Targeted phosphoproteomics of insulin signaling using data-independent acquisition mass spectrometry.

A major goal in signaling biology is the establishment of high-throughput quantitative methods for measuring changes in protein phosphorylation of entire signal transduction pathways across many different samples comprising temporal or dose data or patient samples. Data-independent acquisition (DIA) mass spectrometry (MS) methods, which involve tandem MS scans that are collected independently of precursor ion information and then are followed by targeted searching for known peptides, may achieve this goal. We applied DIA-MS to systematically quantify phosphorylation of components in the insulin signaling network in response to insulin as well as in stimulated cells exposed to a panel of kinase inhibitors targeting key downstream effectors in the network. We accurately quantified the effect of insulin on phosphorylation of 86 protein targets in the insulin signaling network using either stable isotope standards (SIS) or label-free quantification (LFQ) and mapped signal transmission through this network. By matching kinases to specific phosphorylation events (based on linear consensus motifs and temporal phosphorylation) to the quantitative phosphoproteomic data from cells exposed to inhibitors, we investigated predicted kinase-substrate relationships of AKT and mTOR in a targeted fashion. Furthermore, we applied this approach to show that AKT2-dependent phosphorylation of GAB2 promoted insulin signaling but inhibited epidermal growth factor (EGF) signaling in a manner dependent on 14-3-3 binding. Because DIA-MS can increase throughput and improve the reproducibility of peptide detection across multiple samples, this approach should facilitate more accurate, comprehensive, and quantitative assessment of signaling networks under various experimental conditions than are possible using other MS proteomic methods.