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• W2783014139 abstract "Secretome analysis faces several challenges including detection of low abundant proteins and the discrimination of bona fide secreted proteins from false-positive identifications stemming from cell leakage or serum. Here, we developed a two-step secretomics approach and applied it to the analysis of secreted proteins of C2C12 skeletal muscle cells since the skeletal muscle has been identified as an important endocrine organ secreting myokines as signaling molecules. First, we compared culture supernatants with corresponding cell lysates by mass spectrometry-based proteomics and label-free quantification. We identified 672 protein groups as candidate secreted proteins due to their higher abundance in the secretome. On the basis of Brefeldin A mediated blocking of classical secretory processes, we estimated a sensitivity of >80% for the detection of classical secreted proteins for our experimental approach. In the second step, the peptide level information was integrated with UniProt based protein information employing the newly developed bioinformatics tool Lysate and Secretome Peptide Feature Plotter (LSPFP) to detect proteolytic protein processing events that might occur during secretion. Concerning the proof of concept, we identified truncations of the cytoplasmic part of the protein Plexin-B2. Our workflow provides an efficient combination of experimental workflow and data analysis to identify putative secreted and proteolytic processed proteins." @default.
• W2783014139 created "2018-01-26" @default.
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• W2783014139 date "2018-01-24" @default.
• W2783014139 modified "2023-10-01" @default.
• W2783014139 title "Mining the Secretome of C2C12 Muscle Cells: Data Dependent Experimental Approach To Analyze Protein Secretion Using Label-Free Quantification and Peptide Based Analysis" @default.
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• W2783014139 doi "https://doi.org/10.1021/acs.jproteome.7b00684" @default.
• W2783014139 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29322779" @default.
• W2783014139 hasPublicationYear "2018" @default.
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