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Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics.


ABSTRACT: Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored.

SUBMITTER: Mo Y 

PROVIDER: S-EPMC10227022 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics.

Mo Yanquan Y   Wang Kunhao K   Li Liuju L   Xing Shijia S   Ye Shouhua S   Wen Jiayuan J   Duan Xinxin X   Luo Ziying Z   Gou Wen W   Chen Tongsheng T   Zhang Yu-Hui YH   Guo Changliang C   Fan Junchao J   Chen Liangyi L  

Nature communications 20230529 1


Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled str  ...[more]

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