A method and system for illumination light field correction for quantitative fluorescence resonance energy transfer microscopy

Abstract

The application discloses a kind of illumination light field correction method and system for quantitative fluorescence resonance energy transfer microscopic imaging, its method includes the following steps: S1, establishing image non-uniformity correction model;S2, dark field signal distribution function is determined;S3, background signal distribution function and background correction coefficient are determined;S4, foreground signal distribution function and foreground correction coefficient are determined;S5, steps S2-S4 are repeated to obtain the dark field, background and foreground signal distribution function of FRET three channels and background and foreground correction coefficient;S6, based on image non-uniformity correction model, the non-uniformity of live cell FRET three channel fluorescence image is corrected using the signal distribution function and correction coefficient determined.The application does not need to repeat preparation reference sample, only needs to be determined once to be able to effectively correct the illumination non-uniformity in microscopic imaging system, and the ratio between FRET three channel image gray can be kept, so that while greatly improving the amount of data in single frame large field of view image, the accuracy of quantitative FRET measurement is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of image correction technology of optical microscopic imaging technology, and in particular to an illumination light field correction method and system for quantitative fluorescence resonance energy transfer (FRET) microscopic imaging. BACKGROUND

[0002] Fluorescence microscopic imaging is a widely used imaging technology in biomedical research, which can realize the visualization observation of the internal structure and function of cells. In the field of fluorescence microscopic imaging, quantitative FRET microscopic imaging is currently the only imaging technology that can monitor weak, reversible and dynamic molecular events in living cells in situ and in real time.

[0003] Accurate measurement of fluorescence intensity is crucial for obtaining reliable quantitative FRET analysis results. However, due to the illumination non-uniformity of the optical system itself, as well as the interference of background fluorescence and camera noise, the phenomenon of light and dark appears during the imaging process, which seriously affects the accuracy of quantitative FRET analysis.

[0004] To solve this problem, the prior art proposes a variety of illumination correction methods. The most common one is the prospective method using reference images (Model MA. Intensity calibration and shading correction for fluorescence microscopes. Curr Protoc Cytom. 2006 Aug; Chapter 10: Unit 10.14.), which requires a pure white or gray uniform reference image to be taken in advance. The intensity distribution of the reference image is measured to estimate the illumination distribution for later correction. This method is relatively simple to operate, but it requires a reference image in advance. The reference image is generally selected as a fluorescent solution sample to be collected, but the thickness of the fluorescent solution is not consistent with the thickness of the actual sample to be corrected, and the acquisition conditions of the reference image are not completely consistent with the conditions of the actual sample imaging. The above limits the use of this method.

[0005] Another is a retrospective correction method based on multiple images. This method does not require a reference standard, but it has certain requirements for the number of input training images. When the number is low, it is difficult to ensure the correction effect. Moreover, for multi-channel fluorescence imaging, this correction method starts from the image itself, and it is difficult to ensure the proportional relationship between the gray scales of multi-channel images. Therefore, there is an urgent need to develop a new illumination correction technology to improve the accuracy and reliability of quantitative analysis in fluorescence microscopic imaging. SUMMARY

[0006] In order to solve the problems in the prior art, the application provides a method and system for correcting an illumination light field for quantitative fluorescence resonance energy transfer microscopic imaging, a correction model containing three factors causing image non-uniformity is established, that is, non-uniformity of an illumination light source, non-uniformity of background fluorescence and inconsistency of proportional relationships between multi-channel image gray scales, and corresponding model parameter determination and correction methods are provided.

[0007] The method of the application is implemented by the following technical scheme: a method for correcting an illumination light field for quantitative fluorescence resonance energy transfer microscopic imaging, comprising the following steps:

[0008] S1, a non-uniformity correction model of an image is established, the image is divided into a foreground signal, a background signal and a dark field signal, non-uniformity of different signals is corrected by determining the distribution of the different signals, so that an image with uniform illumination is obtained;

[0009] S2, a dark field signal distribution function is determined, a microscopic imaging system illumination light source is turned off and external environmental light interference is shielded, a plurality of images are continuously collected under the lowest exposure time of a camera, and the collected images are averaged to obtain the dark field signal distribution function;

[0010] S3, a background signal distribution function and a background correction coefficient are determined, a culture medium without cells is taken as a sample, a plurality of images are continuously collected under the same imaging conditions as the cells, the collected images are averaged, then the dark field signal distribution function obtained in step S2 is subtracted, so that the background signal distribution function is obtained, and the background correction coefficient is calculated pixel by pixel based on the obtained background signal distribution function;

[0011] S4, a foreground signal distribution function and a foreground correction coefficient are determined, a fluorescence calibration slide is taken as a sample, a plurality of images are continuously collected under non-exposure conditions, the collected images are averaged, then the dark field signal distribution function and the background signal distribution function are subtracted, so that the foreground signal distribution function is obtained, and the foreground correction coefficient is calculated pixel by pixel based on the obtained foreground signal distribution function;

[0012] S5, steps S2-S4 are repeated to obtain the dark field, background and foreground signal distribution functions and the background and foreground correction coefficients of the FRET three channels;

[0013] S6, based on the image non-uniformity correction model, the signal distribution function and the correction coefficient are used to correct the non-uniformity of the live cell FRET three-channel fluorescence image; based on the image non-uniformity correction model established in step S1, the average gray of the background uniform region is taken as the ideal gray, the FRET three-channel fluorescence image of the live cell is corrected pixel by pixel according to the FRET three-channel background signal distribution function and the background correction coefficient obtained in step S5, the average gray of the foreground uniform region is taken as the ideal gray, the FRET three-channel fluorescence image of the live cell is corrected pixel by pixel according to the foreground signal distribution function and the foreground correction coefficient obtained in step S5, and finally the corrected uniform FRET three-channel fluorescence image is output.

