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Yin, Xianzhe

University of British Columbia

Bottom-up proteomics characterizes proteins from the analysis of peptides released from proteolysis. In this process, proteins are digested, and the mass spectrometry (MS) information acquired from the resulting peptides is used to infer the identity and quantity of the proteins. Various methodologies have been established for the bottom-up approach to achieve high-throughput analysis of proteins. However, the performance of bottom-up proteomics, such as protein coverage, reproducibility, and efficiency still needs to be improved by modifying the existing methods. Chapter 1 provided a general introduction to the field of proteomics and the main techniques used in this thesis. In Chapter 2, seven lysis protocols were compared and three of them were selected as best for global proteomics since they had more proteins detected, with less variation. The selected protocols could increase protein coverage and reproducibility in future studies. Chapter 3 introduced one of the limitations that the data-dependant acquisition (DDA) mode has. Although DDA is widely used in proteomics, the abundance-dependent sampling of DDA often leads to the irreproducible or non-detection of low-abundance peptides. In this chapter, a method termed Isobaric Peptide Doping (isoDoping), which utilizes synthesized peptides in conjugation with tandem mass tags (TMT) was developed to improve the detection of targeted low-abundance peptides within a large-scale global proteomics experiment. In Chapter 4, four different methods were compared to characterize the surfaceome of multiple myeloma (MM) cell lines. Among the methods, global proteome profiling was demonstrated promising for MM since it had low sample consumption, more identified membrane proteins, and the ability to compare the protein expression between MM cells and negative controls. The method could help to find potential therapeutic targets for the MM. In Chapter 5, a method was established to estimate the overall peptide concentration by using a quality control (QC) run after the peptide extraction and before the TMT labeling. The method could improve the normalization of sample amounts for a TMT-based global proteome profiling experiment, thus improving the quantification accuracy.

Text

2024-05-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/84722

Chemistry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/