Enhancing untargeted metabolomics using metadata-based source annotation. - Gauglitz, Julia M.; West, Kiana A.; Bittremieux, Wout; Williams, Candace L.; Weldon, Kelly C.; Panitchpakdi, Morgan; Di Ottavio, Francesca; Aceves, Christine M.; Brown, Elizabeth; Sikora, Nicole C.; Jarmusch, Alan K.; Martino, Cameron; et al. (Nature Biotechnology, 2022)
Human untargeted metabolomics studies annotate only 10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.
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