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<p>This article reviews recent super-resolution (SR) optical imaging techniques for cellular study, encompassing structured illumination microscopy (SIM), point-scanning super-resolution (PS-SR) microscopy, single-molecule localization microscopy (SMLM), mathematical and deep learning (DL) SR algorithms. Historically, the resolution of traditional far-field optical imaging was constrained by the diffraction limit. The emergence of SR imaging techniques and image processing algorithms has propelled biological research into nanoscale realm. SIM enhances resolution by manipulating spatial frequency content, effectively doubling the resolution capacity of traditional microscopy. PS-SR imaging, on the other hand, offers superior optical sectioning and a high signal-to-noise ratio. SMLM has achieved a remarkable spatial resolution of approximately 20 nm and supports multi-color, wide-field-of-view (FOV), automated 3D high-throughput imaging, thus broadening the horizons for advanced biomedical investigations. Additionally, both mathematical and DL-based SR algorithms have significantly advanced the conversion of low-resolution images to high-resolution counterparts, extending the capabilities of conventional microscopes. This review underscores the principles, recent developments, and diverse applications of these cutting-edge SR imaging methodologies in biological research.</p>

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Cellular optical imaging techniques: a dynamic advancing frontier

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Yaning Li,
  • Chuankang Li,
  • Caiwei Zhou,
  • Jie Wang,
  • Weixing Li,
  • Tianying Pan,
  • Chenying He,
  • Xiaodong Guan,
  • Yichen Wang,
  • Yingda Huang,
  • Xianao Zhao,
  • Lusheng Gu,
  • Peng Xi,
  • Cuifang Kuang,
  • Yujie Sun,
  • Peng Fei,
  • Wei Ji,
  • Liangyi Chen

摘要

This article reviews recent super-resolution (SR) optical imaging techniques for cellular study, encompassing structured illumination microscopy (SIM), point-scanning super-resolution (PS-SR) microscopy, single-molecule localization microscopy (SMLM), mathematical and deep learning (DL) SR algorithms. Historically, the resolution of traditional far-field optical imaging was constrained by the diffraction limit. The emergence of SR imaging techniques and image processing algorithms has propelled biological research into nanoscale realm. SIM enhances resolution by manipulating spatial frequency content, effectively doubling the resolution capacity of traditional microscopy. PS-SR imaging, on the other hand, offers superior optical sectioning and a high signal-to-noise ratio. SMLM has achieved a remarkable spatial resolution of approximately 20 nm and supports multi-color, wide-field-of-view (FOV), automated 3D high-throughput imaging, thus broadening the horizons for advanced biomedical investigations. Additionally, both mathematical and DL-based SR algorithms have significantly advanced the conversion of low-resolution images to high-resolution counterparts, extending the capabilities of conventional microscopes. This review underscores the principles, recent developments, and diverse applications of these cutting-edge SR imaging methodologies in biological research.