[0014] The system of the application adopts the following technical scheme to realize it: an illumination light field correction system for quantitative fluorescence resonance energy transfer microscopic imaging, comprising:

[0015] An image non-uniformity correction model establishing module is used to divide the image into foreground signals, background signals and dark field signals, to correct the non-uniformity by measuring the distribution of different signals, so as to obtain an illumination uniform image;

[0016] A dark field signal distribution function measuring module is used to close the illumination light source of the microscopic imaging system and shield the interference of external environmental light, to continuously collect multiple images under the lowest exposure time of the camera, and to obtain the dark field signal distribution function by averaging the collected images;

[0017] A background signal distribution function and background correction coefficient measuring module is used to take the culture medium without cells as a sample, to continuously collect multiple images under the same imaging conditions as the cells, to average the collected images, to subtract the dark field signal distribution function obtained by the dark field signal distribution function measuring module, so as to obtain the background signal distribution function, and to calculate the background correction coefficient pixel by pixel based on the obtained background signal distribution function;

[0018] A foreground signal distribution function and foreground correction coefficient measuring module is used to take a fluorescent calibration slide as a sample, to continuously collect multiple images under non-exposure conditions, to average the collected images, to subtract the dark field signal distribution function and the background signal distribution function, so as to obtain the foreground signal distribution function, and to calculate the foreground correction coefficient pixel by pixel based on the obtained foreground signal distribution function;

[0019] A FRET three-channel measuring module is used to repeat the dark field signal distribution function measuring module, the background signal distribution function and the background correction coefficient measuring module, and the foreground signal distribution function and the foreground correction coefficient measuring module, to obtain the dark field, the background and the foreground signal distribution functions of the FRET three channels and the background and the foreground correction coefficients, respectively;

[0020] The non-uniformity correction module of the live cell FRET three-channel fluorescence image is used for correcting the live cell FRET three-channel fluorescence image by the following steps: establishing an image non-uniformity correction model, taking the average gray value of the background uniform area as the ideal gray value to correct the FRET three-channel fluorescence image of the live cell according to the FRET three-channel background signal distribution function and the background correction coefficient obtained by the FRET three-channel measurement module, and taking the average gray value of the foreground uniform area as the ideal gray value to correct the FRET three-channel fluorescence image of the live cell according to the FRET three-channel foreground signal distribution function and the foreground correction coefficient obtained by the FRET three-channel measurement module, and finally outputting the corrected uniform FRET three-channel fluorescence image.

[0021] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0022] 1. The present application can effectively correct the illumination non-uniformity in the microscopic imaging system and the inconsistent proportion between the gray scales of the multi-channel images, thereby significantly improving the accuracy and reliability of the quantitative FRET measurement.

[0023] 2. The present application uses a fluorescent calibration slide for illumination correction, which only needs to determine the correction parameters of the system once, simplifies the correction process, and eliminates the cumbersome operation of the traditional prospective correction method.

[0024] 3. The present application uses a fluorescent calibration slide for illumination correction, and the foreground signal distribution functions at different focal planes in a certain focal plane range are the same, so it is not necessary to adjust the focal plane of the fluorescent calibration slide to the corresponding cell sample to determine the correction parameters.

[0025] 4. The present application uses a fluorescent calibration slide for illumination correction, and the foreground signal distribution functions under different imaging conditions can be linearly converted, so even if the imaging conditions are different from the cells, the foreground correction coefficient can be accurately determined.

[0026] 5. The present application uses a fluorescent calibration slide for illumination correction, and the fluorescent medium is more uniform than the fluorescent solution in the traditional prospective correction method, which eliminates the influence of the non-uniformity of the fluorescent sample itself on the determination of the foreground correction coefficient.

[0027] 6. The present application uses the same substrate solution as the cell sample, which can approximately correct the background fluorescence non-uniformity caused by the substrate solution in the cell sample, thereby being applicable to samples in various substrate solutions and effectively correcting the imaging non-uniformity caused by the background.

[0028] 7、The present application is based on the established illumination correction model, the fluorescence signal of the to-be-measured region is normalized to maximum value correction;This normalization processing based on physical model can effectively correct the measurement error caused by uneven illumination;Taking MCF7 cell sample as an example, the fluorescence intensity error of different regions in the sample is more than 50% without correction, and after correction by the method of the present application, the error is reduced to within 10%, thereby greatly improving the amount of usable data in a single frame large field image while ensuring the accuracy of quantitative FRET measurement.

