WO2015089910A1 - 深层细胞超分辨率成像的方法、***及棱镜光薄片装置 - Google Patents

深层细胞超分辨率成像的方法、***及棱镜光薄片装置 Download PDF

Info

Publication number
WO2015089910A1
WO2015089910A1 PCT/CN2014/001138 CN2014001138W WO2015089910A1 WO 2015089910 A1 WO2015089910 A1 WO 2015089910A1 CN 2014001138 W CN2014001138 W CN 2014001138W WO 2015089910 A1 WO2015089910 A1 WO 2015089910A1
Authority
WO
WIPO (PCT)
Prior art keywords
resolution
super
fluorescent
sample
microscopy
Prior art date
Application number
PCT/CN2014/001138
Other languages
English (en)
French (fr)
Inventor
雷明德
杜胜望
赵腾
王莹
Original Assignee
香港科技大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 香港科技大学 filed Critical 香港科技大学
Publication of WO2015089910A1 publication Critical patent/WO2015089910A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G01N21/6458—Fluorescence microscopy
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00—Microscopes
    • G02B21/36—Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/365—Control or image processing arrangements for digital or video microscopes
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B27/00—Optical systems or apparatus not provided for by any of the groups G02B1/00 - G02B26/00, G02B30/00
    • G02B27/58—Optics for apodization or superresolution; Optical synthetic aperture systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1006—Investigating individual particles for cytology

