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超分辨结构光照明显微成像技术(super-resolution structured illumination microscopy, SR-SIM)具有时间分辨率高、光漂白和光毒性低和对荧光探针的要求少等优点, 适用于活细胞的长时程超分辨成像. 采用二维晶格结构光作为照明光, 可以实现更快的成像速度和更低的光毒性, 但同时也增加了系统的复杂性. 为了解决此问题, 本文提出了一种基于数字微镜器件的快速超分辨晶格结构光照明显微成像方法(digital micromirror device-based lattice SIM , DMD-Lattice-SIM), 通过同步分时触发DMD和sCMOS相机的方式实现二维正交晶格结构光的产生, 且只需要采集5幅相移原始图像即可重构出超分辨图像, 相比于传统SR-SIM需要9幅相移原始图像的方法, 图像采集时间减少了约44.4%. 同时, 在基于空域和频域联合的SIM重构算法(joint space and frequency reconstruction method-SIM, JSFR-SIM)的基础上, 本文还发展了用于Lattice-SIM的JSFR超分辨图像重构方法(Lattice-JSFR-SIM), 先在频域对原始图像进行预滤波处理; 然后, 在空域对滤波后的图像进行超分辨重构处理. 与传统频域图像重构处理对比, 该方法在512 ×512 像素数的成像视场下重构时间减少了约55.6%, 对于实现活细胞实时超分辨成像具有重要意义和应用价值.Super-resolution structured illumination microscopy (SR-SIM) offers numerous advantages such as high temporal resolution, low photobleaching and phototoxicity, and no special requirements for fluorescent probes. It is particularly suitable for long-term SR imaging of living cells. By using two-dimensional lattice structured light serving as illumination, SR-SIM can achieve faster imaging speed and reduce phototoxicity, however, it is accompanied with system complexity increasing. To address this problem, in this work, we propose a fast SR lattice structured illumination microscopy imaging method based on a digital micromirror device (DMD), called DMD-Lattice-SIM. This method utilizes a DMD and synchronous time-sharing triggering with sCMOS to generate two-dimensional orthogonal lattice structured light. The proposed method only requires the collection of five phase-shifted raw images for SR image reconstruction, reducing the acquisition time by approximately 44.4% compared with the traditional SR-SIM method that requires nine phase-shifted raw images. In this work, we also introduce a rapid SR image reconstruction method called Lattice-JSFR-SIM, which combines the advantages of joint space and frequency reconstruction (JSFR)-SIM and Lattice-SIM. The raw images are pre-filtered in the frequency domain and then undergo SR reconstruction in the spatial domain. This approach reduces reconstruction time by approximately 55.6% compared with traditional frequency domain image reconstruction processing, within an imaging field of view of 512 pixels×512 pixels. The feasibility of the proposed method is demonstrated through experiments on cell microtubules and the observation of mitochondrial division and fusion in living cells. The findings presented in this paper hold great significance and application value for enabling real-time SR imaging of living cells.
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Keywords:
- structured illumination microscopy /
- super-resolution imaging /
- lattice structured illumination /
- spatial domain reconstruction
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Google Scholar
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图 2 产生晶格结构光照明的原理示意图 (a) 数据采集卡的控制信号; (b) DMD加载第一张模板时相机采集的模拟数据; (c) DMD加载第二张模板时相机采集的模拟数据; (d) 相机在一次曝光时间内采集的模拟数据; (e)—(g) 图(b)—(d)的傅里叶频谱
Fig. 2. Schematic diagram of the principle of generating lattice structured illumination: (a) Control signal of the data acquisition board; (b) analog data collected by the camera when the DMD loads the first template; (c) analog data collected by the camera when the DMD loads the second template; (d) analog data collected by the camera within one exposure time; (e)–(g) frequency spectra of panel (b)–(d).
图 4 模拟数据重构结果 (a) 真实图像; (b) 宽场图像; (c) fairSIM超分辨重构图像; (d) Lattice-SIM超分辨重构图像; (e) Lattice-JSFR-SIM超分辨重构图像; (f) 图(b)—(e)中曲线的切面强度分布
Fig. 4. Reconstructed results of the simulated data: (a) Ground truth; (b) wide-field image; (c) fairSIM super-resolution reconstructed image; (d) Lattice-SIM super-resolution reconstructed image; (e) Lattice-JSFR-SIM super-resolution reconstructed image; (f) intensity distribution of the profile in panel (b)–(e).
