A spatial reconstruction-based multiplexed immunofluorescence detection method, device and medium

Abstract

The application discloses a kind of multiple immunofluorescence detection method, equipment and medium based on space reconstruction, it is related to histopathological analysis technical field, including, preparation tissue sample and join fluorescent reference microbead to obtain continuous section;To the tissue sample cycle pretreatment, and utilize fluorescent reference microbead to carry out intensity normalization and bleaching time shift correction, obtain correction image sequence;Spectral unmixing is carried out under three-dimensional voxel coordinate system and introduces space regular constraint, obtain voxel level fluorescence intensity vector;Combining nuclear dye signal and fluorescent reference microbead, using rigid and non-rigid registration and supplemented with three-dimensional deconvolution, reconstruct high-resolution voxel stack and segment cell voxel set;The adjacent relationship and colocalization index of different phenotype cells are calculated, and detection result is generated.The application realizes high-resolution tissue and cell reconstruction under three-dimensional space, improves spatial continuity and accuracy, and improves the stability and accuracy of voxel level fluorescence signal quantification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of histopathological analysis, and in particular to a multi-immunofluorescence detection method based on spatial reconstruction, a device and a medium. BACKGROUND

[0002] In histopathology and cell biology research, multi-immunofluorescence detection as an important molecular marker and spatial distribution analysis method has been widely used in cell phenotype identification, molecular interaction research and tissue microenvironment analysis. The conventional method usually includes fixing the tissue sample, slicing and immunofluorescence staining, marking specific molecules or cells by different fluorescent markers, and then collecting image data by using an optical microscopic imaging system. Subsequently, researchers identify and quantitatively analyze different fluorescent signals by spectral separation or channel separation technology to obtain the molecular distribution at the tissue or cell level. This method can present the distribution characteristics of cells and their markers in two-dimensional images, and provides important data support for histopathological diagnosis, immune function research and precision medicine. Due to its repeatability and strong universality, this detection method has become one of the basic tools for related experimental research and clinical analysis.

[0003] However, the conventional immunofluorescence detection method based on two-dimensional images has certain limitations in spatial information expression. On the one hand, it often relies on local reference signals in the image stitching and cross-slice registration process, which may lead to insufficient continuity of the overall spatial structure of the tissue, thereby affecting the accurate determination of cell positioning and proximity relationship in three-dimensional scale. On the other hand, the spectral unmixing and background correction of multi-channel fluorescent signals in the two-dimensional detection method are mainly carried out in the plane, which is difficult to fully consider the signal continuity and spatial constraints at the voxel level, thereby affecting the stable extraction and quantitative accuracy of weak fluorescent signals in complex tissues. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a multi-immunofluorescence detection method based on spatial reconstruction to solve the problems of insufficient three-dimensional spatial registration continuity between tissue slices and limited quantitative accuracy of voxel-level fluorescent signals.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a multi-immunofluorescence detection method based on spatial reconstruction, which comprises preparing a tissue sample and adding fluorescent reference microbeads, and obtaining continuous slices.

[0008] Collecting blank slice signal to establish autofluorescence baseline, and performing monochromatic spectrum calibration on each fluorescent dye to obtain complete dataset of spectral mixing matrix and baseline parameters;

[0009] Performing cyclic pretreatment on the tissue sample in sequence, and using the signal emitted by the fluorescent reference microbead to perform intensity normalization and bleaching time drift correction to obtain a corrected multi-channel image sequence;

[0010] Performing spectral unmixing on the multi-channel image sequence under a three-dimensional voxel coordinate system, and performing constraint processing and continuity spatial constraint on the unmixing result vector through three-dimensional space regular constraint to obtain a voxel-level fluorescent intensity vector;

[0011] According to the components of the voxel-level fluorescent intensity vector and the fluorescent reference microbead, using rigid and non-rigid registration methods to perform unified spatial alignment on the multi-channel images obtained in different cycles, and combining three-dimensional deconvolution processing to obtain high-resolution reconstructed voxel values, and performing cell segmentation under a unified coordinate system to obtain a cell voxel set;

[0012] Calculating the proximity relationship and colocalization index of different phenotype cells in three-dimensional space for the cell voxel set to obtain a multiple immunofluorescence detection result.

[0013] As a preferred scheme of the multiple immunofluorescence detection method based on spatial reconstruction, the method comprises the steps of:

[0014] After the tissue to be detected is fixed and dehydrated, the tissue is prepared into a tissue sample, and the fluorescent reference microbead is added to the tissue sample;

[0015] The tissue sample containing the fluorescent reference microbead is sliced to obtain continuous slices.

[0016] As a preferred scheme of the multiple immunofluorescence detection method based on spatial reconstruction, the method comprises the steps of:

[0017] Signal collection is performed on the blank slice, and autofluorescence baseline is obtained through fluorescence calculation on the blank slice signal;

[0018] Monochromatic excitation collection is performed on each fluorescent dye in sequence, and spectral mixing calculation is performed on the monochromatic signal of each fluorescent dye to obtain a spectral mixing matrix;

[0019] The autofluorescence baseline and the spectral mixing matrix are combined to obtain a complete dataset of the spectral mixing matrix and the baseline parameters.

[0020] As a preferred scheme of the spatial reconstruction-based multiplexed immunofluorescence detection method, the blank slice refers to a tissue slice without any fluorescent dye treatment.

