Date of Award

12-2013

Degree Type

Thesis

Degree Name

Master of Science

Major

Chemistry

Major Professor

Robert N. Compton

Committee Members

Robert L. Hettich, Shawn R. Campagna, Michael J. Sepaniak

Abstract

Multiple Reaction Monitoring (MRM) is a powerful tandem mass spectrometry (MS/MS) tool frequently implemented in proteomic studies to provide targeted analysis of proteins and peptides. The selectivity that MRM delivers is so strong that it provides the quadrupole mass spectrometers (QQQ), on which it is commonly employed, with pertinence to proteomic studies that they would otherwise lack for their relatively low resolution. Additionally, this increased level of selectivity is sufficient enough to supplant complicated fractionation techniques, additional dimensions of chromatography, and 24 hour long MS/MS experiments in simplistic biological samples. But there is a deficiency of evidence to determine the applicability of MRM to complex samples such as those containing the entire proteome of single cellular organisms. These samples are often employed to profile entire metabolic pathways at a cellular level using the complete set of proteins involved in the pathway’s characteristic enzyme driven reactions. This sweeping view of gene expression is vital to understand cellular response, and profiling these expressions would benefit greatly from the introduction of MRM as a viable approach for characterizing metabolic networks. This thesis takes two significant steps towards this viability by first demonstrating MRM reproducibility in complex samples, and characterizing degrees to which certain design related factors influence the quality of these MRM. The next step applies knowledge gained by the first to exhibit the MRM profiling a vital metabolic pathway from a complex sample. This step also demonstrates the self-sufficient utility an ab initio method designing MRM. Combining the ab initio design approach with the MRM of complex samples represents substantially shorter experimental preparations for profiling metabolic networks, and renders the characterization gene expression on a cellular level as a more widely accessible study within proteomics.

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