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多维度单分子成像研究进展

李孟帆,陈剑威,石伟,傅爽,李昀泽,罗婷丹,陈俊帆,李依明

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李孟帆, 陈剑威, 石伟, 傅爽, 李昀泽, 罗婷丹, 陈俊帆, 李依明. 多维度单分子成像研究进展[J]. , 2022, 15(6): 1243-1257. doi: 10.37188/CO.2022-0088
引用本文: 李孟帆, 陈剑威, 石伟, 傅爽, 李昀泽, 罗婷丹, 陈俊帆, 李依明. 多维度单分子成像研究进展[J]. , 2022, 15(6): 1243-1257.doi:10.37188/CO.2022-0088
LI Meng-fan, CHEN Jian-wei, SHI Wei, FU Shuang, LI Yun-ze, LUO Ting-dan, CHEN Jun-fan, LI Yi-ming. Advances in multi-dimensional single molecule imaging[J]. Chinese Optics, 2022, 15(6): 1243-1257. doi: 10.37188/CO.2022-0088
Citation: LI Meng-fan, CHEN Jian-wei, SHI Wei, FU Shuang, LI Yun-ze, LUO Ting-dan, CHEN Jun-fan, LI Yi-ming. Advances in multi-dimensional single molecule imaging[J].Chinese Optics, 2022, 15(6): 1243-1257.doi:10.37188/CO.2022-0088

多维度单分子成像研究进展

doi:10.37188/CO.2022-0088
基金项目:广东省基础与应用基础研究基金区域联合基金项目(No. 2020A1515110380);山东省重点研发计划项目(No. 2021CXGC010212);深圳市高层次人才团队项目(No. KQTD20200820113012029)
详细信息
    作者简介:

    李孟帆(1998—),男,广东佛山人,硕士研究生,2020年于黑龙江大学获得学士学位,主要从事多维度单分子定位成像方面研究。E-mail:limf2020@mail.sustech.edu.cn

    李依明(1988—),男,福建莆田人,博士,南方科技大学研究员,博士生导师,2009、2010、2015年分别于上海交通大学、海德堡大学、卡尔斯鲁厄理工学院获得生物医学工程学士、医学物理硕士和生物物理博士学位。2016—2019年受玛丽居里博士后奖学金资助,分别在欧洲分子生物实验室和耶鲁大学任职博士后和访问学者。2020年底入选国家高层次人才青年项目。长期从事三维亚10 nm多色超分辨成像相关研究,近五年来发表高影响力论文13篇,其中第一/通讯作者论文7篇,包括Nature Methods,Nature Communications,Optics Letters,Engineering等。E-mail:liym2019@sustech.edu.cn

  • 中图分类号:TH742

Advances in multi-dimensional single molecule imaging

Funds:Supported by Guangdong Natural Science Foundation Joint Fund (No. 2020A1515110380); Shandong Key Research and Development Program(No. 2021CXGC010212); Shenzhen Science and Technology Innovation Commission (No. KQTD20200820113012029)
  • 摘要:

    单分子成像方法被广泛应用于亚细胞结构的三维空间定位。点扩散函数是分析单分子信息的重要窗口,除了能反映空间坐标外还蕴含着丰富的额外信息。本文介绍了从点扩散函数中解析空间位置、荧光波长、偶极子朝向及干涉相位等多维度单分子成像研究进展,简要地概括了目前主流定位方法,并对该技术的发展方向进行了展望。

  • 图 1单分子二维定位[21]。(a)在采集步骤中,将会获取稀疏分布的单分子闪烁图像;(b)分析步骤中,从单帧图像中准确定位的单分子二维位置,以及所有单分子点的合成图像。

    Figure 1.Two-dimensional localization of a single molecule[21]. (a) In the acquisition step, sparsely distributed single molecule images are recorded; (b) in the analysis step, the two-dimensional coordinates of the single molecules are precisely localized in each frame and then accumulated to reconstruct the super-resolution image

    图 2各PSF在不同轴向位置的变化及在SLM调制下的光路布局。(a)标准PSF;(b)散光PSF;(c)双螺旋PSF[27-28];(d)相位斜坡PSF[29];(e)螺旋PSF[30];(f)自弯曲PSF[33];(g) SLM调制下的光路布局

    Figure 2.Changes of each PSF at different axial positions and optical path layout for SLM modulation. (a) The standard PSF; (b) astigmatism PSF; (c) double helix PSF[27-28]; (d) phase ramp PSF[29]; (e) spiral PSF[30]; (f) self-bending PSF[33]; (g) optical path layout for SLM modulation

    图 3不同景深优化下的Tetrapod PSF[37]。6 µm优化景深下的光瞳函数(a),理论PSF(b),实验PSF(c),定位精度(d)。(e)~(h)与(a)~(d)相同,但是为10 μm优化景深下的Tetrapod PSF。

    Figure 3.Tetrapod PSF optimized at different depths of field[37]. (a) The pupil function, (b) theoretical PSF, (c) experimental PSF, (d) localizing accuracy of Tetrapod PSF optimized for 6 µm depth of field. (e)~(h) The same as (a)~(d), but for Tetrapod PSF optimized for 10 µm depth of field.

