A method and device for intelligent collection of fluorescence correlation spectroscopy with precise addressing

Abstract

The application provides a fluorescence correlation spectroscopy intelligent acquisition method and device with accurate addressing. The specific steps are as follows: step one, a microscope scanning module performs grating scanning imaging for once; step two, an intelligent acquisition module is used to accurately address a target region with a fluorescence signal, record the coordinates of the target region, and input the coordinates of the region into the microscope scanning module to realize accurate acquisition of the data of the target region. The fluorescence correlation spectroscopy intelligent acquisition method and device with accurate addressing provided by the application solve the randomness of manual selection of a region for single-point FCS data acquisition, and accurate addressing reduces the possibility of wrong estimation of information such as molecular diffusion dynamics due to address selection errors. Meanwhile, FPGA control of a scanning galvanometer realizes automatic acquisition of all accurate addressing coordinates.

Description

Technical Field

[0001] This invention relates to the field of fluorescence microscopy imaging and detection, and in particular to a precise addressing intelligent acquisition method and apparatus for fluorescence correlation spectroscopy. Background Technology

[0002] Fluorescence correlation spectroscopy (FCS), first proposed by Magde et al. in 1972, is a single-molecule detection technique based on optical microscopy. It measures the fluctuations in fluorescence signals caused by Brownian motion of fluorescent molecules within a very small detection region and analyzes these fluctuations using correlation functions to obtain the fluorescence correlation spectral curve. By studying the changes in fluorescence intensity over time or space, it can measure molecular dynamics, interactions, and structural changes in living cells. However, due to limitations in computer technology, optical technology, and optical detection techniques, its sensitivity is not high, and it has not attracted widespread interest. In 1993, the combination of laser confocal scanning technology and FCS (Confocal FCS) was first proposed. Using laser confocal technology, the sample irradiation volume and detection volume were reduced (to 10). -15 L (i.e., below 1 fL) eliminates the interference of scattered light on the test, greatly improving the signal-to-noise ratio of FCS. In 2005, based on the traditional single-point FCS, the grating image correlation spectroscopy (grating image correlation spectroscopy) technology was proposed. The grating image correlation spectroscopy technology is based on the fluorescence signal fluctuations generated by the Brownian motion of fluorescent molecules during grating scanning imaging. In addition to analyzing diffusion dynamics, the traditional single-point FCS can also improve two-dimensional super-resolution images.

[0003] Because traditional confocal FCS only allows data to be acquired once within a small detection volume, it is impossible to accurately describe the spatiotemporal characteristics of lipid or other biological process interactions in a single acquisition process. Furthermore, if the laser power is too high, phototoxicity and photobleaching are also problems that cannot be ignored. At the same time, this traditional acquisition method requires the selection of coordinates for single-point data acquisition. This random location selection will seriously affect the accuracy of the results, thus hindering the judgment of living cell biodynamics.

[0004] In traditional grating image correlation spectroscopy, a large portion of the region does not exhibit fluorescence or interaction during grating scanning imaging, yet scanning these regions takes a significant amount of time. This results in a deterioration in the temporal resolution of grating image correlation spectroscopy. Accurately locating the region of interest and improving the temporal resolution are challenges that current grating image correlation spectroscopy technology must address.

[0005] Therefore, it is necessary to provide a precise addressing intelligent acquisition method and device for fluorescence correlation spectroscopy to solve the above-mentioned technical problems. Summary of the Invention

[0006] This invention provides a precise addressing intelligent acquisition method and apparatus for fluorescence correlation spectroscopy, which solves the above-mentioned problems.

[0007] To solve the above-mentioned technical problems, the present invention provides;

[0008] The method for using a precisely addressed fluorescence correlation spectroscopy intelligent acquisition device includes the following specific steps:

[0009] Step 1: The microscope scanning module performs an addressable raster scanning image.

[0010] Step 2: Use the intelligent acquisition module to accurately locate the target area where the fluorescence signal appears, record the coordinates of the target area, and input the coordinates of these areas into the microscope scanning module to achieve accurate acquisition of data of the target area;

[0011] Step 3: The intelligent acquisition module synchronously controls the microscope detection module to transmit the acquired fluorescence signal to the computer. Based on the time or spatial autocorrelation function, the data is analyzed to obtain the fluorescence correlation spectrum (FCS) curve or the grating image correlation spectrum curve.

