Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics
Abstract Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally cap...
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62471-w |
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| author | Lanxin Zhu Jiahao Sun Chengqiang Yi Meng Zhang Yihang Huang Sicen Wu Mian He Liting Chen Yicheng Zhang Chunhong Zheng Hao Chen Jiang Tang Yu-Hui Zhang Dongyu Li Peng Fei |
| author_facet | Lanxin Zhu Jiahao Sun Chengqiang Yi Meng Zhang Yihang Huang Sicen Wu Mian He Liting Chen Yicheng Zhang Chunhong Zheng Hao Chen Jiang Tang Yu-Hui Zhang Dongyu Li Peng Fei |
| author_sort | Lanxin Zhu |
| collection | DOAJ |
| description | Abstract Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally capturing biological dynamics with merely single snapshot, it suffers from suboptimal resolution insufficient for resolving subcellular structures. Here we propose an Adaptive Learning PHysics-Assisted Light-Field Microscopy (Alpha-LFM) with a physics-assisted deep learning framework and adaptive-tuning strategies capable of light-field reconstruction of diverse subcellular dynamics. Alpha-LFM delivers sub-diffraction-limit spatial resolution (up to ~120 nm) while maintaining high temporal resolution and low phototoxicity. It enables rapid and mild 3D super-resolution imaging of diverse intracellular dynamics at hundreds of volumes per second with exceptional details. Using Alpha-LFM approach, we finely resolve the lysosome-mitochondrial interactions, capture rapid motion of peroxisome and the endoplasmic reticulum at 100 volumes per second, and reveal the variations in mitochondrial fission activity throughout two complete cell cycles of 60 h. |
| format | Article |
| id | doaj-art-df8c5b729bd54b8aacf064b889052d8c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-df8c5b729bd54b8aacf064b889052d8c2025-08-20T03:46:25ZengNature PortfolioNature Communications2041-17232025-08-0116111710.1038/s41467-025-62471-wAdaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamicsLanxin Zhu0Jiahao Sun1Chengqiang Yi2Meng Zhang3Yihang Huang4Sicen Wu5Mian He6Liting Chen7Yicheng Zhang8Chunhong Zheng9Hao Chen10Jiang Tang11Yu-Hui Zhang12Dongyu Li13Peng Fei14School of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyMOE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyInternational Cancer Institute, Peking University Cancer Hospital and Institute, Peking UniversityDepartment of Computer Science and Engineering, Hong Kong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyMOE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologySchool of Optical and Electronic Information—Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyAbstract Long-term and high-spatiotemporal-resolution 3D imaging of living cells remains an unmet challenge for super-resolution microscopy, owing to the noticeable phototoxicity and limited scanning speed. While emerging light-field microscopy can mitigate this issue through three-dimensionally capturing biological dynamics with merely single snapshot, it suffers from suboptimal resolution insufficient for resolving subcellular structures. Here we propose an Adaptive Learning PHysics-Assisted Light-Field Microscopy (Alpha-LFM) with a physics-assisted deep learning framework and adaptive-tuning strategies capable of light-field reconstruction of diverse subcellular dynamics. Alpha-LFM delivers sub-diffraction-limit spatial resolution (up to ~120 nm) while maintaining high temporal resolution and low phototoxicity. It enables rapid and mild 3D super-resolution imaging of diverse intracellular dynamics at hundreds of volumes per second with exceptional details. Using Alpha-LFM approach, we finely resolve the lysosome-mitochondrial interactions, capture rapid motion of peroxisome and the endoplasmic reticulum at 100 volumes per second, and reveal the variations in mitochondrial fission activity throughout two complete cell cycles of 60 h.https://doi.org/10.1038/s41467-025-62471-w |
| spellingShingle | Lanxin Zhu Jiahao Sun Chengqiang Yi Meng Zhang Yihang Huang Sicen Wu Mian He Liting Chen Yicheng Zhang Chunhong Zheng Hao Chen Jiang Tang Yu-Hui Zhang Dongyu Li Peng Fei Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics Nature Communications |
| title | Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics |
| title_full | Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics |
| title_fullStr | Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics |
| title_full_unstemmed | Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics |
| title_short | Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics |
| title_sort | adaptive learning physics assisted light field microscopy enables day long and millisecond scale super resolution imaging of 3d subcellular dynamics |
| url | https://doi.org/10.1038/s41467-025-62471-w |
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