A method for processing FRET sensitization quenching conversion factor measurement data

Abstract

The present invention provides a method for processing FRET sensitization-quenching conversion factor measurement data, comprising the steps of: S1, preparing a FRET sample and acquiring an image; S2, determining and deducting background noise values ​​using a frequency histogram; S3, obtaining a signal region template image; S4, setting an initial sliding window, screening the valid data region with the most non-overlapping sliding windows, and then calculating the average value, substituting the average value into the mPb_G formula to calculate the average value and standard deviation of the sensitization-quenching conversion factor G; S5, changing the sliding window, and determining the window size according to the average value and standard deviation of the G value; S6, plotting the G value data under the appropriate window size as a pseudo-color image and a frequency histogram, and outputting the average value and standard deviation thereof. The present invention filters out discrete data points inside and at the edge of the cell signal region by adopting a method of local signal recognition and mean value calculation, thereby avoiding calculation errors caused by optical system noise and inaccurate multi-channel co-localization.

Description

Technical Field

[0001] The present invention relates to the technical field of fluorescence resonance energy transfer (FRET) measurement, and in particular to a method for processing FRET sensitization-quenching conversion factor measurement data. Background Art

[0002] Fluorescence resonance energy transfer is currently the only imaging technique that can quantitatively study the interactions between macromolecules in living cells in situ and in real time. Among them, the sensitized emission method is the most widely used method for monitoring dynamic living cells. It has the characteristics of being sensitive, rapid, and non-destructive, but it requires additional measurement of crosstalk factors a, b, c, d and the sensitized-quenching conversion factor G. Many methods have been developed for measuring the sensitized-quenching conversion factor. Among them, Jiang Zhang [Jiang Zhang, Lili Zhang, Liuying Chai, Fangfang Yang, Mengyan Du & Tongsheng Chen*. Reliable measurement of the FRET sensitized-quenching transition factor for FRET quantification in living cells. Micron 88, 7-15 (2016)] proposed using a partial acceptor photobleaching method to first measure the FRET efficiency of a FRET tandem structure with a donor-acceptor concentration ratio of 1:n, and then use the plasmid to measure the sensitized-quenching conversion factor. The method proposed by Jiang Zhang typically uses a pixel-by-pixel data processing approach to automatically calculate the G factor. However, the resulting G values ​​are discretely distributed, leading to an overestimation of the final calculated G value. Artificially limiting the range will also result in an underestimation of the G value. Therefore, to address this issue, a data processing method was proposed to eliminate image noise and random fluctuations by filtering out discrete signal points within and at the edges of the cell signal region. This method effectively reduces the discreteness of the FRET sensitization-quenching conversion factor measurement data, making the results more reliable and accurate. This is particularly true when the degree of photobleaching is low, as the data contains more discrete outliers and the effect is more pronounced. Summary of the Invention

[0003] To address the technical problems of the prior art, the present invention provides a method for processing FRET sensitization-quenching conversion factor measurement data. This method uses local signal recognition and mean calculation to filter out discrete data points within and at the edges of the cell signal region, while avoiding calculation errors caused by optical system noise and inaccurate multi-channel co-localization. Compared with the pixel-by-pixel method, the data obtained by the present invention is more realistic, stable, and robust.

[0004] The present invention adopts the following technical solution to achieve: a method for processing FRET sensitization quenching conversion factor measurement data, comprising the following steps:

[0005] S1. Prepare FRET samples and collect images of the FRET channels AA, DA, and DD before photobleaching. Then, rapidly bleach the sample using a short, high-intensity excitation method. Finally, collect images of the FRET channels AAP and DDP after photobleaching. Repeat the above steps to obtain multiple sets of live cell FRET sample images to obtain statistical results.

[0006] S2. Generate grayscale value frequency histograms of several sample images AA, DA, DD, AAP, and DDP, use the grayscale value of the first non-zero peak of the grayscale value frequency histogram as the background noise value, deduct the background noise of the several images pixel by pixel, and set the grayscale value to zero if it is less than zero after deducting the background noise;

[0007] S3, performing binarization processing on the AA, DA, and DD images before photobleaching using an appropriate threshold, and then performing AND processing on these binarized images to obtain a binary signal region template image of the set of sample images;

