{"title":"自适应学习物理辅助光场显微镜可以实现长达一天和毫秒级的3D亚细胞动力学超分辨率成像","authors":"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","doi":"10.1038/s41467-025-62471-w","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"37 1","pages":""},"PeriodicalIF":15.7000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics\",\"authors\":\"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\",\"doi\":\"10.1038/s41467-025-62471-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"37 1\",\"pages\":\"\"},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-025-62471-w\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-62471-w","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Adaptive-learning physics-assisted light-field microscopy enables day-long and millisecond-scale super-resolution imaging of 3D subcellular dynamics
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.
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.