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Gear-like MOF microrobots for single cell mechanotransduction of microvilli
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  • Published: 26 February 2026

Gear-like MOF microrobots for single cell mechanotransduction of microvilli

  • Xiaoxia Liu1,2,
  • Yong Wang3,
  • Lin Lin4,
  • Ning Liu5,
  • Zihao Yang6,
  • Peng Wang1,
  • Xiaohui Yan  ORCID: orcid.org/0000-0001-6089-06644,
  • Jinhong Guo  ORCID: orcid.org/0000-0003-2659-31503,7,
  • Dongdong Jin  ORCID: orcid.org/0000-0003-4833-538X1 &
  • …
  • Xing Ma  ORCID: orcid.org/0000-0002-2248-48061 

Nature Communications , Article number:  (2026) Cite this article

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Subjects

  • Actuators
  • Biomedical engineering
  • Nanobiotechnology

Abstract

Cellular mechanotransduction, mediated by specialized structures such as microvilli, regulates processes ranging from tissue homeostasis to disease progression. Existing tools for microvilli-specific biomechanical intervention suffer from limited spatiotemporal precision and non-physiological constraints, restricting mechanistic studies and targeted therapies. Here, we develop a magnetically driven gear-like metal-organic framework microrobot (MOFbot) for programmable mechanical manipulation of single-cell microvilli. MOFbots are fabricated through epitaxial growth of heterogeneous MOF structures followed by deposition of Ni/Au nanofilms. Under a rotating magnetic field, they perform rolling and obstacle negotiation. Their rotating gear structure entangles microvilli, exerting quantified pulling forces via Förster resonance energy transfer and traction force microscopy. This mechanical stimulation triggers intracellular calcium influx and enhanced focal adhesion kinase phosphorylation, indicating mechanotransduction pathway activation. Consequently, rotating MOFbots increase membrane permeability, enabling on-demand transmembrane delivery of therapeutics into targeted single cells. This work establishes a targeted cellular mechanomodulation strategy and informs future micro/nanorobotic biomedical designs.

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Data availability

The data generated or analysed during this study are included in this published article/Supplementary Information/Source Data file. Source data is available for Figs.1d–g, 2c, 2d, 2f, 2i, 2k, 3e–g, 4c, 4d, 4h, 4i, 5c–f, 5h, 6d, 6f–h and Supplementary Figs. 3, 4, 6, 8, 10b, 12b, 12c, 13b, 13c, 15b, 19c, 20b, 21, 22a, 22b, 24 in the associated source data file. The raw data generated in this study have also been deposited in the Zenodo database under accession code [10.5281/zenodo.18495219]. Source data are provided with this paper.

Code availability

Code associated with our paper is available at https://github.com/alfredyang93/Demo-datasets.

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Acknowledgements

This work was supported by Shenzhen Science and Technology Program (RCJC20231211090000001 to X.M.), Shenzhen Medical Research Fund (A2403068 to D.J.), Shenzhen Science and Technology Program (GXWD20231129101105001 to X.M., KJZD20231023100302006 to D.J.), National Natural Science Foundation of China (52472280 to D.J., 82402730 to Y.W.), Macau Foundation for Development of Sicence and Technology (0008/2024/RIA1 to X.M.), China Postdoctoral Science Foundation (2024M763882 to X.L.).

Author information

Authors and Affiliations

  1. Sauvage Laboratory for Smart Materials, School of Integrated Circuits, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Xiaoxia Liu, Peng Wang, Dongdong Jin & Xing Ma

  2. School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, China

    Xiaoxia Liu

  3. Key Laboratory of Clinical Laboratory Diagnostics (Chinese Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China

    Yong Wang & Jinhong Guo

  4. State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China

    Lin Lin & Xiaohui Yan

  5. School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China

    Ning Liu

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Zihao Yang

  7. School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China

    Jinhong Guo

Authors
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Contributions

X.L. synthesized and characterized the MOFbots, analyzed the interaction between MOFbots and cellular microvilli, and wrote the paper. Y.W. was responsible for video recording of the MOFbots and data analysis. L.L. simulated the flow field around the MOFbots. N.L. simulated the interaction between the gear-like MOFbot and cellular microvilli. Z.Y. analyzed the distribution of gear-like MOFbots within cellular microvilli using the Unet + + network model. P.W. analyzed the cell substrate deformation induced by the MOFbot. X.Y. and J.G. revised the manuscript. Y.W., D.J. and X.M. conceived the initial idea, supervised the project, and edited the paper.

Corresponding authors

Correspondence to Yong Wang, Dongdong Jin or Xing Ma.

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Nature Communications thanks Pierre Bongrand and Jianguo Guan for their contribution to the peer review of this work. [A peer review file is available].

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Supplementary information

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Liu, X., Wang, Y., Lin, L. et al. Gear-like MOF microrobots for single cell mechanotransduction of microvilli. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70052-8

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  • Received: 27 April 2025

  • Accepted: 17 February 2026

  • Published: 26 February 2026

  • DOI: https://doi.org/10.1038/s41467-026-70052-8

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