Nuclear magnetic resonance and mass spectrometry based metabolomics

Zhengzheng Pan, Purdue University

Abstract

Metabolomics is the systematic study of the biochemical changes occurring in a living system as studied by the measurement of multiple metabolites in parallel. As two of the most important tools in this field, nuclear magnetic resonance (NMR) and mass spectrometry (MS) provide relatively comprehensive measurements of metabolic profiles in biological systems. In addition, multivariate data analyses, when combined with NMR and MS, provide enormous possibilities for metabolomics research beyond simple data reduction methods. Various unsupervised and supervised statistical methods create robust mathematical models to detect significant differences between groups of samples that are due to perturbation caused by diseases, toxins, therapy or even diet. Key metabolites can be identified from the statistical results and then validated as biomarker candidates. In this dissertation, NMR, MS and their combination with multivariate analyses are used to detect important diseases such as inborn errors of metabolism (IEM) and several cancers. Newly-developed analytical techniques involving ambient sample MS were used in metabolomics-based research and proven to be powerful methods for profiling. The addition of NMR-based metabolomics improved the statistical analysis. Important metabolites were identified using these advanced techniques and various multivariate statistical methods. Based on the identified biomarker candidates, intrinsic disease-related mechanisms were evaluated and suggested for further studies. In particular, emerging technologies in metabolomics discussed in this thesis are shown to be effective, opening a number of potential avenues for further development.

Degree

Ph.D.

Advisors

Raftery, Purdue University.

Subject Area

Analytical chemistry|Physical chemistry

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