[0021] As a preferred scheme of the spatial reconstruction-based multiplexed immunofluorescence detection method, the tissue sample is sequentially subjected to cyclic pretreatment, and the signal emitted by the fluorescent reference microbead is used for intensity normalization and bleaching time drift correction to obtain a corrected multi-channel image sequence, and the specific steps are as follows.

[0022] The tissue sample is subjected to cyclic pretreatment to obtain a cyclic immunofluorescence acquisition sequence.

[0023] The pretreatment includes immunofluorescence staining, imaging, and bleaching.

[0024] The signal emitted by the fluorescent reference microbead is collected, and an intensity normalization coefficient is calculated.

[0025] The cyclic immunofluorescence acquisition sequence is subjected to numerical normalization processing by channel using the intensity normalization coefficient to obtain an intensity-normalized multi-channel image sequence.

[0026] The bleaching time drift correction parameter is calculated using the signal emitted by the fluorescent reference microbead.

[0027] The intensity-normalized multi-channel image sequence is corrected by the drift correction parameter to obtain a corrected multi-channel image sequence.

[0028] As a preferred scheme of the spatial reconstruction-based multiplexed immunofluorescence detection method, the multi-channel image sequence is subjected to spectral unmixing in a three-dimensional voxel coordinate system, and the unmixing result vector is subjected to constraint processing and continuity spatial constraint through three-dimensional spatial regular constraint to obtain a voxel-level fluorescence intensity vector, and the specific steps are as follows.

[0029] The corrected multi-channel image sequence is mapped to a three-dimensional voxel coordinate system.

[0030] The multi-channel image mapped to the three-dimensional voxel coordinate system and the spectral mixing matrix are used for spectral unmixing calculation to obtain an initial unmixing result vector.

[0031] The initial unmixing result vector is subjected to constraint processing using three-dimensional spatial regular constraint, and the constraint-processed unmixing result vector is subjected to continuity constraint using continuity spatial constraint to obtain an unmixing result vector.

[0032] The unmixing result vector is subjected to voxel-level intensity calculation to obtain a voxel-level fluorescence intensity vector.

[0033] As a preferred scheme of the spatial reconstruction-based multiplexed immunofluorescence detection method, the components of the voxel-level fluorescence intensity vector and the fluorescence reference microbeads are subjected to rigid and non-rigid registration to unify the spatial alignment of the multi-channel images obtained in different cycles, and high-resolution reconstructed voxel values are obtained by combining three-dimensional deconvolution processing, and cell segmentation is performed in a unified coordinate system to obtain a cell voxel set, and the specific steps are as follows,

[0034] The nuclear dye signals of the corrected multi-channel image sequence and the signals emitted by the fluorescence reference microbeads are extracted as registration references;

[0035] The multi-channel images obtained in different cycles are subjected to rigid and non-rigid registration by using the established registration references to obtain unified spatial alignment data;

[0036] The unified spatial alignment data are subjected to three-dimensional deconvolution processing to generate high-resolution reconstructed voxel values;

[0037] The high-resolution reconstructed voxel values are subjected to cell segmentation in a unified coordinate system to extract a cell voxel set.

[0038] As a preferred scheme of the spatial reconstruction-based multiplexed immunofluorescence detection method, the components of the voxel-level fluorescence intensity vector and the fluorescence reference microbeads are subjected to rigid and non-rigid registration to unify the spatial alignment of the multi-channel images obtained in different cycles, and high-resolution reconstructed voxel values are obtained by combining three-dimensional deconvolution processing, and cell segmentation is performed in a unified coordinate system to obtain a cell voxel set, and the specific steps are as follows,

[0039] The cell voxel set is subjected to phenotype determination by using the voxel-level fluorescence intensity vector to obtain a different phenotype cell set;

[0040] The proximity relationship is calculated by using the positional relationship of the different phenotype cell set in the three-dimensional space;

[0041] The colocalization index is calculated by using the signal distribution of the different phenotype cell set in the three-dimensional space;

[0042] The multiplexed immunofluorescence detection result is generated based on the voxel-level fluorescence intensity vector, the proximity relationship and the colocalization index;

[0043] The multiplexed immunofluorescence detection result includes voxel-level fluorescence signals, cell-level quantitative parameters and spatial microenvironment distribution characteristics.

[0044] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program is executed by the processor to implement any step of the spatial reconstruction-based multiplexed immunofluorescence detection method according to the first aspect of the present application.

[0045] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements any step of the method for spatial reconstruction-based multiplexed immunofluorescence detection according to the first aspect of the present application.

[0046] The present application has the advantages that: by adding fluorescent reference microbeads in the tissue sample, and combining the nuclear dye signal with rigid and non-rigid registration, high-resolution tissue and cell reconstruction in three-dimensional space is realized, and the continuity and accuracy of three-dimensional space reconstruction are improved; by performing spectral unmixing under the three-dimensional voxel coordinate system and optimizing through three-dimensional space regularization constraint, not only the detection capability of weak fluorescent signal is enhanced, but also the stability and accuracy of voxel-level fluorescent signal quantification are improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Fig. 1 Flowchart of the method for spatial reconstruction-based multiplexed immunofluorescence detection.

[0049] Fig. 2 Flowchart of establishing autofluorescence baseline and monochrome spectral calibration.

