Abstract
Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism's environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.
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