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

Author

Listed:
  • Yanquan Mo

    (Peking University)

  • Kunhao Wang

    (South China Normal University)

  • Liuju Li

    (Peking University)

  • Shijia Xing

    (Peking University)

  • Shouhua Ye

    (Guangzhou Computational Super-resolution Biotech Co., Ltd)

  • Jiayuan Wen

    (Guangzhou Computational Super-resolution Biotech Co., Ltd)

  • Xinxin Duan

    (Huazhong University of Science and Technology)

  • Ziying Luo

    (Guangzhou Computational Super-resolution Biotech Co., Ltd)

  • Wen Gou

    (Chongqing University of Posts and Telecommunications)

  • Tongsheng Chen

    (South China Normal University)

  • Yu-Hui Zhang

    (Huazhong University of Science and Technology)

  • Changliang Guo

    (Peking University)

  • Junchao Fan

    (Chongqing University of Posts and Telecommunications)

  • Liangyi Chen

    (Peking University
    PKU-IDG/McGovern Institute for Brain Research
    Beijing Academy of Artificial Intelligence
    National Biomedical Imaging Center)

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.

Suggested Citation

  • Yanquan Mo & Kunhao Wang & Liuju Li & Shijia Xing & Shouhua Ye & Jiayuan Wen & Xinxin Duan & Ziying Luo & Wen Gou & Tongsheng Chen & Yu-Hui Zhang & Changliang Guo & Junchao Fan & Liangyi Chen, 2023. "Quantitative structured illumination microscopy via a physical model-based background filtering algorithm reveals actin dynamics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38808-8
    DOI: 10.1038/s41467-023-38808-8
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    References listed on IDEAS

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    1. Marcel Müller & Viola Mönkemöller & Simon Hennig & Wolfgang Hübner & Thomas Huser, 2016. "Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ," Nature Communications, Nature, vol. 7(1), pages 1-6, April.
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