Date of Award

Winter 12-15-2019

Author's School

Graduate School of Arts and Sciences

Author's Department


Degree Name

Doctor of Philosophy (PhD)

Degree Type



Liquid chromatography/mass spectrometry (LC/MS)-based untargeted metabolomics is an established technology being applied by many laboratories for comprehensive analysis of metabolites in a biological sample to yield insight into the regulation of biochemical processes. A major goal of untargeted metabolomics is to find a biomarker that is correlated with disease processes. Conventional metabolomics, however, has limitations when dealing with a large number of samples, analyzing complex metabolomic data, determining metabolic fates of specific nutrients, resolving the spatial distribution of metabolites, and studying large sample sizes. The major goal of this dissertation is to improve upon existing approaches to better characterize the metabolome at the atomic, molecular, and tissue levels.Quenching metabolism is a critical component in metabolomics procedures. If metabolism is not rapidly quenched during sample preparation, artifacts can arise in metabolomic datasets because some metabolites turn over in seconds. In such situations, the metabolite concentrations will be the result of sample preparation instead of normal cellular physiology. The quenching step typically requires washing the plate with water prior to quenching to remove surrounding medium completely. This can take from minutes up to an hour when the sample sizes are large. Therefore, the rapid quenching methods that most investigators follow can be problematic. Here, we assessed the error associated with conventional metabolomic quenching procedures and explored the possible role of crosslinking proteins in performing metabolite extraction. We conclude that quenching with crosslinking agents is effective and reliable for deactivating enzymes as well as preventing cell proliferation.To enhance resolution at the molecular level, we developed a strategy to facilitate identification of metabolites. Much evidence supports that a large percentage of the peaks detected by LC/MS in untargeted metabolomics do not correspond to unique metabolites of biological origin. Rather, most of the mass spectrometry signals arise from artifacts, contaminants, and molecular degenerates (e.g., adducts, multimers, fragments, and naturally occurring isotopes of the same metabolites). Our strategy has been to remove noise from datasets on the fly by annotating adducts and contaminants. We found that this reduces the data burden of metabolomics by more than an order of magnitude, allowing us to acquire high-resolution MS/MS data on all relevant signals in a dataset on an Orbitrap mass spectrometer in a single run.To enhance resolution at the atomic level, we improved upon metabolomic workflows involving isotopically labeled atoms. By labeling one atom in a single nutrient and then tracking it through metabolism as it is transformed into other compounds, we could track metabolites downstream of precursors. We first developed a database called isoMETLIN to facilitate tracking of stable isotopic labels between metabolic intermediates. isoMETLIN provides two major criteria: one is the capability of searching all computed isotopologues of common metabolites on the basis of mass-to-charge (m/z) values and specified isotopes of interest (e.g., 13C and 15N), and the other is offering hundreds of experimental MS/MS data on specific isotopomers.1 Previously, investigators had obtained biochemical information by identifying isotopologue enrichment. Now, because of our work, investigators can obtain greater insight into metabolic pathways by monitoring isotopomers.Lastly, to enhance resolution at the tissue level, we optimized desorption electrospray ionization (DESI) mass spectrometry-based imaging for detection of water-soluble metabolites in tissue. To the best of our knowledge, the platform developed is the only commercially available technology to image water-soluble metabolites with mass spectrometry. Mass spectrometry imaging is a powerful tool for investigating the localization of metabolites in situ, which enables investigators to correlate key metabolites with specific tissues in an animal.


English (en)

Chair and Committee

Gary J. Patti

Committee Members

Joseph A. Fournier, Michael L. Gross, Richard A. Loomis, Jacob Schaefer,


Permanent URL: https://doi.org/10.7936/f38h-sd29

Available for download on Friday, December 15, 2119