Extracellular vesicles (EVs) and the RNAs contained within them (EV-RNAs) are secreted into biofluids by every cell type. EV-RNAs have emerged as potential prognostic or predictive biomarkers of a wide range of diseases, providing a targetable, accurate real-time representation of the disease state. However, advancement of EV- RNAs in the clinic as biomarkers of disease has been impeded by challenges in their isolation and characterization. Most notably, there is a lack of tools and techniques to i) isolate and precisely characterize tissue-specific EV-RNA populations; ii) define heterogeneity in surface markers and RNA content in tissue- specific EV populations; and iii) determine changes in EV-RNAs associated with disease state. In this multi-PI proposal, we will use a collaborative and innovative approach to advance technology that would allow isolation and granular characterization of EV populations from hematopoietic cells, brain, and heart. Our objective in the UG3 phase is to identify cell/tissue specific markers for isolation of EVs using computational analysis, transcriptomics, and EV-tracking in genetic mouse models; this approach would allow for fluorescence/antibody-based identification of EVs in a cell-specific manner. Information will also be obtained on individual EVs with a novel quantitative single molecule localization microscopy (qSMLM) approach. qSMLM, a sensitive fluorescence-based imaging method, will be used to quantify the number of affinity isolated EVs, their size, and key RNA content using molecular beacons. The identified tissue-specific EV-markers and EV-RNAs will be used in the UH3 phase for validation in a variety of human models. Our objective in the UH3 phase is to determine cellular/tissue contribution to EV-RNAs from Tissue-Chip effluents; and assess dynamic changes in EVs from human plasma from subjects with acute disease (coronary ischemia or cerebrovascular accident) or physiological processes (exercise). We will validate key EV-RNAs (using nano-flow cytometry and molecular beacons) and use qSMLM with molecular beacons to provide a quantitative profile of EVs at a single vesicle resolution. Notably, proposed experiments will help determine contribution of different tissues to the plasma biofluid RNA landscape at baseline, and in response to physiological or disease stressors. Together, the tools and techniques developed in the UG3 phase, and validated in the UH3 phase would serve as a road-map for the discovery and development of EV markers and EV-RNAs specific to other tissues. Ultimately, the use of tissue-specific EV-RNAs to probe disease state would provide a dynamic window into disease progression or regression with higher sensitivity and fidelity compared to currently available technologies.
Extracellular vesicles (EVs) and their RNA contents have emerged as novel diagnostic and prognostic biomarkers for a wide range of human diseases. However, there is a lack of tools and techniques to identify and characterize tissue-specific EVs. Here, we will focus on discovery and validation of tissue-specific EVs and EV-RNAs to improve specificity and fidelity of these emerging biomarkers and thus address a significant unmet need.
