Spatial transcriptome permeabilization time analysis method and system, medium, product and terminal

Abstract

The invention provides a spatial transcriptome permeabilization time analysis method and system, a medium, a product and a terminal. A plurality of biological tissue slice permeabilization images generated based on different permeabilization time are acquired and preprocessed; obtaining target tissue cDNA fluorescence images corresponding to the biological tissue slice permeation images based on an edge detection algorithm according to the preprocessed biological tissue slice permeation images; respectively calculating a brightness index value and a sharpness index value corresponding to the cDNA fluorescence image of each target tissue; and obtaining an optimal permeabilization time analysis image and corresponding optimal permeabilization time according to the brightness index value and the sharpness index value corresponding to each target tissue cDNA fluorescence image. According to the method, the clearest permeabilization condition of the internal organizational structure can be rapidly and automatically interpreted in combination with the image brightness index and the sharpness index, the interpretation accuracy and efficiency are greatly improved, and subjectivity and time consumption of manual judgment are avoided.

Description

Technical Field

[0001] The present application relates to the field of biotechnology, and in particular to a spatial transcriptome permeabilization time analysis method, system, medium, product and terminal. Background Art

[0002] As a cutting-edge molecular biology research method, spatial transcriptome sequencing technology has shown great application potential in the field of life sciences in recent years. This technology can achieve comprehensive detection of the entire transcriptome in intact tissue sections and draw a tissue spatial location map of gene expression, providing an unprecedented perspective for revealing gene and cell functions, disease occurrence and development mechanisms, etc.

[0003] In the frozen section spatial transcriptome experiment, a crucial step is tissue permeabilization, a process designed to effectively penetrate RNA from the tissue so that it can be captured by a gene chip with a spatial position capture probe. Subsequently, reverse transcription is performed on the chip to generate cDNA with spatial position sequence barcodes. These cDNAs are mixed to construct a sequencing library and finally sequenced. By parsing the different spatial position sequence barcodes carried by each RNA, the spatial position response and gene expression analysis of the sequencing data can be achieved, thereby revealing the precise expression pattern of genes in tissues.

[0004] However, the cell and stroma composition of different tissues vary significantly, which leads to different time conditions required for different tissues during the enzymatic permeabilization process. Therefore, it is particularly important to conduct optimization test experiments for multiple tissue permeabilization times for each sample. Traditional optimization test experiments usually use a capture matrix with multiple identical poly dT capture probes, and after tissue patching, fixation, staining and other steps, permeabilization treatment is performed at multiple time points in parallel. Subsequently, reverse transcription is performed on the chip, and cDNA is generated using fluorescently labeled nucleotides to visualize the synthesized cDNA. After the tissue is removed by the enzyme, the fluorescently labeled cDNA is left behind, which is observed under a fluorescence microscope to select the optimal permeabilization time.

[0005] However, this method based on fluorescence microscopy observation is significantly subjective. Especially when the cDNA fluorescence intensity at multiple time points is similar, the experience of the experimental operator becomes a key factor affecting the accuracy of the judgment, and the experience of the actual experimental operator is required to be high. This not only increases the uncertainty of the experiment, leading to deviations in the selection of permeabilization time, but also affects the accuracy and reliability of subsequent sequencing results.

[0006] At present, although some commercial spatial transcriptome sequencing reagents include products that involve the optimization of tissue permeabilization conditions, such as reagents from some biological companies, these products also rely on experienced experimenters to make manual judgments in actual applications. This manual reliance is not only inefficient, but may also lead to inconsistent results due to individual differences.

[0007] In addition, although a method has been proposed to quantify and evaluate the images of tissue slice permeabilization treatment using fast Fourier transform (FFT), the FFT method also has many shortcomings. First, FFT requires a lot of computing resources, which increases the cost and time cost of the experiment. Secondly, before performing FFT on the image, it is usually necessary to zero-fill the image to avoid frequency aliasing. However, if the filling is improper, it may cause erroneous frequency information, thus affecting the accuracy of the evaluation. In addition, in the frequency domain, noise may be amplified, further increasing the difficulty and uncertainty of the evaluation. Summary of the invention

[0008] In view of the shortcomings of the prior art described above, the purpose of the present application is to provide a spatial transcriptome permeabilization time analysis method, system, medium, product and terminal, which are used to solve the technical problems in the prior art that the evaluation of the optimal permeabilization time mainly relies on manual judgment, low efficiency and low accuracy.