[0029] 8、The illumination correction method provided by the present application has the advantages of simple operation, wide applicability, high correction precision, etc., and can effectively solve the problems in the prior art, and provides a reliable solution for fluorescence quantitative analysis in the biomedical field, especially quantitative FRET imaging. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a flow chart of the method of the present application;

[0031] Figure 2 is a schematic diagram of the optical path of the quantitative FRET microscopic imaging system;

[0032] Figure 3 is a flow chart for determining the background signal distribution function and the background correction coefficient;

[0033] Figure 4 (a) is the imaging spectrum diagram of the fluorescence calibration slide obtained in the AA detection channel;

[0034] Figure 4 (b) is the imaging spectrum diagram of the fluorescence calibration slide obtained in the DA detection channel;

[0035] Figure 4 (c) is the imaging spectrum diagram of the fluorescence calibration slide obtained in the DD detection channel;

[0036] Figure 5 is a linear relationship diagram of the foreground correction coefficient and the optical power;

[0037] Figure 6 is a linear relationship diagram of the foreground correction coefficient and the exposure time;

[0038] Figure 7 is a Quantile-Quantile diagram of the foreground correction coefficient and the focal plane position;

[0039] Figure 8 is a flow chart for determining the foreground signal distribution function and the foreground correction coefficient;

[0040] Figure 9It is a flow chart for determining the dark field, background and foreground signal distribution functions and background and foreground correction coefficients of the three FERT channels;

[0041] Figure 10 This is a comparison of cell fluorescence images before and after illumination correction;

[0042] Figure 11 (a) Box plot of the quantitative FRET analysis before and after illumination correction for the system correction factor G;

[0043] Figure 11 (b) Box plot of the k-factor before and after illumination correction for quantitative FRET analysis;

[0044] Figure 11 (c) is the efficiency E D Box plots of quantitative FRET analysis before and after illumination correction;

[0045] Figure 11 (d) is the acceptor-donor concentration ratio R C Box plots of quantitative FRET analysis before and after illumination correction;

[0046] Figure 12 (a) Error bar graph of quantitative FRET analysis before and after illumination correction for the system correction factor G;

[0047] Figure 12 (b) is the error bar graph of the k factor before and after illumination correction for quantitative FRET analysis;

[0048] Figure 12 (c) is the efficiency E D Error bars of quantitative FRET analysis before and after illumination correction;

[0049] Figure 12 (d) is the acceptor-donor concentration ratio R C Error bars of quantitative FRET analysis before and after illumination correction. DETAILED DESCRIPTION

[0050] 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.

[0051] Example

[0052] like Figure 1 As shown, this embodiment provides an illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy imaging, comprising the following steps:

[0053] S1. Establish an image non-uniformity correction model, divide the image into foreground signal, background signal, and dark field signal, measure the distribution of different signals, and correct their non-uniformity to obtain an image with uniform illumination;

[0054] S2, determining the dark-field signal distribution function, turning off the illumination light source of the microscopic imaging system and shielding the interference of the external environment light, continuously collecting multiple images under the lowest exposure time of the camera, and taking the average of the collected images to obtain the dark-field signal distribution function;

[0055] S3, determining the background signal distribution function and the background correction coefficient, taking the culture medium without cells as the sample, continuously collecting multiple images under the same imaging conditions as the cells, taking the average of the collected images, then subtracting the dark-field signal distribution function obtained in step S2 to obtain the background signal distribution function, and calculating the background correction coefficient based on the obtained background signal distribution function pixel by pixel;

[0056] S4, determining the foreground signal distribution function and the foreground correction coefficient, taking the fluorescent calibration slide as the sample, continuously collecting multiple images under the non-exposure condition, taking the average of the collected images, then subtracting the dark-field signal distribution function and the background signal distribution function to obtain the foreground signal distribution function, and calculating the foreground correction coefficient based on the obtained foreground signal distribution function pixel by pixel;

[0057] S5, repeating steps S2-S4 to obtain the dark-field, background and foreground signal distribution functions of the FRET three-channel and the background and foreground correction coefficients;

[0058] S6, based on the image non-uniformity correction model, using the determined signal distribution function and correction coefficient to correct the non-uniformity of the live cell FRET three-channel fluorescence image; based on the image non-uniformity correction model established in step S1, according to the FRET three-channel background signal distribution function and the background correction coefficient obtained in step S5, taking the average gray value of the background uniform area as the ideal gray value, performing pixel-by-pixel background correction on the live cell FRET three-channel fluorescence image, according to the foreground signal distribution function and the foreground correction coefficient obtained in step S5, taking the average gray value of the foreground uniform area as the ideal gray value, performing pixel-by-pixel foreground correction on the live cell FRET three-channel fluorescence image, and finally outputting the corrected uniform FRET three-channel fluorescence image.

[0059] As Figure 2As shown, the light path of the quantitative FRET microscopic imaging system is shown in the figure, the LED light source is folded through the collimating mirror, the mirror and the dichroic mirror, a part of the objective lens is irradiated on the sample, the emission light of the sample is enlarged through the objective lens and the dichroic mirror, the mirror, and then the intermediate image is formed through the tube lens. The intermediate image is divided into two channels through the relay lens, the dichroic mirror and the filter, and finally imaged through the camera sensor. The factors causing the non-uniformity of the image include three aspects: first, the light source irradiated on the sample surface shows a trend of attenuation from the middle to the periphery; second, the non-uniformity of the autofluorescence of the sample background; third, the difference in the proportional change of the image gray scale between each channel. Fully considering the above three factors, the image non-uniformity correction model is established as follows in step S1:

[0060]

[0061] F cell (x,y)=I cell (x,y)-B(x,y)-D(x,y)

[0062] Wherein, is the corrected cell gray value, FCF(x,y) is the foreground correction coefficient, F cell (x,y) is the foreground signal distribution function of the cell fluorescence image, I cell (x,y) is the measured gray value of the cell fluorescence image, BCF(x,y) is the background correction coefficient, B(x,y) is the background signal distribution function of the cell fluorescence image, and D(x,y) is the dark field signal distribution function.

