Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Metabolite Annotation
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TISSUE(S): Worms
PROVIDER: ST001778 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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Project description:Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.
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Project description:Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.
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Project description:The composition of the juice from grape berries is at the basis of the definition of technological ripeness before harvest, historically evaluated from global sugar and acid contents. If many studies have contributed to the identification of other primary and secondary metabolites in whole berries, deepening knowledge about the chemical composition of the sole flesh of grape berries (i.e., without considering skins and seeds) at harvest is of primary interest when studying the enological potential of widespread grape varieties producing high-added-value wines. Here, we used non-targeted DI-FT-ICR-MS and RP-UHPLC-Q-ToF-MS analyses to explore the extent of metabolite coverage of up to 290 grape juices from four Vitis vinifera grape varieties, namely Chardonnay, Pinot noir, Meunier, and Aligoté, sampled at harvest from 91 vineyards in Europe and Argentina, over three successive vintages. SPE pretreatment of samples led to the identification of more than 4500 detected C,H,O,N,S-containing elemental compositions, likely associated with tens of thousands of distinct metabolites. We further revealed that a major part of this chemical diversity appears to be common to the different juices, as exemplified by Pinot noir and Chardonnay samples. However, it was possible to build significant models for the discrimination of Chardonnay from Pinot noir grape juices, and of Chardonnay from Aligoté grape juices, regardless of the geographical origin or the vintage. Therefore, this metabolomic approach opens access to a remarkable holistic molecular description of the instantaneous composition of such a biological matrix, which is the result of complex interplays among environmental, biochemical, and vine growing practices.
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Project description:Native mass spectrometry (MS) involves the analysis and characterization of macromolecules, predominantly intact proteins and protein complexes, whereby as much as possible the native structural features of the analytes are retained. As such, native MS enables the study of secondary, tertiary, and even quaternary structure of proteins and other biomolecules. Native MS represents a relatively recent addition to the analytical toolbox of mass spectrometry and has over the past decade experienced immense growth, especially in enhancing sensitivity and resolving power but also in ease of use. With the advent of dedicated mass analyzers, sample preparation and separation approaches, targeted fragmentation techniques, and software solutions, the number of practitioners and novel applications has risen in both academia and industry. This review focuses on recent developments, particularly in high-resolution native MS, describing applications in the structural analysis of protein assemblies, proteoform profiling of─among others─biopharmaceuticals and plasma proteins, and quantitative and qualitative analysis of protein-ligand interactions, with the latter covering lipid, drug, and carbohydrate molecules, to name a few.
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Project description: Not available
| ST001777 | MetabolomicsWorkbench
Project description:IntroductionMass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.ObjectivesThis study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.MethodsHere we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.ResultsIn total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.ConclusionsThe approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.
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