图 5 固定细胞微管实验结果 (a) 宽场图像; (b) 超分辨图像; (c) 图(a)中所选区域的放大; (d) 图(b)中所选区域的放大图; (e) 图(a)的去相关分辨率估计; (f) 图(b)的去相关分辨率估计; (g) 图(c)和图(d)中划线位置的切面强度分布
Fig. 5. Experimental results of fixed cell microtubules: (a) Wide-field image; (b) super-resolution image; (c) magnification of the selected area in panel (a); (d) magnification of the selected area in panel (b); (e) corresponding decorrelation analysis of panel (a), C.c., cross-correlation; (f) corresponding decorrelation analysis of panel (b); (g) intensity distribution of the profile at the line position in panel (c) and (d).
图 6 BSC-1活细胞线粒体动态实验结果 (a) 宽场图像; (b) 超分辨图像; (c) 图(a)的去相关分辨率估计; (d) 图(b)的去相关分辨率估计; (e) 图(b)中所选区域放大图, 时间间隔为0.5 s; (f) 图(b)中所选区域放大图, 时间间隔为0.05 s
Fig. 6. Experimental results of BSC-1 live cell mitochondria dynamics: (a) Wide-field image; (b) super-resolution image; (c) corresponding decorrelation analysis of panel (a); (d) corresponding decorrelation analysis of panel (b); (e) magnification of the selected area in panel (b) with a time interval of 0.5 s; (f) magnification of the selected area in panel (b) with a time interval of 0.05 s.
表 1 不同照明图案的SIM对比
Table 1. Comparison of SIM with different illumination patterns.
名称 照明图案 特点 优缺点 blind-SIM 散斑 使用散斑照明代替传统条纹照明 不需要求解照明图案参数, 简化了图像重构过程; 但需要采集较多的原始图像进行迭代重建, 成像速度慢 Lattice SIM for ZEISS Elyra 7 正方晶格、
六边形晶格物理光栅产生晶格, 振镜产生相移, 并且结合解卷积算法重构 成像速度和成像对比度高, 实现了60 nm的空间分辨率 Lattice-SIM 正方晶格 物理光栅产生晶格, SLM产生相移 成像视场大, 但系统复杂, 速度慢 
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表 2 不同重构算法的SIM对比
Table 2. Comparison of reconstruction algorithms.
名称 类别 特点 优缺点 Hessian-SIM 迭代 利用生物结构的连续性进行重建 减少了重构伪影, 光毒性低、成像速度快、
分辨率高, 但参数设置敏感Sparse-SIM 迭代 将生物结构的稀疏性加入到
Hessian-SIM中进行重建分辨率高, 但需要人为控制重构参数设置 HiFi-SIM 频域 利用点扩散函数改造方法优化频谱 图像伪影少、光学层切能力高、保真度高、参数设置不敏感 SP-SIM 空域 无需估计光照模式的频率和相位 重构速度比传统算法快约5.4倍, 但在重构过程中
丢失了低频分量, 导致图像对比度低SDR-SIM 空域 利用数学中函数的级数展开的概念 重构速度比传统算法快约7倍, 但没有考虑离焦背景的问题 JSFR-SIM 空频域混合 首先在频域预滤波, 然后进行空域重构 图像重建速度高、背景荧光和周期性计算伪影低,
相比Wiener-SIM算法快近两个数量级direct-SIM 空频域混合 首先进行空域重构, 然后进行频域频谱优化 无需估计结构光条纹参数、分辨率高、伪影低、
样本要求低, 但对条纹对比度和相移精确度要求较高
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表 3 算法运行速度对比
Table 3. Comparison of algorithm running speed.