[0008] S4. Set the size of the sliding window to n*n, traverse the binary signal region template image, and select the maximum number of non-overlapping valid data windows in all signal regions using the idea of ​​dynamic programming; use the mapping relationship between the binary signal region template image and the signal region position of the original image to determine the coordinates of the valid data window in the original image according to the selected valid data window coordinates; average the grayscale values ​​of all data points in each window in the original image, substitute the average value as a data point into the mPb_G formula for calculation, and save the calculated value G in the valid data list of the n*n window;

[0009] S5. Increase the sliding window to m*m, where m=n+1, and repeat step S4 to calculate the mean and standard deviation of the valid data lists of the m*m window and the n*n window. If the difference between the two standard deviations is less than the respective standard deviations, and the coefficient of variation of the two windows is less than 0.3, the data is stable and there is no need to increase the sliding window. Otherwise, continue to increase the window and repeat steps S4 and S5 until the conditions are met.

[0010] S6. If the data tends to be stable, convert the effective data of the photobleaching degree x and the sensitization-quenching conversion factor G into a pseudo-color image and a frequency histogram, and output their average values.

[0011] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0012] 1. The present invention adopts a method of automatically extracting cell regions, which saves the time of manually circling cell regions and improves the efficiency of experimenters in analyzing and processing large amounts of experimental data.

[0013] 2. The present invention adopts the method of local signal recognition and mean calculation to filter out discrete data points inside and at the edge of the cell signal area, and avoids calculation errors caused by optical system noise and inaccurate multi-channel co-localization. Compared with the pixel-by-pixel method, the data obtained by the present invention is more realistic, stable, and robust.

[0014] 3. The present invention adopts the method of standard deviation analysis data stability, which avoids the error caused by artificial prior conditions and makes the calculation results more real and objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention;

[0016] Figure 2 (a) is a schematic diagram showing five images of DD, DA, AA, DDP, and AAP of the standard plasmid C4Y captured in this example;

[0017] Figure 2 (b) is a histogram and pseudo-color image of the photobleaching degree and sensitization-quenching conversion factor of C4Y in this example after image processing and data processing;

[0018] Figure 2 (c) is a comparison chart of G values ​​calculated by various methods. DETAILED DESCRIPTION

[0019] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0020] Example

[0021] like Figure 1 As shown, this embodiment provides a method for processing FRET sensitization quenching conversion factor measurement data, comprising the following steps:

[0022] S1. Prepare FRET samples and collect images of the FRET channels AA, DA, and DD before photobleaching. Then, rapidly bleach the sample using a short, high-intensity excitation method. Finally, collect images of the FRET channels AAP and DDP after photobleaching. Repeat the above steps to obtain multiple sets of live cell FRET sample images to obtain statistical results.

[0023] S2. Generate grayscale value frequency histograms of five sample images AA, DA, DD, AAP, and DDP, use the grayscale value of the first non-zero peak of the grayscale value frequency histogram as the background noise value, and deduct the background noise of the five images pixel by pixel. If the grayscale value after deducting the background noise is less than zero, set it to zero;

[0024] S3, performing binarization processing on the AA, DA, and DD images before photobleaching using an appropriate threshold, and then performing AND processing on the three binarized images to obtain a binary signal region template image of the set of sample images;

[0025] S4. Set the size of the sliding window to n*n, traverse the binary signal region template image, and select the maximum number of non-overlapping valid data windows in all signal regions using the idea of ​​dynamic programming; use the mapping relationship between the binary signal region template image and the signal region position of the original image to determine the coordinates of the valid data window in the original image according to the selected valid data window coordinates; average the grayscale values ​​of all data points in each window in the original image, substitute the average value as a data point into the mPb_G formula for calculation, and save the calculated value G in the valid data list of the n*n window;

[0026] S5. Increase the sliding window to m*m, where m=n+1, and repeat step S4 to calculate the mean and standard deviation of the valid data lists of the m*m window and the n*n window. If the difference between the two standard deviations is less than the respective standard deviations, and the coefficient of variation of the two windows is less than 0.3, it is considered that the data is stable and there is no need to increase the sliding window. Otherwise, continue to increase the window and repeat steps S4 and S5 until the conditions are met.

[0027] S6. If the data tends to be stable, convert the effective data of the photobleaching degree x and the sensitization-quenching conversion factor G into a pseudo-color image and a frequency histogram, and output their average values.

[0028] Specifically, in this embodiment, the FRET sample in step S1 is a living cell sample with a donor-acceptor concentration ratio of 1:1.

