|Matt Foster Ph.D. is an Assistant Professor in the Division of Pulmonary, Allergy and Critical Care Medicine at Duke University and is also on the staff of the Duke Proteomics and Metabolomics Shared Resource. His formal training is in the areas of bioinorganic chemistry and nitric oxide biology. Read More |
He has a keen interest in the application of discovery-based and targeted proteomics to both clinical and basic science research problems.
A Targeted Proteomic Assay Quantifies the Periodic Expression of Cell-cycle Regulators in Yeast S. Cerevisae.
Analysis of yeast mRNA has identified a cell cycle-regulated network of transcription factors (TFs) that control periodic gene expression. However, it has not been possible to readily quantify the protein expression of these TFs. Here, we used Skyline to develop, optimize and deploy a parallel reaction monitoring (PRM) assay for over 40 low abundance TFs, including several that have not previously been identified by mass spectrometry. Analysis of synchronized wild-type yeast S. cerevisiae, sampled over ~2 cell cycles (20 time points per replicate), confirmed the periodic protein expression of numerous TFs that was highly correlated with mRNA expression. In cyclin-deficient yeast, the cell cycle-dependent proteasomal degradation of many, but not all, TFs was abrogated. These data help to establish a model of (post)-transcriptional regulation of cell cycle-regulated yeast TFs and more generally highlight the utility of targeted proteomic assays for high resolution temporal profiling of protein expression. Skyline addition: In my research (and the work that I do with the Duke Proteomics and Metabolomics Shared Resource), Skyline is an essential tool for targeted proteomics, for assessing longitudinal system suitability, and for tracking system performance during acquisition of large unbiased proteomics datasets. I also use Skyline, to a lesser extent, for label-free MS1 and MS2/DIA quantitation and metabolomics. I will highlight how Skyline was used for assay development in order to insure that we achieved maximum sensitivity for detection of a large set of very low abundance peptides while still maintaining sufficient points across the peak for quantitation. I will discuss our experience with manual peak/transition picking versus automation of these steps in Skyline. Finally, I will use Skyline to highlight a few aspects of this work that could be improved upon for future large-scale studies.