留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Recent progress on the reconstruction algorithms of structured illumination microscopy

ZHOU Bo WANG Kun-hao CHEN Liang-yi

周博, 王昆浩, 陈良怡. 结构光照明显微镜重建算法研究进展[J]. 中国光学(中英文), 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011
引用本文: 周博, 王昆浩, 陈良怡. 结构光照明显微镜重建算法研究进展[J]. 中国光学(中英文), 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011
ZHOU Bo, WANG Kun-hao, CHEN Liang-yi. Recent progress on the reconstruction algorithms of structured illumination microscopy[J]. Chinese Optics, 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011
Citation: ZHOU Bo, WANG Kun-hao, CHEN Liang-yi. Recent progress on the reconstruction algorithms of structured illumination microscopy[J]. Chinese Optics, 2022, 15(6): 1211-1227. doi: 10.37188/CO.EN.2022-0011

结构光照明显微镜重建算法研究进展

详细信息
  • 中图分类号: O43, TH742, TH744

Recent progress on the reconstruction algorithms of structured illumination microscopy

doi: 10.37188/CO.EN.2022-0011
Funds: Supported by National Natural Science Foundation of China (No. 81925022, No. 92054301, No. 91750203, No. 31821091); National Key R&D Program of China (No. SQ2016YFJC040028); Beijing Natural Science Foundation (No. Z200017, No. Z201100008420005, No. Z20J00059); National Science and Technology Major Project Programme (No. 2016YFA0500400)
More Information
    Author Bio:

    Zhou Bo (1997—), male, was born in Anqing, Anhui province. He received his Master degree from Peking University in 2020. Currently, he is a Ph.D student in Cell Secretion and Metabolism Laboratory of Institute of Molecular Medicine, Peking University. His research interests are the reconstruction algorithms of super-resolution fluorescence microscopy. E-mail: 2001111937@pku.edu.cn

    WANG Kun-hao (1999—), male, was born in Handan, Hebei province. He received his bachelor’s degree from Xinjiang University in 2019. Currently, he is a postgraduate student in South China Normal University. His research interests are the pathogenicity of EGFR family mutations in breast cancer. E-mail: wkh1999@126.com

    Liangyi Chen (1975—), male, was born in wuhan, Hubei province. is Boya Professor of Peking University. He obtained his undergraduate degrees Biomedical engineering in Xi’an JiaoTong University, then majored in Biomedical engineering in pursuing PhD degree in Huazhong University of Science and Technology. His lab focused on two interweaved aspects: the development of new imaging and quantitative image analysis algorithms, and the application of these technology to study how glucose-stimulated insulin secretion is regulated in the health and disease at multiple levels (single cells, islets and in vivo) in the health and disease animal models. The techniques developed included ultrasensitive Hessian structured illumination microscopy (Hessian SIM) for live cell super-resolution imaging, the Sparse deconvolution algorithm for extending spatial resolution of fluorescence microscopes limited by the optics, Super-resolution fluorescence-assisted diffraction computational tomography (SR-FACT) for revealing the three-dimensional landscape of the cellular organelle interactome, two-photon three-axis digital scanned lightsheet microscopy (2P3A-DSLM) for tissue and small organism imaging, and fast High-resolution Miniature Two-photon Microscopy (FHIRM-TPM) for Brain Imaging in Freely-behaving Mice. He is also recipient of the National Distinguish Scholar Fund project from National Natural Science Foundation of China. E-mail: lychen@pku.edu.cn

    Corresponding author: lychen@pku.edu.cn
  • 摘要:

    作为现代超分辨成像技术的早期组成部分,结构照明显微镜(SIM)已经发展了近20年。其近期在活细胞中实现了高达60 nm和564 Hz的最佳时空分辨率组合,但也存在一些源于内在重建过程的缺点。本文综述了SIM技术的最新进展,包括超分辨率(SR)重建算法、性能评估及SIM与其他成像技术的集成,以便为生物学家提供实用指导。

     

