Tracking extracellular vesicle subpopulations and their cargo to advance therapeutic RNA delivery

Over the past decade, RNA-based therapeutics have become a promising class of drugs for treating diseases at the genetic level. They include approaches like messenger RNA (mRNA), small interfering RNA (siRNA), and CRISPR-associated guide RNAs, each providing unique mechanisms to address genetic mutations. However, delivering RNA within the cell cytosol remains challenging, as RNA cannot independently cross cell barriers and can be degraded by RNAses in circulation. Lipid nanoparticles (LNPs) have been widely used for RNA delivery, evidenced by their role in COVID-19 vaccines and the siRNA therapeutic Onpattro. While effective, LNPs have limitations like low delivery efficiency, liver accumulation, and immunogenicity. Extracellular vesicles (EVs) present a promising alternative due to their natural ability to deliver RNA, biocompatibility, and potential for targeted delivery based on their origin. Despite their potential, challenges in understanding EV processing, uptake mechanisms, and the heterogeneity among EV subpopulations need addressing. This thesis explores these challenges by focusing on EV processing in recipient cells, molecular factors influencing EV uptake and RNA delivery, and variability in RNA delivery efficiency among EV subpopulations. In Chapter 2, RNA labeling techniques are reviewed to visualize EV-RNA trafficking. Chapter 3 introduces a metabolic labeling approach using 5-ethynyl uridine, enabling RNA detection in EVs and visualization in recipient cells. Chapter 4 identifies molecular mediators of EV uptake and RNA delivery, highlighting the interaction between fibronectin on EVs and integrin α4β1 on recipient cells as critical for efficient delivery. Chapter 5 investigates EV subpopulations using imaging and tagging techniques, revealing differences in intracellular trafficking and RNA delivery efficiencies. Chapter 6 presents a platform for enriching EV subpopulations based on surface marker expression, allowing detailed study of their uptake and delivery efficiencies. Finally, Chapter 7 summarizes the findings, offering perspectives for advancing EV-based RNA delivery systems.