RRC ID 83466
著者 Kapoor KS, Kong S, Sugimoto H, Guo W, Boominathan V, Chen YL, Biswal SL, Terlier T, McAndrews KM, Kalluri R.
タイトル Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.
ジャーナル ACS Nano
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 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.
巻・号 18(18)
ページ 11717-11731
公開日 2024-5-7
DOI 10.1021/acsnano.3c12556
PMID 38651873
MeSH Cryoelectron Microscopy* Extracellular Vesicles* / chemistry Extracellular Vesicles* / metabolism Humans Image Processing, Computer-Assisted / methods Microscopy, Electron, Transmission Neural Networks, Computer
IF 14.588
リソース情報
ヒト・動物細胞 T3M-4(RCB1021)