Quantitative fluorescent nanoparticle tracking analysis and nano-flow cytometry enable advanced characterization of single extracellular vesicles.

Journal of extracellular biology Pub Date : 2025-01-08 eCollection Date: 2025-01-01 DOI:10.1002/jex2.70031
Danilo Mladenović, Joseph Brealey, Ben Peacock, Kairi Koort, Nataša Zarovni
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Abstract

Current state-of-the-art tools for analysing extracellular vesicles (EVs) offer either highly sensitive but unidimensional bulk measurements of EV components, or high-resolution multiparametric single-particle analyses which lack standardization and appropriate reference materials. This limits the accuracy of the assessment of marker abundance and overall marker distribution amongst individual EVs, and finally, the understanding of true EV heterogeneity. In this study, we aimed to define the standardized operating procedures and reference material for fluorescent characterization of EVs with two commonly used EV analytical platforms-nanoparticle tracking analysis (NTA) and nano-flow cytometry (nFCM). We achieved quantitative fluorescence analyses on ZetaView NTA and NanoAnalyzer nFCM instruments, by utilizing yellow-green FluoSpheres (FS) with assigned ERF (equivalent reference fluorophore) values. This standardization technique allowed for fluorescent EV signal to be expressed in ERF units (indicative of bound fluorescent antibodies per EV), thus enabling measurement of target protein marker abundance on individual EVs, and in the whole EV population. The NTA's and nFCM's limits of detection (LoD) were evaluated at 21 and 9 Alexa Fluor 488 (AF488) molecules, respectively. To complement the limited quantification of markers expressed in a few copies per single EV, in-line bulk fluorescence measurements with a plate reader were performed. This provided absolute marker quantification and more insightful analyses of EV heterogeneity and marker stoichiometry. The standardization method outlined in this work unlocks the full analytical potential of NTA and nFCM, enabling cross-platform data comparison. At the same time, it highlights some of the technical challenges and considerations and thus contributes to the ongoing efforts towards the development of EV analytical tools.

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