Back

In situ investigation of extracellular vesicles in viscous formulations: interplay of nanoparticle transport and nanorheology through interferometric light microscopy analysis

Alexandre, L.; DUBROVA, A.; KUNDURU, A.; BERGER, M.; BOUCENNA, I.; GAZEAU, F.; SILVA, A. K. A.; MANGENOT, S.; AUBERTIN, K.

2024-04-15 biophysics
10.1101/2024.04.12.589108 bioRxiv
Show abstract

While extracellular vesicles (EVs) demonstrate growing potential as innovative therapeutics in diverse medical context (cancer, regenerative medicine, etc.) or as naturally circulant diagnostic / prognostic probes, their physical properties (size, transport, etc.) remains a critical concern. Here, we introduce a pipeline that relies on interferometric light microscopy (ILM) for measuring not only nanoparticle concentration and size distribution but also for analyzing the interactions of these nanoparticles with their environment. The analysis of interference patterns allows for the physical characterization of (bio)nanoparticles not only in aqueous solutions but also in challenging media with relatively high viscosity, particularly pertinent for characterizing gel-based EV-delivery systems. Through exploration of the instruments functionality and the use of calibrated NPs of various known sizes, we successfully obtained information about the local viscosity characteristics of a complex fluid embedding EVs. We present a proof-of-concept for characterizing EVs suspended in unconventional media and their interactions with their surroundings. Leveraging the outcomes of this investigation, we not only highlight the advantages of using ILM for characterizing EVs in complex fluid, particularly pertinent for the development of optimized biological carriers for targeted drug delivery and therapeutic applications, but we also validate a new method for measuring viscosity at the nanoscale.

Published in Small Science · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.