Super-resolution FRET image reconstruction method based on acceptor dipole resonance constraint between donor and acceptor molecules

Abstract

The application discloses a kind of super-resolution FRET image reconstruction methods based on donor-acceptor intermolecular dipole resonance constraint, first by the structured light illumination super-resolution microscopic imaging system obtains FRET three-channel original image data set and carries out image pre-processing;Then based on donor-acceptor molecular dipole resonance prior information, the super-resolution SIM image is associatedly constrained, constructs SI-FRET unified forward model and objective function and carries out forward iteration solution, obtains the FRET three-channel fluorescence distribution of reconstruction;Finally, super-resolution FRET efficiency distribution and donor concentration ratio distribution are obtained by pixel-by-pixel calculation.The application uses donor-acceptor molecular dipole resonance prior information to associate the constraint of super-resolution SIM imaging, realizes spectrum expansion by forward iteration solution to carry out FRET reconstruction, improves the fidelity of super-resolution quantitative FRET imaging and the accuracy of quantitative analysis.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of super-resolution fluorescence resonance energy transfer (FRET) imaging, and particularly relates to a super-resolution FRET image reconstruction method based on acceptor-donor intermolecular dipole resonance constraint. BACKGROUND

[0002] Fluorescence resonance energy transfer (FRET) is an important technical means for detecting the structure and dynamic interaction of biological macromolecules in living cells. FRET can quantitatively and dynamically analyze the interaction and conformational changes of biological macromolecules at a scale of 1-10 nm in living cells. However, due to the limitation of spatial resolution by the optical diffraction limit, the spatial resolution of FRET imaging based on wide-field microscopy or confocal microscopy is greater than 200 nm, making it difficult to reveal the structural information of biological macromolecules at the subcellular structure. By combining super-resolution structured illumination microscopy (SR-SIM) with FRET imaging technology, super-resolution FRET microscopy (SIM-FRET) based on structured illumination is developed to improve the spatial resolution of FRET technology and make FERT detection more accurate at the subcellular level, providing a new method for analyzing the dynamic process of biological macromolecule interaction in subcellular organelles of living cells.

[0003] Currently, super-resolution SIM-FRET imaging adopts a two-step image reconstruction strategy of first SIM reconstruction and then quantitative FRET analysis, and its fidelity is restricted by SIM reconstruction artifacts. The linear Wiener SIM reconstruction method can maintain the gray scale fidelity of the reconstructed image, but it will produce significant magnified noise artifacts, and the artifacts will further accumulate in FRET algebraic operation, thereby affecting the quantitative FRET analysis. A method is proposed in the patent with application number 2022103326816 to filter out random noise artifacts after SIM reconstruction and then perform quantitative FRET analysis using the co-localization mask. This method generates a binary mask using the spatial co-localization characteristics of FRET, and filters out false positive FRET signals caused by noise artifacts through the mask; however, this method relies on manual threshold selection for mask generation, and cannot completely eliminate the noise artifacts superimposed on the FRET signal. Another Chinese patent (application number: 202310581280.9) proposes a method of super-resolution SIM-FRET reconstruction after denoising the original image data by a self-supervised learning denoising neural network. This method denoises the original fluorescence image by a self-supervised learning denoising neural network for preprocessing.

[0004] The prior arts above are all based on the super-resolution SIM-FRET two-step reconstruction strategy, but have the following shortcomings: first, due to the lack of FRET prior information constraint in the first step SIM image reconstruction process, the SIM reconstruction and FRET analysis are two independent processes, resulting in the super-resolution FRET signal not being faithful. Second, the artifacts caused by the step-by-step reconstruction steps are difficult to completely eliminate, affecting the accuracy of quantitative FRET analysis. SUMMARY

[0005] The main purpose of the present application is to overcome the shortcomings and deficiencies of the prior art, and provide a super-resolution FRET image reconstruction method based on the intermolecular dipole resonance constraint of the donor and acceptor molecules, which uses the donor-acceptor molecular dipole resonance prior information to correlate and constrain the super-resolution SIM imaging, and then constructs a SI-FRET unified forward model and a reconstruction objective function, realizes the reconstruction of the super-resolution FRET sample fluorescence signal through forward iterative solution, and obtains the super-resolution FRET efficiency distribution and the donor concentration ratio distribution, thereby improving the fidelity of the super-resolution quantitative FRET imaging and the accuracy of the quantitative analysis.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] The first purpose is to provide a super-resolution FRET image reconstruction method based on the intermolecular dipole resonance constraint of the donor and acceptor molecules, comprising the following steps:

[0008] Obtain a FRET three-channel raw image data set for imaging a FRET sample of living cells by a structured light illumination super-resolution microscopic imaging system; the FRET three-channel includes a DD channel of donor excitation donor emission, a DA channel of donor excitation acceptor emission and an AA channel of acceptor excitation acceptor emission;

[0009] Perform image preprocessing on the FRET three-channel raw image data set to obtain a preprocessed FRET three-channel image data set;

[0010] Correlate and constrain the super-resolution SIM imaging based on the donor-acceptor molecular dipole resonance prior information, and construct a SI-FRET unified forward model; the SI-FRET unified forward model includes a FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, a FRET three-channel cosine structured light field and a FRET three-channel point spread function;

[0011] Take the FRET sample fluorescence distribution as the reconstruction target, establish an objective function according to the SI-FRET unified forward model and perform forward iterative solution to obtain the reconstructed FRET sample fluorescence distribution; the FRET sample fluorescence distribution includes a super-resolution donor fluorescence total distribution, a super-resolution acceptor fluorescence total distribution and a super-resolution sensitized FRET fluorescence distribution;

[0012] The super-resolution FRET efficiency distribution and the donor concentration ratio distribution are obtained by pixel-by-pixel calculation according to the reconstructed super-resolution donor total fluorescence distribution, the super-resolution acceptor total fluorescence distribution and the super-resolution sensitized FRET fluorescence distribution.