  • Figure 1.  Schematic diagram of structured illumination microscopy. (a) In sinusoidal illumination microscopy, interference between multiple beams (usually generated by a diffraction grating or spatial light modulator) creates a 2D or 3D striped pattern with spatial frequency ${k}_{{\rm{ex}}}$ illuminating on the sample. This pattern shifts the sample's spatial frequency spectrum $ \stackrel{~}{S}\left(k\right) $ to $\stackrel{~}{S}\left(k+{k}_{{\rm{ex}}}\right)$ and $\stackrel{~}{S}\left(k-{k}_{{\rm{ex}}}\right)$, translating high-frequency SR information into the diffraction-limited detection passband ${OTF}_{{\rm{em}}}\left(k\right)$ with the spatial cutoff frequency ${k}_{{\rm{em}}}$. After computational processing, the sample's highest detectable frequency can be extended to ${k}_{{\rm{ex}}}+{k}_{{\rm{em}}}$. (b) Spot-scanning illumination microscopy where fluorescence is collected by an array detector, and pixels offset by a distance from the excitation spot detect a shifted but higher-resolution, low-signal confocal image. The reconstruction algorithm corrects the shift and restores the signal by reassigning the detected fluorescence toward the illumination axis, with the final resolution ${PS F}_{{\rm{sys}}}$ determined by the product of the excitation PSF (${PS F}_{{\rm{ex}}}$) and the emission PSF (${PS F}_{{\rm{em}}}$). After deconvolution, this process improves resolution similar to that obtained with sinusoidal illumination microscopy

    Figure 2.  The schematic diagram of TIRF-SIM (a) and instant SIM (b). Adapted from Kner et al.[35] and York et al.[40]

    Figure 3.  Schematic diagram of early implemented 2P SIM (a), 2P-ISIM (b), and 2P SIM with the resonant scanner (c). Adapted from Ingaramo et al.[42], Peter et al.[43] and Gregor et al.[44]

    Figure 4.  (a) Schematic diagram of 3D STED-SIM. (b) The cross-section comparison of lateral PSF (top, left), axial PSF (bottom, left), lateral OTF (top, right), and axial OTF (bottom, right) of the widefield microscopy (red) and 3D STED-SIM (blue). Adapted from Xue et al.[49]

    Table  1.   Comparison of SIM SR reconstruction algorithm

    PrincipleEffectCodeReference
    TV-SIMAppend TV regularization to reconstructionSuppress reconstruction artifactsNot open-sourceChu et al. 2014[14]
    Hessian-SIMAppend Hessian regularization to reconstructionSuppress reconstruction artifacts, avoid over-sharpening boundariesOpen-sourceHuang et al. 2018[15]
    HiFi-SIMEngineering the effective SIM PSF into an ideal formSuppress reconstruction artifacts, improve axial sectioningOpen-sourceWen et al. 2021[17]
    Sparse-SIMAppend Sparse and Hessian regularization to reconstructionIncreases SIM resolution ~2-fold laterallyOpen-sourceZhao et al. 2021[26]
    sCMOS Noise-corrected SIMIntroduce sCMOS imaging noise model to reconstructionSuppress sCMOS noise-induced reconstruction artifactsNot open sourceZhou et al. 2022[16]
    Two-step RL deconvolution SIMIntroduce two-step RL deconvolution to reconstructioneliminate ad hoc tuneable parametersNot open sourcePerez et al. 2016[18]
    Noise-controlled SIMIntroduce a physically realistic noise model to reconstructionSuppress reconstruction artifacts, eliminate ad hoc tuneable parameters, maintain resolution and contrastOpen-sourceSmith et al. 2021[19]
    GAN TIRF-SIMUse GAN for transforming TIRF images into TIRF SIM imagesReconstruct rapidlyOpen-sourceWang et al. 2019[20]
    U-Net SIMUse U-net for producing SIM imagesTrain efficiently and reconstruct with fewer low-intensity input imagesOpen-sourceJin et al. 2020[21]
    3D RCANUse 3D RCAN for increasing SIM resolutionIncreases SIM resolution ~1.9-fold laterally and ~3.6-fold axiallyOpen-sourceChen et al. 2021[23]
    DFCAN/DFGANUse DFCAN/DFGAN for producing SIM imagesReconstruct with low SNR input imagesOpen-sourceQiao et al. 2021[24]
    下载: 导出CSV