[0009] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a spatial transcriptome permeabilization time analysis method, comprising: obtaining a plurality of biological tissue slice permeation images and performing preprocessing; wherein each of the biological tissue slice permeation images is generated based on different permeabilization times; according to each of the preprocessed biological tissue slice permeation images and based on an edge detection algorithm, obtaining a target tissue cDNA fluorescence image corresponding to each of the biological tissue slice permeation images; respectively calculating a brightness index value and a sharpness index value corresponding to each of the target tissue cDNA fluorescence images; according to the brightness index value and the sharpness index value corresponding to each of the target tissue cDNA fluorescence images, obtaining an optimal permeabilization time analysis image and the corresponding optimal permeabilization time.

[0010] In some embodiments of the first aspect of the present application, the method for calculating the brightness index value corresponding to each target tissue cDNA fluorescence image includes: screening out a number of effective permeabilization observation points in each target tissue cDNA fluorescence image, and obtaining the characteristic signal value corresponding to each effective permeabilization observation point; calculating the brightness index value corresponding to each target tissue cDNA fluorescence image based on the characteristic signal value corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image.

[0011] In some embodiments of the first aspect of the present application, the method of screening out several effective permeabilization observation points in each target tissue cDNA fluorescence image includes: screening out several original permeabilization observation points in each target tissue cDNA fluorescence image, and calculating the characteristic signal value corresponding to each original permeabilization observation point; according to the characteristic signal value corresponding to each original permeabilization observation point in each target tissue cDNA fluorescence image, and based on a preset value range, screening out several effective permeabilization observation points in each target tissue cDNA fluorescence image.

[0012] In some embodiments of the first aspect of the present application, the method for calculating the brightness index value corresponding to each target tissue cDNA fluorescence image includes: calculating the average value of the characteristic signal values ​​corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image to obtain the brightness index value corresponding to each target tissue cDNA fluorescence image.

[0013] In some embodiments of the first aspect of the present application, the method for calculating the sharpness index value corresponding to each target tissue cDNA fluorescence image includes: using the Sobel operator to perform a convolution operation on each effective permeabilization observation point in each target tissue cDNA fluorescence image, so as to obtain the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image; according to the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image, calculate the sharpness index value corresponding to each target tissue cDNA fluorescence image.

[0014] In some embodiments of the first aspect of the present application, according to the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image, a method for obtaining an optimal permeabilization time analysis image and a corresponding optimal permeabilization time includes: sorting the brightness index value corresponding to each target tissue cDNA fluorescence image, and selecting the target tissue cDNA fluorescence image corresponding to the highest brightness index value and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value; wherein the target tissue cDNA fluorescence image corresponding to the highest brightness index value is used as the first permeabilization time analysis image, and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value is used as the second permeabilization time analysis image. The corresponding target tissue cDNA fluorescence image is used as the second permeabilization time analysis image; it is determined whether the difference between the highest brightness index value and the second highest brightness index value is greater than a preset threshold value; if so, the first permeabilization time analysis image is selected as the optimal permeabilization time analysis image, and the permeabilization time corresponding to the first permeabilization time analysis image is used as the optimal permeabilization time; if not, the sharpness index values ​​of the first permeabilization time analysis image and the second permeabilization time analysis image are determined, the image with a high sharpness index value is selected as the optimal permeabilization time analysis image, and the permeabilization time corresponding to the image with a high sharpness index value is used as the optimal permeabilization time.

[0015] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a spatial transcriptome permeabilization time analysis system, including: a preprocessing module, used to obtain and preprocess a plurality of biological tissue slice permeation images; wherein each of the biological tissue slice permeation images is generated based on different permeabilization times; a cDNA fluorescence image acquisition module, used to obtain the target tissue cDNA fluorescence image corresponding to each of the biological tissue slice permeation images based on the preprocessed biological tissue slice permeation images and based on an edge detection algorithm; a brightness sharpness index calculation module, used to respectively calculate the brightness index value and sharpness index value corresponding to each of the target tissue cDNA fluorescence images; an optimal permeabilization time analysis module, used to obtain an optimal permeabilization time analysis image and the corresponding optimal permeabilization time according to the brightness index value and sharpness index value corresponding to each of the target tissue cDNA fluorescence images.

