Global metabolite profiling via LC-MS is often used for detecting metabolites from biological or environmental samples and obtaining relative quantification in an unbiased manner. The main application is to compare the case group with the control group to find metabolites that show significant differences and thereby providing clues to the metabolic pathways and genes that are involved in the process. It is often the first metabolomics assessment in biomarker development, disease pathogenesis, and drug development. Aside from the commonly known Untargeted Metabolomics, Metware offers a unique Widely-Targeted Metabolomics workflow that combines high resolution, wide coverage, high sensitivity and precise quantification by applying high-resolution mass spectrometry with QQQ MRM scanning in tandem.
The LC-MS untargeted metabolomics is used for unbiased detection of metabolites in samples by liquid chromatography-mass spectrometry and to obtain their qualitative and quantitative information. The main application is to compare the case group with the control group to find the metabolites and metabolic pathways that show significant difference between groups, which can provide clues and directions for the research of disease, biomarker development, disease pathogenesis and drug treatment mechanisms.
Targeted Metabolomics can detect a few known metabolites with high sensitivity, precise qualitation and precise quantification. Targeted Metabolomics aims to validate the differences in various samples.
Untargeted Metabolomics can detect hundreds or thousands of metabolites (known or unknown). It relies on a metabolite database to annotate high-resolution mass spec data to known metabolites Untargeted Metabolomics results in relative quantification that aims to discover significant differences between samples or groups.
Collected ultra-high sensitivity data of over 280,000 metabolites. Each sample can typically identify 1500-3000 metabolites.
A comprehensive identification of metabolites was performed using four qualitative methods:
Matching to in-house standard database;
Matching to integrated public database;
Matching to AI database;
Identify using the metDNA algorithm.
A mature quality control system monitoring all aspects of experimentation from sample preparetion to data collection.
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