[0050] Fig. 3 Flowchart of intensity normalization and bleaching time drift correction.

[0051] Fig. 4 Flowchart of cell segmentation, spatial analysis and result generation. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0053] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the scope of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0054] Secondly, the "one embodiment" or "embodiment" referred to herein is intended to represent a specific feature, structure, characteristic, or combination of features and characteristics, which can be included in at least one implementation of the present application. The various appearances of "in one embodiment" or "in an embodiment" in the specification are not all referring to the same embodiment, nor are they mutually exclusive, although some embodiments can include more features than others.

[0055] With reference to Figs. 1-4 For one embodiment of the present application, the embodiment provides a spatial reconstruction-based multiple immunofluorescence detection method, comprising the following steps:

[0056] S1: Prepare a tissue sample and add fluorescent reference microbeads to obtain continuous sections.

[0057] After the tissue to be detected is fixed and dehydrated, it is prepared into a tissue sample, and fluorescent reference microbeads are added to the tissue sample.

[0058] Further, the tissue to be detected is subjected to conventional histological pretreatment operation, wherein the histological pretreatment operation includes fixation and dehydration treatment. Specifically, the tissue to be detected is placed in a fixing solution, such as a 10% neutral buffered formalin solution, for fixation, so that the protein and cell structure inside the tissue remain stable.

[0059] Further, the fixed tissue to be detected is placed in different concentrations of ethanol solution for gradient dehydration treatment, so as to gradually remove the water in the tissue and avoid structural deformation caused by the presence of water during sectioning.

[0060] Further, after the fixation and dehydration treatment is completed, the tissue sample is prepared, and fluorescent reference microbeads with known concentration and particle size distribution are added to the tissue sample; wherein the fluorescent reference microbeads have stable and traceable fluorescence emission characteristics under different channels, and can be used as signal intensity normalization and spatial anchor points during fluorescence detection.

[0061] The fluorescent reference microbeads are uniformly distributed in the treatment area by mixing or dropping with the tissue sample to be detected.

[0062] The tissue sample containing the fluorescent reference microbeads is subjected to sectioning treatment to obtain continuous sections.

[0063] Further, the tissue sample containing the fluorescent reference microbeads is subjected to paraffin embedding treatment, and a paraffin microtome is used to section the tissue to be detected, with the section thickness controlled to be between 3~5μm to ensure that a high resolution is obtained under optical imaging conditions, and then continuous sections of the tissue sample are obtained.

[0064] It should be noted that the continuity of the original tissue spatial structure should be maintained during the continuous sectioning process to ensure that effective superposition and unification between different sections can be achieved during spatial reconstruction and three-dimensional registration.

[0065] S2: Collecting blank slice signal to establish autofluorescence baseline, and performing single-color spectrum calibration on each fluorescent dye to obtain complete dataset of spectral mixing matrix and baseline parameters.

[0066] Signal acquisition is performed on the blank slice, and autofluorescence baseline is obtained by performing fluorescence calculation on the blank slice signal.

[0067] Further, signal acquisition is performed on the blank slice. Specifically, a blank tissue sample slice that has not been treated with any fluorescent dye is selected, and excitation and acquisition are performed under each detection channel using a fluorescence imager to obtain multi-channel original signals of the blank slice.

[0068] Further, fluorescence calculation is performed on the multi-channel original signals of the blank slice to remove random noise components, and a stable background signal curve is obtained as the autofluorescence baseline, which is used to reflect the endogenous fluorescence characteristics of the to-be-detected tissue under different channels.

[0069] Each fluorescent dye is sequentially subjected to single-color excitation and collection, and spectral mixing calculation is performed using the single-color signals of each fluorescent dye to obtain a spectral mixing matrix.

[0070] Further, in order to obtain the true response of different fluorescent dyes under each channel, each fluorescent dye is sequentially subjected to single-color excitation and collection. Specifically, under the same experimental conditions, control samples containing only a single fluorescent dye are prepared, and excitation and imaging are sequentially performed under each channel to obtain single-color signals corresponding to the single fluorescent dye. The single-color signals are arranged and normalized according to the channel dimension to construct the response relationship of each fluorescent dye under different channels. Based on the single-color signals, the normalized average intensity of each fluorescent dye under single-color excitation in each detection channel is fitted as a linear response coefficient, and a spectral mixing matrix with dimensions of is sequentially generated by taking the channel as the row and the fluorescent dye as the column. .

[0071] The autofluorescence baseline and the spectral mixing matrix are combined to obtain a complete dataset of the spectral mixing matrix and the baseline parameters.

[0072] Further, the complete dataset of the spectral mixing matrix and the baseline parameters includes baseline parameters and spectral response parameters.

[0073] S3: The tissue sample is sequentially subjected to cyclic pretreatment, and the signals emitted by the fluorescent reference microbeads are used for intensity normalization and bleaching time drift correction to obtain a corrected multi-channel image sequence.

[0074] The tissue sample is subjected to cyclic pretreatment to obtain a cyclic immunofluorescence acquisition sequence.

[0075] The pretreatment includes immunofluorescence staining, imaging, and bleaching.

