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Journal Article

Label-free discrimination of extracellular vesicles from large lipoproteins

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Kashkanova,  Anna D.
Sandoghdar Division, Max Planck Institute for the Science of Light, Max Planck Society;
Sandoghdar Division, Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;

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Blessing,  Martin
Sandoghdar Division, Max Planck Institute for the Science of Light, Max Planck Society;
Sandoghdar Division, Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;

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Reischke,  Marie
Sandoghdar Division, Max Planck Institute for the Science of Light, Max Planck Society;

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Sandoghdar,  Vahid
Sandoghdar Division, Max Planck Institute for the Science of Light, Max Planck Society;
Sandoghdar Division, Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;

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Citation

Kashkanova, A. D., Blessing, M., Reischke, M., Baur, J.-O., Baur, A. S., Sandoghdar, V., et al. (2023). Label-free discrimination of extracellular vesicles from large lipoproteins. Journal of extracellular vesicles, 12: 12348. doi:10.1002/jev2.12348.


Cite as: https://hdl.handle.net/21.11116/0000-000B-7A0C-0
Abstract
Extracellular vesicles (EVs) are increasingly gaining interest as biomarkers and therapeutics. Accurate sizing and quantification of EVs remain problematic, given their nanometre size range and small scattering cross-sections. This is compounded by the fact that common EV isolation methods result in co-isolation of particles with comparable features. Especially in blood plasma, similarly-sized lipoproteins outnumber EVs to a great extent. Recently, interferometric nanoparticle tracking analysis (iNTA) was introduced as a particle analysis method that enables determining the size and refractive index of nanoparticles with high sensitivity and precision. In this work, we apply iNTA to differentiate between EVs and lipoproteins, and compare its performance to conventional nanoparticle tracking analysis (NTA). We show that iNTA can accurately quantify EVs in artificial EV-lipoprotein mixtures and in plasma-derived EV samples of varying complexity. Conventional NTA could not report on EV numbers, as it was not able to distinguish EVs from lipoproteins. iNTA has the potential to become a new standard for label-free EV characterization in suspension.