ 2 ] Neuroproteomics School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany
                [ 3 ] Department of Bioinformatics Wissenschaftszentrum Weihenstephan Technical University of Munich Freising Germany
                [ 4 ] Institute of Neuronal Cell Biology Technical University Munich Munich Germany
                [ 5 ] Munich Cluster for Systems Neurology (SyNergy) Munich Germany
                Author notes
                [*] [* ]Corresponding author. Tel: +49 89 440046425; E‐mail: stefan.lichtenthaler@ 123456dzne.de
                Author information
                https://orcid.org/0000-0001-9206-8093
                https://orcid.org/0000-0001-5803-6098
                https://orcid.org/0000-0003-4403-9559
                https://orcid.org/0000-0003-2211-2575
                Article
                EMBJ2020105693
                10.15252/embj.2020105693
                7560198
                32954517
                e27009a9-32bf-4018-886a-229932d7c087
                © 2020 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 May 2020
                : 12 August 2020
                : 14 August 2020
                Page count
                Figures: 12, Tables: 0, Pages: 20, Words: 15947
                Funding
                Funded by: Deutsche Forschungsgemeinschaft (DFG)
                Award ID: EXC 2145 SyNergy project ID 390857198
                Funded by: Bundesministerium für Bildung und Forschung (BMBF)
                Award ID: CLINSPECT‐M and JPND PMG‐AD
                Funded by: Alzheimer Forschung Initiative (AFI)
                Award ID: 18014
                Funded by: Open access funding enabled and organized by Projekt DEAL.
                Categories
                Resource
                Resource
                Custom metadata
                2.0
                15 October 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.2 mode:remove_FC converted:15.10.2020

                Molecular biology
                bace1,brain cells,csf,proteomics,secretomics,neuroscience
                Molecular biology
                bace1, brain cells, csf, proteomics, secretomics, neuroscience

                Comments

                Comment on this article