WO2015089910A1

WO2015089910A1 - Deep-cell super-resolution imaging method and system and prism optical sheet apparatus

Info

Publication number: WO2015089910A1
Application number: PCT/CN2014/001138
Authority: WO
Inventors: 雷明德; 杜胜望; 赵腾; 王莹
Original assignee: 香港科技大学
Priority date: 2013-12-18
Filing date: 2014-12-17
Publication date: 2015-06-25
Also published as: CN107655812A; CN104458683B; CN104458683A

Abstract

A deep-cell super-resolution imaging method and system and a prism optical sheet apparatus are provided. The imaging method uses super-resolution optical fluctuation imaging (SOFI) and super-resolution localisation microscopy (LM), can acquire deep-cell super-resolution imaging by using a computer to eliminate background noise, and can be directly applied to an ordinary fluorescent microscope without requiring modification to the original optical structure. The imaging system loads the prism optical sheet apparatus onto an inverted microscope and uses a physical method to reduce the background noise, obtaining deep-cell super-resolution images by means of localisation microscopy, and can be directly loaded on to a traditional inverted fluorescent microscope.

Description

Method, system and prismatic light sheet device for deep cell super-resolution imaging

Technical field

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.

Background technique

Super-Resolution positioning microscopy provides near-molecular resolution. The development of this technology has greatly advanced the understanding of intracellular structures. However, in essence, this technique relies on imaging and precise positioning of a single fluorescently labeled molecule, and requires a very high image SNR (Signal to Noise Ratio) to ensure positioning accuracy. Localization Microscopy (LM) usually uses Total Internal Reflection (TIRF) or Near-TIRF to reduce background noise by limiting the depth of the illumination area. The area is limited to a few microns above the surface of the sample slide.

Currently, deep imaging of highly autofluorescent or densely packed cells or tissues still relies on other techniques such as Confocal Laser Scanning Microscope (CLSM). On the other hand, recently developed Super-resolution Optical Wave Microscopy (LM) analyzes the signal fluctuations of each fluorescently labeled molecule by applying higher-order correlations, thereby reducing the point spread function of each molecule. (point spread function), and use this to improve the resolution. The resolution of this technique is directly proportional to the order of the higher-order correlation operations, so theoretically the resolution can be further increased by increasing the order of operations, but in reality the resolution limit is 100 nm. In addition, since the signal of the fluorescent marker is only related to itself and has no correlation with the background signal generated by other layer molecules, the high-order correlation operation used in the method can effectively eliminate background noise, thereby realizing optical section imaging.

Currently, slice imaging technology plays an important role in biological research. Scanning confocal microscopy (and turntable confocal) have been used as the primary slice imaging method for 25 years. 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. In addition, the literature has claimed to achieve super-resolution imaging with a positioning microscopy and light film technology and achieve a resolution of 40 nm. However, due to the geometry problems of illumination and detection, this technique is still difficult to apply to the shooting of more common biological samples.

In summary, the prior art obviously has inconveniences and defects in practical use, so it is necessary to improve.

Summary of the invention

In view of the above drawbacks, 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.

In a first aspect of the present application, 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;

Obtaining a fluorescence signal of the fluorescent marker flickering in the deep layer of the sample;

Eliminating background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm;

Calculating a position of the fluorescence signal subjected to noise reduction processing by a super-resolution positioning microscopic algorithm;

A deep cell super-resolution image of the sample is constructed based on the location of the fluorescent signal.

In an embodiment in accordance with the present application, 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.

In an embodiment according to the present application, 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.

For example, 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.

In a second aspect of the present application, 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.

According to some embodiments of the present application, 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.

In a third aspect of the present application, a method of deep cell super-resolution imaging is provided. According to some embodiments of the present application, the method 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.

According to some embodiments of the present application, 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.

In a fourth aspect of the present application, there is provided 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. 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. .

DRAWINGS

1 is a flow chart of a method for super deep resolution imaging of a deep cell of the present invention;

2 is a flow chart of a preferred method for the first deep cell super-resolution imaging of the present invention;

3 is a schematic structural diagram of a first deep cell super-resolution imaging system of the present invention;

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.

detailed description

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the 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. 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.

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.

In 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.

In one embodiment, a fluorescent label (ie, a fluorescent labeling molecule) is attached to a biological sample to be observed using a conventional immunofluorescent labeling method, and the sample is immersed in an imaging buffer. Immunization In the fluorescent labeling method, 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.

