Synthesis of S-Carbamidomethyl Cysteine and Its Use for Quantification of Cysteinyl Peptides by Targeted Proteomics.

Proteomics has played a central role in the identification of reliable disease biomarkers, which are the basis of precision medicine, a promising approach for tackling recalcitrant diseases such as cancer, that elude conventional treatments. Among proteomic methodologies, targeted proteomics employing stable isotope-labeled (SIL) internal standards is particularly suited for the clinical translation of biomarker information owing to its high throughput and accuracy in the quantitative analysis of patient-derived proteomes. Using SIL internal standards ensures the utmost level of confidence in detection and precision in targeted MS experiments. For successfully establishing assays based on targeted proteomics, it is crucial to secure broad coverage when selecting the SIL standard peptide panel. However, cysteinyl peptides have often been excluded because of cysteine's high chemical reactivity. To address this limitation, a new cysteine building block was developed by incorporating a sulfhydryl group configured with an S-carbamidomethyl group, which is commonly used in proteome sampling. This compound was found to be chemically stable and applicable to a variety of solid-phase peptide synthesis (SPPS) campaigns. Furthermore, a direct comparison of the synthesized SIL peptides and tryptic endogenous peptides demonstrated the potential utility of an SPPS flow based on the new cysteine building block for improving the success of targeted proteomic applications.

Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes.

We report a proteogenomic analysis of pancreatic ductal adenocarcinoma (PDAC). Mutation-phosphorylation correlations identified signaling pathways associated with somatic mutations in significantly mutated genes. Messenger RNA-protein abundance correlations revealed potential prognostic biomarkers correlated with patient survival. Integrated clustering of mRNA, protein and phosphorylation data identified six PDAC subtypes. Cellular pathways represented by mRNA and protein signatures, defining the subtypes and compositions of cell types in the subtypes, characterized them as classical progenitor (TS1), squamous (TS2-4), immunogenic progenitor (IS1) and exocrine-like (IS2) subtypes. Compared with the mRNA data, protein and phosphorylation data further classified the squamous subtypes into activated stroma-enriched (TS2), invasive (TS3) and invasive-proliferative (TS4) squamous subtypes. Orthotopic mouse PDAC models revealed a higher number of pro-tumorigenic immune cells in TS4, inhibiting T cell proliferation. Our proteogenomic analysis provides significantly mutated genes/biomarkers, cellular pathways and cell types as potential therapeutic targets to improve stratification of patients with PDAC.

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome.

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry.

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor ß signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

DNA repair and cholesterol-mediated drug efflux induce dose-dependent chemoresistance in nutrient-deprived neuroblastoma cells.

Neuroblastoma is a solid, heterogeneous pediatric tumor. Chemotherapy is widely used to treat neuroblastoma. However, dose-dependent responses and chemoresistance mechanisms of neuroblastoma cells to anticancer drugs remain challenging. Here, we investigated the dose-dependent effects of topotecan on human neuroblastoma cells (SK-N-SH, SH-SY5Y, and SK-N-BE) under various nutrient supply conditions. Serum-starved human neuroblastoma cells showed reduced toxicity. Their survival rate increased upon treatment with a high concentration (1 µM) of topotecan. Quantitative profiling of global and phosphoproteome identified 12,959 proteins and 48,812 phosphosites, respectively, from SK-N-SH cells. Network analysis revealed that topotecan upregulated DNA repair and cholesterol-mediated topotecan efflux, resulting in topotecan resistance. Results of DNA damage assay, cell cycle, and quantitative analyses of membrane cholesterol supported the validity of these resistance factors and their applicability to all neuroblastoma cells. Our results provide a model for high dose-dependent chemoresistance in neuroblastoma cells that could enable a patient-dependent chemotherapy screening strategy.

Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry.

Proteomics research today no longer simply seeks exhaustive protein identification; increasingly, it is also desirable to obtain robust, large-scale quantitative information. To accomplish this, data-independent acquisition (DIA) has emerged as a promising strategy largely owing to developments in advanced mass spectrometers and sophisticated data analysis methods. Nevertheless, the highly complex multiplexed MS/MS spectra produced by DIA remain challenging to interpret. Here, we present a novel strategy to analyze DIA data, based on unambiguous precursor mass assignment through the mPE-MMR (multiplexed post-experimental monoisotopic mass refinement) procedure and combined with complementary multistage database searching. Compared to conventional spectral library searching, the accuracy and sensitivity of peptide identification were significantly increased by incorporating precise precursor masses in DIA data. We demonstrate identification of additional peptides absent from spectral libraries, including sample-specific mutated peptides and post-translationally modified peptides using MS-GF+ and MODa/MODi multistage database searching. This first use of unambiguously determined precursor masses to mine DIA data demonstrates considerable potential for further exploitation of this type of experimental data.