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      The proteomic profile of the human myotendinous junction

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          Summary

          Proteomics analysis of skeletal muscle has recently progressed from whole muscle tissue to single myofibers. Here, we further focus on a specific myofiber domain crucial for force transmission from muscle to tendon, the myotendinous junction (MTJ). To overcome the anatomical constraints preventing the isolation of pure MTJs, we performed in-depth analysis of the MTJ by progressive removal of the muscle component in semitendinosus muscle-tendon samples. Using detergents with increasing stringency, we quantified >3000 proteins across all samples, and identified 112 significantly enriched MTJ proteins, including 24 known MTJ-enriched proteins. Of the 88 novel MTJ markers, immunofluorescence analysis confirmed the presence of tetraspanin-24 (CD151), kindlin-2 (FERMT2), cartilage intermediate layer protein 1 (CILP), and integrin-alpha10 (ITGA10), at the human MTJ. Together, these human data constitute the first detailed MTJ proteomics resource that will contribute to advance understanding of the biology of the MTJ and its failure in pathological conditions.

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          Highlights

          • •

            Deep proteome analysis of human skeletal muscle and tendon

          • •

            The first proteomic map of human myotendinous junction (MTJ)

          • •

            Discovery of 88 novel MTJ-enriched proteins

          • •

            Validation of novel MTJ markers by immunofluorescence

          Abstract

          Biology experimental methods; Molecular physiology; Musculoskeletal anatomy; Proteomics

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          The PRIDE database and related tools and resources in 2019: improving support for quantification data

          Yasset Perez-Riverol, Attila Csordas, Jingwen Bai … (2018)
          Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            Stefka Tyanova, Tikira Temu, Pavel Sinitcyn … (2016)
            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
            Article 0 comments Cited 2250 times – based on 0 reviews     Review now
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              The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

              Stefka Tyanova, Tikira Temu, Juergen Cox (2016)
              MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.
              Article 0 comments Cited 1395 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Abigail L. Mackey
                Atul S. Deshmukh
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 29 January 2022
                Publication date Collection: 18 February 2022
                Publication date (Electronic): 29 January 2022
                Volume: 25
                Issue: 2
                Electronic Location Identifier: 103836
                Affiliations
                [1 ]Institute of Sports Medicine Copenhagen, Department of Orthopedic Surgery, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Denmark and Part of IOC Research Center, Copenhagen, Denmark
                [2 ]Center for Healthy Aging, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
                [3 ]Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
                [4 ]Section for Sports Traumatology M51, Department of Orthopedic Surgery, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Denmark and Part of IOC Research Center, Copenhagen, Denmark
                [5 ]Institute for Dental Research and Oral Musculoskeletal Biology, Center for Biochemistry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
                [6 ]Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
                [7 ]Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
                [8 ]Xlab, Center for Healthy Aging, Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
                [9 ]Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
                Author notes
                [∗ ]Corresponding author atul.deshmukh@ 123456sund.ku.dk
                [∗∗ ]Corresponding author abigailmac@ 123456sund.ku.dk
                [10]

                These authors contributed equally

                [11]

                Senior authors

                [12]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(22)00106-7 Publisher ID: 103836
                DOI: 10.1016/j.isci.2022.103836
                PMC ID: 8851264
                PubMed ID: 35198892
                SO-VID: bf05622c-5a2d-4ca5-af25-95fd0ac333bb
                Copyright © © 2022 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 1 December 2021
                Date revision received : 12 January 2022
                Date accepted : 24 January 2022
                Categories
                Subject: Article

                Keywords: biology experimental methods,molecular physiology,musculoskeletal anatomy,proteomics
                Data availability:
                Keywords: biology experimental methods, molecular physiology, musculoskeletal anatomy, proteomics

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