[0063] In this embodiment, the specific process of step S2 for determining the dark field signal distribution function is as follows:

[0064]

[0065] Wherein, D(x,y) is the dark field signal distribution function, I D(i) (x,y) is the i-th image continuously collected under the condition that the illumination light source of the microscopic imaging system is turned off and other environmental light is shielded, and n is the total number of images continuously collected.

[0066] The digital camera imaging process can be regarded as the mapping of the light reflected (or irradiated) by the object to the image gray value; this process includes the light entering the lens, the image sensor converting the photons incident within the exposure time into electrons to form an analog signal, forming a voltage signal through the output amplifier, and then converting into a digital signal through the analog-to-digital converter, and finally mapping to the image gray value. However, each pixel will have an inherent dark current bias value, and there will be random fluctuations between different pixels, which constitute the dark field signal distribution. Therefore, measuring the dark field signal distribution function in a light-free environment is to calibrate the initial gray bias and part of the noise signal of each pixel.

[0067] In this embodiment, as shown in Figure 3 the specific process of step S3 for determining the background signal distribution function and the background correction coefficient is as follows:

[0068] S301, determining the background signal distribution function B(x, y); taking the culture medium without cells as a sample, continuously collecting multiple images under the same imaging conditions as the cells, and taking the average of the collected images, then subtracting the dark field signal distribution function obtained in step S2, thereby obtaining the background signal distribution function:

[0069]

[0070] wherein B(x, y) is the background signal distribution function, I B(i) (x, y) is the i-th frame of image continuously collected under the same imaging conditions as the cells with the illumination light source of the microscopic imaging system turned on, D(x, y) is the dark field signal distribution function obtained in step S2, and n is the total number of frames of the images continuously collected;

[0071] S302, obtaining the relative background center point (x B ,y B ); performing median filtering processing on the collected background signal distribution function, selecting a neighborhood window size of 3pixel*3pixe for median filtering, and selecting a pixel window size of 100pixel*100pixe, sequentially traversing the pixel window to calculate the average gray value, selecting the center point of the pixel window with the maximum average gray value as the relative background center point, denoted as (x B ,y B );

[0072] S303, determining the background correction coefficient BCF (Background Correction Factor); based on the obtained background signal distribution function, calculating the background correction coefficient BCF(x, y) pixel by pixel for the collected images, and the calculation formula is:

[0073]

[0074] wherein BCF(x, y) is the background correction coefficient, B(x, y) is the background signal distribution function, and B(x B ,y B ) is the intensity of the relative background center point.

[0075] In this embodiment, when the foreground signal distribution function and the foreground correction coefficient are determined in step S4, the illumination light source of the microscopic imaging system needs to be turned on, and the image of the fluorescence calibration slide is collected. The fluorescence calibration slide usually has multiple different colors, which correspond to different imaging channels. In order to ensure accurate calibration of each imaging channel, the fluorescence calibration slide used should be consistent with the spectral composition of the sample to be corrected. That is, different imaging channels should be calibrated using different color fluorescence calibration slides, as shown in Figure 4 (a), Figure 4 (b), Figure 4 (c). Among them, Figure 4 (a) is the imaging spectral graph obtained by the AA detection channel, indicating that the ORANGE and RED color fluorescence calibration slides are more consistent with the spectral composition of the AA channel of the actual cell imaging, so the ORANGE and RED fluorescence calibration slides are selected to correct the AA channel; Figure 4 (b) is the imaging spectral graph obtained by the DA detection channel, indicating that the ORANGE and YELLOW color fluorescence calibration slides are more consistent with the spectral composition of the DA channel of the actual cell imaging, so the ORANGE and YELLOW fluorescence calibration slides are selected to correct the DA channel; Figure 4 (c) is the imaging spectral graph obtained by the DD detection channel, indicating that the ORANGE and YELLOW and GREEN color fluorescence calibration slides are more consistent with the spectral composition of the DD channel of the actual cell imaging, so the YELLOW and GREEN fluorescence calibration slides are selected to correct the DD channel.

[0076] In fluorescence microscopic imaging, the fluorescence intensity is linearly related to the excitation light power I ex , exposure time t, fluorescence quantum yield Q, extinction coefficient ε, etc., as follows:

[0077] I(x,y)=K(x,y)·I ex εQL1L2ct+D(x,y)

[0078] Where I(x,y) is the image gray value, I ex is the excitation light power, ε is the extinction coefficient, L1 and L2 are the transmittance of the laser and emission light path respectively, t is the exposure time, Q is the fluorescence quantum yield, c is the fluorescence solution concentration, K(x,y) is a constant related to the measurement system, and represents the non-uniformity of the fluorescence image.

[0079] Through experimental verification, the foreground correction coefficient is mainly related to the field of view angle corresponding to different positions, and there is a certain linear conversion relationship with the light power and the exposure time, as shown in Figure 5 and Figure 6 . Among them, Figure 5The linear variation of a single point with the increase of light power and the linear conversion relationship between different points are shown. Figure 6 The linear variation of a single point with the increase of exposure time and the linear conversion relationship between different points are shown.

