Yunee Kim

In-depth proteomic analyses of exosomes isolated from expressed prostatic secretions in urine

Expressed prostatic secretions (EPS) are proximal fluids of the prostate that are increasingly being utilized as a clinical source for diagnostic and prognostic assays for prostate cancer (PCa). These fluids contain an abundant amount of microvesicles reflecting the secretory function of the prostate gland, and their protein composition remains poorly defined in relation to PCa. Using expressed prostatic secretions in urine (EPS‐urine), exosome preparations were characterized by a shotgun proteomics procedure. In pooled EPS‐urine exosome samples, ∼900 proteins were detected. Many of these have not been previously observed in the soluble proteome of EPS generated by our labs or other related exosome proteomes. We performed systematic comparisons of our data against previously published, prostate‐related proteomes, and global annotation analyses to highlight functional processes within the proteome of EPS‐urine derived exosomes. The acquired proteomic data have been deposited to the Tranche repository and will lay the foundation for more extensive investigations of PCa derived exosomes in the context of biomarker discovery and cancer biology.

Isolation of cell surface proteins for mass spectrometry-based proteomics

Defining the cell surface proteome has profound importance for understanding cell differentiation and cell–cell interactions, as well as numerous pathogenic abnormalities. Owing to their hydrophobic nature, plasma membrane proteins that reside on the cell surface pose analytical challenges and, despite efforts to overcome difficulties, remain under-represented in proteomic studies. Limitations in the classically employed ultracentrifugation-based approaches have led to the invention of more elaborate techniques for the purification of cell surface proteins. Three of these methods – cell surface coating with cationic colloidal silica beads, biotinylation and chemical capture of surface glycoproteins – allow for marked enrichment of this subcellular proteome, with each approach offering unique advantages and characteristics for different experiments. In this article, we introduce the principles of each purification method and discuss applications from the recent literature.

In-Depth Proteomic Analyses of Direct Expressed Prostatic Secretions

It is expected that clinically obtainable fluids that are proximal to organs contain a repertoire of secreted proteins and shed cells reflective of the physiological state of that tissue and thus represent potential sources for biomarker discovery, investigation of tissue-specific biology, and assay development. The prostate gland secretes many proteins into a prostatic fluid that combines with seminal vesicle fluids to promote sperm activation and function. Proximal fluids of the prostate that can be collected clinically are seminal plasma and expressed prostatic secretion (EPS) fluids. In the current study, MudPIT-based proteomics was applied to EPS obtained from nine men with prostate cancer and resulted in the confident identification of 916 unique proteins. Systematic bioinformatics analyses using publicly available microarray data of 21 human tissues (Human Gene Atlas), the Human Protein Atlas database, and other published proteomics data of shed/secreted proteins were performed to systematically analyze this comprehensive proteome. Therefore, we believe this data will be a valuable resource for the research community to study prostate biology and potentially assist in the identification of novel prostate cancer biomarkers. To further streamline this process, the entire data set was deposited to the Tranche repository for use by other researchers.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Use of colloidal silica-beads for the isolation of cell-surface proteins for mass spectrometry-based proteomics.

Chaney and Jacobson first introduced the colloidal silica-bead protocol for the coating of cellular plasma membranes in the early 1980s. Since then, this method has been successfully incorporated into a wide range of in vitro and in vivo applications for the isolation of cell-surface proteins. The principle is simple - cationic colloidal silica microbeads are introduced to a suspension or monolayer of cells in culture. Electrostatic interactions between the beads and the negatively charged plasma membrane, followed by cross-linking to the membrane with an anionic polymer, ensure attachment and maintain the native protein conformation. Cells are subsequently ruptured, and segregation of the resulting plasma membrane sheets from the remaining- cell constituents is achieved by ultracentrifugation through density gradients. The resulting membrane-bead pellet is treated with various detergents or chaotropic agents (i.e., urea) to elute bound proteins. If proteomic profiling by mass spectrometry is desired, proteins are denatured, carbamidomethylated, and digested into peptides prior to chromatography.

Proteotranscriptomic analysis reveals stage specific changes in the molecular landscape of clear-cell renal cell carcinoma

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

Current Status of Biomarker Discovery in Human clear Cell Renal Cell Carcinoma

The incidence of clear cell renal cell carcinoma (ccRCC) continues to increase while very few treatment options are available for therapy. Development of metastatic disease dramatically decreases patient survival; therefore detection at an early stage while still localized to the renal capsule is imperative for a favorable prognosis. With recent advances in molecular technologies and subsequent understanding of the underlying pathology of disease, molecular markers have been defined that are predicative of tumor stage, metastatic potential and patient prognosis. Analysis of these biomarkers along with current staging techniques already used in the clinic may allow for implementation of personalized post-surgical treatment plans as well provide further molecular pathways for targeted therapeutic interventions. In this review we will highlight the potential ccRCC biomarkers that have been identified through a variety of molecular techniques to date, and provide insight into research models for biomarker discovery.

Using proteomics to probe neurons

Advances in mass spectrometry-based proteomics have allowed researchers to quantify the abundances of the different forms of three closely related proteins in the neurons of mice.