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


Other title
Mass spectrometry
Data processing
Metabolite identification
Liquid chromatography
Chemical isotope labeling
Type of item
Degree grantor
University of Alberta
Author or creator
Huan, Tao
Supervisor and department
Li, Liang (Chemistry)
Examining committee member and department
Lin, Guohui (Computer Science)
Raftery, Daniel (Mitochondria and Metabolism Center)
Serpe, Michael (Chemistry)
Lowary, Todd (Chemistry)
Department of Chemistry

Date accepted
Graduation date
Doctor of Philosophy
Degree level
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.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
Tao Huan, Chenqu Tang, Ronghong Li, Yi Shi, Guohui Li and Liang Li, 2015, “MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites”, Analytical ChemistryTao Huan, Yiman Wu, Chenqu Tang, Guohui Lin and Liang Li, 2015, “DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC-MS-Based Metabolomics”, Analytical ChemistryTao Huan and Liang Li, 2015, “Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry”, Analytical Chemistry. 87, 7011-7016.Tao Huan and Liang Li, 2015, “Counting Missing Values in a Metabolite-Intensity Dataset for Measuring the Analytical Performance of a Metabolomics Platform”, Analytical Chemistry. 87, 1306-1313

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