Cited by 140Nanoparticle-Based Drug DeliveryBarrier Structure and Function StudiesAngiogenesis and VEGF in Cancer

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Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures

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Zhu, Mingsheng, Zhuang, Jie, Li, Zhe, Liu, Qiqi, Zhao, Rongping, Gao, Zhanxia, Midgley, Adam C., Qi, Tianyi, Tian, Jingwei, Zhang, Zhixuan, Kong, Deling, Tian, Jie, Yan, Xiyun, Huang, Xinglu·2023·Nature Nanotechnology

DOI 10.1038/s41565-023-01323-4

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Abstract

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.

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Authors: Zhu, M., Zhuang, J., Li, Z., Liu, Q., Zhao, R., Gao, Z., Midgley, A. C., Qi, T., Tian, J., Zhang, Z., Kong, D., Tian, J., Yan, X., & Huang, X.
Affiliations: Nankai University (CN)
University of Science and Technology of China (CN)
Chinese Academy of Sciences (CN)
Corresponding author: Tian, Jingwei
Tian, Jie
Yan, Xiyun
Huang, Xinglu
ORCID: Zhu, Mingsheng
Li, Zhe
Liu, Qiqi
Midgley, Adam C.
Qi, Tianyi
Tian, Jingwei
Zhang, Zhixuan
Kong, Deling
Tian, Jie
Yan, Xiyun
Huang, Xinglu
Venue: Nature Nanotechnology
Publisher: Springer Science and Business Media LLC
Published: 2023-02-13
Volume: 18
Issue: 6
Pages: 657-666
ISSN: 1748-3387 (ISSN-L)
1748-3395
DOI: 10.1038/s41565-023-01323-4
OpenAlex: W4320486498
Cited by: 140
References: 46
Citations by year: 2026: 32 · 2025: 55 · 2024: 31 · 2023: 22
FWCI: 8.96
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Nanotechnology (publishedVersion)
Research Square (acceptedVersion, cc-by)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (91959129, 31870999, 82072054)
Topics: Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
Life Sciences › Neuroscience › Neurology › Barrier Structure and Function Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Angiogenesis and VEGF in Cancer
Keywords: protein nanoparticles, nanoprobes, tumor heterogeneity, transendothelial transport, tumor vasculature
MeSH terms: Capillary Permeability, Humans, Neoplasms, Permeability, Nanomedicine, Nanoparticles

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