Institute Affiliations

Education & Certifications

  • BASc, University of British Columbia, Materials Engineering (2021)

All Publications

  • Enhancing Metabolome Coverage in Data-Dependent LC-MS/MS Analysis through an Integrated Feature Extraction Strategy ANALYTICAL CHEMISTRY Hu, Y., Cai, B., Huan, T. 2019; 91 (22): 14433-14441


    In untargeted metabolomics, conventional data preprocessing software (e.g., XCMS, MZmine 2, MS-DIAL) are used extensively due to their high efficiency in metabolic feature extraction. However, these programs present limitations in recognizing low-abundance metabolic features, thus hindering complete metabolome coverage from the analysis. In this work, we explored the possibility of enhancing the metabolome coverage of data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) results by rescuing metabolic features that are missed by conventional software. To achieve this goal, we first categorized the metabolic features into four confidence levels based on their chromatographic peak shapes and the presence of corresponding MS/MS spectra. We then assessed the false positives and quantitative accuracy of the metabolic features that contain MS/MS spectra but are not recognized by conventional software. Our results indicate that these missed features contain valid and important metabolic information and should be integrated into the conventional metabolomics results. Thus, we developed a data-preprocessing pipeline to extract low-abundance metabolic features and integrate them with the results from conventional programs. This integrated feature extraction strategy was tested on a set of fecal metabolomic data retrieved from mice who have undergone normal diet vs high-fat diet treatments. In our test data set, the integrated feature extraction approach increased the number of significant features being extracted by 24.4% and identified five additional metabolites bearing critical biological meanings. Our results show that this integrated feature extraction strategy remarkably improves the metabolome coverage beyond that of conventional data preprocessing, therefore facilitating the confirmation of metabolites of interest and accomplishment of a higher success rate in de novo metabolite identification.

    View details for DOI 10.1021/acs.analchem.9b02980

    View details for Web of Science ID 000498280100038

    View details for PubMedID 31626534