[0029] Specifically, in this embodiment, pre-experimental determination of appropriate excitation light conditions is required before acquiring dual-channel images before and after photobleaching in step S1. Typically, the excitation light intensity and camera exposure time are adjusted to achieve appropriate conditions, with the degree of photobleaching below 5% after 5 minutes of continuous excitation as the standard. In this embodiment, the camera exposure time is no longer than 300ms, and the excitation light intensity is no more than 20%, determined based on the actual excitation light power of the light source to prevent further photobleaching from interfering with the experimental results.

[0030] Specifically, in this embodiment, during photobleaching in step S1, in order to ensure the bleaching effect and minimize damage to the cells, a strategy of photobleaching with high-power excitation light intensity in a short period of time is adopted. Other experiments and data statistics show that the appropriate photobleaching degree is 30% to 40%.

[0031] Specifically, in the embodiment, the process of step S2 is expressed as: Bg=max{H(Gray)},0 <Gray<2 Bit / r, where bg represents the background noise value, H(Gray) represents the grayscale value frequency histogram function, Gray represents the grayscale value, Bit represents the number of bits of the image, and r represents the parameter for determining the peak range. In this embodiment, it is set to 6 and can be limited according to the background noise value that may appear in actual conditions.

[0032] Specifically, in this embodiment, the appropriate threshold is selected in step S3 by using the threshold obtained by the OTSU algorithm to perform image binarization segmentation. Numerous experimental statistics indicate that a threshold of 3 is most suitable in this embodiment. Using a threshold less than 3 for signal region calculations can be severely affected by optical noise, which is not an ideal image condition.

[0033] Specifically, in this embodiment, when the three dual-channel images before bleaching are compared to obtain a binary signal region template image in step S3, the co-localization of the three dual-channel images needs to be consistent; otherwise, an erroneous or misplaced signal region will be extracted; in the binary signal region template image, a gray value of 1 represents a signal region, and a gray value of 0 represents a non-signal region; the specific formula is: Among them, Gray represents the grayscale value, and Threshold represents the threshold value.

[0034] Specifically, in this embodiment, the valid data in step S4 means that all data points in the window are signal points, that is, the grayscale values ​​of all data points are 1. If at least one data point is 0, it is considered to be an invalid area; the specific formula is: Among them, W i Indicates the current i-th window, Gray j Indicates the grayscale value of the j-th pixel. TRUE indicates a valid area, and FALSE indicates an invalid area. If an invalid area is traversed, it will not be processed and the next valid area will be searched.

[0035] Specifically, in this embodiment, the dynamic programming idea is used in step S4 to select the maximum number of non-overlapping valid data windows in all signal areas in order to ensure the maximum utilization of valid data; the specific formula is: dp(x i ,y i ,x j ,yj )=

[0036] max{dp(x i ,y i ,x j ,y j ),dp(x i ,y i ,x k ,y k )+dp(x i ,y k+1 ,x k ,y j )+dp(x k+1 ,y i ,x j ,y k )+dp(x k+1 ,y k+1 ,x j ,y j )},i≤k≤j

[0037] Among them, dp(x i ,y i ,x j ,y j ) means from the upper left corner (x i ,y i ) to the lower right corner (x j ,y j ) interval does not overlap the maximum number of valid data windows. If the current position (x i ,y i ) of n*n window W i = TRUE, the next non-overlapping valid data window starts at (x i+n ,y i+n ), otherwise, the next non-overlapping valid data window starts at (x i+1 ,y i+1 ) to ensure that the windows do not overlap.

[0038] Specifically, in this embodiment, the data in step S5 tends to be stable and satisfies the following formula: (|σ m -σ n |<σ m )∩(|σ m -σ n |<σ n )∩(cv m <0.3)∩(cv n <0.3), where σ m Represents the standard deviation of the m*m window, σ n Indicates the standard deviation of the n*n window, cv mRepresents the coefficient of variation of the m*m window, cv n It represents the coefficient of variation of n*n window, and ∩ represents the intersection.