[0076] Further, the tissue sample is sequentially added to different target fluorescent antibodies, and after completing one staining, microscopic imaging is performed, and the combined fluorescent signal is eliminated through photobleaching or chemical methods, thereby providing conditions for the next cycle of fluorescent labeling, and the cycle is executed to obtain a cycle immunofluorescence acquisition sequence containing multiple staining results.

[0077] The combined fluorescent signal refers to the signal generated by the fluorescent label remaining on the sample after completing one immunofluorescence staining and imaging.

[0078] The signal emitted by the fluorescent reference microbeads is collected, and the intensity normalization coefficient is calculated.

[0079] Further, the signal emitted by the fluorescent reference microbeads embedded in the tissue sample is collected separately, and the intensity normalization coefficient is calculated using the ratio of the average intensity of the reference microbeads under each channel to the reference intensity of the fluorescent reference microbeads.

[0080] It should be noted that the reference intensity of the fluorescent reference microbeads refers to the average signal intensity of the standard fluorescent reference microbeads under the corresponding channel.

[0081] The standard fluorescent reference microbeads are commercial fluorescent microbeads with known concentration and particle size distribution, stable and traceable emission characteristics in each detection channel, and are mixed with the tissue sample after preparation according to the instructions and embedded with the tissue sample to obtain a slice for use as a fluorescent reference microbead reference intensity and spatial anchor.

[0082] The intensity normalization coefficient is used to perform numerical normalization processing on the cycle immunofluorescence acquisition sequence channel by channel to obtain an intensity-normalized multi-channel image sequence.

[0083] The signal emitted by the fluorescent reference microbeads is used to calculate the bleaching time drift correction parameter.

[0084] Further, the signal decay law of the fluorescent reference microbeads in the time sequence is used to calculate the bleaching time drift correction parameter, and the bleaching time drift correction parameter is obtained through exponential regression fitting.

[0085] The intensity-normalized multi-channel image sequence is corrected by the drift correction parameter to obtain a corrected multi-channel image sequence.

[0086] Further, the drift correction parameter is used to correct the intensity-normalized multi-channel image sequence channel by channel.

[0087] The correction calculation formula is represented as:

[0088] ;

[0089] wherein, denotes the image signal value in channel at time , denotes the image corrected signal value in channel at time , denotes the bleaching rate constant of channel , denotes time.

[0090] It should be noted that the bleaching rate constant is greater than or equal to 0.

[0091] S4: Spectral unmixing is performed on the multi-channel image sequence in the three-dimensional voxel coordinate system, and the unmixing result vector is constrained and continuously spatially constrained through three-dimensional space regular constraints, to obtain a voxel-level fluorescence intensity vector.

[0092] The corrected multi-channel image sequence is mapped to the three-dimensional voxel coordinate system.

[0093] Further, the multi-channel image sequence corrected by the bleaching time shift is uniformly mapped to the three-dimensional voxel coordinate system. Specifically, the two-dimensional images under different cycles and different channels are spatially stacked and registered through slice indexes and imaging parameters, to obtain a three-dimensional multi-channel data body with spatial coordinate identification, so that each pixel point corresponds to a unique three-dimensional voxel coordinate .

[0094] wherein, is a three-dimensional voxel coordinate vector, used to represent the position of a voxel in three-dimensional space, denotes the coordinate component of the voxel in the horizontal direction, denotes the coordinate component of the voxel in the vertical direction, denotes the coordinate component of the voxel in the depth direction.

[0095] Spectral unmixing calculation is performed using the multi-channel image mapped to the three-dimensional voxel coordinate system and the spectral mixing matrix, to obtain an initial unmixing result vector.

[0096] Further, the spectral unmixing optimization formula is represented as:

[0097] ;

[0098] wherein, denotes the initial unmixing result vector of the voxel , denotes the multi-channel observation signal vector of the voxel , i.e., the actual intensity values collected by each channel, Represents the spectral mixing matrix. Let represent the intensity vector of the fluorescence component to be determined. Represents the spontaneous fluorescence baseline vector. Let represent the intensity vector of the fluorescence component to be determined.

[0099] It should be noted that the fluorescence component intensity vector to be determined in the spectral unmixing optimization formula corresponds to the intensity value of different fluorescent dyes at the current voxel, and is used as an optimization variable for solving, and satisfies the non-negativity constraint condition ≥ 0; the initial unmixing result vector of the voxel in the spectral unmixing optimization formula represents the optimal solution obtained by minimizing the objective function at the three-dimensional voxel coordinates, i.e., the initial unmixing result vector; in short, the fluorescence component intensity vector to be determined is the candidate variable in the optimization problem, while the initial unmixing result vector of the voxel is the fluorescence component intensity vector obtained by optimization calculation at a specific voxel position, and has practical numerical significance.

[0100] The initial unmixed result vector is constrained by three-dimensional spatial regularity constraints, and the unmixed result vector after constraint processing is constrained by continuous spatial constraints to obtain the unmixed result vector.

[0101] Furthermore, based on the spectral unmixing optimization formula, noise is suppressed and the stability of weak observation signals is improved by three-dimensional spatial regularization constraints. Specifically, a three-dimensional gradient regularization term is added to the spectral unmixing optimization formula to perform spatial constraint processing on the unmixing result vector.

