Comparison and optimization of nanoscale extracellular vesicle imaging by scanning electron microscopy for accurate size-based profiling and morphological analysis

Extracellular Vesicles
/References

Cavallaro, Sara, Petra Hååg, Kristina Viktorsson, Anatol Krozer, Kristina Fogel, Rolf Lewensohn, Jan Linnros, and Apurba Dev. "Comparison and optimization of nanoscale extracellular vesicle imaging by scanning electron microscopy for accurate size-based profiling and morphological analysis." Nanoscale Advances (2021).

Nanosized extracellular vesicles (EVs) have been found to play a key role in intercellular communication, offering opportunities for both disease diagnostics and therapeutics. However, lying below the diffraction limit and also being highly heterogeneous in their size, morphology and abundance, these vesicles pose significant challenges for physical characterization. Here, we present a direct visual approach for their accurate morphological and size-based profiling by using scanning electron microscopy (SEM). To achieve that, we methodically examined various process steps and developed a protocol to improve the throughput, conformity and image quality while preserving the shape of EVs. The study was performed with small EVs (sEVs) isolated from a non-small-cell lung cancer (NSCLC) cell line as well as from human serum, and the results were compared with those obtained from nanoparticle tracking analysis (NTA). While the comparison of the sEV size distributions showed good agreement between the two methods for large sEVs (diameter > 70 nm), the microscopy based approach showed a better capacity for analyses of smaller vesicles, with higher sEV counts compared to NTA. In addition, we demonstrated the possibility of identifying non-EV particles based on size and morphological features. The study also showed process steps that can generate artifacts bearing resemblance with sEVs. The results therefore present a simple way to use a widely available microscopy tool for accurate and high throughput physical characterization of EVs.

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2023
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