[0012] Furthermore, the intelligent acquisition module is controlled in real time by a field-programmable gate array (FPGA) development board. The intelligent acquisition module first acquires data from the coordinates obtained by precise addressing. Two acquisition methods are used: FCS analysis of single-point scanning of coordinates or correlation spectrum analysis of grating image of a region scanned by grating. The module also controls the X and Y axes of the scanning galvanometer to acquire data from all target areas.

[0013] Furthermore, after the microscope scanning system completes the addressing raster scan, the single-point scan and raster scan adopt the following scanning methods:

[0014] The first method involves recording coordinates sequentially in space. The FPGA development board first transmits the coordinates of the first pixel to the microscope scanning system, controls the deflection angles of the X and Y axes of the scanning galvanometer, fixes the scanning time, and uses a single-photon counter to detect the data. After the scan is completed, the FPGA development board transmits the second coordinate to the microscope scanning system. The above process is repeated until all precisely located coordinates on the entire two-dimensional pixel surface have been acquired. The acquired data is then transmitted to a computer for time autocorrelation analysis.

[0015] The second method involves recording coordinates arranged sequentially in space. The FPGA development board finds the 256×256 pixels containing the most addressable coordinates and performs a traditional raster scanning mode, starting from the beginning of the first row and scanning until the 256th pixel of the first row is scanned. Then, it moves to the second row and repeats the above process until all 256 rows are scanned. Then, it starts from the first pixel again and repeats the raster scanning. At least 10 complete 256×256 pixel images are scanned. The fluorescence signal is detected by a photodetector and the signal is sent to the computer for spatial autocorrelation analysis.

[0016] Furthermore, the intensity of the fluorescence signal within the detection micro-region at any given time... Fluctuation caused by changes It can be expressed by the formula:

[0017]

[0018] in, represent The total fluorescence intensity of the system at that time. The symbol represents the average value of the solution function over a certain time period; it detects fluorescence fluctuations within the micro-region. Related to the delay time, the normalized time autocorrelation function It can be represented as:

[0019]

[0020] Among them, delay time This represents the time that the fluorescent molecule remains in the detection volume. Represents at any given moment During the delay time Subsequent fluorescence intensity, Represents delay time The changes in molecular motion state within the volume were then detected, and the data collected by the single-photon counter was partitioned using the time autocorrelation function to obtain the FCS curve, thus exploring information such as molecular dynamics in living cells.

[0021] Furthermore, molecular dynamics information is extracted from the grating scan image using grating image correlation spectroscopy analysis. Grating image correlation spectroscopy analysis involves the following two steps:

[0022] The first step is background subtraction, which removes stationary or slowly moving objects. The average background subtraction method is used to subtract the average value of a set of consecutive images from the image that needs to be analyzed.

[0023] The second step involves understanding the hidden temporal structure between any two pixels under raster scanning. If there is a correlation between the fluorescence intensities of two pixels, this correlation can be revealed using a spatial autocorrelation function. This spatial autocorrelation function can be expressed as:

[0024]

[0025] in, Represents the fluorescence intensity at each pixel. and Representing the raster scan image direction and Changes in direction space The symbol represents the average value. The spatial autocorrelation function is used to analyze the acquired grating images to obtain the grating image correlation spectrum curves, and to explore information such as molecular dynamics in living cells.

[0026] Furthermore, the fluorescent probe on the photodetector can be any fluorescent dye such as quantum dots, organic dyes, or rare-earth upconversion nanoparticles. Rare-earth upconversion nanoparticles can interact with NOB... The reaction removes oleic acid ligands from the surface of the particles, allowing the rare earth upconversion nanoparticles to dissolve in water for application.

[0027] A precisely addressed fluorescence correlation spectrum intelligent acquisition device includes an excitation light generation module, a microscopic scanning module, an FPGA development board, and a photoelectric detection module;

[0028] The excitation light generation module includes an infrared continuous laser, a filter, a collimating beam expander, a half-wave plate, and a polarizer;

[0029] The microscopic scanning module includes a scanning galvanometer, a scanning lens, a tube mirror, a high-reflection, low-transmission dichroic mirror, and an objective lens;

[0030] The photoelectric detection module includes a focusing lens, a photodetector, and a single-photon counter.