[0013] As a preferred technical solution, the image preprocessing comprises:

[0014] Taking the AA channel original image as a reference, the DD channel and DA channel original images are aligned pixel by pixel through affine transformation to obtain a FRET three-channel registration image data set; the affine transformation is a linear transformation operation, including rotation, translation, scaling and reflection;

[0015] The FRET three-channel registration image data set is subjected to pixel-by-pixel gray value statistics, and the first peak value in the gray value histogram is taken as the background gray value to correct the FRET three-channel registration image data set pixel by pixel to obtain a preprocessed FRET three-channel image data set.

[0016] As a preferred technical solution, the SI-FRET unified forward model is constructed, specifically:

[0017] The FRET three-channel spectral mixing parameter matrix is measured by the FRET reference sample; the FRET three-channel spectral mixing parameter matrix is used to represent the mixing degree of spectral overlap and crosstalk between the FRET three channels caused by the donor-acceptor molecular dipole resonance physical process, specifically:

[0018]

[0019] Wherein m ij , i, j ∈ [1, 3] represents the contribution degree of the donor fluorophore, the acceptor fluorophore or the sensitized FRET fluorophore in the FRET sample fluorescence distribution X to the fluorescence signal collected by the FRET three channels; the FRET reference sample usually includes a single-turn donor sample, a single-turn acceptor sample and a standard plasmid sample with a donor-to-acceptor ratio of 1:1;

[0020] The preprocessed FRET three-channel image data set is estimated by using a structured light field parameter estimation method to obtain a FRET three-channel cosine structured light field, which is represented as:

[0021]

[0022] Wherein, is the FRET three-channel cosine structured light field, is the DD channel cosine structured light field, the DA channel cosine structured light field or the AA channel cosine structured light field, r is the spatial coordinates of the corresponding channel fluorescence image pixel, and θ and n represent different direction angles and different phases of the structured light field, respectively, a wave vector of the DD channel cosine structured light field, the DA channel cosine structured light field or the AA channel cosine structured light field, a modulation depth of the DD channel cosine structured light field, the DA channel cosine structured light field or the AA channel cosine structured light field, an initial phase of the DD channel cosine structured light field, the DA channel cosine structured light field or the AA channel cosine structured light field;

[0023] The FRET three-channel point spread function is obtained by actually measuring or simulating based on a theoretical model by using a microscopic imaging system, and is expressed as:

[0024]

[0025] wherein H FRET is the FRET three-channel point spread function, H DD (r) is a point spread function of the DD channel, H DA (r) is a point spread function of the DA channel, H AA (r) is a point spread function of the AA channel, and r is a spatial coordinate of a pixel of a fluorescence image of a corresponding channel;

[0026] According to a forward physical process of a structured light excited FRET sample and FRET three-channel image acquisition, a unified forward model of SI-FRET is constructed by combining a donor-acceptor molecular dipole resonance priori information to correlate and constrain super-resolution SIM imaging.

[0027] The unified forward model of SI-FRET includes a FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, a FRET three-channel cosine structured light field and a FRET three-channel point spread function, and is expressed as:

[0028]

[0029] wherein, is the unified forward model of SI-FRET, H FRET is the FRET three-channel point spread function, is the FRET three-channel cosine structured light field, θ and n respectively represent different direction angles and different phases of the structured light field, M is a FRET three-channel spectral mixing parameter matrix, and X = [X1, X2, X3] T is the FRET sample fluorescence distribution, X1 is a super-resolution donor fluorescence total distribution, X2 is a super-resolution acceptor fluorescence total distribution, and X3 is a super-resolution sensitized FRET fluorescence distribution.

[0030] As a preferred technical solution, the FRET sample fluorescence distribution is taken as a reconstruction target, and a reconstruction objective function of the unified forward model of SI-FRET is established, and is expressed as:

[0031]

[0032] Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; The likelihood term is represented using the least squares method:

[0033]

[0034] Where H FRET For FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter;

[0035] Based on the functional properties of the data likelihood term, a suitable optimization algorithm is designed to perform forward iteration to solve the reconstruction objective function of the SI-FRETM unified forward model, thereby obtaining the reconstructed fluorescence distribution of the FRET samples.

[0036] As a preferred technical solution, a regularization term constraining the reconstruction objective function of the SI-FRETM unified forward model is added using prior information from the fluorescence image, establishing the regularized reconstruction objective function of the SI-FRETM unified forward model, expressed as:

[0037]

[0038] Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; Let R(X) be the data likelihood term, R(X) be the regularization term, and λ be the weighting factor that balances the data likelihood term and the regularization term.

[0039] The data likelihood item The likelihood term is characterized using the least squares method:

[0040]

[0041] Where H FRET For FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter;

[0042] The regularization term R(X) is used to incorporate prior information about the fluorescence image;

[0043] Based on the functional properties of the regularization term, a suitable optimization algorithm is designed to perform forward iteration to solve the regularized reconstruction objective function of the SI-FRETM unified forward model, thereby obtaining the reconstructed fluorescence distribution of the FRET samples.

