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AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging

Nature Methods (2025)Cite this article

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Abstract

Super-resolution microscopy (SRM) has revolutionized nanoscale cellular imaging, providing detailed insights into cellular architecture, organelle organization, molecular interactions and subcellular dynamics. Artificial intelligence (AI) has shown its transformative potential for improving SRM to advance our understanding of complex cellular structures and dynamics. This Review begins by offering a comprehensive overview of AI techniques in computer vision, focusing on their application to SRM. Additionally, this Review provides a thorough summary of publicly available code and datasets that can support the development and evaluation of AI-empowered SRM. Notably, many AI techniques in the domain of computer vision remain underexplored in SRM. The ongoing evolution of AI promises to unlock new potential in SRM, and the integration of cutting-edge AI technologies is poised to pioneer breakthroughs in nanoscale cellular imaging.

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Fig. 1: A unifying framework of AI-SRM studies.
Fig. 2: AI-based image reconstruction methods.
Fig. 3: AI-SRM.
Fig. 4: Representative algorithms of AI-SRM.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (82273890 to Y.Z., 81925022, T2288102 and 32227802 to L.C.), the National Key Research and Development Program of China (2022YFC3400600 to L.C.), a Shenzhen Stable Support Grant (GXWD 20231130103401001 to Y.Z.), the Shenzhen Science and Technology Program (JCYJ20240813104817024 to Y.Z.) and the High-performance Computing Platform of Peking University. L.C. is also supported by New Cornerstone Science Foundation. All authors participated in the discussions and data interpretation.

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  1. These authors contributed equally: Sen Li, Xiangjie Meng.

Authors and Affiliations

  1. School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Sen Li  (李森), Xiangjie Meng  (孟翔杰) & Yang Zhang  (张阳)

  2. School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China

    Sen Li  (李森), Xiangjie Meng  (孟翔杰) & Yang Zhang  (张阳)

  3. New Cornerstone Science Laboratory, National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China

    Bo Zhou  (周博), Wenfeng Tian  (田文丰) & Liangyi Chen  (陈良怡)

  4. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

    Liangyi Chen  (陈良怡)

  5. FuRong Laboratory, Changsha, China

    Liangyi Chen  (陈良怡)

  6. Shenzhen Loop Area Institute, Shenzhen, China

    Yang Zhang  (张阳)

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  1. Sen Li  (李森)
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Contributions

Y.Z. conceived the study. Y.Z., S.L. and X.M. wrote the manuscript and designed the figures. Y.Z., L.C., S.L., X.M., B.Z. and W.T. revised the manuscript and figures. Y.Z. and L.C. supervised the project.

Corresponding authors

Correspondence to Liangyi Chen  (陈良怡) or Yang Zhang  (张阳).

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Nature Methods thanks Jiji Chen and Yi Xue for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Li, S., Meng, X., Zhou, B. et al. AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02871-4

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