Dataset Information

Single muscle fiber proteomics reveals unexpected mitochondrial specialization

ABSTRACT: Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here we developed a high sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. The fiber type resolved proteomes can be applied to a variety of physiological and pathological conditions and illustrate the utility of single cell type analysis for dissecting proteomic heterogeneity.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Skeletal Muscle, Muscle Cell

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD001641 | Pride | 2015-02-05

REPOSITORIES: Pride

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Publications

Single muscle fiber proteomics reveals unexpected mitochondrial specialization.

Murgia Marta M Nagaraj Nagarjuna N Deshmukh Atul S AS Zeiler Marlis M Cancellara Pasqua P Moretti Irene I Reggiani Carlo C Schiaffino Stefano S Mann Matthias M

EMBO reports 20150202 3

Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here, we developed a high-sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype-specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. T ...[more]

PMID: 25643707

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Project description:Epithelial cells and differentiated fiber cells represent distinct compartments in the ocular lens. While previous studies have revealed proteins that are preferentially expressed in epithelial vs. fiber cells, a comprehensive proteomics library comparing the molecular composition of epithelial vs. fiber cells is essential for understanding lens formation, function, disease and regenerative potential, and for efficient differentiation of pluripotent stem cells for modeling of lens development and pathology in vitro. To compare protein composition between the lens epithelium and fibers, we employed tandem mass spectrometry (2DLC/ MS) analysis of micro-dissected mouse P0.5 lenses. Functional classifications of the top 525 identified proteins into gene ontology categories by molecular process and subcellular localization, were adapted for lens. Expression levels of both epithelial and fiber proteomes were compared with their temporal and spatial mRNA levels using E14.5, E16.5, E18.5, and P0.5 RNA-Seq data sets. During this developmental time window, multiple complex biosynthetic and catabolic processes generate the molecular and structural foundation for lens transparency. As expected, crystallins showed a high correlation between their mRNA and protein levels. Comprehensive data analysis confirmed and/or predicted roles for transcription factors (TFs), RNA-binding proteins, translational apparatus including ribosomal heterogeneity and initiation factors, microtubules, cytoskeletal and membrane proteins in lens formation and maturation. Our data highlighted many proteins with unknown function in the lens that were preferentially enriched in epithelium or fibers, setting the stage for future studies to further dissect the roles of these proteins in fiber cell differentiation vs. epithelial cell maintenance. In conclusion, the present proteomic datasets established reference mouse lens epithelium and fiber cell proteomes, provided quantitative analyses of protein and RNA-Seq data, and probed the major proteome remodeling required to form the mature lens fiber cells.

Dataset Information

Single muscle fiber proteomics reveals unexpected mitochondrial specialization

Publications

Single muscle fiber proteomics reveals unexpected mitochondrial specialization.

Similar Datasets

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