[0031] Compared with related technologies, the precise addressing fluorescence correlation spectroscopy intelligent acquisition method and device provided by the present invention have the following beneficial effects:

[0032] 1. Compared with traditional confocal FCS technology, this invention utilizes a single-address grating scanning imaging method to accurately address the target area where fluorescence appears, and sequentially collects data from the target area through an intelligent acquisition module. This helps to solve the randomness of single-point FCS data acquisition when manually selecting the area. Precise addressing reduces the possibility of incorrect estimation of molecular diffusion dynamics and other information due to addressing errors. At the same time, FPGA-controlled scanning galvanometer realizes the automated acquisition of all precisely addressed coordinates.

[0033] 2. Compared with traditional grating image correlation spectroscopy, this invention utilizes a single grating scan imaging technique to precisely locate the target region exhibiting fluorescence. A smart acquisition module then performs a grating scan within the located region, shortening the imaging time. Furthermore, the smaller target region also reduces post-processing time. This helps to reduce the imaging time for regions of uninterested interest during the scanning process in traditional grating image correlation spectroscopy, thereby improving temporal resolution.

[0034] 3. Compared with traditional fluorescence correlation spectroscopy, the device of this invention, controlled by FPGA, solves some of the shortcomings of traditional confocal FCS and traditional grating image correlation spectroscopy. At the same time, the device can directly switch between the two acquisition modes without the need for adjustment and switching of optical path and instrument equipment. The device of this invention achieves intelligent acquisition only through FPGA, which is lower in cost and more feasible. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of the device of the present invention.

[0036] Figure 2 This is a schematic diagram illustrating the principle of the FCS intelligent acquisition method in this invention;

[0037] Figure 3 This is a schematic diagram illustrating the principle of the intelligent acquisition method for grating image correlation spectrum in this invention;

[0038] Figure 4 This is a set of Time-Trace trajectory diagrams obtained by FCS intelligent acquisition in this invention;

[0039] Figure 5 This is the FCS trajectory map obtained by FCS intelligent acquisition in this invention;

[0040] Figure 6 This is a two-dimensional laser scanning fluorescence image obtained by intelligent acquisition of grating image correlation spectrum in this invention;

[0041] Figure 7 This is a three-dimensional trajectory diagram of the grating image correlation spectrum obtained by intelligent acquisition of grating image correlation spectrum in this invention.

[0042] The following are the labels in the diagram: 1. Infrared continuous laser, 2. Filter, 3. Collimating beam expander, 4. Half-wave plate, 5. Polarizer, 6. Scanning galvanometer, 8. Scanning lens, 9. Tube mirror, 11. Objective lens, 12. High-reflection, low-transmission dichroic mirror, 13. Focusing lens, 14. Photodetector, 15. Single-photon counter, 16. FPGA development board. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] Please refer to the following: Figure 1-7 As shown, a precise addressing fluorescence correlation spectrum intelligent acquisition device includes an excitation light generation module, a microscopic scanning module, an FPGA development board 16, and a photoelectric detection module;

[0045] The excitation light generation module includes an infrared continuous laser 1, a filter 2, a collimating beam expander 3, a half-wave plate 4, and a polarizer 5;

[0046] The microscopic scanning module includes a scanning galvanometer 6, a scanning lens 8, a tube mirror 9, a high-reflection, low-transmission dichroic mirror 12, and an objective lens 11;

[0047] The photoelectric detection module includes a focusing lens 13, a photodetector 14, and a single-photon counter 15.

[0048] The excitation light generation module generates a continuous near-infrared steady-state laser beam as the excitation light. The infrared continuous laser 1 emits a stable laser beam, which is filtered by the filter 2 to remove other wavelengths of laser light. The collimating beam expander 3 is a biconvex lens that collimates the beam and has a pinhole filter inside. After the laser beam is shaped by the collimating beam expander 3, it enters the microscope scanning system and is focused by the objective lens 11 to obtain a Gaussian spot that reaches the diffraction limit size. The microscope scanning module performs grating scanning imaging. The focused Gaussian spot excites a fluorescent nanoprobe. The photoelectric detection module receives the fluorescence signal to generate a two-dimensional laser scanning image and accurately locates the fluorescent area in the scanning image.

[0049] Near-infrared laser 1 generates a continuous near-infrared steady-state laser beam output. The laser beam is filtered and collimated by filter 2 and collimating and expanding lens 3. Its power is adjusted by half-wave plate 4 and polarizer 5.

