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Kinase-catalyzed protein phosphorylation is fundamental to eukaryotic signal transduction, regulating most cellular processes. Kinases are frequently dysregulated in cancer, inflammation, and degenerative diseases, and because they can be inhibited with small molecules, they became important drug targets. Accordingly, analytical approaches that determine kinase activation states are critically important to understand kinase-dependent signal transduction and to identify novel drug targets and predictive biomarkers. Multiplexed inhibitor beads (MIBs or kinobeads) efficiently enrich kinases from cell lysates for liquid chromatography-mass spectrometry (LC-MS) analysis. When combined with phosphopeptide enrichment, kinobead/LC-MS can also quantify the phosphorylation state of kinases, which determines their activation state. However, an efficient kinobead/LC-MS kinase phospho-profiling protocol that allows routine analyses of cell lines and tissues has not yet been developed. Here, we present a facile workflow that quantifies the global phosphorylation state of kinases with unprecedented sensitivity. We also found that our kinobead/LC-MS protocol can measure changes in kinase complex composition and show how these changes can indicate kinase activity. We demonstrate the utility of our approach in specifying kinase signaling pathways that control the acute steroidogenic response in Leydig cells; this analysis establishes the first comprehensive framework for the post-translational control of steroid biosynthesis.

Stable isotope labeling is widely used to encode and quantify proteins in mass-spectrometry-based proteomics. We compared metabolic labeling with stable isotope labeling by amino acids in cell culture (SILAC) and chemical labeling by stable isotope dimethyl labeling and find that they have comparable accuracy and quantitative dynamic range in unfractionated proteome analyses and affinity pull-down experiments. Analyzing SILAC- and dimethyl-labeled samples together in single liquid chromatography-mass spectrometric analyses minimizes differences under analytical conditions, allowing comparisons of quantitative errors introduced during sample processing. We find that SILAC is more reproducible than dimethyl labeling. Because proteins from metabolically labeled populations can be combined before proteolytic digestion, SILAC is particularly suited to studies with extensive sample processing, such as fractionation and enrichment of peptides with post-translational modifications. We compared both methods in pull-down experiments using a kinase inhibitor, dasatinib, and tagged GRB2-SH2 protein as affinity baits. We describe a StageTip dimethyl-labeling protocol that we applied to in-solution and in-gel protein digests. Comparing the impact of post-digest isotopic labeling on quantitative accuracy, we demonstrate how specific experimental designs can benefit most from metabolic labeling approaches like SILAC and situations where chemical labeling by stable isotope-dimethyl labeling can be a practical alternative.