In-vivo tracking of deuterium metabolism in mouse organs using LC-MS/MS.

Mass spectrometry-based metabolomics with stable isotope labeling (SIL) is an established tool for sensitive and precise analyses of tissue metabolism, its flux, and pathway activities in diverse models of physiology and disease. Despite the simplicity and broad applicability of deuterium (2H)-labeled precursors for tracing metabolic pathways with minimal biological perturbations, they are rarely employed in LC-MS/MS-guided metabolomics. In this study, we have developed a LC-MS/MS-guided workflow to trace deuterium metabolism in mouse organs following 2H7 -glucose infusion. The workflow includes isotopically labeled glucose infusion, mouse organ isolation and metabolite extraction, zwitterion-based hydrophilic interaction liquid chromatography (HILIC) coupled to high-resolution tandem mass spectrometry, targeted data acquisition for sensitive detection of deuterated metabolites, a spectral library of over 400 metabolite standards, and multivariate data analysis with pathway mapping. The optimized method was validated for matrix effects, normalization, and quantification to provide both tissue metabolomics and tracking the in-vivo metabolic fate of deuterated glucose through key metabolic pathways. We quantified more than 100 metabolites in five major mouse organ tissues (liver, kidney, brain, brown adipose tissue, and heart). Furthermore, we mapped isotopologues of deuterated metabolites from glycolysis, tricarboxylic acid (TCA) cycle, and amino acid pathways, which are significant for studying both health and various diseases. This study will open new avenues in LC-MS based analysis of 2H-labeled tissue metabolism research in animal models and clinical settings.

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.