[0050] The scanning galvanometer 6 controls the optical path deflection of the laser beam to perform two-dimensional scanning of the sample. The scanning lens 8 and the field lens focus and collimate the laser beam emitted from the scanning galvanometer 6. The high-reflection, low-transmission dichroic mirror 12 can reflect near-infrared excitation light and transmit sample fluorescence, and is used to separate excitation light and fluorescence. The laser beam is focused onto the sample by the objective lens 11.

[0051] When acquiring FCS data, the FPGA development board 16 controls the deflection angles of the X and Y axes of the scanning mirror 6. When acquiring spectral data related to the grating image, it sends waveform data to the X and Y axes of the scanning mirror 6 to control the scanning mirror 6 to scan.

[0052] The photoelectric detection module includes a focusing lens 13, a photodetector 14, and a single-photon counter 15, all placed coaxially. The focusing lens 13 and photodetector 14 are positioned along the forward direction of the fluorescence collected by the objective lens 11. The objective lens 11 collects a portion of the fluorescence signal, which passes through a high-reflection, low-transmission dichroic mirror 12 and the focusing lens 13, and is received by the photodetector 14. After receiving each detection signal, the photodetector 14 sends the signal to the FPGA development board 16. The FPGA development board 16 controls the rotation of the scanning galvanometer 6 via waveform input, moving the focused spot to scan the next pixel, thereby obtaining a two-dimensional laser scanning image. The single-photon counter 15 records the arrival time of each photon and transmits the signal to the computer via the FPGA development board 16. Based on the photon arrival time, the computer can obtain a curve of fluorescence intensity changing over time. After acquiring a set of data, the FPGA controls the deflection angles of the scanning galvanometer along the X and Y axes to continue acquiring data at the next pixel position obtained through precise addressing, until all precisely addressed areas have been acquired.

[0053] The method for using a precisely addressed fluorescence correlation spectroscopy intelligent acquisition device includes the following specific steps:

[0054] Step 1: The microscope scanning module performs an addressable raster scanning image.

[0055] Step 2: Use the intelligent acquisition module to accurately locate the target area where the fluorescence signal appears, record the coordinates of the target area, and input the coordinates of these areas into the microscope scanning module to achieve accurate acquisition of data of the target area;

[0056] Step 3: The intelligent acquisition module synchronously controls the microscope detection module to transmit the acquired fluorescence signal to the computer. Based on the time or spatial autocorrelation function, the data is analyzed to obtain the fluorescence correlation spectrum (FCS) curve or the grating image correlation spectrum curve.

[0057] The intelligent acquisition module of this invention is controlled in real time by a field-programmable gate array (FPGA) development board 16. The intelligent acquisition module first acquires data from the coordinates obtained by precise addressing. Two acquisition methods are used: FCS analysis of single-point scanning of coordinates or correlation spectrum analysis of grating image of a region scanned by grating. The module also controls the X and Y axes of the scanning galvanometer to acquire data from all target areas.

[0058] After the microscope scanning system of this invention completes the addressing grating scan, the single-point scan and grating scan adopt the following scanning methods:

[0059] The first method involves recording coordinates sequentially in space. The FPGA development board 16 transmits the coordinates of the first pixel to the microscope scanning system, controls the deflection angles of the X and Y axes of the scanning galvanometer, fixes the scanning time, and uses the single-photon counter 15 to detect the data. After the scan is completed, the FPGA development board 16 transmits the second coordinate to the microscope scanning system. The above process is repeated until all precisely located coordinates on the entire two-dimensional pixel surface have been acquired. The acquired data is then transmitted to the computer for time autocorrelation analysis.

[0060] The second method involves recording coordinates arranged sequentially in space. The FPGA development board 16 finds the 256×256 pixels containing the most addressable coordinates and performs a traditional raster scanning mode, starting from the beginning of the first row and scanning until the 256th pixel of the first row is scanned. Then, it moves to the second row and repeats the above process until all 256 rows are scanned. Then, it starts from the first pixel again and repeats the raster scanning, scanning at least 10 complete 256×256 pixel images. The photodetector 14 detects the fluorescence signal, and the signal is sent to the computer for spatial autocorrelation analysis.

[0061] This invention detects the intensity of fluorescence signals within a micro-region at any given time. Fluctuation caused by changes It can be expressed by the formula:

[0062]

[0063] in, represent The total fluorescence intensity of the system at that time. The symbol represents the average value of the solution function over a certain time period; it detects fluorescence fluctuations within the micro-region. Related to the delay time, the normalized time autocorrelation function It can be represented as:

[0064]

[0065] Among them, delay time This represents the time that the fluorescent molecule remains in the detection volume. Represents at any given moment During the delay time Subsequent fluorescence intensity, Represents delay time The changes in molecular motion state within the volume were then detected. The data collected by the single-photon counter 15 were partitioned using the time autocorrelation function to obtain the FCS curve, and information such as molecular dynamics in living cells was explored.