[0044] As a preferred technical solution, the regularization term is spatial continuity regularization and / or non-negative regularization.

[0045] As a preferred technical solution, the calculation method for the super-resolution FRET efficiency distribution and the donor-recipient concentration ratio distribution is as follows:

[0046]

[0047] Among them, E D For donor-centric apparent efficiency, E A For donor-centric apparent efficiency, R C X1 represents the donor-recipient concentration ratio distribution, X2 represents the reconstructed super-resolution donor fluorescence distribution, X3 represents the reconstructed super-resolution acceptor fluorescence distribution, and X4 represents the reconstructed super-resolution sensitized FRET fluorescence distribution.

[0048] The second objective is to provide a super-resolution FRET image reconstruction device based on donor-acceptor intermolecular dipole resonance constraint, which is applied to the above-mentioned super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint, including an image acquisition module, an image preprocessing module, a model building module, an objective solving module, and a FRET calculation module.

[0049] The image acquisition module is used to acquire raw FRET three-channel image data set for imaging live cell FRET samples through a structured light illumination super-resolution microscopy system; the FRET three channels include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission.

[0050] The image preprocessing module is used to preprocess the FRET three-channel raw image data group to obtain the preprocessed FRET three-channel image data group.

[0051] The model building module is used to perform correlation constraints on super-resolution SIM imaging based on donor-acceptor molecular dipole resonance prior information, and to construct a unified SI-FRETM forward model; the unified SI-FRETM forward model includes FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field and FRET three-channel point spread function.

[0052] The objective solving module is used to establish a reconstruction objective function based on the SI-FRETM unified forward model, with the fluorescence distribution of FRET samples as the objective, and to perform forward iterative solving to obtain the reconstructed fluorescence distribution of FRET samples; the fluorescence distribution of FRET samples includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution, and the super-resolution sensitized FRET fluorescence distribution.

[0053] The FRET calculation module is used to obtain the super-resolution FRET efficiency distribution and the donor-recipient concentration ratio distribution by calculating pixel by pixel based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution.

[0054] The third objective is to provide an electronic device, comprising:

[0055] At least one processor; and a memory communicatively connected to said at least one processor; wherein,

[0056] The memory stores computer program instructions that can be executed by the at least one processor, which enables the at least one processor to perform the super-resolution FRET image reconstruction method based on donor-receptor intermolecular dipole resonance constraint described above.

[0057] The fourth objective is to provide a computer-readable storage medium storing a program that, when executed by a processor, implements the aforementioned super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint.

[0058] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0059] 1. This invention introduces FRET physical process information into the image reconstruction process by establishing a structured light-excited FRET unified forward model (SI-FRETM unified forward model) based on donor-acceptor intermolecular dipole resonance constraint, thereby improving the fidelity of super-resolution FRET reconstruction results and the accuracy of quantitative analysis.

[0060] 2. This invention establishes an objective function based on the SI-FRETM unified forward model, and achieves forward iterative reconstruction of the SI-FRETM unified forward model by optimizing and solving the objective function. This eliminates the need for prior FERT three-channel super-resolution SIM reconstruction; instead, it directly utilizes structured light to excite the original FRET three-channel image group to obtain the super-resolution quantitative FRET signal through forward iterative reconstruction. This avoids noise amplification from Wiener SIM reconstruction and noise accumulation from FRET algebraic operations, thus more effectively suppressing noise artifacts. Super-resolution quantitative FRET analysis is achieved for live cell biological samples under low signal-to-noise ratio and low-light conditions.

[0061] 3. The present invention further provides an SI-FRETM regularization objective function, which has good regularization extensibility and can introduce different prior information of fluorescence images to further constrain the solution space. Super-resolution quantitative FRET analysis is achieved by optimizing the solution of the SI-FRETM regularization objective function, which further enhances the applicability and extensibility of the method. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is an overall flowchart of the super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint in an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram showing the FRET efficiency and donor-recipient concentration ratio results of reconstructing a simulated FRET three-channel original image containing 30% Gaussian white noise in an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of the FRET efficiency results of reconstructing experimental data from MCF7 live cells expressing Acta-G17M in an embodiment of the present invention.

[0066] Figure 4 This is a block diagram of the super-resolution FRET image reconstruction system based on intermolecular dipole resonance constraint of donor and acceptor molecules in an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0069] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0070] like Figure 1 As shown, this embodiment of the super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint includes the following steps:

[0071] S1. Obtain raw image data set of FRET three channels for imaging live cell FRET samples using a structured light illumination super-resolution microscopy system; wherein, the FRET three channels include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission.

[0072] Specifically, the FRET three-channel raw image data set includes the donor-excited donor-emitted DD channel raw image data set. Raw image dataset of DA channels emitted by donor-excited receptors Raw image dataset of AA channels eliciting receptor firing Where r = (x, y) are the spatial coordinates of the fluorescence image pixels, and θ and n represent different directional angles and different phases of the structured light field, respectively; in this embodiment, θ = (1, 2, 3) and n = (-1, 0, 1), therefore each channel contains three different directions, and each direction contains three different phases, for a total of 9 original images. The FRET three channels yield a total of 27 original images of structured light modulation. In this embodiment... Figure 3 The GFP-mCherry fluorescent protein pair was selected as the FRET donor-acceptor pair. A 488nm laser was used as the donor excitation light, and the 525±15nm band was used as the detection channel for donor fluorescence emission. A 561nm laser was used as the acceptor excitation light, and the 600±15nm band was used as the detection channel for acceptor fluorescence emission.