    Table  2.   Summary of SIM performance evaluation algorithms

    FunctionCodeReference
    FRC/FSCDetermine SIM resolution by cross-correlationOpen-sourceNieuwenhuizen et al. 2013[28]
    Decorrelation analysisDetermine SIM resolution by partial phase correlationOpen-sourceDescloux et al. 2019[31]
    NanoJ-SQUIRRELEvaluate SIM artifacts with the resolution scaling functionOpen-sourceCulley et al. 2018[32]
    SIMcheckEvaluate SIM stripe modulation contrast by computing the standard deviationOpen-sourceBall et al. 2015[33]
    下载: 导出CSV
  • [1] BETZIG E, TRAUTMAN J K, HARRIS T D, et al. Breaking the diffraction barrier: optical microscopy on a nanometric scale[J]. Science, 1991, 251(5000): 1468-1470. doi: 10.1126/science.251.5000.1468
    [2] AXELROD D. Total internal reflection fluorescence microscopy in cell biology[J]. Traffic, 2001, 2(11): 764-774. doi: 10.1034/j.1600-0854.2001.21104.x
    [3] HELL S W. Toward fluorescence nanoscopy[J]. Nature Biotechnology, 2003, 21(11): 1347-1355. doi: 10.1038/nbt895
    [4] EGGELING C, RINGEMANN C, MEDDA R, et al. Direct observation of the nanoscale dynamics of membrane lipids in a living cell[J]. Nature, 2009, 457(7233): 1159-1162. doi: 10.1038/nature07596
    [5] BETZIG E, PATTERSON G H, SOUGRAT R, et al. Imaging intracellular fluorescent proteins at nanometer resolution[J]. Science, 2006, 313(5793): 1642-1645. doi: 10.1126/science.1127344
    [6] RUST M J, BATES M, ZHUANG X W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)[J]. Nature Methods, 2006, 3(10): 793-796. doi: 10.1038/nmeth929
    [7] THOMPSON R E, LARSON D R, WEBB W W. Precise nanometer localization analysis for individual fluorescent probes[J]. Biophysical Journal, 2002, 82(5): 2775-2783. doi: 10.1016/S0006-3495(02)75618-X
    [8] GUSTAFSSON M G L. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy[J]. Journal of Microscopy, 2000, 198(2): 82-87. doi: 10.1046/j.1365-2818.2000.00710.x
    [9] WU Y, SHROFF H. Faster, sharper, and deeper: structured illumination microscopy for biological imaging[J]. Nature Methods, 2018, 15(12): 1011-1019. doi: 10.1038/s41592-018-0211-z
    [10] GUSTAFSSON M G L, SHAO L, CARLTON P M, et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination[J]. Biophysical Journal, 2008, 94(12): 4957-4970. doi: 10.1529/biophysj.107.120345
    [11] SHROFF S A, FIENUP J R, WILLIAMS D R. Phase-shift estimation in sinusoidally illuminated images for lateral superresolution[J]. Journal of the Optical Society of America A, 2009, 26(2): 413-424. doi: 10.1364/JOSAA.26.000413
    [12] WICKER K, MANDULA O, BEST G, et al. Phase optimisation for structured illumination microscopy[J]. Optics Express, 2013, 21(2): 2032-2049. doi: 10.1364/OE.21.002032
    [13] ZHOU X, LEI M, DAN D, et al. Image recombination transform algorithm for superresolution structured illumination microscopy[J]. Journal of Biomedical Optics, 2016, 21(9): 096009. doi: 10.1117/1.JBO.21.9.096009
    [14] CHU K Q, MCMILLAN P J, SMITH Z J, et al. Image reconstruction for structured-illumination microscopy with low signal level[J]. Optics Express, 2014, 22(7): 8687-8702. doi: 10.1364/OE.22.008687
    [15] HUANG X SH, FAN J CH, LI L J, et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy[J]. Nature Biotechnology, 2018, 36(5): 451-459. doi: 10.1038/nbt.4115
    [16] ZHOU B, HUANG X SH, FAN J CH, et al. sCMOS noise-corrected superresolution reconstruction algorithm for structured illumination microscopy[J]. Photonics, 2022, 9(3): 172. doi: 10.3390/photonics9030172