[0039] Specifically, in this embodiment, the theoretical formula mPb_G for calculating the FRET sensitization quenching conversion factor is: Where n is the number of receptors; x is the degree of photobleaching, and the calculation formula is: F c is the fluorescence intensity of the energy transferred from the donor to the acceptor, and the calculation formula is: F c =I DA -a(I AA -cI DD )-d(I DD -bI AA ), where a, b, c, and d are crosstalk factors, I DA It represents the fluorescence intensity detected in the acceptor fluorescence detection channel when the acceptor is excited by the donor excitation light before photobleaching. DD It represents the donor fluorescence intensity detected in the donor fluorescence detection channel when the acceptor is excited by the donor excitation light before photobleaching, I DDP It represents the donor fluorescence intensity detected in the donor fluorescence detection channel when the acceptor is excited by the donor excitation light after photobleaching, I AA represents the acceptor fluorescence intensity detected in the acceptor fluorescence detection channel when the acceptor is excited by the acceptor excitation light before the acceptor is photobleached, I AAP represents the acceptor fluorescence intensity detected in the acceptor fluorescence detection channel when excited by acceptor excitation light after acceptor photobleaching.

[0040] Specifically, in this embodiment, since the discreteness of the results obtained with different sliding window sizes is different, the difference in standard deviation can be used to compare the current sliding window with the previous sliding window, and the coefficient of variation of each window can be calculated to determine whether it is currently stable; when the conditions are met, even if the sliding window size is increased, it will not have a significant impact on the results; usually when the conditions are met, the coefficient of variation is within 30%, and the discreteness is low.

[0041] Specifically, when automatically processing and calculating images, a pixel-by-pixel approach is typically used. However, images acquired from electron microscopes are commonly subject to thermal noise and dark current noise. Furthermore, environmental noise and the influence of fluorescent self-luminescence on the acquired signal are present during acquisition, and this noise is difficult to completely eliminate. Therefore, a pixel-by-pixel calculation approach can lead to outliers that deviate from the original value. Because our method uses partial receptor photobleaching, we typically use the lowest possible photobleaching level to minimize cell damage. This can result in insufficient photobleaching depth and uneven photobleaching across different cell regions. Furthermore, despite the fastest possible image acquisition time after photobleaching, subpixel flow within the cell is unavoidable. Using pixel-by-pixel calculation results can result in significant variability. Furthermore, we need to acquire three images from two channels under different excitation light sources, necessitating consistent colocalization of the three images across the two channels. However, complete hardware registration is difficult to achieve, resulting in non-one-to-one correspondence between pixel grayscale values ​​at the same coordinate in the three images, leading to offsets.

[0042] The present invention solves the above-mentioned problem by adopting the signal area averaging method, selecting the maximum number of non-overlapping valid data windows in the signal area, taking the average value of all data in the valid data window as a valid data point, and then substituting it into the formula to calculate the G factor. When using pixel-by-pixel calculation, it is observed that the abnormal values ​​caused by the above-mentioned reasons are more evenly distributed inside and at the edge of the cell. If the distribution of the G factor is directly smoothed by the mean, the value of the G factor will be artificially changed, and the result obtained in this way is not authentic. If the original data is processed, the local averaging method inside the signal area is similar to the method in which the experimenter manually circles a certain window size data when manually processing the cell image. This method has authenticity and reliability. From the results, this method can solve the above-mentioned problems caused by pixel-by-pixel calculation, and the data is more accurate and stable.

[0043] Specifically, because the samples we used to measure the G factor using the partial receptor photobleaching method were standard plasmids transfected in the cytoplasm, the fluorescence of the organelles inside the cell was weak, and the edge width of the cell edge increased due to diffusion of the fluorophore or defocus. Therefore, after image processing, discrete points may appear inside and at the edge of the cell signal area. These discrete points will be taken into account during pixel-by-pixel processing, increasing the volatility of the data.

[0044] When selecting a valid data window in a signal area, the present invention considers it to be a valid data window when all the data inside the window are signal data. Otherwise, as long as there is one data point in the window that is not a signal data point, it is considered to be an invalid data window. Only if it is a valid data window will mean processing be performed and then calculated. If it is an invalid data window, no processing is performed. In this way, when the window size increases to a certain extent, all discrete signal data points can be effectively filtered out. For the selection of window size, the strategy of comparing the standard deviation of the current window with the previous window is currently adopted to determine whether the current window size is appropriate. If the difference between the standard deviations of the two is less than their respective standard deviations, and the coefficient of variation of the two windows is small, it is considered that there is no need to increase the window size at this time, and the data has stabilized. The coefficient of variation at this time is usually less than 30%, and it is considered that the data volatility is small, and the discrete signal points caused by the above problems are effectively removed.