[0073] S2. Perform image preprocessing on the FRET three-channel raw image data set to obtain the preprocessed FRET three-channel image data set.

[0074] Further, the image preprocessing steps are as follows:

[0075] S201, Channel Registration: Using the original image of the AA channel as a reference, the original images of the DD and DA channels are aligned pixel by pixel through affine transformation to obtain the FRET three-channel registered image data set; where affine transformation is a linear transformation operation, including linear transformation operations such as rotation, translation, scaling and reflection.

[0076] S202, Background Subtraction: Perform pixel-by-pixel grayscale value statistics on the FRET three-channel registered image data set, and use the grayscale value corresponding to the first peak in the grayscale value histogram as the background grayscale value to perform pixel-by-pixel background correction on the FRET three-channel registered image data set to obtain the preprocessed FRET three-channel image data set.

[0077] S3. Based on the prior information of donor-acceptor molecular dipole resonance, super-resolution SIM imaging is correlated and constrained to construct the SI-FRETM unified forward model (a unified forward model of structured light-excited FRET based on donor-acceptor molecular dipole resonance constraint), including FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field, and FRET three-channel point spread function.

[0078] Furthermore, the construction steps of the SI-FRETM unified forward model are as follows:

[0079] S301. Measure the FRET three-channel spectral mixing parameter matrix using a FRET reference sample. In this application, the FRET three-channel spectral mixing parameters are used to characterize the degree of spectral overlap and crosstalk between the three FRET channels caused by the donor-acceptor molecular dipole resonance physical process. Specifically:

[0080]

[0081] Where m ij , i,j∈[1,3] represent the contribution of donor fluorophores, acceptor fluorophores, or sensitized FRET fluorophores in the fluorescence distribution X of the FRET sample to the fluorescence signal acquired by the three channels of FRET. The FRET three-channel spectral mixing parameter matrix M requires additional FRET reference samples to be determined in advance; the FRET reference samples include single-transfer donor samples, single-transfer acceptor samples, and standard plasmid samples with a donor-acceptor ratio of 1:1.

[0082] This embodiment provides a method for obtaining FRET three-channel spectral mixing parameters using a FRET reference sample. The measurement steps are as follows:

[0083] First, calibration parameters a, b, c, and d for fluorescence crosstalk between donor and acceptor emission spectra were measured using single-transfer donor and single-transfer acceptor samples; a and b were measured using single-transfer donor samples, and c and d were measured using single-transfer acceptor samples. Next, the FRET sensitization quenching conversion factor G (the ratio of expression-sensitized acceptor emission to donor emission quenching) and the ratio k of donor fluorescence intensity to acceptor fluorescence intensity at equimolar concentrations without FRET were determined using a standard plasmid reference sample with a donor-acceptor ratio of 1:1. Finally, the FRET three-channel spectral mixing parameter matrix was obtained, expressed as:

[0084]

[0085] Where a, b, c, and d are calibration parameters for fluorescence crosstalk between the donor and acceptor emission spectra, G is the FRET sensitization quenching conversion factor, and k is the ratio of donor fluorescence intensity to acceptor fluorescence intensity at an equimolar concentration in the absence of FRET. It should be noted that parameters a, b, c, d, G, and k will differ depending on the FRET donor-acceptor fluorescence pair used.

[0086] S302. The FRET three-channel image data set is preprocessed using a structured light field parameter estimation method to estimate and obtain the FRET three-channel cosine structured light field. Specifically, the preprocessed FRET three-channel image data set is estimated using the structured light field parameter estimation method to obtain the wave vector, modulation depth, and initial phase of the cosine structured light field, thus obtaining the FRET three-channel cosine structured light field, expressed as:

[0087]

[0088] The cosine structured light field of each channel is represented as:

[0089]

[0090] in, This represents the FRET three-channel cosine structured optical field. Let represent the cosine structured light field of the DD channel, DA channel, or AA channel, where r is the spatial coordinate of the corresponding pixel in the fluorescence image after processing, and θ and n represent different orientation angles and phases of the structured light field, respectively. Let be the wave vector of the DD-channel cosine structured light field, the DA-channel cosine structured light field, or the AA-channel cosine structured light field. The modulation depth of the DD-channel cosine structured optical field, DA-channel cosine structured optical field, or AA-channel cosine structured optical field. This represents the initial phase of the cosine structured light field in the DD channel, DA channel, or AA channel. Methods for estimating structured light parameters include, but are not limited to, Peak Phase (POP), Non-Iterative Autocorrelation Reconstruction (ACR), Image Reconstruction Transform (IRT), and Cross-Correlation Iteration (COR). In this example, the Cross-Correlation Iteration (COR) method is used for estimating the structured light field parameters.

[0091] S303. The FRET three-channel point spread function is obtained based on actual measurements of the microscopic imaging system or generated based on a theoretical model.

[0092] Specifically, obtaining the point spread function includes, but is not limited to, the FRET three-channel point spread function generated by actual measurement using a microscopic imaging system or simulation based on a theoretical model, expressed as:

[0093]

[0094] Among them, H FRET H is the FRET three-channel point spread function. DD (r) is the point spread function of the DD channel, H DA (r) is the point spread function of the DA channel, H AA (r) is the point spread function of the AA channel, and r is the spatial coordinate of the pixel in the fluorescence image after the corresponding channel is processed.

