In untargeted metabolomics, the quest for discovery, the quest to find something that has not been found before is a true struggle of desire and effort versus reward. Potentially the most challenging approach to metabolomics and biomarker discovery lies in the complex interpretation of “differential features” and how to convert lists of such features into metabolite identifications and contextualize them in biology.
Approaches to untargeted metabolite profiling generally involve statistical treatment of LC-MS data to generate a list of features that can be potentially identified. Often this can involve multiple injections of a sample to ensure information can be collected on features of interest. Ultimately the nature of a metabolomics dataset is such that often no identity can be assigned to a given feature due to it being an artifact or simply that its structure cannot be inferred from the data.
SCIEX solutions for untargeted and discovery metabolomics focus on generating comprehensive datasets containing large amounts of information through unique, modern approaches like SWATH acquisition, that can be curated repeatedly using comprehensive statistics, along with tools developed by our collaborators aimed at identifying metabolites of interest through simplification and deconvolution of the data.
