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EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open-source ImageJ/Fiji plugin
M. Schürz, J. Danmayr, M. Jaritsch, E. Klinglmayr, HM. Benirschke, CT. Matea, P. Zimmerebner, J. Rauter, M. Wolf, FG. Gomes, Z. Kratochvil, Z. Heger, A. Miller, T. Heuser, V. Stanojlovic, J. Kiefer, T. Plank, L. Johnson, M. Himly, C. Blöchl, CG....
Language English Country United States
Document type Journal Article, Research Support, Non-U.S. Gov't
NLK
Directory of Open Access Journals
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Free Medical Journals
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PubMed Central
from 2012
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from 2022-01-01
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from 2012-01-01
Open Access Digital Library
from 2012-01-01
Wiley-Blackwell Open Access Titles
from 2012
PubMed
36437554
DOI
10.1002/jev2.12282
Knihovny.cz E-resources
- MeSH
- Biomarkers metabolism MeSH
- Diagnostic Imaging MeSH
- Extracellular Vesicles * metabolism MeSH
- Silicon Dioxide * metabolism MeSH
- Flow Cytometry methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
Cell Therapy Institute Spinal Cord Injury and Tissue Regeneration Centre Salzburg Salzburg Austria
Department of Biosciences and Medical Biology Paris Lodron University Salzburg Salzburg Austria
Department of Chemistry and Biochemistry Mendel University in Brno Brno Czech Republic
Department of Informatics and Mathematics Fernuniversität Hagen Hagen Germany
EvoBiotiX SA Lugano Switzerland
KP Therapeutics sro Brno Czech Republic
References provided by Crossref.org
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- $a 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.
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