Date of Award


Document Type

Campus Access Thesis

Degree Name

Master of Science (MS)



First Advisor

Jason Evans

Second Advisor

Hannah Sevian

Third Advisor

Neil Reilly


Proteomics has become one of the most important fields in biological science research. It addresses the analysis of all proteins present in a specific cell. Quantitative proteomics seeks to measure the relative amount of proteins present in the sample. This work evaluates the influence of the number of precursor ions selected for MS3 analysis in a nanoflow HPLC tandem mass spectrometry experiment using TMT labeling on the degree of co-isolation occurrences that degrade the accuracy of the ion ratios used to measure relative protein expression. A two proteome inference model, with a HeLa whole cell digest as target proteome and a yeast whole cell digest as interfering proteome, was utilized to measure and evaluate interferences caused by co-isolation of HeLa and yeast peptides under various experimental settings. The results show that as the number of selected MS3 precursors is increased, the inference effects from co-isolation increase significantly, increasingly distorting the measured TMT reporter ion ratios.

Due to enormous complexity of whole cell protein digests, peptides frequently co-elute limiting the number of peptides that can be sequenced. In addition, isobaric peptides are often co-isolated during the tandem mass spectrometry experiment, causing poor accuracy in measuring relative protein expression levels. This issue is often addressed by fractionating the complex mixtures of peptides using a separation technique which is orthogonal to the low pH reversed phase HPLC separation and which is used prior to mass spectrometric analysis. This work shows that reducing the sample complexity using 2D-HPLC fractionation, in conjunction with an effective concatenation strategy, not only significantly increases protein identifications and average protein coverage, but also greatly mitigates the problems caused by co-isolation that can plague quantification using TMT labeling.


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