Development and Application of Chemical Isotope Labeling Methods for Metabolomics Data Processing and Liquid Chromatography-Mass Spectrometry-Based Metabolomics

  • Author / Creator
    Huan, Tao
  • Metabolomics is the study of chemical processes involving small-molecule metabolites in a given biological system. These small molecules are downstream outputs of cellular activities. An understanding of the metabolome change can help us to visualize perturbations in the genome or proteome from the environmental impacts. To study metabolomics, our lab has developed a differential chemical isotope labeling (CIL) platform. In this platform, dansylation reaction is used to label the amine/phenol submetabolome with a dansyl tag. The benefit of using this platform includes better LC separation efficiency, MS detection sensitivity, and metabolite quantification accuracy. We can typically detect over 1000 metabolites in human urine samples and over 600 metabolites in human serum samples. In this thesis, Chapter 2 and Chapter 3 described the development of new data processing programs to better handle the large LC-MS metabolomics dataset from CIL LC MS platform. The two programs, zero-fill and IsoMS-Quant, were aimed to reduce the number of missing values in the LC-MS dataset and to improve the accuracy of the quantitative metabolomics result. Chapters, 4 and 5 focus on the development of metabolite identification methods. As described in Chapter 4, a retention time correction algorithm was combined with a dansyl labeled metabolite standard library, providing a possibility for quick metabolite identification through RT and m/z matching with 278 dns-standards. In Chapter 5, a library of predicted fragment-ion-spectra containing 383,830 possible human metabolites was developed, which allowed the search of experimental MS/MS spectra for metabolite identification. An application of these analytical techniques to biomarker discovery work is included in Chapter 6, specifically. The CIL LC-MS platform was used in the study of potential biomarkers and diagnostic models for early-stage diagnosis of Alzheimer’s disease and mild cognitive impairment. Overall, these research activities have provided technique improvements and demonstrated the enhanced analytical performance as well as the capability of CIL LC-MS-based metabolomics methods. All these enabled analytical technique further our understanding of metabolomics and its role in system biology.

  • Subjects / Keywords
  • Graduation date
    Spring 2016
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Raftery, Daniel (Mitochondria and Metabolism Center)
    • Lin, Guohui (Computer Science)
    • Lowary, Todd (Chemistry)
    • Serpe, Michael (Chemistry)