In step S102, a fluorescent signal of the fluorescent labeling molecule of the deep layer (for example, the deep layer of the sample) is obtained. Because 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. In general, in order to reduce the background, only the fluorescence signal of the cell surface structure is collected by special means such as total internal reflection microscopy. In this method, a deep cell fluorescence signal is acquired in order to achieve deep cell structure imaging. Fluorescent molecules only emit a scintillation signal in the presence of imaging buffer.

In step S103, the background noise of the fluorescent signal is eliminated by the SOFI algorithm. In one embodiment, the fluorescent signal is processed by a second order or higher order correlation analysis algorithm to remove uncorrelated background noise in the fluorescent signal.

In step S104, the position of the fluorescence signal subjected to the noise reduction processing is calculated by the LM algorithm. In one embodiment, 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.

In step S105, a deep cell super-resolution image of the sample is constructed based on the position of the fluorescent signal.

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;

In 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.

According to an embodiment of the present application, the method may optionally further include: step S206, The animation group of noise reduction processing is reorganized into a new animation. Thus, in 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.

This is a software-based approach that does not require additional hardware changes to existing LM mirrors.

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. In one embodiment, 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. In one embodiment of the present application, 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. In one embodiment of the present application, 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.

As shown, 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.

In an embodiment of the present application, the SOFI module 20 further includes:

Recording sub-module 21 for recording a fluorescent signal as an animation;

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 according to an embodiment of the present application 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.

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;

In 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.

As shown in FIG. 5, 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. The positioning software then 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. However, when performing deep imaging of cells, 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). When the area to be located cannot be found, the resulting reconstructed image is sparse and discontinuous.

To solve this problem, 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.

First, the background removal capability of SOFI will be confirmed by numerical simulation. In the 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. In the first step, the 10 frames of images are processed directly using the positioning program. In the second step, the image is denoised using SOFI and then processed using positioning software. The results show that for images that have not been processed by SOFI, as shown in Figure 6a, the defocus signal severely interferes with the positioning procedure, so only one position per frame can be detected. As a comparison, the noise reduction process is performed by SOFI, and the program can recognize and locate More locations, as shown in Figure 6b. And SOFI+LM provides greater efficiency in super-resolution imaging of thick samples.

In the experiment, two samples were tested: (1) mitochondrial outer membrane in SHSY-5Y (human neuroblastoma) cell line and (2) multivesicular in tobacco root BY-2 cells. Both samples were fluorescently labeled with Alexa-647 and immersed in standard localization microbuffers. First, a region was photographed using a confocal microscope to compare with the SOFI+LM method. For mitochondrial samples, the scanning plane is 12 microns up, 10 microns and 8 microns up the surface of the sample. For multivesicular samples, the scanning plane is 15 microns deep inside the cell surface. All confocal microscopy images were taken by the Zeiss LSM7DUO microscope. Then, the above-mentioned regional level was photographed with a self-made positioning microscope. 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. For the 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. For 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.

To further demonstrate the performance of SOFI+LM, it was used to image multivesicles deep within 15 microns of tobacco BY-2 type cells. Due to the large size and strong autofluorescence of this structure, it is difficult to image it using localization microscopy. Figures 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. 8f, 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. In summary, 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. As shown in 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. Moreover, 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. 9c is a theoretical calculation result in which 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. By further increasing the number of incident angles and adjusting the position of the prism, a very thin light sheet illumination can be introduced, theoretically up to one micron, and the illumination direction is parallel to the microscope stage, so the existing inspection objective can be used to observe the sample. This structure is very useful for single-molecule super-resolution imaging of thicker samples. The addition of such a device to an inverted microscope and the use of a piezoelectric ceramic moving in a vertical direction facilitates deep cell optical tomography. A system that combines a prismatic light sheet illumination system with an Olympus inverted microscope can introduce illumination light using an optical fiber. It should be noted that the structure of the prism allows the light sheet to be very close to the sample.

Of course, the invention is also capable of other various embodiments. A person skilled in the art can make various changes and modifications to the above-described embodiments without departing from the spirit and scope of the invention, and such changes and modifications are intended to fall within the scope of the appended claims.

