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Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy.

Weisong ZhaoShiqun ZhaoLiuju LiXiaoshuai HuangShijia XingYulin ZhangGuohua QiuZhenqian HanYingxu ShangDe-En SunChunyan ShanRunlong WuLusheng GuShuwen ZhangRiwang ChenJian XiaoYanquan MoJianyong WangWei JiXing ChenBaoquan DingYanmei LiuHeng MaoBao- Liang SongJiubin TanJian LiuHaoyu LiLiangyi Chen
Published in: Nature biotechnology (2021)
A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.
Keyphrases
  • single molecule
  • high resolution
  • living cells
  • photodynamic therapy
  • healthcare
  • high throughput
  • deep learning
  • label free
  • monte carlo