[0066] This invention utilizes grating image correlation spectral analysis to extract molecular dynamics information from grating scan images. The grating image correlation spectral analysis involves the following two steps:

[0067] The first step is background subtraction, which removes stationary or slowly moving objects. The average background subtraction method is used to subtract the average value of a set of consecutive images from the image that needs to be analyzed.

[0068] The second step involves understanding the hidden temporal structure between any two pixels under raster scanning. If there is a correlation between the fluorescence intensities of two pixels, this correlation can be revealed using a spatial autocorrelation function. This spatial autocorrelation function can be expressed as:

[0069]

[0070] in, Represents the fluorescence intensity at each pixel. and Representing the raster scan image direction and Changes in direction space The symbol represents the average value. The spatial autocorrelation function is used to analyze the acquired grating images to obtain the grating image correlation spectrum curves, and to explore information such as molecular dynamics in living cells.

[0071] The fluorescent probe on the photodetector 14 of this invention can be any one of quantum dots, organic dyes, or rare-earth upconversion nanoparticles. The rare-earth upconversion nanoparticles can react with NOB... The reaction removes oleic acid ligands from the surface of the particles, allowing the rare earth upconversion nanoparticles to dissolve in water for application.

[0072] A precise addressing-based intelligent fluorescence correlation spectroscopy acquisition method includes the following two acquisition methods:

[0073] S1. Based on a precise addressing-based point-scan FCS acquisition method, before acquiring FCS data, an addressing raster scan imaging is performed first. Based on the raster scan imaging results, the FPGA development board 16 precisely addresses and locates the target areas where fluorescence appears. The FPGA development board 16 records the coordinates of all target areas and controls the deflection angle of the scanning galvanometer to acquire Time-Trace curves for a sufficiently long time until all target areas have been acquired. Figure 2 As shown, Figure 2 This invention demonstrates a point scanning FCS acquisition method based on precise addressing.

[0074] S2. Grating Image Correlation Spectrum Acquisition Method Based on Precise Addressing. Before acquiring the grating image correlation spectral data, an addressing grating scan imaging is performed first. Based on the scan imaging results, the FPGA development board will precisely address and locate the target area where fluorescence appears. The FPGA development board will then control the scanning mirror to perform grating scanning within the target area, such as... Figure 3 As shown, Figure 3 This invention demonstrates a grating image correlation spectrum acquisition method based on precise addressing.

[0075] Example 1

[0076] Based on the precise addressing fluorescence correlation spectroscopy intelligent acquisition method in a specific embodiment, this example demonstrates the data acquired by point scanning FCS based on precise addressing;

[0077] NaY was excited using a continuous near-infrared excitation beam. Yb / Tm (18 / 2%) nanoparticles, selected with a wavelength of 980 nm in this example, were excited by a 980 nm near-infrared laser of a certain power. A set of time-trace trajectories collected by a single-photon counter 15 are shown below. Figure 4 As shown, the fluorescence signals of all target areas were collected in a 60s acquisition time. The data from the six target areas need to be divided before data analysis.

[0078] In this example, precise addressing was used to locate six fluorescent target areas for data acquisition. The FPGA development board 16 controlled the deflection angle of the scanning galvanometer 6 to perform single-point FCS data acquisition on the eight target areas and transmitted the signals to an external computer. The external computer analyzed the acquired data using the time autocorrelation function to obtain six FCS curves.

[0079] like Figure 5 As shown, Figure 5 The trajectory plots of eight FCS curves are shown. This analysis yielded an average diffusion time of 7.687 ms and a diffusion coefficient of 3.9467 μm. 2 ·s -1 .

[0080] This embodiment provides a precise addressing intelligent fluorescence correlation spectrum acquisition device, the structure of which is described in [reference needed]. Figure 1 It includes an excitation light generation module, a microscopic scanning module, a photoelectric detection module, and an FPGA development board 16.

