High-fidelity structured illumination microscopy by point-spread-function engineering

Gang Wen; Simin Li; Linbo Wang; Xiaohu Chen; Zhenglong Sun; Yong Liang; Xin Jin; Yifan Xing; Yaming Jiu; Yuguo Tang; Hui Li

doi:10.1038/s41377-021-00513-w

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High-fidelity structured illumination microscopy by point-spread-function engineering

Articles

- High-fidelity structured illumination microscopy by point-spread-function engineering
- Light: Science & Applications Vol. 10, Issue 5, Pages: 812-823(2021)
- Affiliations：
  
  1.Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
  2.Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
  3.The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200031, China
- Author bio：
  
  Yuguo Tang (tangyg@sibet.ac.cn)
  Hui Li (hui.li@sibet.ac.cn)
- Funds：
- DOI：10.1038/s41377-021-00513-w
  CLC：
- Published：31 May 2021，
  
  Published Online：01 April 2021，
  
  Received：31 July 2020，
  
  Revised：13 March 2021，
  
  Accepted：14 March 2021
- Accepted：
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Wen, G. et al. High-fidelity structured illumination microscopy by point-spread-function engineering. Light: Science & Applications, 10, 812-823 (2021).
DOI：

Wen, G. et al. High-fidelity structured illumination microscopy by point-spread-function engineering. Light: Science & Applications, 10, 812-823 (2021). DOI： 10.1038/s41377-021-00513-w.

摘要

Abstract

Structured illumination microscopy (SIM) has become a widely used tool for insight into biomedical challenges due to its rapid

long-term

and super-resolution (SR) imaging. However

artifacts that often appear in SIM images have long brought into question its fidelity

and might cause misinterpretation of biological structures. We present HiFi-SIM

a high-fidelity SIM reconstruction algorithm

by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background. In particular

HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters; hence

it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment

thus promoting SIM as a daily imaging tool.

关键词

Keywords

references

Gustafsson, M. G. L. et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination.Biophys. J.94, 4957-4970 (2008)..

Heintzmann, R.&Huser, T. Super-resolution structured illumination microscopy.Chem. Rev.117, 13890-13908 (2017)..

Sahl, S. J., Hell, S. W.&Jakobs, S. Fluorescence nanoscopy in cell biology.Nat. Rev. Mol. Cell Biol.18, 685-701 (2017)..

Wu, Y. C.&Shroff, H. Faster, sharper, and deeper: structured illumination microscopy for biological imaging.Nat. Methods15, 1011-1019 (2018)..

Kner, P. et al. Super-resolution video microscopy of live cells by structured illumination.Nat. Methods6, 339-342 (2009)..

Hirvonen, L. M. et al. Structured illumination microscopy of a living cell.Eur. Biophys. J.38, 807-812 (2009)..

Li, D. et al. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics.Science349, aab3500 (2015)..

Huang, X. S. et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy.Nat. Biotechnol.36, 451-459 (2018)..

Guo, Y. T. et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales.Cell175, 1430-1442 (2018). e17..

Dong, D. S. et al. Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome.Light9, 11 (2020)..

Markwirth, A. et al. Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction.Nat. Commun.10, 4315 (2019)..

Sahl, S. J. et al. Comment on "Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics".Science352, 527 (2016)..

Hoffman, D. P.&Betzig, E. Tiled reconstruction improves structured illumination microscopy. Preprint atbioRxivhttps://doi.org/10.1101/895318https://doi.org/10.1101/895318(2020)..

Schaefer, L. H., Schuster, D.&Schaffer, J. Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach.J. Microsc.216, 165-174 (2004)..

Pospíšil, J., Fliegel, K.&Klíma, M.Applications of Digital Image Processing XL(SPIE, 2017).

Young, L. J., Ströhl, F.&Kaminski, C. F. A guide to structured illumination TIRF microscopy at high speed with multiple colors.J. Vis. Exp.111, e53988 (2016)..

Ball, G. et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy.Sci. Rep.5, 15915 (2015)..

Demmerle, J. et al. Strategic and practical guidelines for successful structured illumination microscopy.Nat. Protoc.12, 988-1010 (2017)..

Kraus, F. et al. Quantitative 3D structured illumination microscopy of nuclear structures.Nat. Protoc.12, 1011-1028 (2017)..

Wicker, K. et al. Phase optimisation for structured illumination microscopy.Opt. Express21, 2032-2049 (2013)..

Wicker, K. Non-iterative determination of pattern phase in structured illumination microscopy using auto-correlations in Fourier space.Opt. Express21, 24692-24701 (2013)..

Zhou, X. et al. Image recombination transform algorithm for superresolution structured illumination microscopy.J. Biomed. Opt.21, 096009 (2016)..

Cao, R. Z. et al. Inverse matrix based phase estimation algorithm for structured illumination microscopy.Biomed. Opt. Express9, 5037-5051 (2018)..

Sola-Pikabea, J. et al. Fast and robust phase-shift estimation in two-dimensional structured illumination microscopy.PLoS ONE14, e0221254 (2019)..

Chu, K. Q. et al. Image reconstruction for structured-illumination microscopy with low signal level.Opt. Express22, 8687-8702 (2014)..

Perez, V., Chang, B. J.&Stelzer, E. H. K. Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.Sci. Rep.6, 37149 (2016)..

Karras, C. et al. Successful optimization of reconstruction parameters in structured illumination microscopy - a practical guide.Opt. Commun.436, 69-75 (2019)..

Jin, L. H. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed.Nat. Commun.11, 1934 (2020)..

Christensen, C. N. et al. ML-SIM: a deep neural network for reconstruction of structured illumination microscopy images. Preprint athttps://arxiv.org/abs/2003.11064https://arxiv.org/abs/2003.11064(2020).

Křížek, P. et al. SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.Bioinformatics32, 318-320 (2016)..

Müller, M. et al. Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ.Nat. Commun.7, 10980 (2016)..

Lal, A., Shan, C. Y.&Xi, P. Structured illumination microscopy image reconstruction algorithm.IEEE J. Sel. Top. Quantum Electron.22, 6803414 (2016)..

Heintzmann, R.&Cremer, C. G.Optical Biopsies and Microscopic Techniques III(SPIE, 1999).

Gustafsson, M. G. L. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy.J. Microsc.198, 82-87 (2000)..

Lukeš, T. et al. Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation.Opt. Express22, 29805-29817 (2014)..

O'Holleran, K.&Shaw, M. Optimized approaches for optical sectioning and resolution enhancement in 2D structured illumination microscopy.Biomed. Opt. Express5, 2580-2590 (2014)..

Lai-Tim, Y. et al. Jointly super-resolved and optically sectioned Bayesian reconstruction method for structured illumination microscopy.Opt. Express27, 33251-33267 (2019)..

Johnson, K. A.&Hagen, G. M. Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction.GigaScience9, giaa035 (2020)..

Shabani, H. et al. Optical transfer function engineering for a tunable 3D structured illumination microscope.Opt. Lett.44, 1560-1563 (2019)..

Manton, J. D. et al. Concepts for structured illumination microscopy with extended axial resolution through mirrored illumination.Biomed. Opt. Express11, 2098-2108 (2020)..

Ingerman, E. A., London, R. A.&Gustafsson, M. G. L. Signal, noise and resolution in linear and nonlinear structured-illumination microscopy.J. Microsc.273, 3-25 (2019)..

Chen, B. C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution.Science346, 1257998 (2014)..

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