Quality Control and Applications of Mass Spectrometry-based Metabolomics: From Cell Research to Large-scale Human Cohort Studies

Metabolomics is an analytical approach for systematic profiling of metabolites in biofluids, cells and tissues. Gas chromatography - mass spectrometry (GC-MS) and liquid chromatography - mass spectrometry (LC-MS) are most popular techniques in metabolomics due to their high-throughput and high sensitivity. Common procedures in metabolomics include study design, sample collection, sample extraction, data acquisition, data normalization, statistical analysis, and biological interpretation. All procedures before data normalization may introduce analytical variations as a hurdle for statistical performance. With the purpose of improving quality assurance in metabolomics for human disease studies, my dissertation work started with applying internal standards and external quality controls for quality assurance in untargeted metabolomics as showed in chapter one. A new tool called Systematical Error Removal using Denoising Autoencoder (SERDA) was developed and the median relative standard deviations (RSD) of the training QC samples was reduced to 4.6% RSD after normalized by SERDA. Then, I performed untargeted metabolomics assays to explore the lipidomic alterations and risk prediction of Type 2 Diabetes (T2D) in the cohort of American Indians from the Strong Heart Family Study (SHFS). Multivariate analysis of lipidomics identified distinct lipidomic signatures that can differentiate high- from low-risk groups. Higher baseline level of 33 lipid species, including triacylglycerols, diacylglycerols, phosphoethanolamines, and phosphocholines, was significantly associated with increased risk of T2D at 5-year follow-up. Because studies in humans usually can only denote risk factors but not biochemical mechanisms, I continued my work on animal and cell studies in chapter three and four. In chapter three I showcase how metabolomics data can be integrated with phenotype information in mouse models to decipher the link between gene functions and metabolism and downstream diseases. Finally, I compared how different technologies can be used to measure isotope flux analyses in bacterial cell samples to detail specific differences in metabolic pathways under anaerobic or aerobic conditions. The comparison results revealed that all three instruments can provide similar biological conclusions with each instrument possessing their own advantages