Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism

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Dataset information

Country of origin
Updated
2020.01.24 00:00
Created
2019.08.01
Available languages
English
Keywords
In vitro, Skeletal Muscle, Metabolism, Transcriptomics, Myotube
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Dataset description

ABSTRACT: Rat L6, mouse C2C12 and primary human skeletal muscle cells (HSMC) are commonly used to study biological processes in skeletal muscle and experimental data on these models is abundant. However, consistently-matched experimental data are scarce and comparisons between the different cell-types and adult tissue are problematic. We hypothesized that metabolic differences between these cellular models may be reflected at the mRNA level. Publicly available data were used to profile mRNA levels in myotubes and skeletal muscle tissues. L6, C2C12 and HSMC myotubes were then assessed for proliferation, glucose uptake, mitochondrial activity, substrate oxidation and response to in vitro contraction. Transcriptomic profiling revealed that contractile proteins were most abundant in C2C12 and human primary myotubes, whereas L6 myotubes had the highest level of glucose transporters and mitochondrial genes. Consistently, insulin-stimulated glucose uptake and oxidative capacity were greatest in L6 myotubes. Insulin-induced glycogen synthesis was highest in HSMC, but C2C12 myotubes had higher baseline glucose oxidation. Our analysis reveals a great degree of heterogeneity in the transcriptomic and metabolic profiles of L6, C2C12 or human primary cells. Based on these distinct signatures, we provide recommendations for the appropriate use of these models depending on scientific hypotheses and biological relevance. DATASET: Publicly available data from myotubes and skeletal muscle tissues were selected from the GEO database. Raw files were downloaded and robust multi array (RMA) normalization was performed in unison for all samples from the same platform. For each human ENSEMBL, the rat and mouse orthologs were found using the R package BioMart and the arrays were merged based on the human ENSEMBL annotation. The database was then aggregated according to the official human gene symbol. When multiple ENSEMBL were found for a single gene symbol, an average was calculated.
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2019
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