Quantum Dot-Based Immunolabelling of Extracellular Vesicles and Detection Using Fluorescence-Based Nanoparticle Tracking Analysis

Abstract

Extracellular vesicles (EVs) contain a variety of biomolecules, including DNA, RNA, lipids and proteins. They can interact with target cells to perform various functions, offering potential for therapeutic applications like drug delivery and diagnosis. The growing interest in EVs drives the need for robust methods for EV characterisation. One of the prevalent EV characterisation methods is scatter-based nanoparticle tracking analysis (Sc-NTA). This method measures the size and concentration of particles by tracking the scattered light from individual particles. However, Sc-NTA has limitations in selectivity, as it detects all scattered light and fails to distinguish EVs from other nanoparticles, such as protein aggregates. To overcome this limitation, fluorescence-based NTA (Fl-NTA) is being utilised, where fluorescence tagging is used to selectively detect EVs. In previous studies, lipophilic dyes were employed for membrane labelling, but this resulted in false-positive signals due to the staining of even non-vesicular extracellular particles (NVEPs). Immunolabelling methods using antibodies that specifically bind to EV-specific protein were also introduced; yet challenges with sensitivity and photostability of the organic dyes remained. To address the challenges, we conjugated quantum dots (QDs) to antibodies that specifically bind to EV-specific markers, CD9, CD63 and then immunolabelled the EVs. Labelling conditions were optimised to develop a robust protocol for QD-based immunolabelling. Detection sensitivity was evaluated by comparing QD-based immunolabelling with Alexa dye-based methods. Furthermore, size distribution analysis demonstrated the ability of QDs to detect smaller EV populations. Finally, subpopulations of EVs from various cell lines were profiled. This approach enhances the accurate characterisation of EVs, providing a reliable and reproducible method for EV quality control and improved insights into their heterogeneity.