[0102] The spectral unmixing optimization formula incorporating three-dimensional spatial regularization constraints is expressed as:

[0103] ;

[0104] in, Voxel representation The constraint unmixing result vector, Indicates the first The gradient of each fluorescence component in three-dimensional space. This represents the spatial regularity coefficient, with a value range of

[10] . -4 10 -1 ].

[0105] It should be noted that the multi-channel images of tissue sample slices were unmixed under different spatial regularization coefficients, and the range of values ​​for the spatial regularization coefficients was obtained by using the proportion of positive cells as a parameter.

[0106] It should be noted that the fluorescence component refers to the component in the voxel-level fluorescence intensity vector obtained after spectral unmixing, representing the intensity of a specific fluorescent dye at the voxel.

[0107] Further, in order to ensure the continuity of the unmixed result vector between adjacent voxels, a spatial neighborhood difference field smoothing condition is applied to the unmixed result vector after the spatial constraint processing by a continuity spatial constraint, so as to obtain the final unmixed result vector.

[0108] The unmixed result vector is subjected to voxel-level intensity calculation, so as to obtain a voxel-level fluorescence intensity vector.

[0109] Further, the unmixed result vector obtained through the spectral unmixing, the three-dimensional spatial regular constraint and the field smoothing constraint is directly used as the fluorescence intensity vector of the voxel.

[0110] It should be noted that the components of the final unmixed result vector refer to the fluorescence intensities of various fluorescent dyes at the voxel.

[0111] S5: According to the components of the voxel-level fluorescence intensity vector and the fluorescence reference microbeads, a rigid and non-rigid registration method is used to uniformly spatially align the multi-channel images obtained in different cycles, and combined with three-dimensional deconvolution processing, high-resolution reconstructed voxel values are obtained, and cell segmentation is performed in a unified coordinate system to obtain a cell voxel set.

[0112] The nuclear dye signal of the corrected multi-channel image sequence and the signal emitted by the fluorescence reference microbeads are extracted as registration references.

[0113] Further, the nuclear dye signal is extracted from the corrected multi-channel image sequence, and DAPI nuclear dye is usually selected as the reference. Since the nuclear dye is uniformly distributed in the entire tissue sample and can stably identify the position of the cell nucleus, the spatial form has strong consistency, therefore, the nuclear dye is used as a registration reference of the global structure, which is used to ensure the consistency of the multi-channel images in different cycles in the entire geometric form.

[0114] Further, the signal emitted by the fluorescence reference microbeads is extracted from the corrected multi-channel image sequence. Since the fluorescence reference microbeads exhibit regular distribution of point-like highlight signals in the tissue sample, the fluorescence intensity temperature and spatial position are fixed in different cycles and different channels, therefore, the signal emitted by the fluorescence reference microbeads is used as a local accurate anchor point, which is used to correct the translation, rotation, scaling and local deformation in the process of different cycle acquisition.

[0115] The established registration reference is used to perform rigid and non-rigid registration on the multi-channel images obtained in different cycles, so as to obtain uniformly spatially aligned data.

[0116] Further, based on the registration reference of the nuclear dye signal, rigid registration is performed by extracting the nuclear dye intensity distribution in each cycle multi-channel image, using a matching algorithm of mutual information maximization method to calculate the geometric transformation parameters between the cycle multi-channel images, wherein the geometric transformation parameters include translation vectors, rotation angles and scaling ratios, and the geometric transformation parameters are applied to each cycle multi-channel image to realize the consistency of the overall structure in the three-dimensional coordinate system.

[0117] Further, a matching algorithm of mutual information maximization method is used to calculate the geometric transformation parameters between the cycle multi-channel images, specifically, the nuclear dye channel image is extracted from the multi-channel image sequence after intensity normalization and bleaching drift correction as the registration reference of the global structure, and the coordinates of the fluorescent reference microbeads in each multi-channel image are extracted as local spatial anchor points; assuming that the multi-channel images to be registered are respectively a reference image and a to-be-transformed image, the mutual information objective of the mutual information maximization matching algorithm is defined as the optimal geometric transformation function, so that the multi-channel image after transformation reaches the maximum mutual information in the gray statistical distribution; the initial geometric transformation parameters are defined, wherein the translation vector is the displacement in the X, Y and Z three-axis directions, the rotation angle is the Euler angle rotation around the three axes, and the scaling ratio is the three-axis scaling ratio; based on the defined initial geometric transformation parameters, mutual information is used as the objective function, and random gradient descent is used for parameter iterative update to obtain the optimal geometric transformation parameters, and the optimal geometric transformation parameters are applied to the to-be-registered image to perform consistent geometric transformation on all channel images.

[0118] Further, on the basis of the rigid registration, non-rigid registration is performed using the signals emitted by the fluorescent reference microbeads in the registration reference, specifically, by detecting the spatial coordinates of the signals emitted by the fluorescent microbeads in each cycle multi-channel image, an anchor point set is constructed, and the coordinates of the fluorescent microbeads in the reference cycle are taken as the target, a B-spline free deformation method is used to match the coordinates of the fluorescent microbeads in the to-be-registered image with the coordinates in the reference multi-channel image through nearest neighbor matching, to ensure that each anchor point generates a matching pair with an anchor point in the reference image with similar position.

[0119] A regular three-dimensional grid composed of control points is constructed within the three-dimensional space range of the to-be-registered image, wherein each control point corresponds to a spatial position and has an adjustable three-dimensional displacement vector, which is initially set to zero.