[0016] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the spatial transcriptome permeabilization time analysis method as described above.

[0017] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code is run on a computer, the computer implements the spatial transcriptome permeabilization time analysis method as described above.

[0018] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the spatial transcriptome permeabilization time analysis method as described above.

[0019] As described above, the spatial transcriptome permeabilization time analysis method, system, medium, product and terminal of the present application have the following beneficial effects: it can combine the image brightness index and sharpness index to quickly and automatically judge the clearest permeabilization conditions of the internal tissue structure, greatly improving the accuracy and efficiency of the judgment, and does not need to observe the fluorescence intensity of cDNA at different permeabilization times by fluorescence microscopy, does not rely on manual judgment, avoids the subjectivity and time-consuming nature of manual judgment, can significantly reduce the experimental cost and time cost, quickly and accurately determine the optimal permeabilization time, and enhance the repeatability of the experiment and the consistency of the results. In addition, there is no need to perform complex fast Fourier transform (FFT) and image processing, and the computational complexity is relatively low, which greatly saves computing resources, reduces the difficulty and threshold of operation, and enables more researchers to use it easily. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a schematic diagram of the process of the spatial transcriptome permeabilization time analysis method in one embodiment of the present application.

[0021] Figure 2 Shown is a schematic diagram of the process of obtaining a cDNA fluorescence image of a target tissue in one embodiment of the present application.

[0022] Figure 3 Shown is a schematic diagram of a process for calculating a brightness index value in an embodiment of the present application.

[0023] Figure 4 Shown is a schematic diagram of a process for calculating a sharpness index value in one embodiment of the present application.

[0024] Figure 5 Shown is a schematic diagram of a process for determining the optimal permeabilization time in an embodiment of the present application.

[0025] Figure 6 Shown is a schematic block diagram of a spatial transcriptome permeabilization time analysis system in one embodiment of the present application.

[0026] Figure 7 Shown is a schematic diagram of the structure of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0028] Before further describing the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are applicable to the following interpretations:

[0029] <1> RNA: Ribonucleic acid, is a genetic information carrier that exists in biological cells and some viruses and viroids.

[0030] <2> cDNA (Complementary DNA): The full name is complementary DNA, which is a DNA molecule generated from an mRNA (messenger RNA) template in the laboratory through reverse transcription. This process involves using mRNA as a template and synthesizing a DNA chain complementary to the mRNA base sequence under the action of reverse transcriptase.

[0031] <3> mRNA (Messenger RNA): Messenger RNA is a single-stranded RNA molecule that is widely present in eukaryotes and prokaryotes. Its main function is to serve as a template for protein synthesis, guiding ribosomes to synthesize specific proteins in cells.

[0032] <4> Poly dT: refers to a single-stranded nucleic acid molecule composed of multiple thymine nucleotides connected by phosphodiester bonds, which can complement the 3' end poly (A) tail of mRNA. Its structural characteristics are highly repetitive sequences composed only of thymine nucleotides.

[0033] <5> Poly (A) tail: It is an important feature of eukaryotic mRNA. It is located at the 3' end of the mRNA molecule and is composed of a series of adenylic acid (A) connected by phosphodiester bonds.

[0034] The technical scheme of the embodiment of the present application can be applied to spatial transcriptome sequencing technology, etc., and after the steps of tissue patch, fixation, staining, etc., the permeabilization treatment of multiple time points in parallel is performed. Subsequently, reverse transcription is performed on the chip, and cDNA is generated using fluorescently labeled nucleotides to visualize the synthesized cDNA. After the tissue is removed by enzyme, fluorescently labeled cDNA is left, and several biological tissue slice penetration images are obtained, and the corresponding target tissue cDNA fluorescence image is identified by edge detection of each of the biological tissue slice penetration images, and the brightness index value and sharpness index value corresponding to the target tissue cDNA fluorescence image are combined to select the optimal permeabilization time. After determining the optimal permeabilization time, the optimal permeabilization time can be used to implement spatial transcriptome sequencing technology, and the formal experiment of spatial gene expression is started. Therefore, suitable tissue permeabilization process conditions become the key to realizing this technology.

