Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures
Menée à l'aide de 32 modèles murins de tumeurs et d'une méthode d'analyse tissulaire utilisant un système d'imagerie par nanosondes et un algorithme d'apprentissage automatique, cette étude examine, en fonction des types de tumeur, la perméabilité des vaisseaux tumoraux aux nanomédicaments
The central dogma that nanoparticle delivery to tumours requires enhanced leakiness of vasculatures is a topic of debate. To address this, we propose a single-vessel quantitative analysis method by taking advantage of protein-based nanoprobes and image-segmentation-based machine learning (nano-ISML). Using nano-ISML, >67,000 individual blood vessels from 32 tumour models were quantified, revealing highly heterogenous vascular permeability of protein-based nanoparticles. There was a >13-fold difference in the percentage of high-permeability vessels in different tumours and >100-fold penetration ability in vessels with the highest permeability compared with vessels with the lowest permeability. Our data suggest passive extravasation and transendothelial transport were the dominant mechanisms for high- and low-permeability tumour vessels, respectively. To exemplify the nano-ISML-assisted rational design of nanomedicines, genetically tailored protein nanoparticles with improved transendothelial transport in low-permeability tumours were developed. Our study delineates the heterogeneity of tumour vascular permeability and defines a direction for the rational design of next-generation anticancer nanomedicines.
Nature Nanotechnology , résumé, 2023