[0120] Based on the constructed regular three-dimensional grid, a free deformation function is defined, wherein the defined free deformation function is based on the B-spline interpolation principle, and the position offset of any control point is determined by the displacement values of the 16 adjacent control points in the control point grid block.

[0121] To solve the optimal displacement value of the control point, an optimization objective function is defined, wherein the optimization objective function is composed of two parts of anchor point matching error term and regularization term; the anchor point matching error term refers to the sum of squares of Euclidean distances between the anchor points of the fluorescent microbeads in the image to be registered after deformation and the corresponding anchor points in the reference multi-channel image, which is used to minimize the spatial deviation between the anchor points; the regularization term refers to using the sum of squares of displacement gradients of the control points as a smoothness constraint to limit the deformation intensity change of the control points to suppress the image distortion caused by excessive deformation; the overall objective of the optimization objective is to minimize the total error of the anchor point matching error term and the regularization term.

[0122] By iteratively optimizing the three-dimensional displacement vector of the control point, the optimal control point displacement vector is finally obtained.

[0123] The optimal control point displacement vector is applied to all channels of the image to be registered, specifically, the B-spline deformation function is applied to the coordinates of each voxel in the multi-channel image sequence to obtain a new spatial position, and the pixel value of the voxel is recalculated through trilinear interpolation according to the new spatial position after transformation, and the image data of each channel in the three-dimensional space after transformation is generated through a loop operation to obtain a unified spatially aligned data set.

[0124] It should be noted that the unified spatially aligned data set is obtained through rigid registration and non-rigid registration, wherein the unified spatially aligned data set realizes global consistency and local accuracy between different cyclic multi-channel images in a three-dimensional coordinate system.

[0125] The unified spatially aligned data is subjected to three-dimensional deconvolution processing to generate high-resolution reconstructed voxel values.

[0126] Further, the unified spatially aligned data is subjected to three-dimensional deconvolution processing to eliminate the point spread effect caused by optical instruments during imaging, specifically, based on the point spread function of the microscopic imaging instrument, the Richardson-Lucy iterative deconvolution algorithm is used to perform convolution processing on the unified spatially aligned data; through multiple iterations, when the change rate between the voxel intensities of two iterations is less than a set convergence threshold, the iteration is stopped, and high-resolution reconstructed voxel values are obtained.

[0127] wherein the iterative update formula is represented as:

[0128] ;

[0129] wherein, represents the reconstructed voxel value of the i-th iteration, represents the reconstructed voxel value of the i-th iteration, represents the reconstructed voxel value of the i-th iteration, represents the reconstructed voxel value of the i-th iteration, represents the observation signal value of the unified spatially aligned data, denotes a point spread function, is a convolution operation, denotes an inverse function of the point spread function.

[0130] It should be noted that the convergence threshold setting of three-dimensional deconvolution is specifically determined when the voxel intensity change rate between two consecutive iterations is less than 1%; if the iteration number reaches 40 times and still does not converge, the 40th result is taken as the final result to avoid excessive iteration and excessive noise.

[0131] The high-resolution reconstructed voxel value is used for cell segmentation in a unified coordinate system to extract a cell voxel set.

[0132] Further, on the basis of the high-resolution reconstructed voxel value, cell segmentation is performed to extract a cell voxel set. Specifically, a nuclear dye signal channel in the multi-channel image sequence is extracted and smoothed by three-dimensional Gaussian filtering to suppress interference; three-dimensional threshold segmentation is performed on the nuclear dye signal based on the Otsu threshold method to obtain a preliminary mask of the cell nucleus; isolated noise points of the preliminary mask are removed and local missing areas are filled by using three-dimensional morphological reconstruction of nuclear connected domain analysis to obtain a continuous and complete nuclear region; finally, the three-dimensional watershed algorithm is used to expand to the cytoplasmic region with the nuclear region as a seed point, to extract a complete cell voxel set, and each cell voxel set is given a unique identifier in a unified coordinate system.

[0133] S6: Calculate the proximity relationship and colocalization index of different phenotype cells in three-dimensional space for the cell voxel set, to obtain a multi-immunofluorescence detection result.

[0134] The phenotype of the cell voxel set is determined in combination with the voxel-level fluorescence intensity vector to obtain a different phenotype cell set.

[0135] Further, the cell voxel set is associated with the corresponding voxel-level fluorescence intensity vector. Specifically, for each cell voxel set, the voxel-level fluorescence intensity vector of all voxels in the cell voxel set is counted, and a cell-level fluorescence intensity feature vector is obtained by average value calculation.

[0136] Further, the cell-level fluorescence intensity feature vector is determined using a preset phenotype determination threshold; wherein the phenotype determination threshold is determined by the fluorescence intensity of the negative control cells, and is used to distinguish the expression states of different target points. Specifically, each fluorescence component signal is normalized, and the normalized fluorescence component signal is compared with the corresponding phenotype determination threshold. When the intensity of the fluorescence component signal exceeds the phenotype determination threshold, it is determined that the cell corresponding to the fluorescence component signal is positive for the marker. The positive or negative determination results of each cell under each fluorescence component are combined as different phenotype feature vectors. Cells with the same phenotype feature vector are classified into the same phenotype, and different phenotype cell sets are obtained.