    [17] WEN G, LI S M, WANG L B, et al. High-fidelity structured illumination microscopy by point-spread-function engineering[J]. Light:Science &Applications, 2021, 10(1): 70.
    [18] PEREZ V, CHANG B J, STELZER E H K. Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution[J]. Scientific Reports, 2016, 6: 37149. doi: 10.1038/srep37149
    [19] SMITH C S, SLOTMAN J A, SCHERMELLEH L, et al. Structured illumination microscopy with noise-controlled image reconstructions[J]. Nature Methods, 2021, 18(7): 821-828. doi: 10.1038/s41592-021-01167-7
    [20] WANG H D, RIVENSON Y, JIN Y Y, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy[J]. Nature Methods, 2019, 16(1): 103-110. doi: 10.1038/s41592-018-0239-0
    [21] JIN L H, LIU B, ZHAO F Q, et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed[J]. Nature Communications, 2020, 11(1): 1934. doi: 10.1038/s41467-020-15784-x
    [22] ZHANG Y L, LI K P, LI K, et al. . Image super-resolution using very deep residual channel attention networks[C]. Proceedings of the 15th European Conference on Computer Vision, Springer, 2018: 294-310.
    [23] CHEN J J, SASAKI H, LAI H, et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes[J]. Nature Methods, 2021, 18(6): 678-687. doi: 10.1038/s41592-021-01155-x
    [24] QIAO CH, LI D, GUO Y T, LIU CH, et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy[J]. Nature Methods, 2021, 18(2): 194-202. doi: 10.1038/s41592-020-01048-5
    [25] SCHULZ O, PIEPER C, CLEVER M, et al. Resolution doubling in fluorescence microscopy with confocal spinning-disk image scanning microscopy[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(52): 21000-21005. doi: 10.1073/pnas.1315858110
    [26] ZHAO W S, ZHAO SH Q, LI L J, et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy[J]. Nature Biotechnology, 2022, 40(4): 606-617. doi: 10.1038/s41587-021-01092-2
    [27] NIEUWENHUIZEN R P J, LIDKE K A, BATES M, et al. Measuring image resolution in optical nanoscopy[J]. Nature Methods, 2013, 10(6): 557-562. doi: 10.1038/nmeth.2448
    [28] BANTERLE N, BUI K H, LEMKE E A, et al. Fourier ring correlation as a resolution criterion for super-resolution microscopy[J]. Journal of Structural Biology, 2013, 183(3): 363-367. doi: 10.1016/j.jsb.2013.05.004
    [29] ROSENTHAL P B, HENDERSON R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy[J]. Journal of Molecular Biology, 2003, 333(4): 721-745. doi: 10.1016/j.jmb.2003.07.013
    [30] SAXTON W O, BAUMEISTER W. The correlation averaging of a regularly arranged bacterial cell envelope protein[J]. Journal of Microscopy, 1982, 127(2): 127-138. doi: 10.1111/j.1365-2818.1982.tb00405.x
    [31] DESCLOUX A, GRUßMAYER K S, RADENOVIC A. Parameter-free image resolution estimation based on decorrelation analysis[J]. Nature Methods, 2019, 16(9): 918-924. doi: 10.1038/s41592-019-0515-7
    [32] CULLEY S, ALBRECHT D, JACOBS C, et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts[J]. Nature Methods, 2018, 15(4): 263-266. doi: 10.1038/nmeth.4605
    [33] BALL G, DEMMERLE J, KAUFMANN R, et al. . SIMcheck: a toolbox for successful super-resolution structured illumination microscopy[J]. Scientific Reports, 2015, 5: 15915.
    [34] FIOLKA R, BECK M, STEMMER A. Structured illumination in total internal reflection fluorescence microscopy using a spatial light modulator[J]. Optics Letters, 2008, 33(14): 1629-1631. doi: 10.1364/OL.33.001629