[0095] In this embodiment, based on the parameters of the microscopic imaging system, including the objective lens numerical aperture, magnification, and emission channel wavelength, an optical transfer function (OTF) is generated using a theoretical model. Then, an inverse Fourier transform is performed on the OTF to obtain the FRET three-channel point spread function (PSF). The optical transfer function (OTF) is:

[0096]

[0097] in, Let OTF be the optical transfer function, and k be the value of k. c The cutoff frequency of the microscopic imaging system is k, and k is the spectral coordinate.

[0098] Then consider the optical transfer function Performing an inverse Fourier transform yields the FRET three-channel point spread function H. CH (k),H=(DD,DA,AA).

[0099] S304. Based on the forward physical process of structured light excitation of FRET samples and acquisition of FRET three-channel images, and combined with the prior information of donor-acceptor molecular dipole resonance, a unified SI-FRETM forward model is constructed to constrain super-resolution SIM imaging. This model includes the fluorescence distribution of FRET samples, the mixing parameters of FRET three-channel spectra, the cosine structured light field of FRET three channels, and the point spread function of FRET three channels, expressed as:

[0100]

[0101] in, For the SI-FRETM unified forward model, H FRET For FRET three-channel point spread function, Let X be the FRET three-channel cosine structured light field, where θ and n represent different directional angles and phases of the structured light field, respectively, and M be the FRET three-channel spectral mixing parameters, X = [X1, X2, X3]. T X1 represents the total fluorescence distribution of the super-resolution donor, X2 represents the total fluorescence distribution of the super-resolution acceptor, and X3 represents the fluorescence distribution of the super-resolution sensitized FRET.

[0102] S4. Taking the fluorescence distribution of FRET samples as the target, establish the reconstruction objective function according to the SI-FRETM unified forward model and perform forward iteration to obtain the reconstructed fluorescence distribution of FRET samples; wherein, the fluorescence distribution of FRET samples includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution and the super-resolution sensitized FRET fluorescence distribution.

[0103] Furthermore, using the fluorescence distribution of FRET samples as the reconstruction target, the reconstruction objective function of the SI-FRETM unified forward model is established, expressed as:

[0104]

[0105] Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; The likelihood term measures the fidelity of the reconstructed image and is characterized using the least squares method.

[0106]

[0107] Among them, H FRET For FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter;

[0108] Based on the functional properties of the data likelihood term, a suitable optimization algorithm is designed to perform forward iteration to solve the reconstruction objective function of the SI-FRETM unified forward model, thereby obtaining the reconstructed fluorescence distribution of the FRET samples.

[0109] For the reconstruction objective function (6) in this embodiment, given the initial value X 0 Let k = k + 1, and generate the search direction d according to certain criteria. k and step size α k Thus, the iterative format X is obtained. k+1 =X k+1 +α k d k The forward iterative solution is gradually implemented; further, based on the error, it is determined whether the convergence to the optimal reconstruction result X is achieved. k+1 When the error is less than the threshold ε = 1 × 10 -6 That is, ||(X) k+1 -X k ) / (X k When || < ε, the optimal reconstruction result X of the fluorescence distribution of the FRET sample is obtained. k+1 Stop iterating.

[0110] Furthermore, a regularization term constraining the reconstruction objective function of the SI-FRETM unified forward model is added using prior information from the fluorescence image, establishing the regularized reconstruction objective function of the SI-FRETM unified forward model, expressed as:

[0111]

[0112] Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; first term The first term is the data likelihood term, the second term R(X) is the regularization term, and λ>0 is the weighting factor that balances the data likelihood term and the regularization term.

[0113] First item The likelihood term measures the fidelity of the reconstructed image, and it is also represented using the least squares method.

[0114]

[0115] Where H FRETFor FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter;

[0116] The second term, R(X), is a regularization term used to incorporate prior information from the FRET three-channel fluorescence image to improve the well-posedness of the solution. Since the structure of biological samples is continuously changing while noise is random and discontinuous, and the minimum signal collected during fluorescence imaging is zero, based on the above priors, the regularization term in this application employs spatial continuity regularization and / or non-negative regularization. In this embodiment, the spatial continuity regularization uses TV regularization constraints, expressed as:

[0117]

[0118] in , respectively, are the horizontal and vertical first-order gradients of the fluorescence image, and ||·||1 is the L1 norm;

[0119] Nonnegative positive coercion introduces a nonnegative constraint on fluorescence intensity in fluorescence images, expressed as:

[0120]

[0121] Finally, based on the functional properties of the regularization term, a suitable optimization algorithm is designed to iteratively solve the regularized reconstruction objective function of the SI-FRETM unified forward model, obtaining the reconstructed fluorescence distribution of the FRET samples. In this embodiment, the split Bregman method is used to introduce splitting and auxiliary variables into the regularized reconstruction objective function of the SI-FRETM unified forward model, decoupling it into several sub-problems that are solved iteratively step by step to obtain the reconstructed fluorescence distribution of the FRET samples.

[0122] S5. Based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution, the super-resolution FRET efficiency distribution and acceptor-donor concentration ratio distribution are obtained through pixel-by-pixel calculation:

[0123]

[0124] Among them, E D For donor-centric apparent efficiency, E A For receptor-centric epigenetic efficiency, R C X1 represents the donor-recipient concentration ratio distribution, X2 represents the reconstruction result of the total super-resolution donor fluorescence distribution, X3 represents the reconstruction result of the total super-resolution acceptor fluorescence distribution, and X4 represents the reconstruction result of the super-resolution sensitized FRET fluorescence distribution.

