Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging
Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 μm, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.
NI Journal Papers
First ; Corresponding
Natural Science Foundation of Guangdong Province[2020A1515110380];Department of Science and Technology of Shandong Province[2021CXGC010212];Science, Technology and Innovation Commission of Shenzhen Municipality[KQTD20200820113012029];Science, Technology and Innovation Commission of Shenzhen Municipality[KQTD20210811090115021];
|WOS Accession No|
|ESI Research Field|
BIOLOGY & BIOCHEMISTRY
Cited Times [WOS]:3
|Document Type||Journal Article|
|Department||Department of Biomedical Engineering|
1.Guangdong Provincial Key Laboratory of Advanced Biomaterials,Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,China
2.School of Life Sciences,Southern University of Science and Technology,Shenzhen,China
3.Key Laboratory of Biomedical Engineering of Hainan Province,School of Biomedical Engineering,Hainan University,Haikou,China
4.European Molecular Biology Laboratory,Cell Biology and Biophysics,Heidelberg,Germany
5.Department of Biomedical Engineering,College of Future Technology,Peking University,Beijing,China
6.Institute for Biomedical Materials and Devices (IBMD),Faculty of Science,University of Technology Sydney,Sydney,Australia
|First Author Affilication||Department of Biomedical Engineering|
|Corresponding Author Affilication||Department of Biomedical Engineering|
|First Author's First Affilication||Department of Biomedical Engineering|
Fu，Shuang,Shi，Wei,Luo，Tingdan,et al. Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging[J]. Nature Methods,2023,20(3):459-468.
Fu，Shuang.,Shi，Wei.,Luo，Tingdan.,He，Yingchuan.,Zhou，Lulu.,...&Li，Yiming.(2023).Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.Nature Methods,20(3),459-468.
Fu，Shuang,et al."Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging".Nature Methods 20.3(2023):459-468.
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