Definitions

  • the invention relates to the field of optical microscopy technology and biological cell imaging technology, and in particular to a method, a system and a prism light sheet device for deep cell super-resolution imaging.
  • LM Localization Microscopy
  • TIRF Total Internal Reflection
  • Near-TIRF Near-TIRF
  • slice imaging technology plays an important role in biological research.
  • Scanning confocal microscopy and turntable confocal
  • This technique utilizes small holes to filter out background signals from the imaging plane.
  • the existing stimulated radiation loss microscopy technology is a super-resolution microscopy technology developed based on scanning confocal microscopy. Its resolution can reach 50 nanometers.
  • the limitation of this technique is that the sample taken must be able to withstand extremely high laser exposures, thus limiting the types of samples that can be taken.
  • Slice imaging can also be achieved from the lateral illumination of the sample using light sheet technology, which greatly reduces the light intensity that the cells are subjected to compared to confocal techniques.
  • the literature has claimed to achieve super-resolution imaging with a positioning microscopy and light film technology and achieve a resolution of 40 nm.
  • this technique is still difficult to apply to the shooting of more common biological samples.
  • the present application aims to provide a method, system and prismatic light sheet device for deep cell super-resolution imaging, which can realize deep-cell super-resolution imaging without changing the existing microscope structure.
  • a method of deep cell super-resolution imaging is provided.
  • the method can be applied to a super-resolution positioning microscope and includes:
  • a fluorescent label is attached to the sample to be observed, and the sample is immersed in an imaging buffer;
  • a deep cell super-resolution image of the sample is constructed based on the location of the fluorescent signal.
  • the step of canceling background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm comprises: processing the fluorescent signal by a second-order or higher-order correlation analysis algorithm to Removing the background noise that is not associated in the fluorescent signal; and the step of calculating the position of the fluorescence signal processed by the noise reduction by the super-resolution positioning microscopy algorithm includes: by Gaussian fitting, maximum similarity, or A quality center algorithm is sought to calculate the center position of the fluorescence signal subjected to the noise reduction process.
  • the step of eliminating background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm further comprises: recording the fluorescent signal as an animation; recombining the animation into a a series of animation groups, each of the animation groups comprising a predetermined number of frames; and performing a second-order correlation operation on each of the animation groups by the super-resolution optical fluctuation microscopy algorithm, To remove the background noise that is not associated in the fluorescent signal.
  • the noise-reduced animation can be reorganized into a new animation.
  • the step of calculating the position of the fluorescence-reduced fluorescent signal by the super-resolution positioning microscopy algorithm further comprises: analyzing each frame in the new animation by matching a predetermined point spread function Locating the image to identify the non-overlapping fluorescent signals; acquiring each of the non-overlapping fluorescent signals; and locating a center position of each of the non-overlapping fluorescent signals according to the super-resolution positioning microscopy algorithm, In this way, a corresponding resolution positioning microscopic image is constructed.
  • the step of constructing a deep cell super-resolution image of the sample based on the location of the fluorescent signal may further comprise: superimposing a central location of each of the fluorescent signals to construct a deep cell super-resolution image of the sample.
  • a system for deep cell super-resolution imaging is provided.
  • the system is used in a super-resolution positioning microscope and can include:
  • a signal acquisition module configured to acquire a fluorescent signal of a fluorescent labeling molecule in the deep layer of the sample, the sample being previously connected with the fluorescent label and immersed in the imaging buffer;
  • a SOFI module for canceling background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm
  • An LM module configured to calculate a position of the fluorescence signal processed by the noise reduction by a super-resolution positioning microscopy algorithm
  • An imaging module for constructing a deep cell super-resolution image of the sample based on the location of the fluorescent signal.
  • the SOFI module is configured to process the fluorescent signal by a second-order or high-order correlation analysis algorithm to remove the unrelated associated background noise in the fluorescent signal;
  • the center position of the fluorescence signal subjected to the noise reduction process is calculated by Gaussian fitting, maximum similarity, or finding a quality center algorithm.
  • a method of deep cell super-resolution imaging may include: arranging the prismatic light sheet device on top of the inverted microscope to eliminate uncorrelated background noise by physical means; and super-resolution positioning microscopy when positioning the sample for microscopy An algorithm calculates the position of the fluorescently labeled molecule and constructs according to the position of the fluorescently labeled molecule A deep cell super-resolution image of the sample was constructed.
  • the step of disposing the prismatic light sheet device on top of the inverted microscope comprises: arranging the prismatic light sheet device on top of the inverted microscope, and causing the prismatic light sheet device to There is a predetermined angle between the sample platforms in the horizontal direction.
  • the prismatic light sheet device includes at least an illumination objective lens and a prism mounted on the illumination objective lens, wherein the prism redirects illumination light of the illumination objective lens and compresses a beam thickness of the illumination light.
  • a prismatic light sheet device wherein the prism light sheet device is disposed on top of an inverted microscope and includes a first collimating positive lens, a negative lens, and a second collimating positive lens a cylindrical mirror and an illumination objective, characterized in that the prismatic light sheet device further comprises a prism mounted on the illumination objective for changing a direction of illumination light of the illumination objective such that the illumination is perpendicular to the illumination objective And increasing the illumination field of view and compressing the beam thickness of the illumination light.
  • the first technical solution of the present invention combines Super-resolution Optical Fluctuation Microscopy (SOFI) and Localization Microscopy (LM) to eliminate non-association by computer operation.
  • SOFI Super-resolution Optical Fluctuation Microscopy
  • LM Localization Microscopy
  • the background noise is used to obtain deep super-resolution images of the cells, which can be directly applied to ordinary fluorescence microscopes without modifying their original optical structure.
  • the second technical solution of the present invention is applied to an inverted microscope by using a prism light sheet device, physically reducing background noise and obtaining a super-resolution image of deep cells by positioning microscopy, which can be directly loaded into a conventional inverted fluorescence microscope. .
  • FIG. 1 is a flow chart of a method for super deep resolution imaging of a deep cell of the present invention
  • FIG. 2 is a flow chart of a preferred method for the first deep cell super-resolution imaging of the present invention
  • FIG. 3 is a schematic structural diagram of a first deep cell super-resolution imaging system of the present invention.
  • FIG. 4 is a flow chart of a second deep cell super-resolution imaging method of the present invention.
  • Figure 5 is a schematic structural view of a prism light sheet device of the present invention.
  • 6a-6b are schematic views of simulation results of the present invention using SOFI to eliminate background;
  • 7a to 7i are tomographic images of mitochondrial outer membranes in BSHSY-5Y cells of the present invention.
  • 8a-8f are images of MVBs in tobacco BY-2 cells by super resolution localization microscope of the present invention And a comparison diagram of the results of imaging with SOFI combined with a super-resolution positioning microscope;
  • Figure 9a is a structural view of a prismatic light sheet microscope of the present invention.
  • Fig. 9b is a schematic view showing the deflection angle of the light sheet after the prism is added in the present invention.
  • Fig. 9c is a schematic view showing the simulation results of the deflection angle and the compression coefficient of the optical sheet after the prism is added to the present invention.
  • the first technical solution of the present invention combines Super-resolution Optical Fluctuation Microscopy (SOFI) and Localization Microscopy (LM), which can be operated by computer due to defocusing.
  • SOFI Super-resolution Optical Fluctuation Microscopy
  • LM Localization Microscopy
  • the incoherent background produced by the fluorescent molecules to acquire deep-resolution super-resolution images of the cells, which can be directly applied to ordinary fluorescence microscopes without modifying the original optical structure.
  • a second technical solution of the present invention uses a method of prismatic light sheet illumination.
  • the light sheet illumination method removes out-of-focus fluorescent molecular signals from a physical perspective and is therefore suitable for deep-cell super-resolution imaging in combination with super-resolution localization microscopy.
  • This illumination structure can also be directly loaded into a conventional inverted fluorescence microscope.
  • These methods can expand the range of application of super-resolution localization microscopy, from a few micrometers on the cell surface to a few hundred micrometers deep in the cell, and the ability to know the location of the cell structure and protein can reach TEM (Transmission electron microscope, Transmission electron microscopy) is a similar range, but simpler and easier to implement than TEM.
  • TEM Transmission electron microscope, Transmission electron microscopy
  • FIG. 1 is a flow chart of a method for deep cell super-resolution imaging according to an embodiment of the present application, which is applied to a super-resolution positioning microscope and includes the following steps.
  • step S101 a fluorescent label is attached to the sample to be observed, and a sample (for example, a cell) is immersed in an imaging buffer.
  • a fluorescent label ie, a fluorescent labeling molecule
  • a biological sample to be observed using a conventional immunofluorescent labeling method
  • the sample is immersed in an imaging buffer.
  • Immunization the sample can be labeled, for example, using Alexa 647 or Alexa 750 fluorophore.
  • the imaging buffer component can include, for example, tris(2-carboxyethyl)phosphine salt, glucose oxidase, glucose, catalase, and cyclooctatetraene.
  • a fluorescent signal of the fluorescent labeling molecule of the deep layer (for example, the deep layer of the sample) is obtained.
  • fluorescently labeled molecules also referred to herein as "fluorescent markers” label all conformationally specific structures in a cell (eg, cellular microtubule structures), but deeper structures (also labeled with fluorescent molecules) will behave A strong background.
  • fluorescent markers also referred to herein as "fluorescent markers” label all conformationally specific structures in a cell (eg, cellular microtubule structures), but deeper structures (also labeled with fluorescent molecules) will behave A strong background.
  • fluorescent markers also referred to herein as "fluorescent markers” label all conformationally specific structures in a cell (eg, cellular microtubule structures), but deeper structures (also labeled with fluorescent molecules) will behave A strong background.
  • the fluorescence signal of the cell surface structure is collected by special means such as total internal reflection microscopy.
  • a deep cell fluorescence signal is acquired in order to achieve deep cell structure imaging.
  • step S103 the background noise of the fluorescent signal is eliminated by the SOFI algorithm.
  • the fluorescent signal is processed by a second order or higher order correlation analysis algorithm to remove uncorrelated background noise in the fluorescent signal.
  • step S104 the position of the fluorescence signal subjected to the noise reduction processing is calculated by the LM algorithm.
  • the center position of the noise-reduced fluorescent signal can be calculated, for example, by Gaussian fitting, maximum similarity, or finding a center of mass algorithm.
  • the central position of the fluorescent signal is the precise location of the fluorescently labeled molecule.
  • step S105 a deep cell super-resolution image of the sample is constructed based on the position of the fluorescent signal.
  • FIG. 2 is a flow chart of a preferred method for the first deep cell super-resolution imaging of the present invention, which is applied to a super-resolution positioning microscope, and includes the following steps:
