The purpose of the studies included in this thesis is to develop an effective an efficient method to study the proteome using separation and detection of peptides, when only a limited amount of sample, 10 micrograms of total protein or less, is available. The analysis will be applied to multiple myeloma cancer cells using ultra high-performance liquid chromatography-mass spectrometry for expression proteomics to illustrate utility. To detect low abundance peptides in a complex proteome, we use different strategies, including basic pH reversed-phase liquid chromatography (bRPLC), mass-to-charge fractionation in the mass spectrometer, and various liquid chromatography gradients to increase peptide separation to improve opportunities for detection and quantification. The different methods are optimized and compared by the number of peptides detected. Step-wise elution of bRP spin columns proved to yield more than 36,000 peptides using only 10 μg of protein. Mass-to-charge (m/z) fractionation was tested in mass analyzer Q-Exactive Plus (Thermo Scientific). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of an unfractionated sample was analyzed 4 times at different mass ranges, each mass range width of 150 m/z, resulting from 4 spectra combined, 31,732 peptides representing 3,967 proteins. Showingcomparable results to those form high pH reversed phase fractionation spin columns 5 fractions. Establishing a benchmark where the LC-MS/MS analysis of 600 μg of 10plex TMT-labeled peptides fractionated with bRPLC into 24 fractions yielded over 74,000 peptides from 7,700 proteins, we compared those results with analysis of 10 μg of total TMT-labeled peptides fractionated by bRP spin columns into 5 fractions, which produced 14,019 peptides from 3,538 proteins. These experiments were used to relatively quantify protein expression in naïve and drug resistant multiple myeloma cells lines as an example application in cancer research.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-8723 |
| Date | 01 November 2018 |
| Creators | Izumi, Victoria |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
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