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Glioblastoma (GBM) cancer stem cells (GSCs) contribute to GBM's origin, recurrence, and resistance to treatment. However, the understanding of how mRNA expression patterns of GBM subtypes are reflected at global proteome level in GSCs is limited. To characterize protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry. We quantified > 10 000 proteins in two independent GSC panels and propose a GSC-associated proteomic signature characterizing two distinct phenotypic conditions; one defined by proteins upregulated in proneural and classical GSCs (GPC-like), and another by proteins upregulated in mesenchymal GSCs (GM-like). The GM-like protein set in GBM tissue was associated with necrosis, recurrence, and worse overall survival. Through proteogenomics, we discovered 252 non-canonical peptides in the GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered non-protein-coding, including variants of the heterogeneous ribonucleoproteins implicated in RNA splicing. In summary, GSCs express two protein sets that have an inverse association with clinical outcomes in GBM. The discovery of non-canonical protein sequences questions existing gene models and pinpoints new protein targets for research in GBM.

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine., QC 20220228

Extracellular vesicles (EVs) are increasingly tested as therapeutic vehicles and biomarkers, but still EV subtypes are not fully characterised. To isolate EVs with few co-isolated entities, a combination of methods is needed. However, this is time-consuming and requires large sample volumes, often not feasible in most clinical studies or in studies where small sample volumes are available. Therefore, we compared EVs rendered by five commonly used methods based on different principles from conditioned cell medium and 250 mu l or 3 ml plasma, that is, precipitation (ExoQuick ULTRA), membrane affinity (exoEasy Maxi Kit), size-exclusion chromatography (qEVoriginal), iodixanol gradient (OptiPrep), and phosphatidylserine affinity (MagCapture). EVs were characterised by electron microscopy, Nanoparticle Tracking Analysis, Bioanalyzer, flow cytometry, and LC-MS/MS. The different methods yielded samples of different morphology, particle size, and proteomic profile. For the conditioned medium, Izon 35 isolated the highest number of EV proteins followed by exoEasy, which also isolated fewer non-EV proteins. For the plasma samples, exoEasy isolated a high number of EV proteins and few non-EV proteins, while Izon 70 isolated the most EV proteins. We conclude that no method is perfect for all studies, rather, different methods are suited depending on sample type and interest in EV subtype, in addition to sample volume and budget.

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of

Purpose: Liquid biopsy has great potential to improve the management of brain tumor patients at high risk of surgery-associated complications. Here, the aim was to explore plasma extracellular vesicle (plEV) immunoprofiling as a tool for noninvasive diagnosis of glioma. Experimental Design: PlEV isolation and analysis were optimized using advanced mass spectrometry, nanoparticle tracking analysis, and electron microscopy. We then established a new procedure that combines size exclusion chromatography isolation and proximity extension assay-based ultrasensitive immunoprofiling of plEV proteins that was applied on a well-defined glioma study cohort (n = 82). Results: Among potential candidates, we for the first time identify syndecan-1 (SDC1) as a plEV constituent that can discriminate between high-grade glioblastoma multiforme (GBM, WHO grade IV) and low-grade glioma [LGG, WHO grade II; area under the ROC curve (AUC): 0.81; sensitivity: 71%; specificity: 91%]. These findings were independently validated by ELISA. Tumor SDC1 mRNA expression similarly discriminated between GBM and LGG in an independent glioma patient population from The Cancer Genome Atlas cohort (AUC: 0.91; sensitivity: 79%; specificity: 91%). In experimental studies with GBM cells, we show that SDC1 is efficiently sorted to secreted EVs. Importantly, we found strong support of plEV(SDC1) originating from GBM tumors, as plEVSDC1 correlated with SDC1 protein expression in matched patient tumors, and plEV(SDC1) was decreased postoperatively depending on the extent of surgery. Conclusions: Our studies support the concept of circulating plEVs as a tool for noninvasive diagnosis and monitoring of gliomas and should move this field closer to the goal of improving the management of cancer patients.

Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy., QC 20190610

Superparamagnetic iron oxide nanoparticles (SPIONs) have emerged as promising contrast agents for magnetic resonance imaging. The influence of different surface coatings on the biocompatibility of SPIONs has been addressed, but the potential impact of the so-called corona of adsorbed proteins on the surface of SPIONs on their biological behavior is less well studied. Here, we determined the composition of the plasma protein corona on silica- coated versus dextran-coated SPIONs using mass spectrometry-based proteomics approaches. Notably, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed distinct protein corona compositions for the two different SPIONs. Relaxivity of silica-coated SPIONs was modulated by the presence of a protein corona. Moreover, the viability of primary human monocyte-derived macrophages was influenced by the protein corona on silica-coated, but not dextran-coated SPIONs, and the protein corona promoted cellular uptake of silica-coated SPIONs, but did not affect internalization of dextran-coated SPIONs., QC 20151109