This summer seems to be very productive for our research teams. We keep announcing new publications, and today we want to highlight two papers with Dr. Liang Li’s technology of Chemical Isotope Labeling (CIL) LC-MS. These publications use high-coverage quantitative metabolomics in different application areas with various sample types.
First of these papers “High-Coverage Quantitative Metabolomics of Human Urine: Effects of Freeze–Thaw Cycles on the Urine Metabolome and Biomarker Discovery” is published in the Analytical Chemistry. As follows from the title, the authors discuss the impact of unavoidable freeze-thaw cycles (FTC), as there is no clarity on how FTC affects the urine biomarker studies. The study involved two centres located in Hangzhou (China) and Edmonton (Canada), and the metabolome analysis was performed with two separate cohorts of samples.
Hangzhou cohort consisted of 80 healthy subjects, while Edmonton cohort had 44 subjects. Each urine sample was subjected to four FTCs, followed by relative quantification of amine/ phenol submetabolome (one out of four available channels in chemical isotope labeling LC-MS approach). Over 3000 metabolites were identified and mass-matched. Multivariate and univariate analysis indicated that significant variations from FTCs were only observed in a very small fraction of the metabolites.
Another paper utilizing chemical isotope labeling (CIL) LC-MS method developed by Dr. Liang Li and his team discusses the analysis of food samples. “Development of a High-Coverage Quantitative Metabolome Analysis Method Using Four-Channel Chemical Isotope Labeling LC–MS for Analyzing High-Salt Fermented Food” is published in the Journal of Agricultural and Food Chemistry.
Understanding the salts interference on the sample processing and analysi makes it clear that the metabolome study of high-salt fermented food is an analytical challenge. To overcome this challenge, the authors suggest four-channel chemical isotope labeling LC-MS approach for a comprehensive metabolome analysis of this type of food. The workflow includes metabolite extraction, chemical labeling, LC-UV measurement of the total concentration of dansyl-labeled metabolites for sample normalization, mixing of 13C- and 12C-reagent-labeled samples, high resolution LC-MS analysis, and data processing.
Metabolome analysis of fermented foods, including fermented red pepper sauce, soy sauce, and sufu (a fermented soybean food), showed high metabolic coverage. The authors conclude that this CIL LC-MS approach can be used for metabolomic studies of high-salt fermented food, as the method allows high-coverage identification and quantification.
Dr. Liang Li is scientific co-director and Node leader of TMIC. His team is focused on the application of the CIL LC-MS methods, metabolomics and lipidomics, and microbiome metabolites. More details about these assays are below:
Global (Untargeted) Metabolomics by Chemical Isotope Labeling LC-MS increases metabolome coverage and achieves accurate quantification for all detectable metabolites. The whole metabolome is analyzed by combining the analysis of four submetabolomes: amine/phenol, carboxyl, carbonyl and hydroxyl submetabolome. The combined results from four channels are able to cover 85% to 95% of the entire chemical space of the metabolome. (Zhao S. et al., Anal. Chem. 2019, 91, 12108−12115 https://doi.org/10.1021/acs.analchem.9b03431)
Global (Untargeted) Lipidomics Profiling uses a cutting-edge method to analyze the lipidome in both positive and negative ionization. It typically detects, identifies and relatively quantifies more than 5,000 lipids for positive ionization and more than 2,000 lipids for negative ionization. Approximately 1,000 lipids can be typically identified by MS/MS, while 3000 to 5000 lipids can be putatively identified by accurate mass-match.
Microbiome Metabolite Assay allows accurate relative quantification of 851 microbiome metabolites in comparative samples, including known microbiota-related metabolites (short-chain and medium-chain fatty acids, bile acids, nucleosides, benzoates and uremic compounds, tryptophan metabolism, tyrosine metabolism, phenylalanine metabolism, vitamins and derivatives, polyamines, neurotransmitters) and other microbiota-host-related endogenous metabolites (amino acid derivatives, glycolysis, TCA cycle and other energy metabolism, amino acid metabolisms).
