Single extracellular vesicle imaging and computational analysis identifiesinherent architectural heterogeneity

Kapoor, K. S.; Kalluri, R.; Kong, S.; Sugimoto, H.; Guo, W.; Boominathan, V.; Chen, Y.-L.; Biswal, S. L.; Terlier, T.; McAndrews, K.

doi:10.1101/2023.12.11.571132

bioRxiv · 10.1101/2023.12.11.571132

Single extracellular vesicle imaging and computational analysis identifiesinherent architectural heterogeneity

Kapoor, K. S.·Kalluri, R.·Kong, S.·Sugimoto, H.·Guo, W.·Boominathan, V.·Chen, Y.-L.·Biswal, S. L.·Terlier, T.·McAndrews, K.

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Abstract

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM) which has an inherent ability to image biological samples without harsh labeling methods and while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources such as cancer cells, normal cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical (S. Spherical), rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution EV images and offer insights into their potential biological impact.

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Kapoor, K. S., Kalluri, R., Kong, S., Sugimoto, H., Guo, W., Boominathan, V., Chen, Y.-L., Biswal, S. L., Terlier, T., McAndrews, K.. 2023-12-12. Single extracellular vesicle imaging and computational analysis identifiesinherent architectural heterogeneity. https://doi.org/10.1101/2023.12.11.571132

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