High-throughput ultra-performance liquid chromatography-mass spectrometry characterization of metabolites guided by a bioinformatics program

摘要: Metabolite profiling in biomarker discovery research requires new data preprocessing approaches to correlate specific metabolites their biological origin. Mass spectrometry-based metabolomics often results the observation of hundreds thousands features that are differentially regulated biosamples. Extracting biomedical information from large metabolomic datasets by multivariate analysis is considerable complexity. Therefore, more efficient and optimized processing technologies needed improve MS applications discovery. Here we use a sensitive ultra-performance LC-ESI/quadrupole-TOF high-definition mass spectrometry (UPLC-ESI-Q-TOF-MS) approach, negative ion mode, characterize metabolites. XCMS online was used which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, matching. software can facilitate prioritization greatly increases probability identifying causally related phenotype interest. 26 urinary differential contributing complete separation HCC patients healthy controls were identified involving key metabolic pathways including tyrosine metabolism, glutathione phenylalanine ascorbate aldarate arginine proline metabolism. It demonstrates high-throughput UPLC-ESI-Q-TOF-MS metabonomics combined with proposed bioinformatic approach (based on XCMS) pivotal elucidate developing biomarkers physiological mechanism disease clinical setting.