Bar Coding MS2 Spectra for Metabolite Identification

Jonathan L. Spalding, Kevin Cho, Nathaniel G. Mahieu, Igor Nikolskiy, Elizabeth M. Llufrio, Stephen L. Johnson, Gary J. Patti

Research output: Contribution to journalLetterpeer-review

5 Scopus citations

Abstract

Metabolite identifications are most frequently achieved in untargeted metabolomics by matching precursor mass and full, high-resolution MS2 spectra to metabolite databases and standards. Here we considered an alternative approach for establishing metabolite identifications that does not rely on full, high-resolution MS2 spectra. First, we select mass-to-charge regions containing the most informative metabolite fragments and designate them as bins. We then translate each metabolite fragmentation pattern into a binary code by assigning 1's to bins containing fragments and 0's to bins without fragments. With 20 bins, this binary-code system is capable of distinguishing 96% of the compounds in the METLIN MS2 library. A major advantage of the approach is that it extends untargeted metabolomics to low-resolution triple quadrupole (QqQ) instruments, which are typically less expensive and more robust than other types of mass spectrometers. We demonstrate a method of acquiring MS2 data in which the third quadrupole of a QqQ instrument cycles over 20 wide isolation windows (coinciding with the location and width of our bins) for each precursor mass selected by the first quadrupole. Operating the QqQ instrument in this mode yields diagnostic bar codes for each precursor mass that can be matched to the bar codes of metabolite standards. Furthermore, our data suggest that using low-resolution bar codes enables QqQ instruments to make MS2-based identifications in untargeted metabolomics with a specificity and sensitivity that is competitive to high-resolution time-of-flight technologies.

Original languageEnglish
Pages (from-to)2538-2542
Number of pages5
JournalAnalytical Chemistry
Volume88
Issue number5
DOIs
StatePublished - Mar 1 2016

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