37
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      High-throughput proteomics fiber typing (ProFiT) for comprehensive characterization of single skeletal muscle fibers

      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

          Background

          Skeletal muscles are composed of a heterogeneous collection of fiber types with different physiological adaption in response to a stimulus and disease-related conditions. Each fiber has a specific molecular expression of myosin heavy chain molecules (MyHC). So far, MyHCs are currently the best marker proteins for characterization of individual fiber types, and several proteome profiling studies have helped to dissect the molecular signature of whole muscles and individual fibers.

          Methods

          Herein, we describe a mass spectrometric workflow to measure skeletal muscle fiber type-specific proteomes. To bypass the limited quantities of protein in single fibers, we developed a Proteomics high-throughput fiber typing (ProFiT) approach enabling profiling of MyHC in single fibers. Aliquots of protein extracts from separated muscle fibers were subjected to capillary LC-MS gradients to profile MyHC isoforms in a 96-well format. Muscle fibers with the same MyHC protein expression were pooled and subjected to proteomic, pulsed-SILAC, and phosphoproteomic analysis.

          Results

          Our fiber type-specific quantitative proteome analysis confirmed the distribution of fiber types in the soleus muscle, substantiates metabolic adaptions in oxidative and glycolytic fibers, and highlighted significant differences between the proteomes of type IIb fibers from different muscle groups, including a differential expression of desmin and actinin-3. A detailed map of the Lys-6 incorporation rates in muscle fibers showed an increased turnover of slow fibers compared to fast fibers. In addition, labeling of mitochondrial respiratory chain complexes revealed a broad range of Lys-6 incorporation rates, depending on the localization of the subunits within distinct complexes.

          Conclusion

          Overall, the ProFiT approach provides a versatile tool to rapidly characterize muscle fibers and obtain fiber-specific proteomes for different muscle groups.

          Related collections

          Most cited references43

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

          Skeletal muscle: a brief review of structure and function.

          Skeletal muscle is one of the most dynamic and plastic tissues of the human body. In humans, skeletal muscle comprises approximately 40% of total body weight and contains 50-75% of all body proteins. In general, muscle mass depends on the balance between protein synthesis and degradation and both processes are sensitive to factors such as nutritional status, hormonal balance, physical activity/exercise, and injury or disease, among others. In this review, we discuss the various domains of muscle structure and function including its cytoskeletal architecture, excitation-contraction coupling, energy metabolism, and force and power generation. We will limit the discussion to human skeletal muscle and emphasize recent scientific literature on single muscle fibers.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards*

            Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a “proteomic ruler” because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset—even in retrospective analysis—and we demonstrate its usefulness with a series of mouse organ proteomes.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data

              Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, the presence of protein domains or the membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.
                Bookmark

                Author and article information

                Contributors
                marcus.krueger@uni-koeln.de
                Journal
                Skelet Muscle
                Skelet Muscle
                Skeletal Muscle
                BioMed Central (London )
                2044-5040
                23 March 2020
                23 March 2020
                2020
                : 10
                : 7
                Affiliations
                [1 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, CECAD Research Center, Institute for Genetics, , University of Cologne, ; 50931 Cologne, Germany
                [2 ]GRID grid.419502.b, ISNI 0000 0004 0373 6590, Max Planck Institute for the Biology of Aging, ; 50931 Cologne, Germany
                [3 ]GRID grid.428736.c, Venetian Institute of Molecular Medicine (VIMM), ; Via Orus 2, 35129 Padova, Italy
                [4 ]GRID grid.418032.c, ISNI 0000 0004 0491 220X, Max Planck Institute for Heart and Lung Research, ; 61231 Bad Nauheim, Germany
                [5 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Center for Molecular Medicine (CMMC), , University of Cologne, ; 50931 Cologne, Germany
                Article
                226
                10.1186/s13395-020-00226-5
                7087369
                32293536
                26315fe9-c70b-4231-9e2f-52b442d88beb
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 2 December 2019
                : 4 March 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: EXC 2030 – 390661388
                Categories
                Methodology
                Custom metadata
                © The Author(s) 2020

                Rheumatology
                muscle fiber proteomics,myhc profiling,protein turnover,phosphoproteomics
                Rheumatology
                muscle fiber proteomics, myhc profiling, protein turnover, phosphoproteomics

                Comments

                Comment on this article