Untargeted detection of the carbonyl metabolome by chemical derivatization and liquid chromatography-tandem mass spectrometry in precursor ion scan mode: Elucidation of COVID-19 severity biomarkers.

Metabolomics (both targeted and untargeted) has become the gold standard in biomarker discovery. Whereas targeted approaches only provide information for the selected markers, thus hampering the determination of out-of-the-box markers, the common bottleneck of untargeted metabolomics is the identification of detected biomarkers. In this study, we developed a strategy based on derivatization and LC-MS/MS detection in a precursor ion scan for the untargeted determination of a specific part of the metabolome (carbonyl-containing metabolites). The usefulness of this guided metabolomics approach has been demonstrated by elucidating carbonyl-containing biomarkers of COVID-19 severity. First, the LC-MS/MS behavior of 63 model compounds after O-benzylhydroxylamine derivatization was studied. A precursor ion scan of m/z 91 was selected as a suitable approach for the untargeted detection of carbonyl-containing metabolites. The method was able to detect ≈300 potential carbonyl-containing molecules in plasma, including mono-/di-/tricarbonylic compounds with satisfactory intra-day and inter-day repeatability and RSDs commonly <15%. Additionally, the semiquantitative nature of the precursor ion scan method was confirmed by comparison with a fully validated targeted method. The application of the guided metabolomics method to COVID-19 plasma samples revealed the presence of four potential COVID-19 severity biomarkers. Based on their LC-MS/MS behavior, these biomarkers were elucidated as 2-hydroxybutyrate, 2,3-dihydroxybutyrate, 2-oxobutyrate and 2-hydroxy-3-methylbutyrate. Their structures were confirmed by comparison with reference materials. The alterations of these biomarkers with COVID-19 severity were confirmed by a target analysis of a larger set of samples. Our results confirm that guided metabolomics is an alternative approach for the untargeted detection of selected families of metabolites; this approach can accelerate their elucidation and provide new perspectives for the establishment of health/disease biomarkers.

Gomez-Gomez A ，Rodríguez-Morató J ，Haro N ，Marín-Corral J ，Masclans JR ，Pozo OJ ... -  《-》

Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list.

Metabolite identification is one of the major bottlenecks in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics owing to the difficulty of acquiring MS/MS information of most metabolites detected. Data dependent acquisition (DDA) has been currently used to acquire MS/MS data in untargeted metabolomics. When dealing with the complex biological samples, top-n-based DDA method selects only a small fraction of the ions for fragmentation, leading to low MS/MS coverage of metabolites in untargeted metabolomics. In this study, we proposed a novel DDA method to improve the performance of MS/MS acquisition in LC-MS-based untargeted metabolomics using target-directed DDA (t-DDA) with time-staggered precursor ion lists (ts-DDA). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment, ion filtration, and ion fusion, the target precursor ion list was generated for subsequent t-DDA and ts-DDA. Compared to the conventional DDA, the ts-DDA exhibits the better MS/MS coverage of metabolomes in a plasma sample, especially for the low abundant metabolites. Even in high co-elution zones, the ts-DDA also showed the superiority in acquiring MS/MS information of co-eluting ions, as evidenced by better MS/MS coverage and MS/MS efficiency, which was mainly attributed to the pre-selection of precursor ion and the reduced number of concurrent ions. The newly developed method might provide more informative MS/MS data of metabolites, which will be helpful to increase the confidence of metabolite identification in untargeted metabolomics.

Wang Y ，Feng R ，Wang R ，Yang F ，Li P ，Wan JB ... -  《-》

Development of simultaneous targeted metabolite quantification and untargeted metabolomics strategy using dual-column liquid chromatography coupled with tandem mass spectrometry.

Targeted quantification and untargeted global profiling are the two mainstream approaches, a merging of which could provide enhanced analytical potential in metabolomics research. Here, a simultaneous targeted quantification and untargeted metabolomics (STQUM) strategy was developed for more efficient, accurate and comprehensive metabolomics research by using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, we selected 110 cancer-related metabolites as targets and established a dual LC sequential separation method for simultaneous analysis of strong and weak polar metabolites. In order to achieve efficient acquisition for synchronous qualitative and quantitative analysis, high-resolution, data-dependent parallel reaction monitoring (PRM) method and data-independent all ion fragmentation (AIF) method were established. Their performance in targeted confirmation and quantification, and untargeted analysis were systematically investigated and assessed. In total, 78 metabolites were confidently confirmed in positive ion mode in both PRM and AIF assays, in which 73 metabolites can be accurately quantified. In addition, simultaneously untargeted profiling of 4651 features of high reliability and validity were achieved. Both AIF and PRM methods revealed high confidence, sensitivity and accuracy. In the STQUM approach, another 15 metabolites could be accurately quantified in negative ion mode. The method offers a new perspective for merging the hypothesis-based targeted quantitative validation and untargeted biomarkers discovery in one run for improved analysis efficiency and integrity.

Gao Y ，Chen Y ，Yue X ，He J ，Zhang R ，Xu J ，Zhou Z ，Wang Z ，Zhang R ，Abliz Z ... -  《-》

Novel Strategy for Untargeted Chiral Metabolomics using Liquid Chromatography-High Resolution Tandem Mass Spectrometry.

Pandey R ，Collins M ，Lu X ，Sweeney SR ，Chiou J ，Lodi A ，Tiziani S ... -  《-》

Enhancing coverage in LC-MS-based untargeted metabolomics by a new sample preparation procedure using mixed-mode solid-phase extraction and two derivatizations.

Wu Q ，Xu Y ，Ji H ，Wang Y ，Zhang Z ，Lu H ... -  《-》