  • Step S201 connecting a fluorescent label to the sample to be observed, and immersing the sample in the imaging buffer;
  • Step S202 acquiring a fluorescent signal of the fluorescent labeling molecule in the deep layer of the sample
  • step S203 the fluorescent signal is recorded as an animation.
  • Step S204 the animation is reorganized into a series of animation groups, each animation group contains a predetermined number of frames; in step S205, a second-order correlation operation is performed on each animation group by the SOFI algorithm to remove non-associated fluorescence signals. Background noise.
  • the method may optionally further include: step S206,
  • the animation group of noise reduction processing is reorganized into a new animation.
  • step S207 each frame in the new animation is analyzed, and the non-overlapping fluorescent signals are identified by the size-located image matching the predetermined point spread function, and then in step S208, each of the non-extracted signals is acquired.
  • Overlapped fluorescent signals, and in step S209, the center position of each of the non-overlapping fluorescent signals is located according to the super-resolution positioning micro-algorithm, and finally correspondingly constructed according to the located central positions in step S210 Resolution to locate microscopic images.
  • FIG. 3 is a schematic diagram of a system structure of deep cell super-resolution imaging according to some embodiments (first technique) of the present application, which is applied to a super-resolution positioning microscope and includes a signal acquisition module 10 and a SOFI module. 20 and LM module 30.
  • the signal acquisition module 10 is configured to acquire a fluorescent signal of a fluorescent labeling molecule in the deep layer of the sample, and the sample is previously connected to the fluorescent label and immersed in the imaging buffer.
  • the fluorescent label is attached to the biological sample to be observed using a common immunofluorescent labeling method, and the sample is immersed in an imaging buffer, which is preferably labeled with Alexa 647 or Alexa 750 fluorophore.
  • the imaging buffer component preferably comprises: tris(2-carboxyethyl)phosphine salt, glucose oxidase, glucose, catalase, and cyclooctatetraene.
  • the SOFI module 20 is used to eliminate background noise of the fluorescent signal by the SOFI algorithm.
  • the SOFI module 20 is configured to process a fluorescent signal by a second order or higher order correlation analysis algorithm to remove uncorrelated background noise in the fluorescent signal.
  • the LM module 30 is for calculating the position of the noise-reduced fluorescent signal by the LM algorithm.
  • the LM module 30 is configured to calculate a center position of the noise-reduced fluorescent signal by Gaussian fitting, maximum similarity, or finding a quality center algorithm.
  • the central position of the fluorescent signal is the precise location of the fluorescently labeled molecule.
  • system 100 can also include an imaging module 40 for constructing a deep cell super-resolution image of a sample based on the location of the fluorescent signal.
  • the SOFI module 20 further includes:
  • a first recombination sub-module 22 configured to reorganize the animation into a series of animation groups, each animation group comprising a predetermined number of frames;
  • An operation sub-module 23, configured to perform a second-order correlation operation on each animation group by the SOFI algorithm to remove uncorrelated background noise in the fluorescent signal;
  • the second recombination sub-module 24 is configured to reorganize the noise-removed animation group into a new animation.
  • the LM module 30 may further include an identification sub-module 31, a positioning sub-module 32, and a first construction sub-module 33.
  • the identification sub-module 31 is configured to analyze each frame in the new animation to identify non-overlapping fluorescent signals by locating the images in a size that matches the predetermined point spread function.
  • the locating sub-module 32 is operative to obtain each of the non-overlapping fluorescent signals and to locate a center position of each of the fluorescent signals in accordance with the super-resolution positioning microscopy.
  • the central position of the fluorescent signal is the precise location of the fluorescently labeled molecule.
  • the first building sub-module 33 constructs a corresponding resolution positioning microscopic image according to the located central position
  • Imaging module 40 then overlays the center position of each fluorescent signal to construct a deep cell super-resolution image of the sample.
  • FIG. 4 is a flow chart of a method for deep-cell super-resolution imaging (the second technique described above) according to another embodiment of the present application, including the steps of:
  • Step S401 the prism light sheet device is disposed on top of any existing inverted microscope to physically eliminate unrelated background noise;
  • step S402 when the sample is subjected to positioning microscopy, the position of the fluorescent label molecule is calculated by the LM algorithm, and the deep cell super-resolution image of the sample is constructed according to the position of the fluorescent label molecule.
  • the prismatic light sheet device comprises: a first collimating positive lens 101; a negative lens 102; a second collimating positive lens 103; a cylindrical mirror 104; and an illumination objective lens 105.
  • the prism light sheet device further includes a prism 106 mounted on the illumination objective lens 105 for changing the direction of the illumination light of the illumination objective lens, causing the illumination light to be perpendicular to the illumination objective lens and increasing the illumination field of view, and compressing the illumination light. Beam thickness.
  • the prismatic light sheeting device can have a predetermined angle with the horizontally oriented sample platform. Direction change Both the angle and the degree of compression can be adjusted by adjusting the direction of the prism 106.
  • Example 1 SOFI+LM: In the implementation of positioning microscopy, firstly, the fluorescent labeling molecules on the sample start to flicker with strong excitation light, and these flashing signals are recorded by a CCD (Charge Coupled Device) camera with electron multiplying function. The signal is recorded as an animation.
  • CCD Charge Coupled Device
  • the positioning software analyzes each frame of the animation by: (1) identifying non-overlapping scintillation molecules by locating the image in a size compatible with the desired point spread function, identifying single molecules based on the point spread function of the system Fluorescent signal; (2) using Gaussian fitting or other methods to obtain the peak position of each scintillation fluorescent labeled molecule, construct an LM image from it, perform a Gaussian fitting on the identified fluorescent signal and find the center of the signal to locate each The precise location of the fluorescently labeled molecule. Superimposing these locations results in a final reconstructed super-resolution image.
  • the intensity of the fluorescent signal (background noise) from other layers will be equal to or greater than the single-molecule fluorescence of the detected layer, thereby changing the shape of the single-fluorescent molecular image of the focal plane, resulting in an algorithm in step (1).
  • the resulting reconstructed image is sparse and discontinuous.
  • the animation recorded with the scintillation fluorescence signal is reorganized into a series of groups, each group consisting of 5 to 15 frames (the number of frames is determined by the signal strength and the blinking speed), and then each group is performed by SOFI. Order related operations. This operation effectively removes or reduces random and uncorrelated background signals. All processed images are then reorganized into new animations and sent to the locator for analysis. In principle, more flickering fluorescent signals can be identified in step (1) of the LM process, and will be further positioned to nanoscale precision in step (2), resulting in a deep super-resolution slice image of the cell without special Optical settings. This method is basically applied to SOFI to remove the background before the LM algorithm, which is called SOFI+STORM.
  • the background removal capability of SOFI will be confirmed by numerical simulation.
  • Matlab program commercial math software from Math Works, USA
  • 20 randomly flashing light-emitting points are first generated in the focal plane, and then 30,000 point light sources are randomly generated in the defocusing area (1.5 micrometers to 4 micrometers).
  • the average illumination time of each illumination point is 2 frames, and a total of 10 frames are generated.
  • This simulation reproduces the environment when using brightfield fluorescence microscopy.
  • the 10 frames of images are processed directly using the positioning program.
  • the image is denoised using SOFI and then processed using positioning software.
  • the self-made microscope is built on a fluorescent brightfield microscope (preferably Nikon Ti-E) with a large numerical aperture lens inverted, which preferably includes 60x CFI Plan Flour and 100x CFI APO TIRF (Nikon objective).
  • the filter set is: fluorescence filter: Semrock FF01-440/521/607/700-25, beam splitter: Semrock FF410/504/582/669-25-36.
  • the camera used is: Andor ixon3EMCCD.
  • Laser illumination is provided by a 656 nm solid-state laser (illumination intensity of 0.5-0.8 kW per square centimeter) and a 405 nm semiconductor laser (illumination intensity of 8 watts per square centimeter). The laser intensity is just about two frames per dye molecule.
  • sample (1) a 100-times lens was used and recorded at a frequency of 20 Hz, and a total of 30,000 frames were recorded.
  • sample (2) the experiment used a 60x lens to bring the focal plane to a depth of 15 microns. Similarly, 40,000 frames are recorded at a frequency of 30 Hz.
  • the SOFI and positioning algorithms used in the experiments came from the localizer (an open source program).
  • Figures 7a-7i compare confocal microscopy (Confocal), SOFI+LM, and different layers of mitochondrial outer membrane images obtained using LM alone. These images clearly demonstrate the layering capabilities of SOFI+LM and the increased resolution. Compared to confocal microscopy and LM, SOFI+LM clearly demonstrates its imaging quality advantages at a depth of 12 microns (um). Even at the weaker 8 micron level of the background, SOFI+LM still outperformed LM. At a more subtle level, LM has outperformed SOFI+LM by using total internal reflection to remove the background.
  • FIG. 8a and 8b show confocal images of a multifoam structure generated by confocal microscopy. Due to the limitations of horizontal and vertical resolution, it is not seen from it. Small structure.
  • Figure 8e shows an image obtained using LM alone. As in the case of numerical simulation, the detection efficiency of this method is greatly limited due to the background, so that no meaningful image can be reconstructed. In contrast, when SOFI+LM is used to process the same raw data, as shown in Fig.
  • the final reconstructed image quality is substantially improved, and the multi-bubble cross section can be clearly seen.
  • the ring structure More importantly, compared to the electron microscopy image of the immunogold label, the image provided by SOFI+LM is consistent with it, and more continuously and stably reflects the position of the marker. As shown in Fig. 8c, the multi-foam surface (black dots in the figure) marked by gold particles and the image of SOFI+LM are completely identical. Figure 8c shows that the resolution of SOFI+LM has reached 40 nm.
  • the SOFI+LM approach has extended the use of localization microscopy from a few microns on the cell surface to a few tens of microns. This approach can be used to obtain near-electron microscope resolutions to resolve intracellular structures and protein distributions with simpler equipment, without the need for complex sample preparation methods, and with the possibility of performing in vivo imaging.
  • Example 2 Prism Light Slice Microscopy:
  • the second part of this patent is a novel method of producing light sheet illumination that can be easily configured on any inverted microscope.
  • Fig. 9a by adding a prism in front of the illumination objective, not only the direction of the illumination light can be changed, it is perpendicular to the detection objective and provides a large illumination field of view, and the thickness of the light sheet can be further reduced.
  • the method of the present invention can be better combined with a commercial microscope than the LSBM, so that the application is simpler and the imaging resolution can be further improved by using a large numerical aperture oil immersion objective.
  • Figure 9b is a deflection angle of the light sheet after the prism is added to the present invention.
  • Fig. 9a is a deflection angle of the light sheet after the prism is added to the present invention.
  • the compression ratio is the ratio of the thickness of the light sheet in the optical path without the prism to the thickness at the time of the prism, and the compression ratio is 2 times when the incident angle is 70 degrees.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Optics & Photonics (AREA)
  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Dispersion Chemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Microscoopes, Condenser (AREA)

