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

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 SiO2 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.

Automated compared to manual office blood pressure and to home blood pressure in hypertensive patients.

We studied the relationships of automated blood pressure (BP), measured in the healthcare centre, with manual office BP and home BP. Stable outpatients treated for hypertension were measured automatically, seated alone in a quiet room, six times after a 5 min rest with the BpTRU device, and immediately afterwards using the auscultatory method. Home BP was measured in a subgroup during 7 days preceding the visit. The automated, office and home BP values were 131.2 ± 21.8/77.8 ± 12.1 mmHg, 146.9 ± 20.8/85.8 ± 12.4 mmHg and 137.7 ± 17.7/79.4 ± 8.2 mmHg, respectively. Limits of agreement between office and automated BP (2 SDs in Bland-Altman plots) were +42.6 to -12.6/+22.6 to -6.6 mmHg for systolic/diastolic BP; for home and automated BP they were +45.8 to -25.8/+20.8 to -12.6 mmHg. For patients with two visits, intraclass correlation coefficients of BP values measured during the first and second visits were 0.66/0.72 for systolic/diastolic automated BP and 0.68/0.74 for systolic/diastolic office BP. Automated BP was lower than home BP and no more closely related to home BP than to office BP. It did not show better repeatability than office BP. Whether automated BP and the "white-coat effect", calculated cas the office BP-automated BP difference, have clinical and prognostic importance deserves further studies.