Input image size/
(pixels×pixels)Output image size/
(pixels×pixels)Acquisition
time/msLattice-SIM/ms Lattice-JSFR-SIM/ms CPU CPU GPU 256×256 512×512 6.4 137.9±6.3 44.9±2.4 4.3±3.7 512×512 1024×1024 12.7 751.0±26.1 333.6±14.0 17.6±4.2 1024×1024 2048×2048 31.3 3007.2±58.8 1749.8±22.9 75.8±6.3
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[1] Gustafsson M G L 2000 J. Microsc.-Oxford 198 82
Google Scholar
[2] Chen X, Zhong S Y, Hou Y W, Cao R J, Wang W Y, Li D, Dai Q H, Kim D, Xi P 2023 Light-Sci. Appl. 12 172
Google Scholar
[3] Chang B J, Chou L J, Chang Y C, Chiang S Y 2009 Opt. Express 17 14710
Google Scholar
[4] Dan D, Lei M, Yao B L, Wang W, Winterhalder M, Zumbusch A, Qi Y J, Xia L, Yan S H, Yang Y L, Gao P, Ye T, Zhao, W 2013 Sci. Rep. 3 1116
Google Scholar
[5] Li M Q, Li Y N, Liu W H, Lal A, Jiang S, Jin D Y, Yang H P, Wang S, Zhanghao K, Xi P 2020 Appl. Phys. Lett. 116 233702
Google Scholar
[6] Li X Y, Xie S Y, Liu W J, Jin L H, Xu Y K, Zhang L H, Hao X, Han Y B, Kuang C F, Liu X 2021 Opt. Express 29 43917
Google Scholar
[7] Mudry E, Belkebir K, Girard J, Savatier J, Le Moal E, Nicoletti C, Allain M, Sentenac A 2012 Nat. Photonics 6 312
Google Scholar
[8] Heintzmann R 2003 Micron 34 283
Google Scholar
[9] Siebenmorgen J, Novikau Y, Wolleschensky R, Weisshart K, Kleppe I 2018 Introducing Lattice SIM for ZEISS Elyra 7 (Germany: Carl Zeiss Microscopy GmbH) pp1–7
[10] Zheng J J, Fang X, Wen K, Li J Y, Ma Y, Liu M, An S, Li J L, Zalevsky Z, Gao P 2022 Opt. Express 30 27951
Google Scholar
[11] Huang X S, Fan J C, Li L J, Liu H S, Wu R L, Wu Y, Wei L S, Mao H, Lal A, Xi P, Tang L Q, Zhang Y F, Liu Y M, Tan S, Chen L Y 2018 Nat. Biotechnol. 36 451
Google Scholar
[12] Zhao W S, Zhao S Q, Li L J, Huang X S, Xing S J, Zhang Y L, Qiu G H, Han Z Q, Shang Y X, Sun D E, Shan C Y, Wu R L, Gu L S, Zhang S W, Chen R W, Xiao J, Mo Y Q, Wang J Y, Ji W, Chen X, Ding B Q, Liu Y M, Mao H, Song B L, Tan J B, Liu J, Li H Y, Chen L Y 2022 Nat. Biotechnol. 40 606
Google Scholar
[13] Wen G, Li S M, Wang L B, Chen X H, Sun Z L, Liang Y, Jin X, Xing Y F, Jiu Y M, Tang Y G, Li H 2021 Light-Sci. Appl. 10 70
Google Scholar
[14] Tu S J, Liu Q L, Liu X, Liu W J, Zhang Z M, Luo T J, Kuang C F, Liu X, Hao X 2020 Opt. Lett. 45 1567
Google Scholar
[15] Dan D, Wang Z J, Zhou X, Lei M, Zhao T Y, Qian J, Yu X H, Yan S H, Min J W, Bianco P, Yao B L 2021 IEEE Photonics J. 13 3900411
Google Scholar
[16] Wang Z J, Zhao T Y, Hao H W, Cai Y N, Feng K, Yun X, Liang Y S, Wang S W, Sun Y J, Bianco P R, Oh K, Lei M 2022 Adv. Photonics 4 026003
Google Scholar
[17] Wang Z J, Zhao T Y, Cai Y A, Zhang J X, Hao H W, Liang Y S, Wang S W, Sun Y J, Chen T S, Bianco P R, Oh K, Lei M 2023 The Innovation 4 100425
Google Scholar
[18] Wen G, Li S M, Liang Y, Wang L B, Zhang J, Chen X H, Jin X, Chen C, Tang Y G, Li H 2023 PhotoniX 4 19
Google Scholar
[19] Müller M, Mönkemöller V, Hennig S, Hübner W, Huser T 2016 Nat. Commun. 7 10980
Google Scholar
[20] Zhou L L, Yu B, Huang L L, Cao H Q, Lin D Y, Jing Y Y, Wali F, Qu J L 2022 ACS Appl. Nano Mater. 5 18742
Google Scholar
[21] Descloux A, Grussmayer K S, Radenovic A 2019 Nat. Methods 16 918
Google Scholar
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