Abstract

提供了一种深层细胞超分辨率成像的方法，***及棱镜光薄片装置。该成像方法使用超分辨率光学波动显微技术（SOFI）和超分辨率定位显微技术（LM），能够通过计算机运算消除非关联的背景噪音来获取细胞深层的超分辨率图像，可以直接应用于普通的荧光显微镜并且无需要修改其原有的光学结构。该成像***将棱镜光薄片装置加载于倒置显微镜上，通过物理手段减少背景噪音并通过定位显微技术来得到细胞深层的超分辨率图像，可以直接加载于传统的倒置的荧光显微镜上。

Description

深层细胞超分辨率成像的方法、***及棱镜光薄片装置 技术领域
本发明涉及光学显微技术和生物细胞成像技术领域，尤其涉及一种深层细胞超分辨率成像的方法、***及棱镜光薄片装置。
背景技术
超分辨率(Super-Resolution)定位显微术可提供近分子级别的分辨率。这一技术的发展极大地推进了人们对细胞内结构的理解。但是，在本质上这一技术依赖于对单个荧光标记分子的成像和精确定位，并且需极高的图像SNR(Signal to Noise Ratio，信噪比)来保证定位的精度。定位显微术(Localization Microscopy，LM)通常使用全内反射(Total Internal Reflection，TIRF)或近全内反射(near-TIRF)方法，通过限制照明区域的深度来降低背景噪音，因此该方法的成像区域受限于样品载玻片表面以上几微米以内。
目前，对于高自发荧光或结构密集的细胞或者组织的深层成像仍然依赖于其他技术如共聚焦显微镜(Confocal Laser Scanning Microscope，CLSM)。在另一方面，近期开发的超分辨率光学波动显微术(Localization Microscopy，LM)通过应用计算高阶相关度来分析每个荧光标记分子的信号波动，从而减小每个分子的点扩散函数(point spread function)，并以此来提高分辨率。此技术的分辨率和高阶相关运算的阶次开方成正比，因此理论上可通过提高运算阶次来进一步增加分辨率，但是在现实中其分辨率极限为100纳米。另外，由于荧光标记物的信号只和自身相关，和其他层面分子所产生的背景信号并无相关性，该方法所使用的高阶相关运算可有效的消除背景噪音，从而实现光学切片成像。
目前切片成像技术在生物学研究中扮演着重要角色。扫描共聚焦显微镜(以及转盘共聚焦)作为主要的切片成像手段已经存在了25年。这一技术利用小孔来滤除非成像平面的背景信号。目前已有的受激辐射损耗显微技术就是基于扫描共聚焦显微镜开发而成的超分辨率显微技术。其分辨率可达50纳米。这一技术的局限在于所拍摄样品必须能够承受极高的激光照射因此限制了可供拍摄样品的种类。
利用光片技术从样品的侧向照明同样可实现切片成像，和共聚焦技术相比，该方法可在很大程度上减小细胞所承受的光强。另外已有文献声称结合定位显微术和光片技术实现超分辨率成像并取得40纳米的分辨率。但是目前受限于由于照明与探测的几何结构问题，这一技术仍然很难被应用于拍摄更普遍的生物样品。
综上可知，现有技术在实际使用上显然存在不便与缺陷，所以有必要加以改进。
发明内容
针对上述的缺陷，本申请的目的在于提供深层细胞超分辨率成像的方法、***及棱镜光薄片装置，其能够在不改变现有显微镜结构的基础上，实现深层细胞超分辨率成像。
在本申请的第一方面中，提供了一种深层细胞超分辨率成像的方法。该方法可应用于超分辨率定位显微镜中，并包括：
将荧光标记物连接于待观察的样品，并将所述样品浸泡在成像缓冲液中；
获取所述荧光标记物在所述样品的深层闪烁的荧光信号；
通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音；
通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置；
根据所述荧光信号的位置构建所述样品的深层细胞超分辨率图像。
在根据本申请的一个实施方式中，所述通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音的步骤包括：通过二阶或高阶相关度分析算法处理所述荧光信号，以去除所述荧光信号中非关联的所述背景噪音；而所述通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置的步骤包括：通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的所述荧光信号的中心位置。
在根据本申请的一个实施方式中，所述通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音的步骤进一步包括：将所述荧光信号记录成动画；将所述动画重组为一系列的动画组，每个所述动画组包含预定个数的帧；以及通过所述超分辨率光学波动显微算法对每个所述动画组进行二阶相关度运算， 以去除所述荧光信号中非关联的所述背景噪音。
经降噪处理的动画可被组重组为新动画。在通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置的步骤则进一步包括：对所述新动画中的每一帧进行分析，通过与预定的点扩散函数相匹配的尺寸定位图像来识别非重叠的所述荧光信号；获取每个所述非重叠的荧光信号；以及根据所述超分辨率定位显微算法定位出每个所述非重叠的荧光信号的中心位置，以此构建相应的分辨率定位显微图像。
例如，所述根据荧光信号的位置构建所述样品的深层细胞超分辨率图像的步骤还可包括：将每个所述荧光信号的中心位置叠加以构建所述样品的深层细胞超分辨率图像。
在本申请的第二方面中，提供了一种深层细胞超分辨率成像的***。该***应用于超分辨率定位显微镜中，并可包括：
信号获取模块，用于获取所述样品深层的荧光标记分子闪烁的荧光信号，所述样品预先与荧光标记物连接并浸泡在成像缓冲液中；
SOFI模块，用于通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音；
LM模块，用于通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置；
成像模块，用于根据所述荧光信号的位置构建所述样品的深层细胞超分辨率图像。
根据本申请的一些实施方式，所述SOFI模块用于通过二阶或高阶相关度分析算法处理所述荧光信号，以去除所述荧光信号中非关联的所述背景噪音；所述LM模块用于通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的所述荧光信号的中心位置。
在本申请的第三方面中，提供了深层细胞超分辨率成像的方法。根据本申请一些实施方式，该方法可包括：将棱镜光薄片装置设置于倒置显微镜的顶部，通过物理手段消除非关联的背景噪音；在对样品进行定位显微时，通过超分辨率定位显微算法计算荧光标记分子的位置，并根据所述荧光标记分子的位置构 建所述样品的深层细胞超分辨率图像。
根据本申请的一些实施方式，所述将棱镜光薄片装置设置于倒置显微镜的顶部的步骤包括：将所述棱镜光薄片装置设置于所述倒置显微镜的顶部，并使得所述棱镜光薄片装置与水平方向的样品平台之间存在一预定角度。所述棱镜光薄片装置至少包括照明物镜和安装在所述照明物镜上的棱镜，其中所述棱镜使所述照明物镜的照明光改变方向，并压缩所述照明光的光束厚度。
在本申请的第四方面中，提供了一种棱镜光薄片装置，其中，所述棱镜光薄片装置设置于倒置显微镜的顶部，并包括有第一准直正透镜、负透镜、第二准直正透镜、柱面镜和照明物镜，其特征在于，所述棱镜光薄片装置还包括安装在所述照明物镜上的棱镜，用于改变照明物镜的照明光的方向，使所述照明光垂直于照明物镜并增加照明视场，并压缩所述照明光的光束厚度。
