Magnetic Bead-Based Workflow for Sensitive and Streamlined Cell Surface Proteomics.

Cell surface proteins represent an important class of molecules for therapeutic targeting and cellular phenotyping. However, their enrichment and detection via mass spectrometry-based proteomics remains challenging due to low abundance, post-translational modifications, hydrophobic regions, and processing requirements. To improve their identification, we optimized a Cell-Surface Capture (CSC) workflow that incorporates magnetic bead-based processing. Using this approach, we evaluated labeling conditions (biotin tags and catalysts), enrichment specificity (streptavidin beads), missed cleavages (lysis buffers), nonenzymatic deamidation (digestion and deglycosylation buffers), and data acquisition methods (DDA, DIA, and TMT). Our findings support the use of alkoxyamine-PEG4-biotin plus 5-methoxy-anthranilic acid, SDS/urea-based lysis buffers, single-pot solid-phased-enhanced sample-preparation (SP3), and streptavidin magnetic beads for maximal surfaceome coverage. Notably, with semiautomated processing, sample handling was simplified and between ∼600 and 900 cell surface N-glycoproteins were identified from only 25-200 µg of HeLa protein. CSC also revealed significant differences between in vitro monolayer cultures and in vivo tumor xenografts of murine CT26 colon adenocarcinoma samples that may aid in target identification for drug development. Overall, the improved efficiency of the magnetic-based CSC workflow identified both previously reported and novel N-glycosites with less material and high reproducibility that should help advance the field of surfaceomics by providing insight in cellular phenotypes not previously documented.

Comparison of sample preparation techniques for large-scale proteomics.

Numerous workflows exist for large-scale bottom-up proteomics, many of which achieve exceptional proteome depth. Herein, we evaluated the performance of several commonly used sample preparation techniques for proteomic characterization of HeLa lysates [unfractionated in-solution digests, SDS-PAGE coupled with in-gel digestion, gel-eluted liquid fraction entrapment electrophoresis (GELFrEE) technology, SCX StageTips and high-/low-pH reversed phase fractionation (HpH)]. HpH fractionation was found to be superior in terms of proteome depth (>8400 proteins detected) and fractionation efficiency compared to other techniques. SCX StageTip fractionation required minimal sample handling and was also a substantial improvement over SDS-PAGE separation and GELFrEE technology. Sequence coverage of the HeLa proteome increased to 38% when combining all workflows, however, total proteins detected improved only slightly to 8710. In summary, HpH fractionation and SCX StageTips are robust techniques and highly suited for complex proteome analysis.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer.

BACKGROUND: The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery. RESULTS: We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC. CONCLUSIONS: This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Proteomics-Derived Biomarker Panel Improves Diagnostic Precision to Classify Endometrioid and High-grade Serous Ovarian Carcinoma.

PURPOSE: Ovarian carcinomas are a group of distinct diseases classified by histotypes. As histotype-specific treatment evolves, accurate classification will become critical for optimal precision medicine approaches. EXPERIMENTAL DESIGN: To uncover differences between the two most common histotypes, high-grade serous (HGSC) and endometrioid carcinoma, we performed label-free quantitative proteomics on freshly frozen tumor tissues (HGSC, n = 10; endometrioid carcinoma, n = 10). Eight candidate protein biomarkers specific to endometrioid carcinoma were validated by IHC using tissue microarrays representing 361 cases of either endometrioid carcinoma or HGSC. RESULTS: More than 500 proteins were differentially expressed (P < 0.05) between endometrioid carcinoma and HGSC tumor proteomes. A ranked set of 106 proteins was sufficient to correctly discriminate 90% of samples. IHC validated KIAA1324 as the most discriminatory novel biomarker for endometrioid carcinoma. An 8-marker panel was found to exhibit superior performance for discriminating endometrioid carcinoma from HGSC compared with the current standard of WT1 plus TP53 alone, improving the classification rate for HGSC from 90.7% to 99.2%. Endometrioid carcinoma-specific diagnostic markers such as PLCB1, KIAA1324, and SCGB2A1 were also significantly associated with favorable prognosis within endometrioid carcinoma suggesting biological heterogeneity within this histotype. Pathway analysis of proteomic data revealed differences between endometrioid carcinoma and HGSC pertaining to estrogen and interferon signalling. CONCLUSIONS: In summary, these findings support the use of multi-marker panels for the differential diagnosis of difficult cases resembling endometrioid carcinoma and HGSC.

Embryonic morphogen nodal promotes breast cancer growth and progression.

Breast cancers expressing human embryonic stem cell (hESC)-associated genes are more likely to progress than well-differentiated cancers and are thus associated with poor patient prognosis. Elevated proliferation and evasion of growth control are similarly associated with disease progression, and are classical hallmarks of cancer. In the current study we demonstrate that the hESC-associated factor Nodal promotes breast cancer growth. Specifically, we show that Nodal is elevated in aggressive MDA-MB-231, MDA-MB-468 and Hs578t human breast cancer cell lines, compared to poorly aggressive MCF-7 and T47D breast cancer cell lines. Nodal knockdown in aggressive breast cancer cells via shRNA reduces tumour incidence and significantly blunts tumour growth at primary sites. In vitro, using Trypan Blue exclusion assays, Western blot analysis of phosphorylated histone H3 and cleaved caspase-9, and real time RT-PCR analysis of BAX and BCL2 gene expression, we demonstrate that Nodal promotes expansion of breast cancer cells, likely via a combinatorial mechanism involving increased proliferation and decreased apopotosis. In an experimental model of metastasis using beta-glucuronidase (GUSB)-deficient NOD/SCID/mucopolysaccharidosis type VII (MPSVII) mice, we show that although Nodal is not required for the formation of small (<100 cells) micrometastases at secondary sites, it supports an elevated proliferation:apoptosis ratio (Ki67:TUNEL) in micrometastatic lesions. Indeed, at longer time points (8 weeks), we determined that Nodal is necessary for the subsequent development of macrometastatic lesions. Our findings demonstrate that Nodal supports tumour growth at primary and secondary sites by increasing the ratio of proliferation:apoptosis in breast cancer cells. As Nodal expression is relatively limited to embryonic systems and cancer, this study establishes Nodal as a potential tumour-specific target for the treatment of breast cancer.