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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 commonly15%. 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.

Formalin-fixed paraffin-embedded (FFPE) tissues play an irreplaceable role in cancer research. Although extensive research has been conducted for the detection of DNA, RNA and proteins in FFPE samples, literature dealing with the FFPE determination of small molecules is scarce. In this study, we aimed to explore the potential of targeted metabolomics in FFPE specimens. For that purpose, we developed a LC-MS/MS method for the quantification of acidic metabolites in FFPE samples. The method involves trimming tissue slices from FFPE blocks, deparaffinization, lysis of the tissue, o-benzyl hydroxylamine derivatization and LC-MS/MS detection. Deparaffinization and lysis steps were optimized to maximize the analytes extraction and to minimize the effect of the ubiquitous presence of some metabolites in the paraffin. Two validation approaches were applied: (i) using blank paraffin as matrix and (ii) using actual human FFPE tissue samples by standard additions. The method quantified 40 metabolites with appropriate accuracy (commonly 80-120%) and precision (CV 2-19%) in both validation approaches. LLOQs ranging 0.88-2001 pg mg-1 with low-moderate matrix effects (commonly 85-115%) were obtained. FFPE samples from 15 patients with colorectal cancer were analyzed and metabolites concentrations in tumor vs matched normal FFPE tissues were compared. Results show that tumor tissues have a well-established fingerprint including an increase in ketogenesis, a decrease in lipogenesis and an imbalance in the tricarboxylic acid cycle.