[0035] In the prior art, the fluorescence intensity of cDNA at different permeabilization times is mainly observed by fluorescence microscope, which depends on the experience and subjective judgment of the experimenter and is inefficient. Especially when the cDNA fluorescence intensity at 2-3 time points is very close, the experience requirement of the experimental operator is relatively high. This subjectivity leads to the deviation of permeabilization time selection, thereby affecting the accuracy and reliability of subsequent sequencing results. In addition, although there is a method for quantifying and evaluating the image of tissue section permeabilization treatment using fast Fourier transform (FFT), the fast Fourier transform method requires a large amount of computing resources, and there are some potential problems in the image processing process. For example, before performing FFT on the image, it is usually necessary to zero-fill the image to avoid frequency aliasing. If improper filling, erroneous frequency information may be caused. In addition, in the frequency domain, noise may be amplified, thereby affecting the quality of the image and the accuracy of the analysis results.

[0036] In order to solve the problems in the above-mentioned background technology, the present application provides a spatial transcriptome permeabilization time analysis method, system, medium, product and terminal, which are used to solve the technical problems in the prior art that the evaluation of the optimal permeabilization time mainly relies on manual judgment, low efficiency and low accuracy.

[0037] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 The schematic diagram of the process of the spatial transcriptome permeabilization time analysis method in the embodiment of the present invention is shown. The spatial transcriptome permeabilization time analysis method in this embodiment mainly includes the following steps:

[0038] S101: Acquire a plurality of biological tissue slice permeation images and perform preprocessing; wherein each of the biological tissue slice permeation images is generated based on a different permeabilization time.

[0039] In this embodiment, after the biological tissue slices are pasted, fixed, stained and other steps, an enzyme permeabilization tissue solution is added to perform permeabilization treatment on the biological tissue at multiple time points in parallel. Subsequently, reverse transcription is performed on the chip, and cDNA is generated using fluorescently labeled nucleotides to visualize the synthesized cDNA. After the tissue is removed by the enzyme, the fluorescently labeled cDNA is left behind, and the biological tissue slices that have been permeabilized and fluorescently labeled are observed using a microscope or imaging system, and the biological tissue slice penetration images are collected to ensure that the corresponding images are obtained at different permeabilization times.

[0040] In this embodiment, the method of preprocessing the biological tissue slice penetration image includes: image denoising preprocessing or image enhancement preprocessing. The image denoising preprocessing is to remove the noise in the biological tissue slice penetration image to prevent the interference of noise, such as high-frequency noise, etc., and use smoothing filtering and other methods to eliminate these noises. The image enhancement preprocessing is to improve the visual effect of the image and the accuracy of subsequent analysis, and the image can be enhanced. This includes improving the image contrast, brightness and other parameters to make the details in the image clearer.

[0041] S102: According to the preprocessed biological tissue slice penetration images and based on an edge detection algorithm, a target tissue cDNA fluorescence image corresponding to each biological tissue slice penetration image is acquired.

[0042] In this embodiment, if Figure 2 FIG. 1 is a flow chart showing a method for obtaining a cDNA fluorescence image of a target tissue according to an embodiment of the present invention. Based on an edge detection algorithm, a method for obtaining a cDNA fluorescence image of a target tissue corresponding to each of the biological tissue slice penetration images includes:

[0043] S1021: performing convolution operations on the preprocessed biological tissue slice penetration images respectively using a Sobel operator to calculate the gradient components in the horizontal direction and the gradient components in the vertical direction of the preprocessed biological tissue slice penetration images.

[0044] In this embodiment, the edge detection algorithm in this application is to use the Sobel operator for edge detection, the main purpose of which is to identify edge features in the image. These edge features correspond to areas where the grayscale value in the image changes significantly, usually corresponding to the boundaries of biological tissues, cell contours, etc. The result of edge detection is usually a binary image or a grayscale gradient image, in which the highlighted portion represents the detected edge.