[0081] The excitation beam generation module includes a near-infrared continuous laser 1, a filter 2, a collimating beam expander 3 (including a pinhole filter), a half-wave plate 4, and a polarizer 5. The near-infrared laser 1 generates Gaussian laser output, the filter 2 filters out stray light from other wavelengths in the laser, the collimating beam expander 3 enlarges the excitation spot size and improves the excitation beam power utilization, and a pinhole filter is placed at the focal point to filter out high-frequency stray light. The half-wave plate 4 is mounted on a rotatable mounting base and is used with a linear polarizer 5 to adjust the power of the laser beam.

[0082] The multiphoton microscopy scanning module includes a scanning galvanometer 6, a scanning lens 8, a tube mirror 9, a high-reflection, low-transmission dichroic mirror 12, and an objective lens 11. The scanning galvanometer 6 controls the deflection of the laser beam to achieve two-dimensional scanning of the sample. The high-reflection, low-transmission dichroic mirror 12 reflects the laser beam. The scanning lens 8 and the tube mirror 9 focus and collimate the beam emitted from the scanning galvanometer 6, so that the laser beam still matches the entrance pupil size of the microscope objective lens during the scanning process. Finally, the objective lens 11 focuses the laser onto the mirror sample.

[0083] The photoelectric detection module includes a focusing lens 13, a photodetector 14, and a single-photon counter 15. The focusing lens 13 and the photodetector 14 are positioned along the forward direction of the fluorescence collected by the objective lens 11. The photodetector is connected to the FPGA development board 16. In the grating image correlation spectrum acquisition mode, after the photodetector 14 receives a detection signal, it sends a signal to the FPGA development board 16. Then, the FPGA development board 16 controls the rotation of the scanning galvanometer 6, using the focused spot to scan the sample point by point and transmits all signals to an external computer for processing. Finally, a two-dimensional laser scanning super-resolution image is obtained for spatial autocorrelation analysis. In the FCS acquisition mode, after the single-photon counter 15 acquires a set of data, the FPGA development board 16 controls the deflection angle of the scanning galvanometer 6 until all regions of interest are acquired. The signal is then transmitted to an external computer for Time-Trace time autocorrelation analysis.

[0084] The FPGA development board 16 performs precise addressing based on the result of a single addressing grating scan imaging image, and controls the photoelectric detection module to acquire data according to two different acquisition modes: FCS and grating image correlation spectrum. In FCS mode, the FPGA controls the deflection angle of the scanning galvanometer 6, and in grating image correlation spectrum mode, the FPGA development board 16 controls the scanning galvanometer 6 to perform grating scanning.

[0085] Example 2

[0086] Based on the precise addressing fluorescence correlation spectrum intelligent acquisition method in a specific embodiment, this example demonstrates the data acquired by raster image correlation spectrum based on precise addressing.

[0087] NaY synthesized using a continuous near-infrared excitation beam Yb / Tm (20 / 10%) nanoparticles were selected with a wavelength of 980 nm in this example. Under the excitation of a 980 nm near-infrared laser of a certain power, the photodetector 14 collected the signal of each grating scanning pixel and transmitted the signal to an external computer. The external computer processed the signal to obtain a two-dimensional laser scanning super-resolution fluorescence image.

[0088] like Figure 6 As shown, Figure 6 A two-dimensional laser scanning fluorescence image obtained in the raster image correlation spectral acquisition mode is shown. This image can be put into a computer for spatial autocorrelation analysis.

[0089] This example obtains a two-dimensional laser-scanned fluorescence image, which is then used in a computer for spatial autocorrelation analysis to obtain a three-dimensional trajectory diagram of the raster image's correlation spectrum, as shown below. Figure 7 As shown;

[0090] Figure 7 The three-dimensional trajectory diagram obtained from the correlation spectral analysis of the raster image is shown. The average molecular diffusion coefficient analyzed in this study is 1.2034 μm. 2 ·s -1 .

