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.

We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (≥2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48–70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 ± 2.03 months in patients with high STOML2 expression, whereas 53.67 ± 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types.

Despite significant efforts in the past decade towards complete mapping of the human proteome, 3564 proteins (neXtProt, 09-2014) are still “missing proteins”. Over one-third of these missing proteins are annotated as membrane proteins, owing to their relatively challenging accessibility with standard shotgun proteomics. Using non-small cell lung cancer (NSCLC) as a model study, we aim to mine missing proteins from disease-associated membrane proteome, which may be still largely under-represented. To increase identification coverage, we employed Hp-RP StageTip pre-fractionation of membrane-enriched samples from 11 NSCLC cell lines. Analysis of membrane samples from 20 pairs of tumor and adjacent normal lung tissue were incorporated to include physiologically expressed membrane proteins. Using multiple search engines (X!Tandem, Comet and Mascot) and stringent evaluation of FDR (MAYU and PeptideShaker), we identified 7702 proteins (66% membrane proteins) and 178 missing proteins (74 membrane proteins) with PSM-, peptide-, and protein-level FDR of 1%. Through multiple reaction monitoring (MRM) using synthetic peptides, we provided additional evidences for 8 missing proteins including 7 with transmembrane helix domains (TMH). This study demonstrates that mining missing proteins focused on cancer membrane sub-proteome can greatly contribute to map the whole human proteome. All data were deposited into ProteomeXchange with the identifier PXD002224.

Brown rot fungi play a critical role in carbon recycling and are of industrial interest. These fungi typically use reactive oxygen species (ROS) to indiscriminately “loosen” wood cell walls at the outset of decay.

Although EGFR tyrosine kinase inhibitors (TKIs) have demonstrated good efficacy in non-small-cell lung cancer (NSCLC) patients harboring EGFR mutations, most patients develop intrinsic and acquired resistance. We quantitatively profiled the phosphoproteome and proteome of drug-sensitive and drug-resistant NSCLC cells under gefitinib treatment. The construction of a dose-dependent responsive kinase-substrate network of 1548 phosphoproteins and 3834 proteins revealed CK2-centric modules as the dominant core network for the potential gefitinib resistance-associated proteins. CK2 knockdown decreased cell survival in gefitinib-resistant NSCLCs. Using motif analysis to identify the CK2 core sub-network, we verified that elevated phosphorylation level of a CK2 substrate, HMGA1 was a critical node contributing to EGFR-TKI resistance in NSCLC cell. Both HMGA1 knockdown or mutation of the CK2 phosphorylation site, S102, of HMGA1 reinforced the efficacy of gefitinib in resistant NSCLC cells through reactivation of the downstream signaling of EGFR. Our results delineate the TKI resistance-associated kinase-substrate network, suggesting a potential therapeutic strategy for overcoming TKI-induced resistance in NSCLC.