Claims

A method for deep cell super-resolution imaging, which is applied to a super-resolution positioning microscope, wherein the method comprises:

A fluorescent label is attached to the sample to be observed, and the sample is immersed in an imaging buffer;

Obtaining a fluorescence signal of the fluorescent marker flickering in the deep layer of the sample;

Eliminating background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm;

Calculating a position of the fluorescence signal subjected to noise reduction processing by a super-resolution positioning microscopy algorithm;

A super-resolution image of the deep cells of the sample is constructed based on the location of the fluorescent signal.
The method of claim 1 wherein said step of canceling background noise of said fluorescent signal by a super-resolution optical fluctuation microscopy comprises: processing said fluorescent signal by a second-order or higher-order correlation analysis algorithm, Removing the background noise that is not associated in the fluorescent signal;

The step of calculating the position of the fluorescence signal processed by the noise reduction by the super-resolution positioning microscopy algorithm comprises: calculating the fluorescence signal of the noise reduction process by Gaussian fitting, maximum similarity or finding a quality center algorithm Central location.
The method according to claim 1 or 2, wherein

The step of eliminating background noise of the fluorescent signal by a super-resolution optical fluctuation microscopy algorithm further includes:

Recording the fluorescent signal as an animation;

Reorganizing the animation into a series of animation groups, each of the animation groups comprising a predetermined number of frames;

A second-order correlation operation is performed on each of the animation groups by the super-resolution optical fluctuation microscopy algorithm to remove the unrelated background noise in the fluorescent signal.
The method according to claim 3, wherein the animation group of the noise reduction processing is reorganized into a new animation, and the step of calculating the position of the fluorescence signal subjected to the noise reduction processing further comprises:

Identifying non-overlapping fluorescent signals in the fluorescent signal by locating an image in each of the new animations by a size that matches a predetermined point spread function;

Acquiring each of the non-overlapping fluorescent signals;

Positioning the center position of each of the non-overlapping fluorescent signals according to the super-resolution positioning microscopy algorithm to construct a corresponding resolution positioning microscopic image.
The method of claim 4 wherein said step of constructing a corresponding resolution localization microscopy image comprises:

The central location of each of the non-overlapping fluorescent signals is superimposed to construct a corresponding resolution localization microscopy image of the deep cells of the sample.
A deep cell super-resolution imaging system for use in a super-resolution positioning microscope, wherein the system comprises:

a signal acquisition module, configured to acquire a fluorescent signal of a deep fluorescent labeling molecule of the sample, wherein the sample is previously connected to the fluorescent labeling molecule 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 for calculating 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 system of claim 5 wherein said SOFI module is operative to process said fluorescent signal by a second order or higher order correlation analysis algorithm to remove said unrelated associated background noise in said fluorescent signal;

The LM module is configured to calculate a center position of the fluorescence signal subjected to noise reduction processing by Gaussian fitting, maximum similarity, or finding a quality center algorithm.
The system of claim 4 or 5, wherein the SOFI module further comprises:

a recording submodule for recording the fluorescent signal as an animation;

a first recombination sub-module for reorganizing the animation into a series of animation groups, each of the animation groups comprising a predetermined number of frames;

An operation submodule for each of the animation groups by the super resolution optical fluctuation microscopy algorithm A second order correlation operation is performed to remove the background noise that is not associated in the fluorescent signal.
The system of claim 8 wherein said SOFI module further comprises: a second recombination sub-module for reorganizing said reduced animation set into a new animation;

The LM module further includes:

Identifying a sub-module for analyzing each frame in the new animation, and locating the image of each frame by a size matching a predetermined point spread function to identify non-overlapping fluorescence in the fluorescent signal signal;

And a positioning sub-module for obtaining each of the non-overlapping fluorescent signals, and locating a center position of each of the fluorescent signals according to the super-resolution positioning micro-algorithm. The central position of the fluorescent signal, ie the precise location of the fluorescently labeled molecule;

The first building sub-module constructs a corresponding resolution positioning microscopic image according to the located central position.
The system of claim 9 wherein said imaging module is operative to superimpose a central location of each of said fluorescent signals to construct said corresponding resolution localized microscopic image of deep cells of said sample.
A method for deep cell super-resolution imaging, comprising:

Providing a prismatic light sheeting device on top of the inverted microscope to physically remove unrelated background noise;

Calculating the position of the fluorescently labeled molecules in the sample by a super resolution localization microscopy when positioning the sample for microscopy;

A deep cell super-resolution image of the sample is constructed based on the location of the fluorescently labeled molecule.
The method of claim 11 wherein said step of disposing the prismatic light sheeting device on top of the inverted microscope comprises:

The prismatic light sheeting device is placed on top of the inverted microscope such that there is a predetermined angle between the prismatic light sheeting device and the horizontally oriented sample platform.
The method of claim 12 wherein said prismatic light sheeting device comprises at least And a prism mounted on the illumination objective, wherein the prism redirects illumination light of the illumination objective 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, a second collimating positive lens, a cylindrical mirror, and an illumination objective, wherein The prismatic light sheet device further includes a prism mounted on the illumination objective for changing a direction of illumination light illuminating the objective lens, the illumination light being perpendicular to the illumination objective lens and increasing an illumination field of view, and compressing the illumination The beam thickness of the light.