Cost-effective and accurate protein quantification for large-scale targeted proteomics

Ching-Yun Chang, Purdue University


Selected Reaction Monitoring (SRM) is a mass spectrometry(MS)-based experimental approach for quantifying a priori specified protein targeted in biological samples across conditions. The approach is driven by the recent advances in analytical methods and instrumentation, and enables the most sensitive and accurate quantification in targeted proteomics. SRM plays an increasingly important role in system biology, drug discovery, pharmaceutical research, and early detection of disease. A typical goal of which is to identify candidate biomarkers of disease. A challenge of addressing this goal is that SRM experiments generate measurements not on intact proteins, but on two levels of fragments of a protein (peptides and transitions), which do not directly yield protein-level quantities. Additionally, label-based workflow relies on a labeled reference peptide for every endogenous peptide of interest, and as such are somewhat time-consuming and costly. Lastly, the measurements of MS-based studies are frequently incorrect due to systematic and non-systematic noise and result in undermine the quality of the conclusions. This dissertation introduces probabilistic models for addressing these challenges. Through the use of multiple case studies of proteomic investigations, we show that the models are more sensitive and specific for detecting changes in protein abundance than existing methods for a more cost-effective and large-scale experiment.




Vitek, Purdue University.

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