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Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small molecules. In non-targeted metabolite profiling studies, instrument duty cycle limitations and experimental costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor-product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor-product relationships in idMS/MS data by appending a dataset-wide correlational analysis to existing tools. The utility of the approach was demonstrated using a dataset of standard compounds spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to standard MS/MS spectra of authentic standards, even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable molecular feature, which will streamline the identification process for non-targeted metabolite profiling studies. [ABSTRACT FROM AUTHOR]

Non-targeted metabolomic profiling is used to simultaneously assess a large part of the metabolome in a biological sample. Here, we describe both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. At present, more than 200 metabolites have been manually annotated in more than 3600 participants using an in-house library of standards and publically available spectral databases. Data available at the metabolights repository include individual raw unprocessed data, processed data, basic demographic variables and spectra of annotated metabolites. Additional phenotypical and genetic data is available upon request to cohort steering committees. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases. [ABSTRACT FROM AUTHOR]