Metabolomics by GC×GC-TOFMS: Overcoming Challenges from Sample Preparation to Data Analysis Towards Standardization of the Workflow

  • Author / Creator
    Nam, Seo Lin
  • Metabolomics is an ‘omics’ field, which involves the comprehensive characterization of metabolites using analytical chemistry technologies and statistical methods to interpret the results. The term metabolomics was coined about two decades ago as an analogy to other precedent omics approaches: genomics, transcriptomics, and proteomics. Although the term is relatively new, studies of health and life science have existed since ancient times, with the understanding that the biological samples contain information that can be linked to the health state of an organism.
    Advances in analytical technologies and statistical tools have made metabolomics useful for a broad range of biomedical, agricultural, and other applications. The development of technologies, which include novel sampling techniques, sensitive analytical platforms, and computational methods, enables the profiling of thousands of metabolites. With the cutting-edge techniques, metabolomics has evolved into an essential tool providing insights into the molecular complexity of living systems.
    Despite the recent improvement in both analytical and data handling technologies, analyzing the metabolome presents many challenges. For genomics or proteomics, using a single instrument is generally sufficient because genome and proteome chemistry is fundamentally established on combinations of four nucleotide bases and 20 amino acids, respectively. Unlike genome and proteome that use a small number of building blocks, metabolome do not purse fixed structural templates. The chemical diversity of metabolites is enormous and there is no single technology that enables full coverage of the entire metabolome. Indeed, the vast differences in physicochemical properties and abundance amongst metabolites are the bottom-line challenges to metabolomics studies. Accordingly, another major challenge is to extract useful information and interpret complex metabolomics data produced from such high-performance analytical techniques.
    Amongst the many analytical platforms that are used for metabolomics studies, comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) is a well-suited analytical platform to study complex biological samples due to its excellent separation capacity. Although GC×GC-TOFMS has been developed as a powerful separation instrument, considerable challenges associated with it, mainly due to the difficulty of method optimization and data handling, have hindered its adoption by the metabolomics community.
    The work presented in this thesis is devoted to making improvements towards the overall metabolomics workflow using GC×GC-TOFMS, from sample preparation to data analysis. The thesis discusses the challenges that remain in the field of metabolomics and GC×GC-TOFMS and suggests improvements to achieve high analytical performance while simplifying the process. The sample preparation methods for GC(×GC)-based metabolomics have been investigated with the particular focus on extraction and derivatization to enhance the coverage of the metabolome while improving the method reproducibility. A new approach to normalize the natural sample variability of biological samples was also explored. In addition, a data analysis strategy to simplify massive GC×GC-TOFMS metabolomics data using scripting filters that classify peaks into their corresponding chemical classes was developed. It is hoped that the work described in the thesis will contribute towards the standardization of the GC×GC-TOFMS metabolomics workflow, which would allow more widespread use of GC×GC-TOFMS and enhance cross-comparability of results in the metabolomics community.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • 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.