[0091] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A precise addressing intelligent acquisition method for fluorescence correlation spectroscopy, characterized in that, The specific steps are as follows: Step 1: The microscope scanning module performs an addressable raster scanning image. Step 2: Use the intelligent acquisition module to accurately locate the target area where the fluorescence signal appears, record the coordinates of the target area, and input the coordinates of these areas into the microscope scanning module to achieve accurate acquisition of data of the target area; Step 3: The intelligent acquisition module synchronously controls the microscope detection module to transmit the acquired fluorescence signal to the computer. Based on the time or spatial autocorrelation function, the data is analyzed to obtain the fluorescence correlation spectrum (FCS) curve or the grating image correlation spectrum curve. The intelligent acquisition module is controlled in real time by the field programmable gate array FPGA development board (16). The intelligent acquisition module first acquires data from the coordinates obtained by precise addressing. It adopts two acquisition methods: sequentially performing FCS analysis of single-point coordinate scanning and spectral analysis of grating image correlation of a region grating scanning, and controlling the X-axis and Y-axis of the scanning galvanometer to achieve data acquisition of all target areas. After the microscope scanning system completes the addressing raster scan, single-point scanning and area raster scanning are performed using the following scanning methods: The first method involves recording coordinates arranged sequentially in space. The FPGA development board (16) transmits the coordinates of the first pixel to the microscope scanning system, controls the deflection angles of the X and Y axes of the scanning galvanometer, fixes the scanning time, and detects the data by the single-photon counter (15). After the scanning is completed, the FPGA development board (16) transmits the second coordinate to the microscope scanning system. The above process is repeated until all precisely located coordinates on the entire two-dimensional pixel surface are collected. The collected data is then transmitted to the computer for time autocorrelation analysis. The second method involves recording coordinates arranged sequentially in space. The FPGA development board (16) finds the 256×256 pixels containing the most addressable coordinates and performs a traditional raster scanning mode, starting from the beginning to scan the first row until the 256th pixel of the first row is scanned. Then it switches to the second row and repeats the above process until all 256 rows are scanned. Then it starts from the first pixel and repeats the raster scan, scanning at least 10 complete 256×256 pixel images. The photodetector (14) detects the fluorescence signal, and the signal is sent to the computer for spatial autocorrelation analysis.

2. The precise addressing intelligent acquisition method for fluorescence correlation spectroscopy according to claim 1, characterized in that, The intensity of the fluorescence signal within the detection micro-region at any given time Fluctuation value caused by changes It can be expressed by the formula as follows: in, represent The total fluorescence intensity of the system at that time. The symbol represents the average value of the solution function over a certain time period; it detects fluorescence fluctuations within the micro-region. Related to the delay time, the normalized time autocorrelation function Represented as: Among them, delay time This represents the time that fluorescent molecules remain in the detection volume; Represents at any given moment During the delay time Subsequent fluorescence intensity, Represents delay time Then, the degree of change in the molecular motion state within the volume was detected, and the data collected by the single-photon counter (15) was partitioned using the time autocorrelation function to obtain the FCS curve, and the molecular dynamics information in living cells was explored.

3. The precise addressing intelligent acquisition method for fluorescence correlation spectroscopy according to claim 2, characterized in that, Molecular dynamics information is extracted from grating scan images using grating image correlation spectroscopy analysis, which involves the following two steps: The first step is background subtraction, which removes stationary or slowly moving objects. The average background subtraction method is used to subtract the average value of a set of consecutive images from the image to be analyzed. The second step involves identifying the hidden temporal structure between any two pixels under raster scanning. If there is a correlation between the fluorescence intensities of two pixels, this correlation can be revealed using a spatial autocorrelation function, which is expressed as follows: in, Represents the fluorescence intensity at each pixel. and Representing the raster scan image direction and Changes in direction space The symbol represents the average value. The spatial autocorrelation function is used to analyze the acquired grating images to obtain the grating image correlation spectrum curves, and to explore the molecular dynamics information in living cells.

4. The precise addressing intelligent acquisition method for fluorescence correlation spectroscopy according to claim 3, characterized in that, The fluorescent probe is any fluorescent dye among quantum dots, organic dyes, or rare-earth upconversion nanoparticles. The rare-earth upconversion nanoparticles react with NOB... The reaction removes oleic acid ligands from the surface of the particles, allowing the rare earth upconversion nanoparticles to dissolve in water for application.

5. The intelligent fluorescence correlation spectrum acquisition device with precise addressing according to claim 4, characterized in that, It includes an excitation light generation module, a microscopic scanning module, an FPGA development board (16), and a photoelectric detection module; The excitation light generation module includes an infrared continuous laser (1), a filter (2), a collimating beam expander (3), a half-wave plate (4), and a polarizer (5); The microscopic scanning module includes a scanning galvanometer (6), a scanning lens (8), a tube mirror (9), a high-reflection, low-transmission dichroic mirror (12), and an objective lens (11). The photoelectric detection module includes a focusing lens (13), a photodetector (14), and a single-photon counter (15).

Images