Abstract
Proteomic studies of post-translational modifications by metal affinity or antibody-based methods often employ data-dependent analysis, providing rich data sets that consist of randomly sampled identified peptides because of the dynamic response of the mass spectrometer. This can complicate the primary goal of programs for drug development, mutational analysis, and kinase profiling studies, which is to monitor how multiple nodes of known, critical signaling pathways are affected by a variety of treatment conditions. Cell Signaling Technology has developed an immunoaffinity-based LC-MS/MS method called PTMScan Direct for multiplexed analysis of these important signaling proteins. PTMScan Direct enables the identification and quantification of hundreds of peptides derived from specific proteins in signaling pathways or specific protein types. Cell lines, tissues, or xenografts can be used as starting material. PTMScan Direct is compatible with both SILAC and label-free quantification. Current PTMScan Direct reagents target key nodes of many signaling pathways (PTMScan Direct: Multipathway), serine/threonine kinases, tyrosine kinases, and the Akt/PI3K pathway. Validation of each reagent includes score filtering of MS/MS assignments, filtering by identification of peptides derived from expected targets, identification of peptides homologous to expected targets, minimum signal intensity of peptide ions, and dependence upon the presence of the reagent itself compared with a negative control. The Multipathway reagent was used to study sensitivity of human cancer cell lines to receptor tyrosine kinase inhibitors and showed consistent results with previously published studies. The Ser/Thr kinase reagent was used to compare relative levels of kinase-derived phosphopeptides in mouse liver, brain, and embryo, showing tissue-specific activity of many kinases including Akt and PKC family members. PTMScan Direct will be a powerful quantitative method for elucidation of changes in signaling in a wide array of experimental systems, combining the specificity of traditional biochemical methods with the high number of data points and dynamic range of proteomic methods.