本发明的第一种技术方案结合了超分辨率光学波动显微术(Super-resolution Optical Fluctuation Microscopy，SOFI)和超分辨率定位显微术(Localization Microscopy，LM)，能够通过计算机运算消除非关联的背景噪音来获取细胞深层的超分辨率图像，可以直接应用于普通的荧光显微镜并且无需修改其原有的光学结构。本发明的第二种技术方案使用棱镜光薄片装置加载于倒置显微镜上，通过物理手段减少背景噪音并通过定位显微术来得到细胞深层的超分辨率图像，可以直接加载于传统的倒置荧光显微镜。
附图说明
图1是本发明第一种深层细胞超分辨率成像的方法流程图；
图2是本发明第一种深层细胞超分辨率成像的优选方法流程图；
图3是本发明第一种深层细胞超分辨率成像的***结构示意图；
图4是本发明第二种深层细胞超分辨率成像的方法流程图；
图5是本发明棱镜光薄片装置的结构示意图；
图6a～图6b是本发明使用SOFI来消除背景的仿真结果的示意图；
图7a～图7i是本发明BSHSY-5Y细胞中线粒体外膜的层析成像；
图8a～图8f是本发明超分辨率定位显微镜对烟草BY-2细胞中MVBs成像 和用超分辨率定位显微镜与SOFI结合成像结果的比较示意图；
图9a是本发明棱镜光薄片显微镜的结构图；
图9b是本发明加入棱镜后光薄片的偏折角度的示意图。
图9c是本发明加入棱镜后光薄片的偏折角度和压缩系数的仿真结果示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，在本文中所描述的具体实施方式仅仅用以解释本申请的一些示例，并不用于限定本发明。
本发明第一种技术方案结合了超分辨率光学波动显微术(Super-resolution Optical Fluctuation Microscopy，SOFI)和超分辨率定位显微术(Localization Microscopy，LM)，能够通过计算机运算由于离焦的荧光分子产生的非相干背景，来获取细胞深层的超分辨率图像，该技术可直接应用于普通的荧光显微镜并且无需修改其原有的光学结构。
本发明第二种技术方案使用棱镜光薄片照明的方法。光薄片照明方法可从物理角度去除离焦的荧光分子信号，因此与超分辨率定位显微术结合适用于深层细胞超分辨率成像。这一照明结构同样可直接加载于传统的倒置荧光显微镜。这些方法可扩大超分辨率定位显微术的适用范围，使其从细胞表面几微米的范围扩大到细胞深层几百微米，并且获知细胞结构和蛋白的位置的能力可达到TEM(Transmission electron microscope，透射电子显微镜)相近的范围，但与TEM相比更简单容易实现。
图1是根据本申请一个实施方式的深层细胞超分辨率成像的方法流程图，该方法应用于超分辨率定位显微镜中，并包括以下的步骤。
在步骤S101中，将荧光标记物连接待观察的样品，并将样品(例如细胞)浸泡在成像缓冲液中。
在一个实施方式中，使用普通免疫荧光标记方法将荧光标记物(即，荧光标记分子)连接需要观察的生物样品，再将样品浸泡在成像缓冲液中。在免疫 荧光标记方法中，可例如使用Alexa 647或者Alexa 750荧光团标记样品。成像缓冲液成分可例如包括：三(2-羧乙基)膦盐，葡萄糖氧化酶，葡萄糖，过氧化氢酶，以及环辛四烯。
在步骤S102中，获取样品深层(例如细胞深层)的荧光标记分子闪烁的荧光信号。因为荧光标记分子(在本文中还称为“荧光标记物”)会标记细胞中所有符合特异性的结构(如：细胞微管结构)，但是处于深层的结构(同样被荧光分子标记)会表现出较强的背景。一般***为了降低背景，通过特殊手段(如全内反射显微术)仅仅采集细胞表层结构的荧光信号。而在该方法中，为了实现细胞深层结构成像而采集细胞深层的荧光信号。荧光分子仅会在有成像缓冲液的条件下发出闪烁的信号。
在步骤S103中，通过SOFI算法消除荧光信号的背景噪音。在一个实施方式中，通过二阶或高阶相关度分析算法处理荧光信号，以去除荧光信号中非关联的背景噪音。
在步骤S104中，通过LM算法计算经降噪处理的荧光信号的位置。在一个实施方式中可例如通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的荧光信号的中心位置。所述荧光信号的中心位置即荧光标记分子的精确位置。
在步骤S105中，根据荧光信号的位置构建样品的深层细胞超分辨率图像。
图2是本发明第一种深层细胞超分辨率成像的优选方法流程图，所述方法应用于超分辨率定位显微镜中，包括的步骤有：
步骤S201，将荧光标记物连接待观察的样品，并将样品浸泡在成像缓冲液中；
步骤S202，获取样品深层的荧光标记分子闪烁的荧光信号；以及
步骤S203，将荧光信号记录成动画。
步骤S204，将动画重组为一系列的动画组，每个动画组包含预定个数的帧；步骤S205，通过SOFI算法对每个动画组进行二阶相关度运算，以去除荧光信号中非关联的背景噪音。
根据本申请的一个实施方式，上述方法可选地还可包括：步骤S206，将经 降噪处理的动画组重组为新动画。这样，在步骤S207中，对新动画中的每一帧进行分析，通过与预定的点扩散函数相匹配的尺寸定位图像来识别非重叠的荧光信号，接着在步骤S208，获取每个所述非重叠的荧光信号，而在步骤S209中，根据所述超分辨率定位显微算法定位出每个所述非重叠的荧光信号的中心位置，最后在步骤S210中根据定位出的中心位置构建相应的分辨率定位显微图像。
这是一种基于软件的方法，不需对于现有的LM镜有其他硬件方面的改动。
图3是根据本申请一些实施方式(第一种技术)的深层细胞超分辨率成像的***结构示意图，所述***100应用于超分辨率定位显微镜中，并包括有信号获取模块10、SOFI模块20和LM模块30。
信号获取模块10用于获取样品深层的荧光标记分子闪烁的荧光信号，样品预先与荧光标记物连接并浸泡在成像缓冲液中。在一个实施方式中，使用普通免疫荧光标记方法将荧光标记物连接需要观察的生物样品，再将样品浸泡在成像缓冲液中，所述免疫荧光标记方法优选使用Alexa 647或者Alexa 750荧光团标记样品；所述成像缓冲液成分优选包括：三(2-羧乙基)膦盐，葡萄糖氧化酶，葡萄糖，过氧化氢酶，以及环辛四烯。
SOFI模块20用于通过SOFI算法消除荧光信号的背景噪音。在本申请的一个实施方式中，所述SOFI模块20用于通过二阶或高阶相关度分析算法处理荧光信号，以去除荧光信号中非关联的背景噪音。
LM模块30用于通过LM算法计算经降噪处理的荧光信号的位置。在本申请的一个实施方式中，所述LM模块30用于通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的荧光信号的中心位置。所述荧光信号的中心位置即荧光标记分子的精确位置。
如图所示，***100还可包括用于根据荧光信号的位置构建样品的深层细胞超分辨率图像的成像模块40。
在本申请的一个实施方式中，所述SOFI模块20进一步包括：
记录子模块21，用于将荧光信号记录成动画；
第一重组子模块22，用于将动画重组为一系列的动画组，每个动画组包含预定个数的帧；
运算子模块23，用于通过SOFI算法对每个动画组进行二阶相关度运算，以去除荧光信号中非关联的背景噪音；以及
第二重组子模块24，用于将经降噪处理的动画组重组为新动画。
根据本申请的一个实施方式的LM模块30可进一步包括识别子模块31、定位子模块32和第一构建子模块33。识别子模块31用于对新动画中的每一帧进行分析，通过与预定的点扩散函数相匹配的尺寸定位图像来识别非重叠的荧光信号。定位子模块32用于获得每个所述非重叠的荧光信号，并根据所述超分辨率定位显微算法定位出每个所述荧光信号的中心位置。所述荧光信号的中心位置即荧光标记分子的精确位置。第一构建子模块33根据定位出的中心位置构建相应的分辨率定位显微图像
成像模块40则将每个荧光信号的中心位置叠加以构建样品的深层细胞超分辨率图像。
图4是根据本申请另一实施方式的(上述第二种技术)深层细胞超分辨率成像的方法流程图，包括步骤有：
