Phosphorylation related perturbations in cellular signaling pathways are at the root of many human diseases. Protein phosphorylation cannot be observed using DNA or RNA sequencing, which means it is extremely important to develop proteomic technologies and methods that reproducibly and accurately quantify phosphorylation events. Tandem mass spectrometry currently provides an excellent platform to deeply catalog the sites of phosphorylation with respect to disease. However, accurate reproducibility and quantitation are hampered by several acquisition tradeoffs made to provide higher identification rates of low-abundant molecules. I propose an approach to improve quantitative reproducibility in phosphorylation experiments, specifically with regard to positional phosphorylation isomers. I intend to build computational tools that separate the tasks of peptide identification and site localization in proteomic workflows. I will perform site localization using several metrics calculated with entire fragment ion profiles made up of multiple scans, instead of current methods that only use a single scan to localize phosphorylation sites. This should significantly improve accuracy and reproducibility. I will apply this technique to monitor phosphorylation events in the IGF-1 signaling pathway of human cell lines with respect to various stimuli to help elucidate specific networking characteristics between members of that pathway. This project will also greatly benefit the signaling community by providing tools and methods to more reliably discover quantitative changes in the phosphoproteome.
This project will improve our understanding of the phosphorylation topology in the IGF-1 signaling pathway with respect to human disease. An understanding of this mechanism could open doors to new approaches to study and treat a wide range of diseases, including cancer, obesity, diabetes, and Alzheimer's disease. This project will also produce software tools and methods to improve reproducibility and accuracy in quantitative phosphoproteomics experiments.
| Searle, Brian C; Pino, Lindsay K; Egertson, Jarrett D et al. (2018) Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat Commun 9:5128 |
| Pino, Lindsay K; Searle, Brian C; Bollinger, James G et al. (2017) The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev : |
