Single-frame structured illumination microscopy for fast live-cell imaging-Reference-Cited by-同舟云学术

Single-frame structured illumination microscopy for fast live-cell imaging

Published:2024-03-01 Issue:3 Volume:9 Page:
ISSN:2378-0967
Container-title:APL Photonics
language:en
Short-container-title:

Author:

Wu Hanmeng¹^ORCID,Li Yueming¹^ORCID,Sun Yile¹^ORCID,Yin Lu²^ORCID,Sun Weiyun³,Ye Zitong¹^ORCID,Yang Xinxun¹^ORCID,Zhu Hongfei⁴^ORCID,Tang Mingwei¹^ORCID,Han Yubing¹⁵^ORCID,Kuang Cuifang¹⁶⁷^ORCID,Liu Xu¹⁶⁷

Affiliation:

1. State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University 1 , Hangzhou 310027, China

2. College of Optical and Electronic Technology, China Jiliang University 2 , Hangzhou 310018, China

3. Institute of Pharmacology, College of Pharmaceutical Sciences, Zhejiang University of Technology 3 , Hangzhou 310014, China

4. Department of Biomedical Engineering, The Chinese University of Hong Kong 4 , Hong Kong, China

5. Britton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology 5 , Wuhan 430074, China

6. ZJU-Hangzhou Global Scientific and Technological Innovation Center 6 , Hangzhou 311200, China

7. Collaborative Innovation Center of Extreme Optics, Shanxi University 7 , Taiyuan 030006, China

Abstract

Observing subcellular structural dynamics in living cells has become the goal of super-resolution (SR) fluorescence microscopy. Among typical SRM techniques, structured illumination microscopy (SIM) stands out for its fast imaging speed and low photobleaching. However, 2D-SIM requires nine raw images to obtain a SR image, leading to undesirable artifacts in the fast dynamics of live-cell imaging. In this paper, we propose a single-frame structured illumination microscopy (SF-SIM) method based on deep learning that achieves SR imaging using only a single image modulated by a hexagonal lattice pattern. The SF-SIM method used the prior knowledge to complete the structure enhancement of SR images in the spatial domain and the expansion of the Fourier spectrum through deep learning, achieving the same resolution as conventional 2D-SIM. Temporal resolution is improved nine times, and photobleaching is reduced by 2.4 times compared to conventional 2D-SIM. Based on this, we observed the fast dynamics of multiple subcellular structures and the dynamic interaction of two organelles. The SF-SIM methods provide a powerful tool for live-cell imaging.

Funder

STI 2030-Major Projects

National Natural Science Foundation of China

Major Program of the Natural Science Foundation of Zhejiang Province

Zhejiang Provincial Ten Thousand Plan for Young Top Talents

Open Project Program of Wuhan National Laboratory for Optoelectronics

Publisher

AIP Publishing

Link

https://pubs.aip.org/aip/app/article-pdf/doi/10.1063/5.0180978/19707761/036102_1_5.0180978.pdf

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