[0080] Meanwhile, for the linear imaging system, changing the focal plane position within a certain range will not significantly affect the determination of the foreground correction coefficient, as shown in Figure 7 . Figure 7 For the Quantile-Quantile diagram between the image of the fluorescent calibration slide with a fixed focal plane and the image of the fluorescent calibration slide with different focal planes, it can be seen that the quantile points between different focal planes are basically maintained on the same fitted straight line. This shows that within a certain range of focal plane changes, the determination result of the foreground correction coefficient remains stable. In summary, for changes in light intensity, exposure time, and focal plane position within a certain range, they will not significantly affect the determination of the foreground correction coefficient.

[0081] Specifically, as shown in Figure 8 , the specific process of step S4 is as follows:

[0082] S401, determining the foreground signal distribution function F(x, y); taking the fluorescent calibration slide as the sample, continuously collecting multiple images under the condition of no overexposure, and taking the average of the collected images, then subtracting the dark field signal distribution function and the background signal distribution function, thereby obtaining the foreground signal distribution function, the specific process is as follows:

[0083]

[0084] Where F(x, y) is the foreground signal distribution function, I F(i) (x, y) is the i-th frame of image continuously collected under the condition of no overexposure by turning on the illumination light source of the microscopic imaging system and taking the fluorescent calibration slide as the sample, B(x, y) is the background signal distribution function obtained in step S3, D(x, y) is the dark field signal distribution function obtained in step S2, and n is the total number of continuously collected images;

[0085] S402, obtaining the relative foreground center point (x F ,y F ); median filtering processing is performed on the collected foreground distribution function, the neighborhood window size selected by the median filtering is 3pixel*3pixel, and a pixel window with a size of 100pixel*100pixel is selected, the average gray value of the pixel window is calculated in turn, and the center point of the pixel window with the maximum average gray value is selected as the relative foreground center point, denoted as (x F ,y F );

[0086] S403, determining a foreground correction factor FCF (Foreground Correction Factor); calculating foreground correction factor FCF(x, y) for each pixel of the FRET three-channel image based on the acquired foreground signal distribution function, the calculation formula is:

[0087]

[0088] Wherein, FCF(x, y) is the foreground correction factor, F(x, y) is the foreground signal distribution function, F(x F ,y F ) is the intensity relative to the foreground center point.

[0089] In this embodiment, as shown in Figure 9 , the specific process of step S5 is as follows:

[0090] S501, repeat step S2 to determine the dark field signal distribution function of FRET three channels:

[0091]

[0092]

[0093]

[0094] Wherein, D AA (x, y), D DA (x, y), D DD (x, y) are the dark field signal distribution functions corresponding to the three imaging channels of FRET respectively, are the i-th frame images acquired by setting the minimum exposure time of the camera continuously under the condition that the illumination light source of the microscopic imaging system is turned off and other ambient light is shielded, n is the total number of images acquired continuously by the three imaging channels of FRET respectively;

[0095] S502, repeat step S3 to determine the background signal distribution function and the background correction factor of FRET three channels;

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] wherein, B AA (x, y), B DA (x, y), B DD (x, y) are the background signal distribution functions of the three imaging channels of FRET, respectively, BCF AA (x, y), BCF DA (x, y), BCF DD (x, y) are the background correction coefficients of the three channels of FRET, respectively, are the ith frame images of the three imaging channels of FRET acquired continuously with the illumination light source of the microscopic imaging system turned on and under the same imaging conditions as the cells, D AA (x, y), D DA (x, y), D DD (x, y) are the dark field signal distribution functions of the three channels of FRET obtained in step S501, n is the total number of frames of images acquired continuously by the three imaging channels of FRET, respectively, are the intensities of the relative background center points corresponding to the three imaging channels of FRET, respectively;

[0103] S503, repeat step S4 to determine the foreground signal distribution functions and foreground correction coefficients of the three channels of FRET:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] wherein, F AA (x, y), F DA (x, y), F DD (x, y) are the foreground signal distribution functions of the three imaging channels of FRET, respectively, FCF AA (x, y), FCF DA (x, y), FCF DD (x, y) are the foreground correction coefficients of the three channels of FRET, respectively, are the ith frame images of the three imaging channels of FRET acquired continuously with the illumination light source of the microscopic imaging system turned on and under the same imaging conditions as the cells, B AA (x, y), BDA (x,y),B DD (x, y) is the FRET three-channel background signal distribution function D obtained in step S502 AA (x,y),D DA (x, y)D DD (x, is the FRET three-channel dark field signal distribution function obtained in step S501, n is the total number of image frames continuously collected by each of the three FRET imaging channels, These are the intensities of the relative foreground center point corresponding to the three FRET imaging channels.

[0111] In this embodiment, the specific process of step S6 is as follows:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, The grayscale values ​​of the cell fluorescence images of the three channels after FRET correction, FCF AA (x,y), FCF DA (x,y), FCF DD (x,y) are the foreground correction coefficients corresponding to the three FRET channels respectively, are the foreground signal distribution functions of the cell fluorescence image in the three FRET channels, The measured gray values ​​of the cell fluorescence images of the three FRET channels are respectively AA (x,y), BCF DA (x,y), BCF DD (x, y) are the background correction coefficients corresponding to the three FRET channels, B AA (x,y),B DA (x,y),B DD (x, y) are the background signal distribution functions corresponding to the three FRET imaging channels, D AA (x,y),D DA (x,y),D DD (x, y) are the dark field signal distribution functions corresponding to the three FRET imaging channels respectively.