    [35] KNER P, CHHUN B B, GRIFFIS E R, et al. Super-resolution video microscopy of live cells by structured illumination[J]. Nature Methods, 2009, 6(5): 339-342. doi: 10.1038/nmeth.1324
    [36] LI D, SHAO L, CHEN B CH, et al. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics[J]. Science, 2015, 349(6251): aab3500. doi: 10.1126/science.aab3500
    [37] NIXON-ABELL J, OBARA C J, WEIGEL A V, et al. Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER[J]. Science, 2016, 354(6311): aaf3928. doi: 10.1126/science.aaf3928
    [38] GUO Y T, LI D, ZHANG S W, et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales[J]. Cell, 2018, 175(5): 1430-1442.e17. doi: 10.1016/j.cell.2018.09.057
    [39] YORK A G, PAREKH S H, NOGARE D D, et al. Resolution doubling in live, multicellular organisms via multifocal structured illumination microscopy[J]. Nature Methods, 2012, 9(7): 749-754. doi: 10.1038/nmeth.2025
    [40] YORK A G, CHANDRIS P, NOGARE D D, et al. Instant super-resolution imaging in live cells and embryos via analog image processing[J]. Nature Methods, 2013, 10(11): 1122-1126. doi: 10.1038/nmeth.2687
    [41] MO Y Q, FENG F, MAO H, et al. Structured illumination microscopy artefacts caused by illumination scattering[J]. Philosophical Transactions of the Royal Society A:Mathematical,Physical and Engineering Sciences, 2021, 379(2199): 20200153. doi: 10.1098/rsta.2020.0153
    [42] INGARAMO M, YORK A G, WAWRZUSIN P, et al. Two-photon excitation improves multifocal structured illumination microscopy in thick scattering tissue[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(14): 5254-5259. doi: 10.1073/pnas.1314447111
    [43] WINTER P W, YORK A G, NOGARE D D, et al. Two-photon instant structured illumination microscopy improves the depth penetration of super-resolution imaging in thick scattering samples[J]. Optica, 2014, 1(3): 181-191. doi: 10.1364/OPTICA.1.000181
    [44] GREGOR I, SPIECKER M, PETROVSKY R, et al. Rapid nonlinear image scanning microscopy[J]. Nature Methods, 2017, 14(11): 1087-1089. doi: 10.1038/nmeth.4467
    [45] HEINTZMANN R, JOVIN T M, CREMER C. Saturated patterned excitation microscopy-a concept for optical resolution improvement[J]. Journal of the Optical Society of America A, 2002, 19(8): 1599-1609. doi: 10.1364/JOSAA.19.001599
    [46] GUSTAFSSON M G L. Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(37): 13081-13086. doi: 10.1073/pnas.0406877102
    [47] ZHANG H, ZHAO M, PENG L L. Nonlinear structured illumination microscopy by surface plasmon enhanced stimulated emission depletion[J]. Optics Express, 2011, 19(24): 24783-24794. doi: 10.1364/OE.19.024783
    [48] DAKE F, NAKAYAMA S, TAKI Y. Optical resolution enhancement and background reduction by stimulated emission depletion structured illumination microscopy with structured excitation[C]. Novel Techniques in Microscopy 2015, OSA, 2015: NM2C. 4.
    [49] XUE Y, SO P T C. Three-dimensional super-resolution high-throughput imaging by structured illumination STED microscopy[J]. Optics Express, 2018, 26(16): 20920-20928. doi: 10.1364/OE.26.020920
    [50] REGO E H, SHAO L, MACKLIN J J, et al. Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(3): E135-E143.
  • 加载中
图(4) / 表(2)
计量
  • 文章访问数:  853
  • HTML全文浏览量:  607
  • PDF下载量:  635
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-07-11
  • 修回日期:  2022-08-01
  • 录用日期:  2022-08-24
  • 网络出版日期:  2022-08-24

目录

    /

    返回文章
    返回