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Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures
Open accessPublished version of a preprint
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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“Subsequently, data analysis of >67,000 individual blood vessels was performed by image-segmentation-based machine learning (nano-ISML) techniques to predict nanoparticle permeability in the tumor vasculatures.”
From Smart nanoparticles for cancer therapy (2023) · cites this paper as [343]
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“And based on the rational design of nano-ISML-assisted nanomedicines, genetically tailored protein nanoparticles were developed to improve transendothelial transport in low-permeability tumors and assists nanoparticles to enter solid tumors.343”
From Smart nanoparticles for cancer therapy (2023) · cites this paper as [343]
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Metadata
- 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
- Venue
- Nature Nanotechnology
- Published
- 2023-02-13
- Volume
- 18
- Issue
- 6
- Pages
- 657-666
- ISSN
- 1748-3387 (ISSN-L)
- 1748-3395
- 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
- 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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