[0119] After the illumination correction, the images acquired by the quantitative FRET microscopy system have good brightness, contrast and uniformity. Taking MCF7 cells as an example, a single cell is selected to move along the diagonal line of the image sensor, and the gray level change of the cell is measured. Before correction, there is a significant difference in the gray distribution of the cell along the diagonal line, and after correction, the gray distribution level of the cell is significantly improved, as shown in Figure 10 In addition, under the above imaging conditions, the level of photobleaching within five minutes is maintained below 5%, basically excluding the influence of photobleaching.

[0120] These results show that the illumination correction technique can effectively correct the non-uniformity factors in the microscopic imaging system, providing high-quality and uniform image data for subsequent quantitative FRET analysis, which is crucial for improving the accuracy and reliability of FRET quantitative analysis.

[0121] FRET technology is a method for indirectly detecting intermolecular interaction by measuring the change of fluorescence intensity. In FRET, the image of the fluorescent sample is calculated by first subtracting the background, and the background is usually selected as the region near the ROI of the fluorescent sample image to be subtracted. However, this operation is tedious and requires high requirements for the circle point; then the calculation is based on the image after subtracting the background, so the non-uniformity of the background seriously affects the calculation accuracy of quantitative FRET. Due to the non-uniformity of illumination, the fluorescence intensity of the ROI at the non-center of the field of view is reduced, which greatly affects the amount of usable data and SBR (Signal Background Ratio) in a single frame of large field of view image. Therefore, by correcting the background and foreground non-uniformity, the background subtraction process is simplified, the amount of usable data and SBR is increased, and the accuracy of quantitative FRET measurement is ensured. Taking MCF7 cell standard plasmid C4Y as an example, as shown in Figure 11 (a)、 Figure 11 (b)、 Figure 11 (c)、 Figure 11 (d)、 Figure 12 (a)、 Figure 12 (b)、 Figure 12 (c)、 Figure 12 (d), after the illumination correction, the standard deviation of the system correction factor G is reduced from the original 0.827 to 0.278, the standard deviation of k is reduced from the original 0.129 to 0.112, the standard deviation of the efficiency E D is reduced from the original 0.022 to 0.007, and the standard deviation of the donor concentration ratio R C is reduced from the original 0.19 to 0.165. The quantitative FRET result is effectively improved, further proving the important role of the illumination correction technique in improving the accuracy of FRET quantification.

[0122] Based on the same inventive concept, the present application also proposes an illumination light field correction system for quantitative fluorescence resonance energy transfer microscopic imaging, comprising:

[0123] An image non-uniformity correction model establishment module is configured to divide the image into foreground signals, background signals and dark field signals, correct the non-uniformity of different signals by determining the distribution of different signals, and obtain an image with uniform illumination.

[0124] A dark field signal distribution function determination module is configured to continuously collect multiple images at the lowest exposure time of the camera by turning off the illumination light source of the microscopic imaging system and shielding the interference of external ambient light, and obtain the dark field signal distribution function by averaging the collected images.

[0125] A background signal distribution function and background correction coefficient determination module is configured to take the culture medium without cells as a sample, continuously collect multiple images under the same imaging conditions as the cells, average the collected images, subtract the dark field signal distribution function obtained by the dark field signal distribution function determination module, and obtain the background signal distribution function, and calculate the background correction coefficient pixel by pixel based on the obtained background signal distribution function.

[0126] A foreground signal distribution function and foreground correction coefficient determination module is configured to take a fluorescent calibration slide as a sample, continuously collect multiple images under non-exposure conditions, average the collected images, subtract the dark field signal distribution function and the background signal distribution function, and obtain the foreground signal distribution function, and calculate the foreground correction coefficient pixel by pixel based on the obtained foreground signal distribution function.

[0127] A FRET three-channel determination module is configured to repeat the dark field signal distribution function determination module, the background signal distribution function and the background correction coefficient determination module, and the foreground signal distribution function and the foreground correction coefficient determination module to obtain the dark field, the background and the foreground signal distribution functions of the FRET three channels and the background and the foreground correction coefficients, respectively.

[0128] A live cell FRET three-channel fluorescence image non-uniformity correction module is configured to perform pixel-by-pixel background correction on the live cell FRET three-channel fluorescence image according to the FRET three-channel background signal distribution function and the background correction coefficient obtained by the FRET three-channel determination module, taking the average gray value of the background uniform area as the ideal gray value, perform pixel-by-pixel foreground correction on the live cell FRET three-channel fluorescence image according to the FRET three-channel foreground signal distribution function and the foreground correction coefficient obtained by the FRET three-channel determination module, taking the average gray value of the foreground uniform area as the ideal gray value, and finally output the corrected uniform FRET three-channel fluorescence image.

[0129] The above embodiments are the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above embodiments, and any changes, modifications, substitutions, combinations, simplifications, etc. made without departing from the spirit and principles of the present application should be equivalent replacement manners and should be included in the protection scope of the present application.

