Automated and unbiased analysis of LC–MS metabolomic data

Abstract

Background: LC–MS metabolomics provides a unique approach for evaluation of perturbations in biochemical pathways. LC–MS is a key technology for measuring endogenous metabolites and, while it has the impressive ability to acquire colossal volumes of data, the overall success of a study depends on the ability to translate the acquired analytical information into biological knowledge. Thus, a significant research effort has been dedicated to the development of informatics tools capable of automatically translating the complexity of acquired LC–MS datasets into meaningful biochemical sample descriptions. Materials & Methods: This article discusses our methodology for automated analysis of high-resolution accurate mass LC–MS data applied to a case study of evaluation of the effects of fasting in male rats. Blood serum samples from male rats were analyzed using an exactive mass spectrometer interfaced with an Accela UHPLC. Results: An obtained list of annotated endogenous metabolites through matching of the detected components with corresponding profiles of synthetic standards served as a basis for statistical evaluation of observed physiological changes. Conclusion: The observed changes in certain endogenous metabolites prove that fasting could be a significant variable in toxicological studies since the fasting status of a particular animal may exacerbate or obscure drug-induced metabolic effects.