Type 2 diabetes results from aberrant regulation of the phosphorylation cascade in beta-cells. Phosphorylation in pancreatic beta-cells has not been examined extensively, except with regard to subcellular phosphoproteomes using mitochondria. Thus, robust, comprehensive analytical strategies are needed to characterize the many phosphorylated proteins that exist, because of their low abundance, the low stoichiometry of phosphorylation, and the dynamic regulation of phosphoproteins. In this study, we attempted to generate data on a large-scale phosphoproteome from the INS-1 rat pancreatic beta-cell line using linear ion trap MS/MS. To profile the phosphoproteome in-depth, we used comprehensive phosphoproteomic strategies, including detergent-based protein extraction (SDS and SDC), differential sample preparation (in-gel, in-solution digestion, and FASP), TiO2 enrichment, and MS replicate analyses (MS2-only and multiple-stage activation). All spectra were processed and validated by stringent multiple filtering using target and decoy databases. We identified 2467 distinct phosphorylation sites on 1419 phosphoproteins using 4 mg of INS-1 cell lysate in 24 LC-MS/MS runs, of which 683 (27.7%) were considered novel phosphorylation sites that have not been characterized in human, mouse, or rat homologues. Our informatics data constitute a rich bioinformatics resource for investigating the function of reversible phosphorylation in pancreatic beta-cells. In particular, novel phosphorylation sites on proteins that mediate the pathology of type 2 diabetes, such as Pdx-1, Nkx.2, and Srebf1, will be valuable targets in ongoing phosphoproteomics studies.
The multiplexed product-ion scan mode using QqQ-MS generates systematically detectable peptide transitions in a single liquid chromatography/MS run, in which we were able to identify SRM peptides that represent known target proteins in complex biological samples. The method presented here is easy to implement and has high-throughput capabilities as a result of the short analysis time. It is therefore well suited for the design of optimal SRM experiments.
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