Claims

1. A method for calibrating the illumination light field for quantitative fluorescence resonance energy transfer microscopy, characterized in that: The following steps are involved: S1. Establish an image non-uniformity correction model, divide the image into foreground signal, background signal, and dark field signal, measure the distribution of different signals, and correct their non-uniformity to obtain an image with uniform illumination; S2. Determine the dark field signal distribution function, turn off the illumination source of the microscopic imaging system and shield the interference of external ambient light, continuously capture multiple frames of images at the minimum exposure time of the camera, and average the captured images to obtain the dark field signal distribution function; S3, determining the background signal distribution function and the background correction coefficient, using the cell-free culture medium as a sample, continuously acquiring multiple frames of images under the same imaging conditions as the cells, averaging the acquired images, and then subtracting the dark field signal distribution function obtained in step S2 to obtain the background signal distribution function, and calculating the background correction coefficient pixel by pixel based on the acquired background signal distribution function; S4. Determine a foreground signal distribution function and a foreground correction coefficient. Using a fluorescent calibration slide as a sample, continuously capture multiple frames of images without overexposure, average the captured images, and then subtract the dark field signal distribution function and the background signal distribution function to obtain a foreground signal distribution function. Calculate a foreground correction coefficient pixel by pixel based on the obtained foreground signal distribution function. S5, repeat steps S2-S4 to obtain the dark field, background and foreground signal distribution functions and background and foreground correction coefficients of the three FRET channels respectively; S6. Based on the image non-uniformity correction model, the non-uniformity of the living cell FRET three-channel fluorescence image is corrected using the measured signal distribution function and correction coefficient. Based on the image non-uniformity correction model established in step S1, the FRET three-channel background signal distribution function and background correction coefficient obtained in step S5 are used, and the average grayscale of the background uniform area is used as the ideal grayscale. The living cell FRET three-channel fluorescence image is subjected to pixel-by-pixel background correction. Based on the foreground signal distribution function and foreground correction coefficient obtained in step S5, the average grayscale of the foreground uniform area is used as the ideal grayscale. Finally, a corrected uniform FRET three-channel fluorescence image is output.

2. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: Step S1 establishes an image non-uniformity correction model as follows: F cell (x,y)=I cell (x,y)-B(x,y)-D(x,y) in, is the corrected cell grayscale value, FCF(x,y) is the foreground correction coefficient, F cell (x,y) is the foreground signal distribution function of the cell fluorescence image, I cell (x, y) is the measured grayscale value of the cell fluorescence image, BCF(x, y) is the background correction coefficient, B(x, y) is the background signal distribution function of the cell fluorescence image, and D(x, y) is the dark field signal distribution function.

3. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: The specific process of determining the dark field signal distribution function in step S2 is as follows: Where D(x,y) is the dark field signal distribution function, I D(i) (x, y) is the i-th frame of image continuously acquired with the camera’s minimum exposure time set while the microscopic imaging system illumination light source is turned off and other ambient light is shielded. n is the total number of image frames acquired continuously.

4. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: The specific process of step S3 is as follows: S301. Determine the background signal distribution function B(x, y). Specifically, take a cell-free culture medium as a sample, continuously capture multiple frames of images under the same imaging conditions as the cells, average the captured images, and then subtract the dark field signal distribution function obtained in step S2 to obtain the background signal distribution function: Among them, B(x,y) is the background signal distribution function, I B(i) (x, y) is the i-th frame of image acquired continuously under the same imaging conditions as the cell with the illumination light source of the microscopic imaging system turned on, D(x, y) is the dark field signal distribution function obtained in step S2, and n is the total number of image frames acquired continuously; S302, obtain the relative background center point (x B ,y B ), specifically: perform median filtering on the collected background signal distribution function, select a neighborhood window size of m pixel*m pixel for median filtering, and select a pixel window of p pixel*p pixel size, traverse the pixel window in turn to calculate its average grayscale value, and select the center point of the pixel window with the largest average grayscale value as the relative background center point, recorded as (x B ,y B ); S303, determining the background correction coefficient BCF(x,y); calculating the background correction coefficient BCF(x,y) pixel by pixel for the collected image based on the acquired background signal distribution function, using the following formula: Among them, BCF(x,y) is the background correction coefficient, B(x,y) is the background signal distribution function, and B(x B ,y B ) is the intensity relative to the background center point.

5. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: The specific process of step S4 is as follows: S401. Determine the foreground signal distribution function F(x, y). Specifically, using a fluorescent calibration slide as a sample, continuously capture multiple frames of images without overexposure, average the captured images, and then subtract the dark field signal distribution function and the background signal distribution function to obtain the foreground signal distribution function. The specific process is as follows: Among them, F(x,y) is the foreground signal distribution function, I F(i) (x, y) is the i-th frame of image acquired continuously without overexposure, with the microscope illumination system turned on and the fluorescence calibration slide as the sample. B(x, y) is the background signal distribution function obtained in step S3, D(x, y) is the dark field signal distribution function obtained in step S2, and n is the total number of image frames acquired continuously. S402, obtain the relative foreground center point (x F ,y F ), specifically: perform median filtering on the collected foreground distribution function, select a neighborhood window size of n pixel*n pixel for median filtering, and select a pixel window of size q pixel*q pixel, traverse the pixel window in turn to calculate its average grayscale value, and select the center point of the pixel window with the largest average grayscale value as the relative foreground center point, recorded as (x F ,y F ); S403, determining the foreground correction coefficient FCF(x,y), specifically: calculating the foreground correction coefficient FCF(x,y) pixel by pixel for the FRET three-channel image based on the acquired foreground signal distribution function, and the calculation formula is: Among them, FCF(x,y) is the foreground correction coefficient, F(x,y) is the foreground signal distribution function, and F(x F ,y F ) is the intensity relative to the foreground center point.

6. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: The specific process of step S5 is as follows: S501, repeat step S2 to measure the dark field signal distribution function of the three FRET channels: Among them, D AA (x,y),D DA (x,y),D DD (x, y) are the dark field signal distribution functions corresponding to the three FRET imaging channels respectively. The i-th frame of image is continuously acquired by turning off the illumination source of the microscopic imaging system and shielding other ambient light for the three FRET imaging channels respectively, and setting the minimum exposure time of the camera. n is the total number of image frames continuously acquired by the three FRET imaging channels respectively; S502, repeat step S3 to determine the background signal distribution function and background correction coefficient of the three FRET channels: Among them, B AA (x,y),B DA (x,y),B DD (x, y) are the background signal distribution functions corresponding to the three FRET imaging channels, BCF AA (x,y), BCF DA (x,y), BCF DD (x, y) are the background correction coefficients corresponding to the three FRET channels respectively. The i-th frame image is acquired by turning on the illumination source of the microscopic imaging system for the three FRET imaging channels and continuously acquiring the image under the same imaging conditions as the cells. AA (x,y),D DA (x,y),D DD (x, y) is the FRET three-channel dark field signal distribution function obtained in step S501, n is the total number of image frames continuously collected by the three FRET imaging channels, are the intensities of the relative background center points corresponding to the three FRET imaging channels; S503, repeat step S4 to determine the foreground signal distribution function and foreground correction coefficient of the three FRET channels: Among them, F AA (x,y),F DA (x,y),F DD (x, y) are the foreground signal distribution functions corresponding to the three FRET imaging channels, FCF AA (x,y), FCF DA (x,y), FCF DD (x, y) are the foreground correction coefficients corresponding to the three FRET channels respectively, The illumination light source of the microscope imaging system is turned on for the three FRET imaging channels respectively, and the fluorescence calibration slide is used as the sample. The i-th frame image is continuously collected without overexposure. AA (x,y),B DA (x,y),B DD (x, y) is the FRET three-channel background signal distribution function obtained in step S502, D AA (x,y),D DA (x,y),D DD (x, y) is the FRET three-channel dark field signal distribution function obtained in step S501, n is the total number of image frames continuously collected by the three FRET imaging channels, These are the intensities of the relative foreground center point corresponding to the three FRET imaging channels.

7. The illumination light field correction method for quantitative fluorescence resonance energy transfer microscopy according to claim 1, characterized in that: The specific process of step S6 is as follows: in, The grayscale values ​​of the cell fluorescence images of the three channels after FRET correction, FCF AA (x,y), FCF DA (x,y), FCF DD (x, y) are the foreground correction coefficients corresponding to the three FRET channels respectively, are the foreground signal distribution functions of the cell fluorescence image in the three FRET channels, The measured gray values ​​of the cell fluorescence images of the three FRET channels are respectively AA (x,y), BCF DA (x,y), BCF DD (x, y) are the background correction coefficients corresponding to the three FRET channels, B AA (x,y),B DA (x,y),B DD (x, y) are the background signal distribution functions corresponding to the three FRET imaging channels, D AA (x,y),D DA (x,y),D DD (x, y) are the dark field signal distribution functions corresponding to the three FRET imaging channels respectively.

8. An illumination light field correction system for quantitative fluorescence resonance energy transfer microscopy, characterized in that: include: Image non-uniformity correction model building module, used to divide the image into foreground signal, background signal and dark field signal, and to obtain a uniformly illuminated image by measuring the distribution of different signals and correcting their non-uniformity; The dark field signal distribution function measurement module turns off the illumination source of the microscopic imaging system and shields the interference of external ambient light, continuously captures multiple frames of images at the camera's minimum exposure time, and averages the captured images to obtain the dark field signal distribution function; A background signal distribution function and background correction coefficient determination module uses a cell-free culture medium as a sample, continuously acquires multiple frames of images under the same imaging conditions as the cells, averages the acquired images, and then subtracts the dark field signal distribution function obtained by the dark field signal distribution function determination module to obtain a background signal distribution function. The background correction coefficient is calculated pixel by pixel based on the obtained background signal distribution function; The foreground signal distribution function and foreground correction coefficient determination module uses a fluorescent calibration slide as a sample, continuously acquires multiple frames of images without overexposure, averages the acquired images, and then subtracts the dark field signal distribution function and the background signal distribution function to obtain the foreground signal distribution function. The background correction coefficient is calculated pixel by pixel based on the obtained foreground signal distribution function. The FRET three-channel measurement module repeats the dark field signal distribution function measurement module, the background signal distribution function and background correction coefficient measurement module, and the foreground signal distribution function and foreground correction coefficient measurement module to obtain the dark field, background and foreground signal distribution functions and background and foreground correction coefficients of the FRET three channels respectively; The non-uniformity correction module for the living cell FRET three-channel fluorescence image is used to perform pixel-by-pixel background correction on the living cell FRET three-channel fluorescence image based on the established image non-uniformity correction model, the FRET three-channel background signal distribution function and background correction coefficient obtained by the FRET three-channel measurement module, and the average grayscale of the background uniform area as the ideal grayscale. The living cell FRET three-channel fluorescence image is also used to perform pixel-by-pixel foreground correction on the living cell FRET three-channel fluorescence image based on the FRET three-channel foreground signal distribution function and foreground correction coefficient obtained by the FRET three-channel measurement module, and the average grayscale of the foreground uniform area as the ideal grayscale. Finally, the corrected uniform FRET three-channel fluorescence image is output.

Images