Compound identification from accurate mass MS/MS spectra is a bottleneck for untargeted metabolomics. In this study, we propose nine rules of hydrogen rearrangement (HR) during bond cleavages in low-energy collision-induced dissociation (CID). These rules are based on the classic even-electron rule and cover heteroatoms and multistage fragmentation. We evaluated our HR rules by the statistics of MassBank MS/MS spectra in addition to enthalpy calculations, yielding three levels of computational MS/MS annotation: "resolved" (regular HR behavior following HR rules), "semiresolved" (irregular HR behavior), and "formula-assigned" (lacking structure assignment). With this nomenclature, 78.4% of a total of 18506 MS/MS fragment ions in the MassBank database and 84.8% of a total of 36370 MS/MS fragment ions in the GNPS database were (semi-) resolved by predicted bond cleavages. We also introduce the MS-FINDER software for structure elucidation. Molecular formulas of precursor ions are determined from accurate mass, isotope ratio, and product ion information. All isomer structures of the predicted formula are retrieved from metabolome databases, and MS/MS fragmentations are predicted in silico. The structures are ranked by a combined weighting score considering bond dissociation energies, mass accuracies, fragment linkages, and, most importantly, nine HR rules. The program was validated by its ability to correctly calculate molecular formulas with 98.0% accuracy for 5063 MassBank MS/MS records and to yield the correct structural isomer with 82.1% accuracy within the top-3 candidates. In a test with 936 manually identified spectra from an untargeted HILIC-QTOF MS data set of human plasma, formulas were correctly predicted in 90.4% of the cases, and the correct isomer structure was retrieved at 80.4% probability within the top-3 candidates, including for compounds that were absent in mass spectral libraries. The MS-FINDER software is freely available at http://prime.psc.riken.jp/ .
Although understanding the high-resolution spatial distribution of bioactive small molecules is indispensable for elucidating their biological or pharmacological effects, there has been no analytical technique that can easily detect the naïve molecular localization in mammalian tissues. We herein present a novel in situ label-free imaging technique for visualizing bioactive small molecules, using a polyphenol. We established a 1,5-diaminonaphthalene (1,5-DAN)-based matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) technique for visualizing epigallocatechin-3-O-gallate (EGCG), the major bioactive green tea polyphenol, within mammalian tissue micro-regions after oral dosing. Furthermore, the combination of this label-free MALDI-MSI method and a standard-independent metabolite identification method, an isotopic fine structure analysis using ultrahigh-resolution mass spectrometer, allows for the visualization of spatially-resolved biotransformation based on simultaneous mapping of EGCG and its phase II metabolites. Although this approach has limitations of the detection sensitivity, it will overcome the drawbacks associated with conventional molecular imaging techniques, and could contribute to biological discovery.
In the present study, a high-throughput analytical method for intracellular metabolites using MALDI-MS has been developed. As an analytical tool, the quantitative performance and dynamic range of MALDI-TOF-MS was confirmed to be suitable for characterizing the trends of intracellular metabolism. The technique was tested by investigating the intracellular metabolism of Escherichia coli by analyzing whole cell samples taken consecutively before and after a perturbation of the environmental carbon source. As the result, dramatic changes of metabolite concentrations responding to the perturbation were observed. The whole analysis process (i.e., sample preparation and MALDI-MS analysis for 24 time points in triplicate) was completed within 4 hours. MALDI-FTICR-MS was used to identify the elemental compositions of detected metabolites to support the reliability of the MALDI-MS-based analysis. The MALDI-MS-based analytical method developed herein should be suitable for high-throughput analysis of dynamic intracellular metabolism events.
In mass spectrometry (MS)-based metabolomics studies, reference-free identification of metabolites is still a challenging issue. Previously, we demonstrated that the elemental composition (EC) of metabolites could be unambiguously determined using isotopic fine structure, observed by ultrahigh resolution MS, which provided the relative isotopic abundance (RIA) of (13)C, (15)N, (18)O, and (34)S. Herein, we evaluated the efficacy of the RIA for determining ECs based on the MS peaks of 20,258 known metabolites. The metabolites were simulated with a ≤25% error in the isotopic peak area to investigate how the error size effect affected the rate of unambiguous determination of the ECs. The simulation indicated that, in combination with reported constraint rules, the RIA led to unambiguous determination of the ECs for more than 90% of the tested metabolites. It was noteworthy that, in positive ion mode, the process could distinguish alkali metal-adduct ions ([M+Na](+) and [M+K](+)). However, a significant degradation of the EC determination performance was observed when the method was applied to real metabolomic data (mouse liver extracts analyzed by infusion ESI), because of the influence of noise and bias on the RIA. To achieve ideal performance, as indicated in the simulation, we developed an additional method to compensate for bias on the measured ion intensities. The method improved the performance of the calculation, permitting determination of ECs for 72% of the observed peaks. The proposed method is considered a useful starting point for high-throughput identification of metabolites in metabolomic research.
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