Extracellular vesicles (EVs) exhibit high heterogeneity in biofluids, a feature that traditional bulk-level analysis approaches fail to capture in terms of individual variations. While numerous techniques exist for single-EV analysis, the majority focus primarily on profiling surface proteins. Transcriptional analysis at the level of individual EVs, however, remains largely unexplored. To bridge this gap, we propose the development of a technology for multimodal profiling of individual EVs, leveraging next-generation sequencing and an optimized method for multiplex library preparation. This proposed platform will serve as a unique tool for high-throughput, integrative profiling of single-EV gene expression and surface proteins. It aims to offer high-sensitivity, multi- dimensional biological insights, thereby potentially accelerating the advancement of EV-based diagnostics and targeted therapies.
Public Health Relevance Statement: NARRATIVE Conventional approaches to characterizing extracellular vesicles (EVs) often analyze the entire sample population (bulk analysis), neglecting the inherent EV heterogeneity and failing to quantify EV subpopulations within biofluids. Our proposed technique aims to address these unmet needs by facilitating multimodal studies at the single-EV level, thereby offering significant advantages to the broader EV research community. Its exceptional resolution and sensitivity are poised to accelerate EV-based biomarker discovery and advance the field of precision medicine. Moreover, this innovative method will facilitate the multimodal analysis of low- abundance EV samples, while also serving as an advanced quality control tool for EV-based drug delivery. Terms:
