Optimisation of imaging flow cytometry for the analysis of single extracellular vesicles by using fluorescence-tagged vesicles as biological reference material.

Extracellular vesicles (EVs) mediate targeted cellular interactions in normal and pathophysiological conditions and are increasingly recognised as potential biomarkers, therapeutic agents and drug delivery vehicles. Based on their size and biogenesis, EVs are classified as exosomes, microvesicles and apoptotic bodies. Due to overlapping size ranges and the lack of specific markers, these classes cannot yet be distinguished experimentally. Currently, it is a major challenge in the field to define robust and sensitive technological platforms being suitable to resolve EV heterogeneity, especially for small EVs (sEVs) with diameters below 200 nm, i.e. smaller microvesicles and exosomes. Most conventional flow cytometers are not suitable for the detection of particles being smaller than 300 nm, and the poor availability of defined reference materials hampers the validation of sEV analysis protocols. Following initial reports that imaging flow cytometry (IFCM) can be used for the characterisation of larger EVs, we aimed to investigate its usability for the characterisation of sEVs. This study set out to identify optimal sample preparation and instrument settings that would demonstrate the utility of this technology for the detection of single sEVs. By using CD63eGFP-labelled sEVs as a biological reference material, we were able to define and optimise IFCM acquisition and analysis parameters on an Amnis ImageStreamX MkII instrument for the detection of single sEVs. In addition, using antibody-labelling approaches, we show that IFCM facilitates robust detection of different EV and sEV subpopulations in isolated EVs, as well as unprocessed EV-containing samples. Our results indicate that fluorescently labelled sEVs as biological reference material are highly useful for the optimisation of fluorescence-based methods for sEV analysis. Finally, we propose that IFCM will help to significantly increase our ability to assess EV heterogeneity in a rigorous and reproducible manner, and facilitate the identification of specific subsets of sEVs as useful biomarkers in various diseases.