步骤S401，将棱镜光薄片装置设置于任何现有的倒置显微镜的顶部，通过物理手段消除非关联的背景噪音；以及
步骤S402，在对样品进行定位显微时，通过LM算法计算荧光标记分子的位置，并根据所述荧光标记分子的位置构建所述样品的深层细胞超分辨率图像。
如图5所示，所述棱镜光薄片装置包括：第一准直正透镜101；负透镜102；第二准直正透镜103；柱面镜104；照明物镜105。其特征在于，所述棱镜光薄片装置还包括安装在照明物镜105上的棱镜106，用于改变照明物镜的照明光的方向，使照明光垂直于照明物镜并增加照明视场，并压缩照明光的光束厚度。所述棱镜光薄片装置可与水平方向的样品平台之间存在预定角度。方向改变的 角度和压缩程度都可通过调整棱镜106的方向来调节。
实例一：SOFI+LM：在实施定位显微时，首先用较强的激发光使样品上的荧光标记分子开始闪烁，并且用具有电子倍增功能的CCD(Charge Coupled Device)相机记录将这些闪烁的信号记录成动画。然后定位软件通过以下步骤对动画的每一帧进行分析：(1)通过用与期望的点扩散函数相容的尺寸定位图像来识别非重叠的闪烁分子，根据***的点扩散函数识别单分子的荧光信号；(2)使用高斯拟合或其他方法获得每个闪烁的荧光标记分子的峰位置，从中构建LM图像，对识别出的荧光信号进行高斯拟合并找到信号的中心从而定位出每一个荧光标记分子的精确位置。将这些位置叠加便可得到最终重构出的超分辨率图像。但是，当进行细胞深层成像时，来自其他层面的荧光信号(背景噪音)的强度会等于或大所探测层面的单分子荧光，从而改变焦平面单荧光分子图像的形状，导致算法在步骤(1)时无法找到需定位的区域，致使最终重构出的图像稀疏而不连续。
为了解决这一问题，将记录有闪烁荧光信号的动画重组为一系列的组，每个组包含5到15帧(帧数由信号强度和闪烁速度决定)，然后用SOFI对每个组进行二阶相关运算。这一运算可有效的去除或减少随机且不相关的背景信号。然后所有经过处理的图像重组为新的动画，并送入定位程序进行分析。原则上在LM处理的步骤(1)中可识别更多闪烁的荧光信号，并且将在步骤(2)中进一步被定位到纳米级精度，从而得到细胞深层超分辨率的切片图像而无需特殊的光学设置。这种方法基本上被应用于SOFI以在LM算法前去除背景，将该方法称为SOFI+STORM。
首先，将通过数值模拟证实SOFI的背景去除能力。在Matlab程序(美国Math Works公司出品的商业数学软件)中，首先在焦平面生成20个随机闪烁的发光点，然后在离焦区域(1.5微米至4微米范围)随机生成30000个点光源。每个发光点的平均发光时间为2帧，一共生成10帧。这一模拟很好的重现了使用明场荧光显微时的环境。第一步，直接用定位程序处理这10帧图像。第二步，先使用SOFI对图像进行降噪再使用定位软件进行处理。结果显示，对于未经SOFI处理的图像，如图6a，离焦信号严重干扰了定位程序，因此每帧最多只有一个位置可被检测出。作为对比，通过SOFI进行降噪处理，程序可识别并定位 更多的位置，如图6b。而且SOFI+LM能在对厚样品的超分辨率成像中提供更高的效率。
在实验中，对两种样品进行了测试：(1)SHSY-5Y(人神经母细胞瘤)细胞系中的线粒体外膜和(2)烟草根BY-2细胞中的多泡体。两种样品都用Alexa-647进行荧光标记，并浸泡于标准的定位显微缓冲液中。首先使用共聚焦显微镜对某一区域进行拍照用来和SOFI+LM方法进行对比。对于线粒体样品，扫描层面为样品表面向上12微米，10微米和8微米处。对于多泡体样品，扫描层面为细胞表面内15微米深。所有的共聚焦显微图片由蔡司LSM7DUO显微镜拍摄。然后，用自制的定位显微镜对以上区域层面进行拍摄。自制显微镜建于一个配有大数值孔径镜头倒置的荧光明场显微镜(优选尼康Ti-E)，所述大数值孔径镜头优选包括60倍CFI Plan Flour(平场荧光物镜)以及100倍CFI APO TIRF(尼康物镜)。所配滤镜组为：荧光滤镜：Semrock FF01-440/521/607/700-25，分束镜：Semrock FF410/504/582/669-25-36。所使用相机为：Andor ixon3EMCCD。激光照明由一台656纳米固体激光器提供(照明强度为0.5-0.8千瓦每平方厘米)以及一台405纳米半导体激光器(照明强度为8瓦每平方厘米)。激光强度为恰好时每个染料分子的发光时间为大约两帧。对于样品(1)，使用100倍镜头并以20赫兹的频率进行记录，一共记录30000帧。对于样品(2)，实验使用60倍镜头从而使焦平面达到15微米的深度。同样用30赫兹的频率记录40000帧。实验所使用的SOFI以及定位算法来自于localizer(一种开源程序)。
图7a～7i对比了共聚焦显微镜(Confocal)，SOFI+LM以及单独使用LM所得的不同层面线粒体外膜图像。这些图像清楚的展示了SOFI+LM的分层能力以及对分辨率的提高。相较于共聚焦显微镜和LM，在12微米(um)深的层面，SOFI+LM明显的展示了其成像质量的优势。即便是在背景较弱的8微米深的层面，SOFI+LM依旧胜过了LM。在更潜的层面，LM由于使用了全内反射来去除背景，其成像质量终于胜过了SOFI+LM。
为了进一步展示SOFI+LM的性能，用其成像在烟草BY-2型细胞内15微米的深处的多泡体。由于这一结构具有较大尺寸和较强的自发荧光，人们很难使用定位显微对其成像。图8a和8b展示了共聚焦显微镜(Confocal)生成的多泡体结构的共聚焦图像。由于横向和纵向分辨率的限制，从其中并不能看出 细小的结构。图8e展示了单独使用LM所得到的图像。正如数值模拟中的情形，由于背景的缘故，这一方法的探测效率被大大限制，从而不能重建出任何有意义的图像。与之形成对比的是，当使用SOFI+LM对同样的原始数据进行处理，如图8f所示，所最终重建出的图像质量有了本质的提高，并且可清晰的看到多泡体横截面的环状结构。更为重要的是，对比于免疫金标记的电子显微镜图像，SOFI+LM所提供的图像与之一致，且更连续稳定的体现了标记物所处的位置。如图8c所示，由金颗粒所标记的多泡体表面(图中黑点)和SOFI+LM的图像完全一致。图8c表明SOFI+LM的分辨率已达40纳米。总而言之，SOFI+LM这一方法已经将定位显微术的使用范围从细胞表面几微米扩展到几十微米的区域。这一手段可用更简单的器材取得近似电子显微镜的分辨率来解析细胞内结构和蛋白分布，无需复杂的样品制备手段，并且有着实施活体成像的可能性。
实例二：棱镜光薄片显微镜：本专利的第二部分为一种产生光薄片照明的新型方法，这种方法可简单的配置在任何倒置显微镜上。如图9a所示，通过在照明物镜之前加入一个棱镜，不仅可改变照明光的方向，使其垂直于探测物镜并提供一个大的照明视场，并且可进一步使光片的厚度下降。而且，与LSBM相比，本发明的方法可与商用显微镜更好的结合在一起，所以应用起来更加简单，成像分辨率可通过使用一个大数值孔径的油浸物镜来得到进一步的提高。图9b是本发明加入棱镜后光薄片的偏折角度。图9c为理论计算的结果，其中压缩率为光路中没有棱镜时光薄片的厚度与有棱镜时厚度之比，在入射角度为70度时，压缩率为2倍。通过进一步的提高入射角度数并且调整棱镜的位置，可引入一个非常薄的光薄片照明，理论上可达到一微米以下，照明的方向平行于显微镜平台，所以可用现有的检测物镜来观测样品。这种结构在对较厚的样品进行单分子超分辨率成像中非常有用。在倒置显微镜中加入这种装置，并使用垂直方向移动的压电陶瓷，可较容易的实现深层细胞光学层析。将棱镜光薄片照明***与奥林巴斯倒置显微镜结合的***，可利用光纤引入照明光，需注意的是这种棱镜的结构可使光薄片非常靠近样品。
当然，本发明还可有其它多种实施例。本领域技术人员在不背离本发明精神及公开内容教导的情况下，可对上述的实施方式作出各种改变和变形，但这些改变和变形都应属于本发明所附的权利要求的保护范围。