[0045] In this embodiment, the Sobel operator is a discrete differential operator that combines Gaussian smoothing and differential derivation. It includes two 3x3 convolution kernels, one for calculating the gradient in the horizontal direction (usually the x direction) and the other for calculating the gradient in the vertical direction (usually the y direction).

[0046] In this embodiment, a Sobel convolution kernel in the horizontal direction is used to perform a convolution operation on each of the preprocessed biological tissue slice penetration images to obtain its gradient component in the horizontal direction. A Sobel convolution kernel in the vertical direction is used to perform a convolution operation on each of the preprocessed biological tissue slice penetration images to obtain its gradient component in the vertical direction. These two gradient components respectively reflect the change rates of the pixel values ​​in each of the preprocessed biological tissue slice penetration images in the horizontal and vertical directions.

[0047] S1022: Calculate the gradient amplitude and gradient direction of each pixel point in each preprocessed biological tissue slice penetration image according to the gradient component in the horizontal direction and the gradient component in the vertical direction of each preprocessed biological tissue slice penetration image.

[0048] In this embodiment, the gradient amplitude represents the intensity of the gray value change of each pixel in the image, and is obtained by calculating the square root of the sum of the squares of the gradient components of the pixel in the horizontal and vertical directions. The gradient direction represents the direction in which the gray value of each pixel in the image changes fastest, and is obtained by calculating the inverse tangent value of the gradient components of the pixel in the horizontal and vertical directions.

[0049] S1023: Acquire the target tissue cDNA fluorescence image corresponding to each of the biological tissue slice penetration images according to the gradient amplitude and gradient direction of each pixel point in each of the preprocessed biological tissue slice penetration images.

[0050] In this embodiment, a gradient amplitude image is generated using the gradient amplitude of each pixel in each of the preprocessed biological tissue slice penetration images. Each pixel value in this image represents the intensity of the gray value change at the corresponding position, and the edge area (i.e., the position where the gray value changes significantly) usually has a higher gradient amplitude. At the same time, a gradient direction image is generated using the gradient direction of each pixel in each of the preprocessed biological tissue slice penetration images. This image provides information about the edge direction, i.e., the direction of gray value change at each pixel, which is helpful for subsequent image segmentation and target recognition.

[0051] In this embodiment, using the gradient magnitude image and the gradient direction image, a gradient-based segmentation method (such as threshold segmentation, region growing, watershed algorithm, etc.) can be used to segment the pre-processed biological tissue slice penetration image into different regions. After segmentation, the result needs to be post-processed, such as removing noise, filling small holes, connecting broken edges, etc., to improve the accuracy of segmentation.

[0052] In this embodiment, the threshold segmentation is to set one or more thresholds according to the gradient amplitude to divide the image into different regions. The region growing starts from a seed point and gradually merges pixels with similar properties into the same region according to the gradient direction and amplitude information. The watershed algorithm regards the gradient amplitude image as a topographic map and uses the gradient direction information to simulate water flow, thereby segmenting different regions.

[0053] In this embodiment, features related to the cDNA fluorescence of the target tissue are extracted from the segmented image. These features include the shape, size, gray value distribution, texture, etc. of the region. Using the extracted features, a machine learning or deep learning method can be used to perform target recognition. For example, a classifier is trained using a known cDNA fluorescence image of the target tissue as a training sample. This classifier can learn the features of the cDNA fluorescence of the target tissue and is used to distinguish the cDNA fluorescence of the target tissue from other tissues or backgrounds. According to the result of target recognition, the pixel points or areas identified as the cDNA fluorescence of the target tissue are set to highlight (such as white or a specific color), and other pixel points or areas are set to low brightness (such as black or gray), thereby constructing a cDNA fluorescence image of the target tissue. Finally, the generated fluorescence image can be further optimized, such as smoothing, denoising, etc., to improve the quality and readability of the image.

[0054] S103: Calculate the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image respectively.

[0055] In this embodiment, if Figure 3 FIG. 1 is a flow chart showing a process for calculating brightness index values ​​in an embodiment of the present invention. The method for calculating the brightness index values ​​corresponding to the cDNA fluorescence images of each target tissue includes:

[0056] S1031: Screening out a number of effective permeabilization observation points in each target tissue cDNA fluorescence image, and obtaining a characteristic signal value corresponding to each effective permeabilization observation point.