[0137] In the bit combination, each fluorescence component corresponds to a Boolean value, i.e. 0 or 1. For example, the determination results of Y cells under three fluorescence components, such as CD3, CD4 and CD8, are [1, 0, 1], and the Y cells are combined as a phenotype code, i.e. 101. All cells with the 101 phenotype code are classified into the same phenotype cell set.

[0138] The phenotype determination formula is represented as:

[0139] ;

[0140] In the bit combination, each fluorescence component corresponds to a Boolean value, i.e. 0 or 1. For example, the determination results of Y cells under three fluorescence components, such as CD3, CD4 and CD8, are [1, 0, 1], and the Y cells are combined as a phenotype code, i.e. 101. All cells with the 101 phenotype code are classified into the same phenotype cell set. The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as: The phenotype determination formula is represented as:

[0141] It should be noted that the phenotype determination threshold is set by statistically analyzing the fluorescence intensity distribution of the negative control cells, and the phenotype determination threshold is set as the average value of the fluorescence intensity of the negative control cells ± 2 times the standard deviation, so as to ensure that the false positive rate is controlled below 5%.

[0142] In another optional embodiment, the phenotype determination threshold can also be calculated based on the ROC curve to select the optimal cutoff point as the phenotype determination threshold, so as to take into account the sensitivity and specificity.

[0143] Calculate the proximity relationship by the position relationship of the different phenotype cell sets in three-dimensional space.

[0144] Further, extract the three-dimensional coordinate point set of the cell centroid in each phenotype cell set, and take the Euclidean distance between the centroids of different phenotype cells as the spatial metric standard. For example, 6-20 μm is the judgment condition, if the distance between the phenotype cells and another phenotype cell is less than or equal to the proximity threshold, it is determined to be in proximity; by traversing all cell centroid coordinates, the proximity relationship coefficient between different phenotype cells is calculated, and the proximity matrix reflecting the spatial contact characteristics of the cells is obtained.

[0145] Calculate the colocalization index by the signal distribution of the different phenotype cell sets in three-dimensional space.

[0146] Further, in a unified three-dimensional coordinate system, the voxel-level fluorescence intensity vector of the two different phenotype cell sets is overlapped, and the normalized intersection method is used to calculate the colocalization index to obtain the colocalization index set.

[0147] Based on the voxel-level fluorescence intensity vector, the proximity relationship, and the colocalization index, generate the multiple immunofluorescence detection result.

[0148] Further, the number distribution of each phenotype cell derived from the voxel-level fluorescence intensity vector, the proximity relationship coefficient matrix, and the colocalization index set are unified and integrated to generate a detection report containing quantitative parameters and spatial structure characteristics. Specifically, the number of cells in each phenotype cell set is counted, the proportion in all cells is calculated, and a table output is generated; based on the three-dimensional centroid coordinates of the different phenotype cell sets, the proximity relationship matrix is output, reflecting the spatial contact probability of different phenotype cells under the proximity threshold; the colocalization index is output in the form of a histogram; the voxel-level fluorescence intensity distribution map and the phenotype cell distribution map are generated in a unified three-dimensional coordinate system, different phenotype cells are marked with pseudo-color, and quality control parameters such as unmixing residual error, registration error, and fluorescence reference microbead normalization coefficient are output synchronously.

[0149] The embodiment also provides a computer device suitable for the multiple immunofluorescence detection method based on spatial reconstruction, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the multiple immunofluorescence detection method based on spatial reconstruction as proposed in the above embodiments.

[0150] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0151] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the method for implementing multiple immunofluorescence detection based on spatial reconstruction as described in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0152] In summary, the present application achieves high-resolution tissue and cell reconstruction in three-dimensional space by adding fluorescent reference microbeads to the tissue sample and combining the nuclear dye signal with rigid and non-rigid registration, thereby improving the continuity and accuracy of three-dimensional spatial reconstruction. By performing spectral unmixing in the three-dimensional voxel coordinate system and optimizing through three-dimensional spatial regularization, the detection capability of weak fluorescent signals is enhanced, and the stability and accuracy of voxel-level fluorescent signal quantification are improved.

[0153] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for spatial reconstruction-based multiplexed immunofluorescence detection, the method comprising: include, ​ Tissue samples were prepared and fluorescent reference beads were added to obtain serial sections; A baseline of autofluorescence was established by acquiring signals from blank slices, and monochromatic spectra of each fluorescent dye were calibrated to obtain a complete dataset of spectral mixing matrix and baseline parameters. The tissue samples were preprocessed cyclically, and the intensity was normalized and the bleaching time drift was corrected using the signal emitted by the fluorescent reference beads to obtain the corrected multi-channel image sequence. In a three-dimensional voxel coordinate system, spectral unmixing of multi-channel image sequences is performed, and the unmixed result vector is constrained by three-dimensional spatial regularization and continuous spatial constraints to obtain voxel-level fluorescence intensity vectors. Based on the component nuclear dye signal of the voxel-level fluorescence intensity vector and the fluorescence reference microbeads, rigid and non-rigid registration methods are used to perform unified spatial alignment on multi-channel images acquired in different cycles. Combined with three-dimensional deconvolution processing, high-resolution reconstructed voxel values ​​are obtained, and cell segmentation is performed in a unified coordinate system to obtain a set of cell voxels. The proximity relationships and co-localization indices of cells with different phenotypes in three-dimensional space were calculated for cytochrome sets to obtain multiplex immunofluorescence detection results.