[0045] Specifically, the specific experimental conditions of this embodiment are as follows:

[0046] 1. Plasmids and reagents:

[0047] Plasmids: CFP (cyan fluorescent protein), YFP (yellow fluorescent protein), C4Y (a fixed FRET standard plasmid in which CFP and YFP molecules are linked by four bases)

[0048] Reagents: DMEM culture medium was purchased from Life Technologies, Inc.; newborn fetal bovine serum was purchased from Hangzhou Sijiqing Biological Company, trypsin was purchased from Huamei Bioengineering Company; transfection reagent TurbofectTM in vitro transfection reagent was purchased from Fermentas Company in the United States; metformin (MET) was purchased from Beijing Solebow Technology Co., Ltd.

[0049] 2. Cell Culture and Transfection

[0050] HeLa cells were purchased from the Cell Bank of the Chinese Academy of Sciences and cultured in DMEM supplemented with 10% newborn calf serum in an incubator maintained at 37°C and 5% CO2. For the experiment, the cells were trypsinized, transferred to a cell culture dish, and cultured in an incubator for approximately 24 hours. When the cells occupied 70-90% of the dish bottom, the prepared plasmids were transiently transfected into the cells using the in vitro transfection reagent Turbofect™.

[0051] Specific steps of plasmid transfection: (1) Take a sterilized EP tube, first add 100 μL serum-free DMEM solution to the EP tube, then add 1-2 μL transfection reagent TurbofectTM to the EP tube, and then add 1-2 μL (500-600 ng / μl) of plasmid to the EP tube, gently blow the mixed solution 3 times with a pipette tip to mix, and let it stand for 20 minutes after mixing; (2) Within 20 minutes, the cell culture dish waiting for transfection can be washed: wash the cells in the culture dish 3 times with PBS. The purpose is to clean dead cells and other impurities; (3) After 20 minutes, add 100 μL of serum-free DMEM solution to the mixed EP tube; (4) Transfer the final mixed solution in (3) above to the culture dish, and then put the culture dish back into the incubator for 4-6 hours; (5) After 4-6 hours, aspirate the solution (transfection solution) in the dish, and wash the cells in the dish 3 times with PBS, then add DMEM culture medium containing newborn calf serum to the culture dish, and finally put it into the incubator for culturing. After 18-24 hours, it can be used for optical experiment measurement.

[0052] 3. Results and Discussion

[0053] 1) Comparison with the pixel-by-pixel method: Using the pixel-by-pixel calculation method, the results were x = 0.303 and G = 7.819. Local mean smoothing, then using the local mean smoothing method, yielded x = 0.306 and G = 4.308. Due to the potential for internal and internal flow in the cell during photobleaching and imaging, as well as the presence of noise in the optical system, this result demonstrates that local mean smoothing provides more accurate results than the automatic pixel-by-pixel calculation method, particularly at low levels of photobleaching, demonstrating improved accuracy and robustness.

[0054] 2) Comparison with other G-factor measurement methods: A comparison was made using the method proposed by Hoppe [A.D. Hoppe, K. Christensen, and J.A. Swanson, “Fluorescence resonance energy transfer based stoichiometry in living cells,” Biophys. J. 83(6), 3652-3664(2002)]. This method obtained G=4.132, which is close to the result G=4.308 obtained by the method proposed in the present invention, verifying the accuracy of the G-factor measurement of the present invention.

[0055] 3) Compare with the manual calculation results: Figure 2 (a) Figure 2 (b) Figure 2As shown in (c), the automatic processing results of the present invention are x=0.306, G=4.308, and the results calculated after manually circling the cells are x=0.312, G=4.475. The automatic processing results are similar to the manual processing results, which proves the accuracy of the automatic image processing and data calculation of the present invention.