    图 4同时测量单分子的发射波长与三维位置[48]。(a)光路设计图,SLM放置在后焦面上;(b)弯曲光栅的光瞳函数;(c)3种波长在不同位置下的PSF分布,波长越长2个旁瓣的距离越远

    Figure 4.Simultaneous measurement of emission wavelength and 3D position of single molecules[48]. (a) Optical path design with an SLM placed on the back focal plane; (b) pupil function of curved grating; (c) PSF distributions of three different wavelengths at different localizations. The longer the wavelength, the farther the distance between the two side lobes

    图 5基于人工神经网络的信息提取方法[52]

    Figure 5.Information extraction method based on artificial neural network[52]

    图 6偶极子方向引起的定位偏差[53]。(a) 转动角、极角、方位角分别为15°、45°、0°时单分子点的PSFxz切面(左图),xy切面(右图),以及其定位偏差;(b)极角、方位角与(a)相同的情况下转动角为60°的PSF;(c)和(d)分别为不同转动角,极角产生的横向偏移值;(e)转动角、极角、方位角在偶极子中的物理意义

    Figure 6.Localization deviation caused by the dipole’s direction[53]. (a) PSFxzsection (left) andxysection (right) of single molecule with a rotation angle, polar angle and azimuth angle of 15°, 45° and 0° respectively, and corresponding localization deviations; (b) PSF with the same polar angle and azimuth angle as (a) and rotation angle of 60°. (c) and (d) are the lateral offsets generated by different rotation angles and polar angles, respectively; (e) physical meaning of rotation angle, polar angle and azimuth angle of the dipole

    图 7基于双螺旋PSF的偶极子方向定位方法[58]。(a)光路布局;(b)和(c)分别为两个偏振方向的成像通道,它们的光瞳函数分别为(i)、(ii);(d)上下图分别为水平和垂直通道的PSF;(e)和(f)分别为LA、LD指标,只考虑LA指标时会出现4种可能的朝向结果。红色和蓝色分别代表透射通道和反射通道的LD指标

    Figure 7.Dipole orientation localization method based on double helix PSF[58]. (a) Optical path layout; (b) and (c) are imaging channels in two polarization directions, respectively, and their pupil functions are (i) and (ii) respectively; (d) the upper and lower figures are PSF of horizontal and vertical channels, respectively; (e) and (f) are LA and LD indicators respectively. There are four possible orientations when only the LA indicator is considered. Red and blue represent LD indexes of transmission channel and reflection channel respectively

    图 8基于vortex PSF的朝向与三维位置同时定位[60]。(a)Vortex PSF光路;在4000个光子10个背景光子的单分子图像中,(b)方位角φ和(c)极角θ的CRLB;(d)λ-DNA的二维位置及方位角,伪色代表该点方位角,大小如左下角

    Figure 8.Simultaneous localization of the single-molecule orientation and three-dimensional location based on vortex PSF[60]. (a) Vortex PSF’s optical path; CRLB of azimuth angle (b) and polar angle (c) in single molecule imaging with 4000 photons and 10 background photons; (d) 2D position and azimuthal angle of theλ-DNA. The false color represents the azimuthal angle, as shown in the lower left corner

    图 9IAB模型分解的4Pi-PSF[65]。(a)四通道4Pi-SMLM光路结构以及各通道的PSF;(b)每个物镜接收2000光子以及20个背景光子下,通过光度法和IAB模型拟合得到的各维度定位精度;(c)理想的4Pi-PSF与分解出来的IAB矩阵

    Figure 9.4Pi-PSF decomposed by IAB model[65]. (a) The optical path layout of 4-channel 4Pi-SMLM and the PSFs of each channel; (b) localization accuracy of each dimension obtained by photometric method and IAB model fitting for single molecule of 2,000 photons collected by each objective lens; (c) ideal 4Pi-PSF and decomposed IAB matrix

    图 10基于卷积神经网络的单分子定位流程图。(a)网络训练步骤;(b)定位步骤

    Figure 10.Flowchart of single molecule localization based on convolutional neural network. (a) Network training steps; (b) localizing steps

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  • 收稿日期:2022-04-30
  • 录用日期:2022-06-28
  • 修回日期:2022-05-19
  • 网络出版日期:2022-08-20

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