Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.
Kshipra S KapoorSeoyun KongHikaru SugimotoWenhua GuoVivek BoominathanYi-Lin ChenSibani Lisa BiswalTanguy TerlierKathleen M McAndrewsRaghu KalluriPublished in: ACS nano (2024)
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.
Keyphrases
- high resolution
- single cell
- electron microscopy
- induced apoptosis
- deep learning
- neural network
- cell cycle arrest
- convolutional neural network
- drinking water
- endoplasmic reticulum stress
- photodynamic therapy
- genome wide
- mass spectrometry
- cell death
- computed tomography
- optical coherence tomography
- endothelial cells
- machine learning
- pet ct
- climate change
- human health
- optic nerve
- data analysis