[0056] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for processing FRET sensitization quenching conversion factor measurement data, characterized in that: The following steps are involved: S1. Prepare FRET samples and collect images of the FRET channels AA, DA, and DD before photobleaching. Then, rapidly bleach the sample using a short, high-intensity excitation method. Finally, collect images of the FRET channels AAP and DDP after photobleaching. Repeat the above steps to obtain multiple sets of live cell FRET sample images to obtain statistical results. S2. Generate grayscale value frequency histograms of several sample images AA, DA, DD, AAP, and DDP, use the grayscale value of the first non-zero peak of the grayscale value frequency histogram as the background noise value, deduct the background noise of the several images pixel by pixel, and set the grayscale value to zero if it is less than zero after deducting the background noise; S3, performing binarization processing on the AA, DA, and DD images before photobleaching using an appropriate threshold, and then performing AND processing on these binary images to obtain a binary signal region template image of the sample image; S4. Set the size of the sliding window to n*n, traverse the binary signal region template image, and select the maximum number of non-overlapping valid data windows in all signal regions using the idea of ​​dynamic programming; use the mapping relationship between the binary signal region template image and the signal region position of the original image to determine the coordinates of the valid data window in the original image according to the selected valid data window coordinates; average the grayscale values ​​of all data points in each window in the original image, substitute the average value as a data point into the mPb_G formula for calculation, and save the calculated value G in the valid data list of the n*n window; S5. Increase the sliding window to m*m, where m=n+1, and repeat step S4 to calculate the mean and standard deviation of the valid data lists of the m*m window and the n*n window. If the difference between the two standard deviations is less than the respective standard deviations, and the coefficient of variation of the two windows is less than 0.3, the data is stable and there is no need to increase the sliding window. Otherwise, continue to increase the window and repeat steps S4 and S5 until the conditions are met. S6. If the data tends to be stable, convert the effective data of the photobleaching degree x and the sensitization-quenching conversion factor G into a pseudo-color image and a frequency histogram, and output their average values; The valid data in step S4 means that all data points in the window are signal points, that is, the grayscale values ​​of all data points are 1. If at least one data point is 0, it is considered an invalid area; the specific formula is: Among them, W i Indicates the current i-th window, Gray j Indicates the grayscale value of the jth pixel. TRUE indicates a valid area, and FALSE indicates an invalid area. If an invalid area is traversed, it will not be processed and the next valid area will be searched. In step S4, dynamic programming is used to select the maximum number of valid data windows that do not overlap in all signal areas. The specific formula is: dp(x i ,y i ,x j ,y j )=max{dp(x i ,y i ,x j ,y j ),dp(x i ,y i ,x k ,y k )+dp(x i ,y k+1 ,x k ,y j )+dp(x k+1 ,y i ,x j ,y k )+dp(x k+1 ,y k+1 ,x j ,y j )},i≤k≤j Among them, dp(x i ,y i ,x j ,y j ) means from the upper left corner (x i ,y i ) to the lower right corner (x j ,y j ) the maximum number of valid data windows that do not overlap within the interval; if the current position (x i ,y i ) of n*n window W i = TRUE, the next non-overlapping valid data window starts at (x i+n ,y i+n ), otherwise, the next non-overlapping valid data window starts at (x i+1 ,y i+1 ).

2. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, wherein: The FRET sample in step S1 is a living cell sample with a donor-acceptor concentration ratio of 1:

1.

3. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, wherein: When collecting dual-channel images before and after bleaching in step S1, a preliminary experiment is first performed to determine appropriate excitation light conditions. The appropriate excitation light intensity and camera exposure time are adjusted and obtained, taking the photobleaching degree after 5 minutes of continuous excitation as the standard to be less than 5%.

4. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, wherein: During the photobleaching in step S1, a strategy of using high-power excitation light intensity for photobleaching in a short time is adopted, and the degree of photobleaching is 30% to 40%.

5. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, wherein: The process of step S2 is expressed as: Bg=max{H(Gray)},0 <Gray<2 Bit / r, where Bg represents the background noise value, H(Gray) represents the gray value frequency histogram function, Gray represents the gray value, Bit represents the number of bits of the image, and r represents the parameter for determining the peak range.

6. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, characterized in that: In step S3, an appropriate threshold is selected and the image is binarized and segmented using the threshold obtained by the OTSU algorithm.

7. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, characterized in that: In step S3, when the three dual-channel images before bleaching are combined to obtain a binary signal region template image, the co-localization of the three dual-channel images must be consistent. Otherwise, an erroneous or misplaced signal region will be extracted. In the binary signal region template image, a grayscale value of 1 indicates a signal region, and a grayscale value of 0 indicates a non-signal region. The specific formula is: Among them, Gray represents the grayscale value, and Threshold represents the threshold value.

8. The method for processing FRET sensitization quenching conversion factor measurement data according to claim 1, characterized in that: In step S5, the data tends to be stable and satisfies the following formula: (|σ m -σ n |<σ m )∩(|σ m -σ n |<σ n )∩(cv m <0.3)∩(cv n <0.3), where σ m Represents the standard deviation of the m*m window, σ n Indicates the standard deviation of the n*n window, cv m Represents the coefficient of variation of the m*m window, cv n It represents the coefficient of variation of n*n window, and ∩ represents the intersection.

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