Jump to main content
Jump to site search

Issue 9, 2013
Previous Article Next Article

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

Author affiliations

Abstract

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

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

Back to tab navigation
Please wait while Download options loads

Supplementary files

Publication details

The article was received on 01 May 2013, accepted on 25 May 2013, published on 29 May 2013 and first published online on 29 May 2013


Article type: Paper
DOI: 10.1039/C3MB70171A
Citation: Mol. BioSyst., 2013,9, 2259-2265
  •   Request permissions

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

    A. Zhang, P. Wang, H. Sun, G. Yan, Y. Han and X. Wang, Mol. BioSyst., 2013, 9, 2259
    DOI: 10.1039/C3MB70171A

Search articles by author