Claims (14)

  1. 一种深层细胞超分辨率成像的方法，应用于超分辨率定位显微镜中，其中，所述方法包括：
    将荧光标记物连接于待观察的样品，并将所述样品浸泡在成像缓冲液中；
    获取所述荧光标记物在所述样品的深层闪烁的荧光信号；
    通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音；
    通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置；以及
    根据所述荧光信号的位置构建所述样品的深层细胞的超分辨率图像。
  2. 根据权利要求1所述的方法，其中，所述通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音的步骤包括：通过二阶或高阶相关度分析算法处理所述荧光信号，以去除所述荧光信号中非关联的所述背景噪音；
    所述通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置的步骤包括：通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的所述荧光信号的中心位置。
  3. 根据权利要求1或2所述的方法，其中，
    所述通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音的步骤进一步包括：
    将所述荧光信号记录成动画；
    将所述动画重组为一系列的动画组，每个所述动画组包含预定个数的帧；以及
    通过所述超分辨率光学波动显微算法对每个所述动画组进行二阶相关度运算，以去除所述荧光信号中非关联的所述背景噪音。
  4. 根据权利要求3所述的方法，其中，经降噪处理的所述动画组重组为新动画，以及计算经降噪处理的所述荧光信号的位置的步骤进一步包括：
    通过与预定的点扩散函数相匹配的尺寸对所述新动画中的每一帧中的图像进行定位，来识别所述荧光信号中非重叠的荧光信号；
    获取每个所述非重叠的荧光信号；以及
    根据所述超分辨率定位显微算法定位出每个所述非重叠的荧光信号的中心位置，以此构建相应的分辨率定位显微图像。
  5. 根据权利要求4所述的方法，其中，所述构建相应的分辨率定位显微图像的步骤包括：
    将每个所述非重叠的荧光信号的中心位置叠加以构建所述样品的深层细胞的相应的分辨率定位显微图像。
  6. 一种深层细胞超分辨率成像的***，应用于超分辨率定位显微镜中，其中，所述***包括：
    信号获取模块，用于获取样品的深层的荧光标记分子闪烁的荧光信号，所述样品预先与荧光标记分子连接并浸泡在成像缓冲液中；
    SOFI模块，用于通过超分辨率光学波动显微算法消除所述荧光信号的背景噪音；
    LM模块，用于通过超分辨率定位显微算法计算经降噪处理的所述荧光信号的位置；以及
    成像模块，用于根据所述荧光信号的位置构建所述样品的深层细胞超分辨率图像。
  7. 根据权利要求5所述的***，其中，所述SOFI模块用于通过二阶或高阶相关度分析算法处理所述荧光信号，以去除所述荧光信号中非关联的所述背景噪音；以及
    所述LM模块用于通过高斯拟合、最大相似度或者寻找质量中心算法计算经降噪处理的所述荧光信号的中心位置。
  8. 根据权利要求4或5所述的***，其中，所述SOFI模块进一步包括：
    记录子模块，用于将所述荧光信号记录成动画；
    第一重组子模块，用于将所述动画重组为一系列的动画组，每个所述动画组包含预定个数的帧；以及
    运算子模块，用于通过所述超分辨率光学波动显微算法对每个所述动画组 进行二阶相关度运算，以去除所述荧光信号中非关联的所述背景噪音。
  9. 根据权利要求8所述的***，其中，所述SOFI模块进一步包括：第二重组子模块，用于将经降噪处理的所述动画组重组为新动画；
    所述LM模块进一步包括：
    识别子模块，用于对所述新动画中的每一帧进行分析，通过与预定的点扩散函数相匹配的尺寸定位所述每一帧的图像来识别所述荧光信号中的非重叠的荧光信号；
    定位子模块，用于获得每个所述非重叠的荧光信号，并根据所述超分辨率定位显微算法定位出每个所述荧光信号的中心位置。所述荧光信号的中心位置即荧光标记分子的精确位置；以及
    第一构建子模块，根据定位出的中心位置构建相应的分辨率定位显微图像。
  10. 根据权利要求9所述的***，其中，所述成像模块用于将每个所述荧光信号的中心位置叠加以构建所述样品的深层细胞的所述相应的分辨率定位显微图像。
  11. 一种深层细胞超分辨率成像的方法，包括：
    将棱镜光薄片装置设置于倒置显微镜的顶部，以通过物理手段消除非关联的背景噪音；
    在对样品进行定位显微时，通过超分辨率定位显微算法计算所述样品中的荧光标记分子的位置；以及
    根据所述荧光标记分子的位置构建所述样品的深层细胞超分辨率图像。
  12. 根据权利要求11所述的方法，其中，所述将棱镜光薄片装置设置于倒置显微镜的顶部的步骤包括：
    将所述棱镜光薄片装置设置于所述倒置显微镜的顶部，并使得所述棱镜光薄片装置与水平方向的样品平台之间存在预定角度。
  13. 根据权利要求12所述的方法，其中，所述棱镜光薄片装置至少包括照 明物镜和安装在所述照明物镜上的棱镜，其中所述棱镜使所述照明物镜的照明光改变方向，并压缩所述照明光的光束厚度。
  14. 一种棱镜光薄片装置，其中，所述棱镜光薄片装置设置于倒置显微镜的顶部，并包括有第一准直正透镜、负透镜、第二准直正透镜、柱面镜和照明物镜，其特征在于，所述棱镜光薄片装置还包括安装在所述照明物镜上的棱镜，用于改变照明物镜的照明光的方向，使所述照明光垂直于照明物镜并增加照明视场，并压缩所述照明光的光束厚度。
PCT/CN2014/001138 2013-12-18 2014-12-17 深层细胞超分辨率成像的方法、***及棱镜光薄片装置 WO2015089910A1 (zh)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201361963905P 2013-12-18 2013-12-18
US61/963,905 2013-12-18
CN201410669870.8A CN104458683B (zh) 2013-12-18 2014-11-20 深层细胞超分辨率成像的方法、***及棱镜光薄片装置
CN201410669870.8 2014-11-20

Publications (1)

Publication Number Publication Date
WO2015089910A1 true WO2015089910A1 (zh) 2015-06-25

Family

ID=52905088

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2014/001138 WO2015089910A1 (zh) 2013-12-18 2014-12-17 深层细胞超分辨率成像的方法、***及棱镜光薄片装置

Country Status (2)

Country Link
CN (2) CN104458683B (zh)
WO (1) WO2015089910A1 (zh)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105319195A (zh) * 2015-11-30 2016-02-10 哈尔滨工业大学 一种超分辨结构探测阵列共焦荧光成像装置及其成像方法
US10908072B2 (en) 2016-12-15 2021-02-02 The Board Of Regents Of The University Of Texas System Total internal reflection and transmission illumination fluorescence microscopy imaging system with improved background suppression
CN112730352A (zh) * 2020-12-08 2021-04-30 北京林业大学 实时观察植物细胞核蛋白的结构、面积和空间分布的方法
CN115100033A (zh) * 2022-05-20 2022-09-23 浙江大学 一种荧光显微图像超分辨重建方法和装置以及计算设备

