Our ability to model the dynamics of signal transduction networks will depend on accurate methods to quantify levels of protein phosphorylation on a global scale. Here we describe a motif-targeting quantitation method for phosphorylation stoichiometry typing. Proteome-wide phosphorylation stoichiometry can be obtained by a simple phosphoproteomic workflow integrating dephosphorylation and isotope tagging with enzymatic kinase reaction. Proof-of-concept experiments using CK2-, MAPK- and EGFR-targeting assays in lung cancer cells demonstrate the advantage of kinase-targeted complexity reduction, resulting in deeper phosphoproteome quantification. We measure the phosphorylation stoichiometry of >1,000 phosphorylation sites including 366 low-abundance tyrosine phosphorylation sites, with high reproducibility and using small sample sizes. Comparing drug-resistant and sensitive lung cancer cells, we reveal that post-translational phosphorylation changes are significantly more dramatic than those at the protein and messenger RNA levels, and suggest potential drug targets within the kinase–substrate network associated with acquired drug resistance.

We describe an improved version of the data independent acquisition (DIA) computational analysis tool DIA-Umpire, and show that it enables highly sensitive, untargeted and direct (spectral library-free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA-Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re-extraction step of DIA-Umpire is updated with an improved scoring function and a more robust, semi-parametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK-293 cells and human liver microtissues, we demonstrate that DIA-Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data dependent acquisition (DDA). We further demonstrate the utility of DIA-Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional DDA and using DIA with different isolation window widths.

Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.

Aberrant protein phosphorylation plays important roles in cancer-related cell signaling. With the goal of achieving multiplexed, comprehensive, and fully automated relative quantitation of site-specific phosphorylation, we present a simple label-free strategy combining an automated pH/acid-controlled IMAC procedure and informatics-assisted SEMI (sequence, elution time, mass-to-charge, and internal standard) algorithm. The SEMI strategy effectively increased the number of quantifiable peptides more than 4-fold in replicate experiments (from 262 to 1171, p < 0.05, false discovery rate = 0.46%) by using a fragmental regression algorithm for elution time alignment followed by peptide cross-assignment in all LC-MS/MS runs. In addition, the strategy demonstrated good quantitation accuracy (10-12%) for standard phosphoprotein and variation less than 1.9 fold (within 99% confidence range) in proteome scale and reliable linear quantitation correlation (R(2) = 0.99) with 4000-fold dynamic concentrations, which was attributed to our reproducible experimental procedure and informatics-assisted peptide alignment tool to minimize system variations. In an attempt to explore metastasis-associated phosphoproteomic alterations in lung cancer, this approach was used to delineate differential phosphoproteomic profiles of a lung cancer metastasis model. Without sample fractionation, the SEMI algorithm enabled quantification of 1796 unique phosphopeptides (false discovery rate = 0.56%) corresponding to 854 phosphoproteins from a series of non-small cell lung cancer lines with varying degrees of in vivo invasiveness. Nearly 40% of the phosphopeptides showed >2-fold change in highly invasive cells; validation of phosphoprotein subsets by Western blotting not only demonstrated the consistency of data obtained by our SEMI strategy but also revealed that such dramatic changes in the phosphoproteome result mostly from translational or post-translational regulation. Mapping of these differentially expressed phosphoproteins in multiple cellular pathways related to cancer invasion and metastasis suggests that the site and degree of phosphorylation might have distinct patterns or functions in the complex process of cancer progression.