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

Nature Nanotechnology volume 18, pages 657–666 (2023)Cite this article

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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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Fig. 1: ML-based single-vessel analysis method.
Fig. 2: Quantitative analysis of heterogeneous vascular permeability using the nano-ISML method.
Fig. 3: Heterogeneity of vascular permeability mechanism.
Fig. 4: Characterization of FTn and FTn variants.
Fig. 5: Enhancement of active transendothelial transport.
Fig. 6: In vivo anticancer ability of different FTn formulations.

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Data availability

The authors declare that data supporting the findings of this study are available within the article and its Supplementary Information. All relevant data can be made available upon reasonable request to the corresponding authors. Source data are provided with this paper.

Code availability

The source code of machine learning-based model can be accessed at https://github.com/balabilibili24/Confocal_images_analysis.git.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China 91959129 (X.H.), 32271448 (X.H.), 82072054 (J.Z.), 31870999 (X.H.), the National Key Research and Development Program of China 2022YFA1105100 (X.H.), Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project TSBICIP-KJGG-014-03 (X.H.) and the Nankai University Hundred Young Academic Leaders Program.

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Author notes

  1. These authors contributed equally: Mingsheng Zhu, Jie Zhuang.

Authors and Affiliations

  1. Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, State Key Laboratory of Medicinal Chemical Biology, and Frontiers Science Center for Cell Responses, Nankai University, Tianjin, China

    Mingsheng Zhu, Qiqi Liu, Adam C. Midgley, Tianyi Qi, Jingwei Tian, Zhixuan Zhang, Deling Kong & Xinglu Huang

  2. School of Medicine, Nankai University, Tianjin, China

    Jie Zhuang, Rongping Zhao & Zhanxia Gao

  3. School of Cyberspace Science and Technology, University of Science and Technology of China, Hefei, China

    Zhe Li

  4. CAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, China

    Zhe Li & Jie Tian

  5. CAS Engineering Laboratory for Nanozymes, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Xiyun Yan

  6. University of Chinese Academy of Sciences, Beijing, China

    Xiyun Yan

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  1. Mingsheng Zhu
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Contributions

M.Z., J.Z. and X.H. conceived the idea, collected data, conducted data analysis and performed all the experiments. Z.L. assisted with the development of the U-net model. M.Z. and X.H extracted data information from U-net. Q.L., Z.G., Z.Z., T.Q., J.T. and R.Z. performed cryosections and immunostaining. A.C.M. provided guidance and edited the manuscript. D.K., J.T. and X.Y. helped to guide part of experiments. X.H. designed and supervised all studies and wrote the manuscript.

Corresponding authors

Correspondence to Jie Tian, Xiyun Yan or Xinglu Huang.

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Nature Nanotechnology thanks Bjoern Menze, Constantinos Mikelis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Zhu, M., Zhuang, J., Li, Z. et al. Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures. Nat. Nanotechnol. 18, 657–666 (2023). https://doi.org/10.1038/s41565-023-01323-4

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