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016188143A1 (en) * 2015-05-22 2016-12-01 The Hong Kong University Of Science And Technology Methods and systems for generating non-diffracting light sheets for multicolor fluorescence microscopy
CN107478620A (zh) * 2017-05-31 2017-12-15 深圳大学 一种同时获取三维纳米定位和荧光寿命的方法及***
CN108095756B (zh) * 2017-11-30 2020-10-30 上海大学 一种基于sofi的超高分辨平面波超声成像方法
CN110246083B (zh) * 2019-05-10 2023-02-24 杭州电子科技大学 一种荧光显微图像超分辨率成像方法
CN111399208B (zh) * 2020-03-31 2022-04-26 上海澜澈生物科技有限公司 生物荧光样本的聚焦拍摄实现方法、显微镜和存储介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN200982860Y (zh) * 2006-12-05 2007-11-28 哈尔滨航空工业(集团)有限公司 叶片前缘曲率半径检查仪
US20090242801A1 (en) * 2008-03-31 2009-10-01 Deutsches Krebsforschungszentrum Method of fluorescence-microscopically imaging a structure in a sample with high three-dimensional spatial resolution
US20090242798A1 (en) * 2008-04-01 2009-10-01 The Jackson Laboratory 3D Biplane Microscopy
CN101655460A (zh) * 2009-08-28 2010-02-24 山西大学 用于快速光学跟踪单分子的方法及其装置
US20110249866A1 (en) * 2010-04-09 2011-10-13 The Regents Of The University Of Colorado Methods and systems for three dimensional optical imaging, sensing, particle localization and manipulation
CN103308496A (zh) * 2012-03-16 2013-09-18 徐涛 一种新型的超高分辨光电融合显微成像***

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1141063C (zh) * 1998-06-18 2004-03-10 中国科学院长春光学精密机械研究所 大视场无畸变指纹识别光学***
CN101435774B (zh) * 2008-12-23 2011-01-26 中国科学院上海光学精密机械研究所 双色单光子横向超分辨成像的方法和装置
WO2011106323A2 (en) * 2010-02-23 2011-09-01 The Board Of Trustees Of The University Of Illinois Photobleaching and intermittency localization microscopy
CN102008288B (zh) * 2010-12-17 2012-02-01 中国科学院光电技术研究所 一种线扫描共焦检眼镜的***和方法
CN102313723B (zh) * 2011-06-20 2014-06-25 河海大学 偏振光激发显微联合超分辨率重建的成像方法
CN102288589B (zh) * 2011-06-23 2013-05-08 深圳大学 单个荧光探针的定位方法、装置及超分辨成像方法、***
WO2013090360A2 (en) * 2011-12-15 2013-06-20 President And Fellows Of Harvard College High resolution dual-objective microscopy

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN200982860Y (zh) * 2006-12-05 2007-11-28 哈尔滨航空工业(集团)有限公司 叶片前缘曲率半径检查仪
US20090242801A1 (en) * 2008-03-31 2009-10-01 Deutsches Krebsforschungszentrum Method of fluorescence-microscopically imaging a structure in a sample with high three-dimensional spatial resolution
US20090242798A1 (en) * 2008-04-01 2009-10-01 The Jackson Laboratory 3D Biplane Microscopy
CN101655460A (zh) * 2009-08-28 2010-02-24 山西大学 用于快速光学跟踪单分子的方法及其装置
US20110249866A1 (en) * 2010-04-09 2011-10-13 The Regents Of The University Of Colorado Methods and systems for three dimensional optical imaging, sensing, particle localization and manipulation
CN103308496A (zh) * 2012-03-16 2013-09-18 徐涛 一种新型的超高分辨光电融合显微成像***

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
DEDECKER P ET AL.: "Localizer: fast, accurate, open-source, and modular software package for Superresolution microscopy", JOURNAL FO BIOMEDICAL OPTICS, vol. 12, no. 17, 3 December 2012 (2012-12-03), pages 126008 - 1 -126008-2 *
DERTINGER ET AL.: "Fast, background free, 3D super-resolution optical fluctuation imaging(SOFI)", PROC. NATL. ACAD. SCI., vol. 106, no. 52, 31 December 2009 (2009-12-31), pages 1 - 4 *
YING S HU ET AL.: "Light-sheet Bayesian microscopy enables deep- cell super-resolution imaging of heterochromatin in live human embryonic stem cells", OPTICAL NANOSCOPY, vol. 1, no. 2, 20 November 2013 (2013-11-20), pages 1 - 5 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105319195A (zh) * 2015-11-30 2016-02-10 哈尔滨工业大学 一种超分辨结构探测阵列共焦荧光成像装置及其成像方法
US10908072B2 (en) 2016-12-15 2021-02-02 The Board Of Regents Of The University Of Texas System Total internal reflection and transmission illumination fluorescence microscopy imaging system with improved background suppression
CN112730352A (zh) * 2020-12-08 2021-04-30 北京林业大学 实时观察植物细胞核蛋白的结构、面积和空间分布的方法
CN115100033A (zh) * 2022-05-20 2022-09-23 浙江大学 一种荧光显微图像超分辨重建方法和装置以及计算设备
CN115100033B (zh) * 2022-05-20 2023-09-08 浙江大学 一种荧光显微图像超分辨重建方法和装置以及计算设备

Also Published As

Publication number Publication date
CN107655812A (zh) 2018-02-02
CN104458683B (zh) 2018-02-02
CN104458683A (zh) 2015-03-25

Similar Documents

Publication Publication Date Title
WO2015089910A1 (zh) 深层细胞超分辨率成像的方法、***及棱镜光薄片装置
US10768402B2 (en) Microscopy of a tissue sample using structured illumination
US10477097B2 (en) Single-frame autofocusing using multi-LED illumination
US8089691B2 (en) Projection device for patterned illumination and microscopy
US20140353522A1 (en) Apparatus and methodology for flow fluorescence microscopic imaging
KR20100045964A (ko) 형광 초점변조 현미경 시스템 및 방법
US20140333750A1 (en) High resolution dual-objective microscopy
JP2010505094A (ja) 解像度を高めたルミネセンス顕微鏡検査
US10018817B2 (en) Adaptive optics for imaging through highly scattering media in oil reservoir applications
CN108227233B (zh) 基于光片结构光的显微层析超分辨率成像方法及***
Buranachai et al. Rapid frequency-domain FLIM spinning disk confocal microscope: lifetime resolution, image improvement and wavelet analysis
US20190331904A1 (en) Highly inclined swept tile (hist) imaging apparatus, methods, and applications
WO2021188839A1 (en) Single-shot autofocusing of microscopy images using deep learning
JP2021510850A (ja) 高空間分解能の時間分解イメージング法
US20180139366A1 (en) System and method for light sheet microscope and clearing for tracing
Qiao et al. Single-scan HiLo with line-illumination strategy for optical section imaging of thick tissues
TWI554740B (zh) 高速三維成像之光學系統
US20140022373A1 (en) Correlative drift correction
CN100516841C (zh) 一种可产生明场或宽场荧光光切片的方法
Zhang et al. Speckle illumination microscopy enables slide-free and non-destructive pathology of human lung adenocarcinoma
JP2005173561A5 (zh)
EP3423882B1 (fr) Miscroscopie optique non-linéaire quantitative
Shen et al. Fast structured illumination microscopy for deep tissue super-resolution imaging
KR20130121224A (ko) 고속 영상 촬영 장치 및 그 방법
Tortarolo et al. Focus-ISM for sharp and gentle super-resolved microscopy

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14871470

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14871470

Country of ref document: EP

Kind code of ref document: A1