[0125] To further demonstrate the effectiveness of this method, this embodiment utilizes a simulation model for testing. Pre-set parameters are a = 0.2, b = 0, c = 0, d = 0.8, G = 5, and k = 0.69. This simulation model assumes a fixed FRET efficiency of 0.3 and a donor-recipient concentration ratio of 1:1 for the radiometric FRET signal. It uses parameters consistent with the actual system to simulate the mixing degree of the three-channel FRET spectrum, structured light modulation, and point spread function (PSF) fuzzy simulation to generate the original three-channel FRET data set. For example... Figure 2 The diagram illustrates the FRET efficiency and donor-recipient concentration ratio results of the method in this embodiment for reconstructing a simulated FRET three-channel original image containing 30% Gaussian white noise. Figure 2 (a) shows the wide-field FRET results. Figure 2 (b) shows the linear Wiener SIM-FRET reconstruction results. Figure 2 (c) shows the iterative reconstruction result of the regularized SI-FRETM objective function provided in this embodiment. Figure 2 In Figure (d), the iterative reconstruction results provided in this embodiment include the TV and non-negative regularized SI-FRETM objective functions. From... Figure 2 It is evident that the FRET efficiency histogram and donor-acceptor concentration ratio histogram of the linear Wiener SIM-FRET are broadened due to noise artifacts. However, the unregularized SI-FRETM model reconstruction effectively suppresses noise artifacts. Furthermore, in the SI-FRETM reconstruction results with the introduction of TV and non-negative regularization constraints, the standard deviations of the FRET efficiency histogram and donor-acceptor concentration ratio histogram are further reduced. This indicates that the super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraints of this invention is more robust to noise, effectively suppresses false-positive FRET signals caused by noise artifacts, and improves the fidelity of super-resolution FRET reconstruction and the accuracy of quantitative analysis.

[0126] To further demonstrate the effectiveness of the super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint, this embodiment also used MCF7 live cells for experimentation. In MCF7 live cells, the Acta-G17M plasmid was used to target and label the mitochondrial outer membrane. Pre-measurements yielded a = 0.045179054, b = 0.000912663, c = 0.001961017, d = 0.054721272, G = 0.381640369, and k = 2.40367731. (The remaining text appears to be unrelated and likely refers to a different example.) Figure 3 As shown, compared with linear Wiener SIM-FRET, the SI-FRETM model iterative reconstruction can resolve the FRET signal in the mitochondrial outer membrane region contaminated by noise in SIM-FRET imaging. This indicates that the super-resolution FRET image reconstruction method based on donor-receptor molecular dipole resonance constraint of the present invention can effectively suppress noise artifacts in live cell FRET imaging.

[0127] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously.

[0128] Based on the same idea as the super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint in the above embodiments, the present invention also provides a super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint. This system can be used to perform the above-described super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint. For ease of explanation, the structural schematic diagram of the embodiment of the super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint only shows the parts related to the embodiments of the present invention. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0129] like Figure 4 As shown, another embodiment of the present invention provides a super-resolution FRET image reconstruction system based on donor-acceptor molecular dipole resonance constraint, including an image acquisition module, an image processing module, a model building module, an objective solution module, and a FRET calculation module;

[0130] The image acquisition module is used to acquire raw FRET three-channel image data set for imaging live cell FRET samples through a structured light illumination super-resolution microscopy imaging system. The FRET three channels include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission.

[0131] The image processing module is used to preprocess the FRET three-channel raw image data set to obtain the preprocessed FRET three-channel image data set.

[0132] The model building module is used to correlate and constrain super-resolution SIM imaging based on donor-acceptor molecular dipole resonance prior information, and to build a unified SI-FRETM forward model, including FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field, FRET three-channel point spread function and FRET sample fluorescence distribution;

[0133] The objective solution module is used to establish a reconstruction objective function based on the SI-FRETM unified forward model and perform forward iteration to obtain the reconstructed FRET sample fluorescence distribution. The FRET sample fluorescence distribution includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution, and the super-resolution sensitized FRET fluorescence distribution.

[0134] The FRET calculation module is used to obtain the super-resolution FRET efficiency distribution and donor-recipient concentration ratio distribution by calculating pixel by pixel based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution.

[0135] It should be noted that the super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint of the present invention corresponds one-to-one with the super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint of the present invention. The technical features and beneficial effects described in the embodiments of the super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint described above are applicable to the embodiments of the super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint. For details, please refer to the description in the embodiments of the method of the present invention, which will not be repeated here.

[0136] Furthermore, in the embodiments of the super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint described above, the logical division of each program module is merely illustrative. In practical applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or software implementation convenience. That is, the internal structure of the super-resolution FRET image reconstruction system based on donor-acceptor intermolecular dipole resonance constraint is divided into different program modules to complete all or part of the functions described above.

[0137] Please see Figure 5 In one embodiment, an electronic device is provided for implementing a super-resolution FRET image reconstruction method based on donor-acceptor intermolecular dipole resonance constraint. The electronic device may include a first processor, a first memory, and a bus, and may also include a computer program stored in the first memory and executable on the first processor, such as a super-resolution FRET image reconstruction program based on donor-acceptor intermolecular dipole resonance constraint.

