BACKGROUND:Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies.

The skeletal muscle is a metabolically active tissue that secretes various proteins. These so-called myokines have been proposed to affect muscle physiology and to exert systemic effects on other tissues and organs. Yet, changes in the secretory profile may participate in the pathophysiology of metabolic diseases. The present study aimed at characterizing the secretome of differentiated primary human skeletal muscle cells (hSkMC) derived from healthy, adult donors combining three different mass spectrometry based non-targeted approaches as well as one antibody based method. This led to the identification of 548 non-redundant proteins in conditioned media from hSkmc. For 501 proteins, significant mRNA expression could be demonstrated. Applying stringent consecutive filtering using SignalP, SecretomeP and ER_retention signal databases, 305 proteins were assigned as potential myokines of which 12 proteins containing a secretory signal peptide were not previously described. This comprehensive profiling study of the human skeletal muscle secretome expands our knowledge of the composition of the human myokinome and may contribute to our understanding of the role of myokines in multiple biological processes. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.