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The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27,245 potential metabolites. All matching precursor ions in the urine LC-MS(n) data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC-MS(n) data from nutritional metabolite profiling experiments.

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.org/magma ) to automatically process and annotate the LC-MS(n) data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MS(n) spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MS(n) (n ≤ 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC-MS(n) run, a total of 116,240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC-MS(n) component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. To determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid-phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used, namely, accurate mass fragmentation (HPLC-FTMS(n)) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMS(n) analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MS(n) fragmentation with the one-dimensional (1D)-(1)H NMR spectra of HPLC-TOFMS-SPE-trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3',4'-di- and 3',4',5'-trihydroxyphenyl-γ-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 h-quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMS(n) and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of less abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine.

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

High-mass resolution multi-stage mass spectrometry (MS(n)) fragmentation was tested for differentiation and identification of metabolites, using a series of 121 polyphenolic molecules. The MS(n) fragmentation approach is based on the systematic breakdown of compounds, forming a so-called spectral tree. A chip-based nanoelectrospray ionization source was used combined with an ion-trap, providing reproducible fragmentation, and accurate mass read-out in an Orbitrap Fourier transform (FT) MS enabling rapid assignment of elemental formulas to the molecular ions and all fragment ions derived thereof. The used protocol resulted in reproducible MS(n) fragmentation trees up to MS(5). Obtained results were stable over a 5 month time period, a concentration change of 100-fold, and small changes in normalized collision energy, which is key to metabolite annotation and helpful in structure and substructure elucidation. Differences in the hydroxylation and methoxylation patterns of polyphenolic core structures were found to be reflected by the differential fragmentation of the entire molecule, while variation in a glycosylation site displayed reproducible differences in the relative intensities of fragments originating from the same aglycone fragment ion. Accurate MS(n)-based spectral tree data are therefore a powerful tool to distinguish metabolites with similar elemental formula, thereby assisting compound identification in complex biological samples such as crude plant extracts.