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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

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]. 中国光学(中英文). 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]. 中国光学(中英文). 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. 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. 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,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,男,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

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。其中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,并简要地概括了目前主流的定位方法,以及对该技术的发展方向进行了展望。

     

  • 图 1  单分子蕴含着丰富的多维度信息。

    Figure 1.  Single molecule contains rich multidimensional information.

    图 2  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 2.  Two-dimensional localization of 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 3  各PSF在不同轴向位置的变化。(A)标准PSF在距离焦点上下位置所形成相似的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 3.  Changes of each PSF at different axial positions. (A) The standard PSF is symmetry with respect to focus. (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 4  不同景深优化下的Tetrapod PSF[37]。6µm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10m优化景深下的Tetrapod PSF。

    Figure 4.  Tetrapod PSF optimized for different depth of field[37]. The pupil function (A), theoretical PSF (B), (C) experimental PSF, (D) localizing accuracy of Tetrapod PSF optimized for 6µm depth of field. (E-H) The same as (A-D), but for Tetrapod PSF optimized for 10µm depth of field.

    图 5  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)三种波长在不同位置下的PSF分布,波长越长两个旁瓣的距离越远。

    Figure 5.  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 of three different wavelengths. The longer the wavelength, the farther the distance between the two side lobes.

    图 6  基于人工神经网络的信息提取方法[52]。(A)颜色分辨训练步骤。(B)轴向位置定位训练步骤。(C)对未知样品的颜色分辨、三维定位拟合流程。

    Figure 6.  Information extraction method based on artificial neural network [52]. (A) Color separation training steps. (B) Training steps for axial localization. (C) Color discrimination and three-dimensional localizing process of unknown samples.

    图 7  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°单分子点的PSF xz切面,右图为xy切面,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 7.  Localization deviation caused by dipole direction[53]. (A) PSF xz section of single molecule with rotation angle, polar angle and azimuth angle of 15°, 45° and 0° respectively, xy section on the right, and its localization deviation. (B) PSF with the same polar angle and azimuth angle as (A) and rotation angle of 60°. (C) and (D) are lateral offset generated by different rotation angles and polar angles, respectively. (E) The physical meaning of rotation angle, polar angle and azimuth angle of the dipole.

    图 8  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),(F)分别为LA,LD指标,只考虑LA指标时会出现四种可能的朝向结果。红色和蓝色分别代表透射通道和反射通道的LD指标。

    Figure 8.  Dipole orientation localization method based on double helix PSF[58] . (A) Optical path layout. (B) and (C) are imaging channels with 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 9  基于Vortex PSF的朝向与三维位置同时定位[60]。(A)Vortex PSF光路。在4000光子10背景光子的单分子图像中,方位角φ(B)和极角θ(C)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 9.  The single-molecule orientation and three-dimensional location are simultaneously localized based on vortex PSF[60] . (A) Vortex PSF optical path. CRLB of azimuth angle (B) and polar angle (C) in for single molecule imaging with 4,000 photons and 10 background photons. (D) The 2D position and azimuthal angle of the λ -DNA. The false color represents the azimuthal angle, as shown in the lower left corner.

    图 10  IAB模型分解的4Pi-PSF[66]。(A)四通道4Pi-SMLM光路结构以及各通道的PSF。(B)每物镜接收2000光子以及20背景下,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 10.  4Pi-PSF decomposed by IAB model[66]. (A) The 4-channel 4Pi-SMLM optical path layout and the PSF of each channel. (B) The 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 11  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 11.  Flowchart of single molecule localization based on convolutional neural network. (A) Network training steps. (B) Localizing steps.

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出版历程
  • 收稿日期:  2022-04-30
  • 录用日期:  2022-06-28
  • 网络出版日期:  2022-08-20

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