Application of 4D proteomics and super-resolution microscopy in extracellular vesicle and particle-borne biomarker discovery for early pancreatic cancer detection

Pancreatic adenocarcinoma (PDAC) ranks among the most lethal cancers due to a late diagnosis and ineffective treatments. Extracellular vesicles and particles (EVPs) are secreted by most cells, including tumor cells, and package selective molecules, including proteins, lipids, nucleic acids, and metabolites. EVPs are actively released into the circulation and mounting evidence suggests that circulating EVPs can serve as biomarkers for early cancer detection. Mass spectrometry (MS) has been extensively utilized for biomarker discovery in liquid biopsies, including EVP protein characterization. However, the scope and depth of the information obtained is limited by (i) the sensitivity and resolution of the analytic technologies and (ii) the proteomic complexity resulting from highly abundant serum-derived contaminants. The objective of this study is to apply an optimized reproducible EVP isolation method in conjunction with asymmetric-flow field-flow fractionation (AF4) technology to isolate EVP subsets with significantly improved purity and to employ three novel analytic technologies, including extremely sensitive timsTOF 4D proteomic MS, super-resolution dSTORM imaging analysis of single EVPs, and photocatalytic proximity labeling-proteomics (µMap) technology, to discover and validate novel, circulating EVP protein biomarkers for early detection of PDAC. In Aim 1, by employing the label-free timsTOF MS and using samples (blood plasma and tumor tissues) collected from patients with newly diagnosed PDAC, we will identify novel circulating EVP protein biomarkers that correlate with early stage disease. Top-ranked candidates will be further validated by robust absolute quantitation assays employing targeted parallel reaction monitoring (PRM) MS. In Aim 2, we will determine the percent representation and structural location of specific EVP biomarker proteins identified in Aim 1 at the single EVP level by utilizing the super-resolution dSTORM imaging analysis. The performance of single EVP analysis will be compared to the bulk analysis of individual protein targets via western blotting and/or ELISA analysis. We will further explore the potential to apply this analytic tool directly to plasma samples without prior EVP isolation. Lastly, in Aim 3, we will define protein-protein interactions (PPIs) of individual biomarkers by employing our recently developed photocatalytic proximity labeling-proteomics (µMap) technology. Three potential biomarker candidates identified in our previous study and novel candidates discovered in this study Aim 1 and Aim 2 will be subjected to the PPI analysis. These interactions will be further validated by super-resolution imaging analysis at the single-EVP, single-molecule level. We will establish if the presence or absence of these interactions provides a more robust approach for early cancer detection. We predict that combined application of these technologies will greatly facilitate novel biomarker discovery for early detection of pancreatic cancer. It also explores EVP PPIs as a new category of biomarkers and provide a rationale for developing therapies targeting these interactive networks in the future.