Systems-Level Annotation of Metabolomics Data Reduces 25,000 Features to Fewer than 1,000 Unique Metabolites

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomic method. We first group multiple features arising from the same analyte, which we call \"degenerate features\", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ~2,961. We then applied an orthogonal approach to remove non-biological features from the data by using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1,000.