Research ResourcePhosphoproteomics

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

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Sci. Signal.  09 Jun 2015:
Vol. 8, Issue 380, pp. rs6
DOI: 10.1126/scisignal.aaa3139

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Cross sample proteomics

Nontargeted proteomic approaches are not well suited for comparisons of different samples and have detection limits, meaning that many biologically important but low abundance proteins are not detected. By applying a proteomic approach called DIA-MS (data-independent acquisition mass spectrometry) to an adipocyte cell line, Parker et al. showed that this method successfully mapped the phosphorylation events that occurred in response to insulin in a quantitative manner. Furthermore, by comparing the quantification of phosphorylation in cells exposed to various kinase inhibitors, the authors assigned specific kinases to many of phosphorylation events, which identified points of crosstalk between signaling pathways. This study provides proof of principle that this approach enables simultaneous analysis of phosphorylation events in multiple samples from different time points, doses, or patients.


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.

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