Integrative, multi-parametric characterization of the EV surface protein and nucleic acid landscape by nano-flow and sorting cytometry - Abstract A novel paradigm in paracrine signaling has recently emerged based on the findings identifying extracellular vesicles (EVs) as intercellular conveyors of biological information both in normal and pathological conditions such as cancer. EVs and their cargo have been shown by us and others to regulate gene expression and alter cell function in various cell types. Moreover, during pathological conditions such as cancer, the number and compositions of EVs alter the host immune response as well as synchronize the behavior of secondary tumors. Isolation and molecular profiling of EVs (i.e. RNAs, proteins, lipids, metabolites) both in health and disease are critical for understanding EVs' biogenesis and for using EV as biomarkers for disease status. EV RNA and proteins are expected to vary, according to tissues of origin and the biological state of the EV-producing cells. Some of the current limitations in EV field that limit their use as disease markers are: i) lack of effective sorting methods that force bulk EVs analyses biasing detection against low abundant species, ii) DNA/RNA/proteins quantification methods and bioinformatics pipelines, which are time consuming expensive. Novel approaches are needed aimed at improving the antigen detection limit, characterization of EV subsets with single EV resolution, while generating reliable and reproducible results. These new standards in EV research are a necessary prerequisite for novel disease diagnostic and prognostic strategies, biomarker-based surveillance for disease progression, treatment efficacy, and relapse. In the present application, we propose a collaborative approach aimed at streamlining EV analyzes and improving antigen detection by i) detection of specific RNA/ssDNA molecules in EV populations by combining nano-flow cytometry and molecular beacons (Drs. Ghiran and Tyagi, BIDMC/HMS, and Rutgers University, respectively), ii) the use plasmon resonance nano-tags for EV antigen detection, using nano-flow cytometry (Dr. Jones, NCI) and the iii) integration of RNA and protein multidimensional analyses by a dedicated cloud- based, free, bioinformatics pipeline, which will extract by (Dr. Milosavljevic, Aleksandar, Baylor College of Medicine). The results of our collaborative effort will provide the scientific community with: i) new methods for EV sorting, detection of specific protein, RNA/ssDNA molecules on EV subpopulations with a sensitivity currently unattained by any large scale technique, ii) and protocols necessary for standardization across the labs and to translation to clinical practice, iii) bioinformatics infrastructure necessary for extraction of subtle but relevant data present in multi-parametric analyses. Importantly, the scientific community will be able to use every component produced by our team either together for comprehensive EV subset analyses, or as stand-alone tools.