[0138] The first memory includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the first memory can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the first memory can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, the first memory can include both internal and external storage units of the electronic device. The first memory can be used not only to store application software and various types of data installed on the electronic device, such as the code of a super-resolution FRET image reconstruction program based on donor-acceptor molecular dipole resonance constraint, but also to temporarily store data that has been output or will be output.

[0139] In some embodiments, the first processor may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The first processor is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the first memory (e.g., a super-resolution FRET image reconstruction program based on donor-acceptor molecular dipole resonance constraint) and calls data stored in the first memory to perform various functions of the electronic device and process data.

[0140] Figure 5 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 5 The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0141] The super-resolution FRET image reconstruction program based on donor-acceptor intermolecular dipole resonance constraint stored in the first memory of the electronic device is a combination of multiple instructions. When run in the first processor, it can achieve the following:

[0142] The raw image data set of the three channels of FRET for imaging live cell FRET samples was obtained by a structured light illumination super-resolution microscopy system; the three channels of FRET include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission.

[0143] Image preprocessing is performed on the raw FRET three-channel image data set to obtain the preprocessed FRET three-channel image data set;

[0144] Based on the prior information of donor-acceptor molecular dipole resonance, super-resolution SIM imaging is correlated and constrained, and a unified forward model of SI-FRETM is constructed, including the FRET three-channel spectral mixing parameters, the FRET three-channel cosine structured light field, the FRET three-channel point spread function, and the FRET sample fluorescence distribution.

[0145] Using the fluorescence distribution of FRET samples as the target, a reconstruction objective function was established based on the SI-FRETM unified forward model and solved iteratively to obtain the reconstructed three-channel fluorescence distribution of FRET. The fluorescence distribution of FRET samples includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution, and the super-resolution sensitized FRET fluorescence distribution.

[0146] Based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution, the super-resolution FRET efficiency distribution and acceptor-donor concentration ratio distribution are obtained by pixel-by-pixel calculation.

[0147] Furthermore, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0148] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint, characterized in that, Includes the following steps: The raw image data set of the three channels of FRET for imaging live cell FRET samples was obtained by a structured light illumination super-resolution microscopy system; the three channels of FRET include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission. Image preprocessing is performed on the raw FRET three-channel image data set to obtain the preprocessed FRET three-channel image data set; Based on the prior information of donor-acceptor molecular dipole resonance, super-resolution SIM imaging is correlated and constrained to construct the SI-FRETM unified forward model; the SI-FRETM unified forward model includes FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field and FRET three-channel point spread function. Using the fluorescence distribution of FRET samples as the reconstruction target, a reconstruction objective function is established based on the SI-FRETM unified forward model and solved iteratively to obtain the reconstructed fluorescence distribution of FRET samples; the fluorescence distribution of FRET samples includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution, and the super-resolution sensitized FRET fluorescence distribution; Based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution, the super-resolution FRET efficiency distribution and acceptor-donor concentration ratio distribution are obtained by pixel-by-pixel calculation.

2. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 1, characterized in that, The image preprocessing includes: Using the original image of the AA channel as a reference, the original images of the DD and DA channels are aligned pixel by pixel through affine transformation to obtain the FRET three-channel registered image data set; the affine transformation is a linear transformation operation, including rotation, translation, scaling and reflection; The grayscale values ​​of the FRET three-channel registered image data group are statistically analyzed pixel by pixel. The grayscale value corresponding to the first peak in the grayscale value histogram is used as the background grayscale value to perform pixel-by-pixel background correction on the FRET three-channel registered image data group, resulting in the preprocessed FRET three-channel image data group.

3. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 1, characterized in that, The construction of the SI-FRETM unified forward model specifically involves: The FRET three-channel spectral mixing parameter matrix was obtained by measuring a FRET reference sample. This matrix characterizes the degree of spectral overlap and crosstalk between the three FRET channels caused by the donor-acceptor molecular dipole resonance physical process. Specifically: Where m ij , i,j∈[1,3] represents the contribution of donor fluorophores, acceptor fluorophores or sensitized FRET fluorophores in the fluorescence distribution X of the FRET sample to the fluorescence signal collected by the three channels of FRET; the FRET reference sample usually includes single-transfer donor sample, single-transfer acceptor sample and standard plasmid sample with a donor-acceptor ratio of 1:

1. The three-channel cosine structured light field of FRET is estimated by using the structured light field parameter estimation method on the preprocessed FRET three-channel image data set, and is expressed as: in, This represents the FRET three-channel cosine structured optical field. Let r represent the cosine structured light field of the DD channel, DA channel, or AA channel, where r is the spatial coordinate of the corresponding channel's fluorescence image pixel, and θ and n represent different orientation angles and phases of the structured light field, respectively. Let be the wave vector of the DD-channel cosine structured light field, the DA-channel cosine structured light field, or the AA-channel cosine structured light field. The modulation depth of the DD-channel cosine structured optical field, DA-channel cosine structured optical field, or AA-channel cosine structured optical field. The initial phase of the DD channel cosine structured light field, the DA channel cosine structured light field, or the AA channel cosine structured light field; The FRET three-channel point spread function, obtained through actual measurement using a microscopic imaging system or simulation based on a theoretical model, is expressed as: Among them, H FRET H is the FRET three-channel point spread function. DD (r) is the point spread function of the DD channel, H DA (r) is the point spread function of the DA channel, H AA (r) is the point spread function of the AA channel, and r is the spatial coordinate of the corresponding channel's fluorescence image pixel; Based on the forward physical process of structured light excitation of FRET samples and acquisition of FRET three-channel images, and combined with the prior information of donor-acceptor molecular dipole resonance, super-resolution SIM imaging is correlated and constrained to construct the SI-FRETM unified forward model. The SI-FRETM unified forward model includes the FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field, and FRET three-channel point spread function, expressed as: in, For the SI-FRETM unified forward model, H FRET For FRET three-channel point spread function, Let X be the FRET three-channel cosine structured light field, where θ and n represent different orientation angles and phases of the structured light field, respectively. M is the FRET three-channel spectral mixing parameter matrix, X = [X1, X2, X3]. T X1 represents the fluorescence distribution of FRET samples, X2 represents the total fluorescence distribution of the super-resolution donor, X3 represents the total fluorescence distribution of the super-resolution acceptor, and X4 represents the fluorescence distribution of the super-resolution sensitized FRET.

4. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 1, characterized in that, Using the fluorescence distribution of FRET samples as the reconstruction target, the reconstruction objective function of the SI-FRETM unified forward model is established, expressed as: Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; The likelihood term is represented using the least squares method: Where H FRET For FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter; Based on the functional properties of the data likelihood term, a suitable optimization algorithm is designed to perform forward iteration to solve the reconstruction objective function of the SI-FRETM unified forward model, thereby obtaining the reconstructed fluorescence distribution of the FRET samples.

5. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 1 or claim 4, characterized in that, By incorporating a regularization term based on prior fluorescence image information into the reconstruction objective function of the SI-FRETM unified forward model, a regularized reconstruction objective function for the SI-FRETM unified forward model is established, expressed as: Where X = [X1, X2, X3] T The fluorescence distribution of the FRET sample was reconstructed for the target. X1 represents the total fluorescence distribution of the donor in super-resolution, X2 represents the total fluorescence distribution of the acceptor in super-resolution, and X3 represents the fluorescence distribution of the super-sensitized FRET. For the SI-FRETM unified forward model; Let R(X) be the data likelihood term, R(X) be the regularization term, and λ be the weighting factor that balances the data likelihood term and the regularization term. The data likelihood item The likelihood term is characterized using the least squares method: H FRET For FRET three-channel point spread function, is the FRET three-channel cosine structured light field, where θ and n represent different directional angles and different phases of the structured light field, respectively, and M is the FRET three-channel spectral mixing parameter; The regularization term R(X) is used to incorporate prior information about the fluorescence image; Based on the functional properties of the regularization term, a suitable optimization algorithm is designed to perform forward iteration to solve the regularized reconstruction objective function of the SI-FRETM unified forward model, thereby obtaining the reconstructed fluorescence distribution of the FRET samples.

6. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 5, characterized in that, The regularization term is a spatial continuity regularization and / or a non-negative regularization.

7. The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint according to claim 1, characterized in that, The calculation method for the super-resolution FRET efficiency distribution and the donor-recipient concentration ratio distribution is as follows: Among them, E D For donor-centric apparent efficiency, E A For receptor-centric epigenetic efficiency, R C X1 represents the donor-recipient concentration ratio distribution, X2 represents the reconstructed super-resolution donor fluorescence distribution, X3 represents the reconstructed super-resolution acceptor fluorescence distribution, and X4 represents the reconstructed super-resolution sensitized FRET fluorescence distribution.

8. A super-resolution FRET image reconstruction device based on donor-acceptor molecular dipole resonance constraint, characterized in that, The super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint as described in any one of claims 1-7 includes an image acquisition module, an image preprocessing module, a model building module, an objective solving module, and a FRET calculation module. The image acquisition module is used to acquire raw FRET three-channel image data set for imaging live cell FRET samples through a structured light illumination super-resolution microscopy system; the FRET three channels include the DD channel for donor-induced donor emission, the DA channel for donor-induced receptor emission, and the AA channel for receptor-induced receptor emission. The image preprocessing module is used to preprocess the FRET three-channel raw image data group to obtain the preprocessed FRET three-channel image data group. The model building module is used to perform correlation constraints on super-resolution SIM imaging based on donor-acceptor molecular dipole resonance prior information, and to construct a unified SI-FRETM forward model; the unified SI-FRETM forward model includes FRET sample fluorescence distribution, FRET three-channel spectral mixing parameters, FRET three-channel cosine structured light field and FRET three-channel point spread function. The objective solving module is used to establish a reconstruction objective function based on the SI-FRETM unified forward model, with the fluorescence distribution of FRET samples as the objective, and to perform forward iterative solving to obtain the reconstructed fluorescence distribution of FRET samples; the fluorescence distribution of FRET samples includes the super-resolution donor fluorescence distribution, the super-resolution acceptor fluorescence distribution, and the super-resolution sensitized FRET fluorescence distribution. The FRET calculation module is used to obtain the super-resolution FRET efficiency distribution and the donor-recipient concentration ratio distribution by calculating pixel by pixel based on the reconstructed super-resolution donor fluorescence distribution, super-resolution acceptor fluorescence distribution, and super-resolution sensitized FRET fluorescence distribution.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores computer program instructions that can be executed by the at least one processor to enable the at least one processor to perform the super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint as described in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the super-resolution FRET image reconstruction method based on donor-acceptor molecular dipole resonance constraint as described in any one of claims 1-7.

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