Bar Coding MS Spectra for Metabolite Identification

Metabolite identifications are most frequently achieved in untargeted metabolomics by matching precursor mass and full, high-resolution MS spectra to metabolite databases and standards. Here we considered an alternative approach for establishing metabolite identifications that does not rely on full, high-resolution MS 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 MS 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 MS 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 MS-based identifications in untargeted metabolomics with a specificity and sensitivity that is competitive to high-resolution time-of-flight technologies. A profiling features from biological samples by untargeted metabolomics is now routine, establishing the chemical identities of those features remains a major challenge. Even when using state-of-the-art liquid chromatography/mass spectrometry (LC/MS) technologies and automated bioinformatic pipelines, analysis of untargeted metabolomic data sets is time-consuming and only a fraction of the thousands of features detected are identified. In this sense, untargeted metabolomics as conventionally performed with LC/MS is highly inefficient. Historically, to support the structural identification of a feature in untargeted metabolomics, high-resolution MS data from in-house standards have been matched to high-resolution MS data from research samples. However, this process is severely limited by the availability of in-house standards in most laboratories. Thus, to help with metabolite identifications, fragmentation data from a variety of instrument platforms have become increasingly available online in recent years. METLIN, which is currently the largest public MS database for metabolomics, has experimental high-resolution MS data for over 14 000 metabolites. Yet, despite the availability of these MS data, efficient identification of large numbers of features in untargeted metabolomic experiments continues to be experimentally challenging. In contrast to untargeted metabolomics, solutions for targeted metabolomics are well established. Most frequently, targeted experiments are performed with a triple quadrupole (QqQ) mass spectrometer in multiple reaction monitoring (MRM) mode. After empirically identifying precursor-toproduct transitions for the metabolites of interest, MRM methods can be optimized to rapidly and efficiently profile those compounds. The resulting data are relatively easy to interpret compared to data from untargeted metabolomic experiments. In addition to their proven sensitivity, quantitative reliability, and robustness, QqQ instruments are also generally less expensive than the high-resolution mass spectrometers that are conventionally used for untargeted metabolomics. Indeed, QqQ-based methods have become the gold standard in the pharmaceutical industry. The main limitation of the MRM-based workflow is that it provides narrow (i.e., targeted) coverage of the metabolome. In this study, we evaluate a novel approach for performing untargeted metabolomics that achieves broad coverage while leveraging the experimental efficiency of a targeted workflow. The basis of our work is a strategy for translating highresolution MS spectra into low-resolution bar codes without sacrificing the diagnostic specificity of the fragmentation patterns (Figure 1 and Figure 2). The efficiency of the bar codes enables low-resolution, QqQ-based metabolomic workReceived: December 30, 2015 Accepted: February 2, 2016 Published: February 2, 2016 Letter