EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open‐source ImageJ/Fiji plugin

Abstract

Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed ‘EVAnalyzer’, an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno‐)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross‐reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid‐based nanoparticles (LNPs) and protein loading of SiO 2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell–single vesicle level, thereby enabling high content EV cell uptake assays and plate‐based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open‐source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.

High efficiency preparation of monodisperse plasma membrane derived extracellular vesicles for therapeutic applications

“…With respect to EV production, a mild and size-based purification method was applied (i.e., ultrafiltration and subsequent size exclusion chromatography). In contrast to ultracentrifugation-based approaches, co-isolation of non-vesicular protein particles, vesicle fusion, aggregation, and/or disruption can thus be reduced 12 , 26 , 74 , 77 , 78 .…”

“…Compared to GPMVs, nPMVs have a more defined and uniform size distribution due to homogenization through extrusion. To further characterize possible heterogeneity of subpopulations, additional studies will be needed to analyze the cargo distribution and the protein and lipid composition of GPMVs or nPMVs at the single vesicle level such as by single vesicle imaging 74 or flow cytometry of GPMVs 27 , 75 . A recent publication 76 suggested that the ratio of particles per ug protein could be used as a measure for particle purity.…”

“…Interestingly, we did not detect CD63 in any nPMV preparations. There could be several explanations for this phenomenon: First, only a fraction of CD63 localizes to the plasma membrane as compared to intracellular compartments (endosome, MVBs, and lysosomes) 74 , 88 – 92 . Second, even in preparations that are enriched for exosomes, CD63 is typically not detected as highly abundant.…”

“…Second, even in preparations that are enriched for exosomes, CD63 is typically not detected as highly abundant. In HEK293 EVs, for example, only 50% of all EVs are CD63 positive as assessed by single vesicle super-resolution imaging 74 . In addition, CD63 is one of the least abundant EV markers detected by LC-MS proteomics 74 .…”

“…In HEK293 EVs, for example, only 50% of all EVs are CD63 positive as assessed by single vesicle super-resolution imaging 74 . In addition, CD63 is one of the least abundant EV markers detected by LC-MS proteomics 74 . This could be due to either a low molecular density of CD63 per vesicle and/or technical limitations of LC-MS detection.…”