2. The spatial reconstruction-based multiplexed immunofluorescence method of claim 1, wherein: The specific steps for preparing tissue samples and adding fluorescent reference beads to obtain serial sections are as follows: The tissue to be tested was fixed and dehydrated to prepare a tissue sample, and fluorescent reference beads were added to the tissue sample. Tissue samples containing fluorescent reference beads were sectioned to obtain serial sections.

3. The spatial reconstruction-based multiplexed immunofluorescence method of claim 2, wherein: The process involves acquiring signals from blank slides to establish an autofluorescence baseline, performing monochromatic spectral calibration on each fluorescent dye, and obtaining a complete dataset of spectral mixing matrices and baseline parameters. The specific steps are as follows: Signals were acquired from blank slides, and fluorescence calculations were performed on the signals from the blank slides to obtain the autofluorescence baseline. Monochromatic excitation and acquisition were performed on each fluorescent dye individually, and spectral mixing calculations were performed using the monochromatic signals of each fluorescent dye to obtain the spectral mixing matrix; By combining the autofluorescence baseline with the spectral mixing matrix, a complete dataset of the spectral mixing matrix and baseline parameters is obtained.

4. The spatial reconstruction-based multiplexed immunofluorescence method of claim 3, wherein: The blank section refers to a tissue section that has not been treated with any fluorescent dye.

5. The spatial reconstruction-based multiplexed immunofluorescence method of claim 4, wherein: The tissue samples are sequentially preprocessed in cycles, and the signals emitted by fluorescent reference microbeads are used for intensity normalization and bleaching time drift correction to obtain a corrected multi-channel image sequence. The specific steps are as follows: Tissue samples were subjected to cyclic pretreatment to obtain cyclic immunofluorescence acquisition sequences; The pretreatment includes immunofluorescence staining, imaging, and bleaching; The signal emitted by the fluorescent reference microbeads was collected, and the intensity normalization coefficient was calculated. The intensity normalization coefficient was used to perform numerical normalization on the cyclic immunofluorescence acquisition sequence channel by channel to obtain the intensity-normalized multi-channel image sequence. The bleaching time drift correction parameters were calculated using the signal emitted by the fluorescent reference microbeads. The intensity-normalized multi-channel image sequence is corrected by using drift correction parameters to obtain the corrected multi-channel image sequence.

6. The spatial reconstruction-based multiplexed immunofluorescence method of claim 5, wherein: The process involves spectral demixing of a multi-channel image sequence in a three-dimensional voxel coordinate system, followed by constraint processing of the demixed result vector using three-dimensional spatial regularization and continuity constraints to obtain voxel-level fluorescence intensity vectors. The specific steps are as follows: mapping the corrected multi-channel image sequence to a three-dimensional voxel coordinate system; performing spectral unmixing calculation using the multi-channel image mapped to the three-dimensional voxel coordinate system and the spectral mixing matrix to obtain an initial unmixing result vector; constraining the initial unmixing result vector using three-dimensional spatial constraints and performing continuity constraint on the constrained unmixing result vector using continuity spatial constraints to obtain an unmixing result vector; performing voxel-level intensity calculation on the unmixing result vector to obtain a voxel-level fluorescence intensity vector.

7. The spatial reconstruction-based multiplexed immunofluorescence method of claim 6, wherein: The components of the voxel-level fluorescence intensity vector and the fluorescence reference microbeads are used to perform spatial alignment on the multi-channel images obtained in different cycles using rigid and non-rigid registration methods, and high-resolution reconstructed voxel values are obtained through three-dimensional deconvolution processing, and cell segmentation is performed in a unified coordinate system to obtain a cell voxel set, and the specific steps are as follows, extracting the nuclear dye signal of the corrected multi-channel image sequence and the signal emitted by the fluorescence reference microbeads as a registration reference; performing rigid and non-rigid registration on the multi-channel images obtained in different cycles using the established registration reference to obtain unified spatial alignment data; performing three-dimensional deconvolution processing on the unified spatial alignment data to generate high-resolution reconstructed voxel values; performing cell segmentation on the high-resolution reconstructed voxel values in a unified coordinate system to extract a cell voxel set.

8. The spatial reconstruction-based multiplexed immunofluorescence method of claim 7, wherein: The proximity relationship and co-localization index of different phenotype cells in three-dimensional space are calculated based on the cell voxel set to obtain a multiplex immunofluorescence detection result, and the specific steps are as follows, phenotype determination is performed on the cell voxel set based on the voxel-level fluorescence intensity vector to obtain a different phenotype cell set; the proximity relationship is calculated using the positional relationship of the different phenotype cell set in three-dimensional space; the co-localization index is calculated using the signal distribution of the different phenotype cell set in three-dimensional space; a multiplex immunofluorescence detection result is generated based on the voxel-level fluorescence intensity vector, the proximity relationship, and the co-localization index; The multiplex immunofluorescence detection result includes voxel-level fluorescence signal, cell-level quantitative parameter, and spatial microenvironment distribution characteristics. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the spatial reconstruction-based multiplex immunofluorescence detection method of any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the spatial reconstruction-based multiplex immunofluorescence detection method of any one of claims 1-8.

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