Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging-Reference-Cited by-同舟云学术

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging

Published:2023-02-23 Issue:3 Volume:20 Page:459-468
ISSN:1548-7091
Container-title:Nature Methods
language:en
Short-container-title:Nat Methods

Author:

Fu Shuang^ORCID,Shi Wei,Luo Tingdan,He Yingchuan,Zhou Lulu,Yang Jie^ORCID,Yang Zhichao,Liu Jiadong,Liu Xiaotian^ORCID,Guo Zhiyong,Yang Chengyu,Liu Chao,Huang Zhen-li^ORCID,Ries Jonas^ORCID,Zhang Mingjie,Xi Peng,Jin Dayong^ORCID,Li Yiming^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Cell Biology,Molecular Biology,Biochemistry,Biotechnology

Link

https://www.nature.com/articles/s41592-023-01775-5.pdf

Reference51 articles.

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3. Holden, S. J. et al. High-throughput 3D super-resolution microscopy reveals Caulobacter crescentus in vivo Z-ring organization. Proc. Natl Acad. Sci. USA 111, 4566–4571 (2014).

4. Mahecic, D. et al. Homogeneous multifocal excitation for high-throughput super-resolution imaging. Nat. Methods 17, 726–733 (2020).

5. Beghin, A. et al. Localization-based super-resolution imaging meets high-content screening. Nat. Methods 14, 1184–1190 (2017).

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