Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry

Abstract

Motivation: The goal of large-scale metabolite profiling is to compare the relative concentrations of as many metabolites extracted from biological samples as possible. This is typically accomplished by measuring the abundances of thousands of ions with high-resolution and high mass accuracy mass spectrometers. Although the data from these instruments provide a comprehensive fingerprint of each sample, identifying the structures of the thousands of detected ions is still challenging and time intensive. An alternative, less-comprehensive approach is to use triple quadrupole (QqQ) mass spectrometry to analyze predetermined sets of metabolites (typically fewer than several hundred). This is done using authentic standards to develop QqQ experiments that specifically detect only the targeted metabolites, with the advantage that the need for ion identification after profiling is eliminated. Results: Here, we propose a framework to extend the application of QqQ mass spectrometers to large-scale metabolite profiling. We aim to provide a foundation for designing QqQ multiple reaction monitoring (MRM) experiments for each of the 82 696 metabolites in the METLIN metabolite database. First, we identify common fragmentation products from the experimental fragmentation data in METLIN. Then, we model the likelihoods of each precursor structure in METLIN producing each common fragmentation product. With these likelihood estimates, we select ensembles of common fragmentation products that minimize our uncertainty about metabolite identities. We demonstrate encouraging performance and, based on our results, we suggest how our method can be integrated with future work to develop large-scale MRM experiments. Availability and implementation: Our predictions, Supplementary results, and the code for estimating likelihoods and selecting ensembles of fragmentation reactions are made available on the lab website at http://pattilab.wustl.edu/FragPred.

Bar Coding MS2 Spectra for Metabolite Identification

“…Previously, we determined the most unique precursor-to-product transitions that could be used to construct the fewest number of MRMs for profiling all metabolites in METLIN. 21 We used these informative fragments as the basis for selecting bins. In brief, we optimized the combination of bins that enabled us to resolve as many compounds with MS 2 spectra in METLIN as possible within 0.1 Da.…”

Clinical Mass Spectrometry in the Bioinformatics Era: A Hitchhiker’s Guide

“…This has been applied in the field of proteomics as digital biobanking of proteomes [ 255 ]. In contrast to the field of proteomics and metabolomics in which the use of open source software as well as open standards for mass spectrometry data has been widespread [ [256] , [257] , [258] , [259] ], the use of mass spectrometry in clinical laboratory remained mostly within the framework of manufacturer-provided software as most applications of mass spectrometry in the clinical laboratory calls for selective quantitation of analytes rather than omic-profiling and data-mining. One starting point for a beginner in mass spectrometry data analysis is bioconductor ( http://bioconductor.org /), an open source platform for bioinformatics data analysis [ 260 , 261 ].…”

Deciphering chemical interactions between Glycyrrhizae Radix and Coptidis Rhizoma by liquid chromatography with transformed multiple reaction monitoring mass spectrometry

“…Then, the molecular mass of the predicted structure (m/z value) was detected by XIC, and the extracted peak (if existed) was subsequently identified by MS data to confirm the result. Taking 65 (licoricesaponin E2) and 66 (glabrolid) in liquorice as an example, the predominant quasimolecular ion [M-H] − at m/z 819.3839 of peak 65 gave the formula C 42 H 60 O 16 , and its fragment ions at m/z 643.3555 (a C 6 H 8 O 6 loss) and 467.3203 (two C 6 H 8 O 6 losses) suggested successive losses of glucuronosyl moieties. A CO 2 loss from C 30 H 44 O 4 (m/z 467.3203) also indicated a potential ester moiety.…”