TY - JOUR
T1 - Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity
AU - Kapoor, Kshipra S.
AU - Kong, Seoyun
AU - Sugimoto, Hikaru
AU - Guo, Wenhua
AU - Boominathan, Vivek
AU - Chen, Yi Lin
AU - Biswal, Sibani Lisa
AU - Terlier, Tanguy
AU - McAndrews, Kathleen M.
AU - Kalluri, Raghu
N1 - Publisher Copyright:
© 2024 American Chemical Society.
PY - 2024/5/7
Y1 - 2024/5/7
N2 - 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 while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized 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 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.
AB - 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 while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized 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 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.
KW - architectural heterogeneity
KW - cryo-EM
KW - extracellular vesicle purification
KW - extracellular vesicles
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85191940256&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191940256&partnerID=8YFLogxK
U2 - 10.1021/acsnano.3c12556
DO - 10.1021/acsnano.3c12556
M3 - Article
C2 - 38651873
AN - SCOPUS:85191940256
SN - 1936-0851
VL - 18
SP - 11717
EP - 11731
JO - ACS Nano
JF - ACS Nano
IS - 18
ER -