[0057] In this embodiment, the method of screening out a number of effective permeabilization observation points in each target tissue cDNA fluorescence image includes:

[0058] (1) Screen out several original permeabilization observation points in the cDNA fluorescence image of each target tissue, and calculate the characteristic signal value corresponding to each original permeabilization observation point.

[0059] (2) According to the characteristic signal values ​​corresponding to the original permeabilization observation points in each target tissue cDNA fluorescence image and based on a preset value range, a number of effective permeabilization observation points in each target tissue cDNA fluorescence image are screened out.

[0060] In this embodiment, after identifying the target tissue cDNA fluorescence image corresponding to each biological tissue slice penetration image, the target tissue cDNA fluorescence image is segmented according to the grid, and a number of original permeabilization observation points in each target tissue cDNA fluorescence image are randomly extracted, each original permeabilization observation point is a grid, and each grid contains multiple pixels. For each grid, the median value of the signal value (such as gray value, fluorescence intensity, etc.) of all the pixels inside it is calculated, and this median value is used as the characteristic signal value of the grid (i.e., the original permeabilization observation point).

[0061] In this embodiment, the characteristic signal values ​​corresponding to each original permeabilization observation point in each target tissue cDNA fluorescence image are sorted. The sorting can be ascending or descending, preferably, ascending, so that the lowest characteristic signal value is ranked first and the highest characteristic signal value is ranked last.

[0062] In this embodiment, the preset value range is to remove the original transparent observation points within the range of 5% of the highest and lowest characteristic signal values, that is, to remove the part with the lowest characteristic signal value (accounting for 5%) and the part with the highest characteristic signal value (also accounting for 5%) among all the original transparent observation points after sorting, thereby removing the extreme values ​​in the original transparent observation points, avoiding the extraction of the maximum and minimum values ​​of the signal, reducing noise, and avoiding deviations caused by improper experimental operation, instrument errors or the particularity of the sample itself, which helps to enhance the repeatability of the experiment and the consistency of the results.

[0063] In this embodiment, assuming that there are N original transparent observation points, the number of original transparent observation points that need to be removed at both ends is calculated, that is, 5% of N. Since the number of original transparent observation points is an integer, it is necessary to round this result to obtain an integer K, then remove the first K original transparent observation points with the lowest characteristic signal value after sorting and the last K original transparent observation points with the highest characteristic signal value. After removing the extreme values, the remaining original transparent observation points are valid transparent observation points. Exemplarily, assuming that N=100, the number of original transparent observation points that need to be removed at both ends is 5, then remove the first 5 original transparent observation points with the lowest characteristic signal value after sorting and the last 5 original transparent observation points with the highest characteristic signal value.

[0064] S1032: Calculate the brightness index value corresponding to each target tissue cDNA fluorescence image according to the characteristic signal value corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image.

[0065] In this embodiment, the method for calculating the brightness index value corresponding to each target tissue cDNA fluorescence image includes: calculating the average value of the characteristic signal values ​​corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image to obtain the brightness index value corresponding to each target tissue cDNA fluorescence image. The calculation formula is as follows:

[0066]

[0067] Where L represents the brightness index value corresponding to the cDNA fluorescence image of the target tissue; n represents the number of effective permeabilization observation points; x n Represents the characteristic signal value corresponding to the nth effective transparent observation point.

[0068] In this embodiment, if Figure 4 , which shows a schematic diagram of a process for calculating sharpness index values ​​in an embodiment of the present invention. The method for calculating sharpness index values ​​corresponding to cDNA fluorescence images of each target tissue includes:

[0069] S1031A: Using the Sobel operator, convolution operations are performed on each effective permeabilization observation point in each target tissue cDNA fluorescence image to obtain the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image.

[0070] In this embodiment, a Sobel convolution kernel in the horizontal direction is used to perform convolution operations on each effective permeabilization observation point in each target tissue cDNA fluorescence image, and a Sobel convolution kernel in the vertical direction is used to perform convolution operations on each effective permeabilization observation point in each target tissue cDNA fluorescence image, and a gradient component in the vertical direction is obtained.

[0071] S1032B: Calculate the sharpness index value corresponding to each target tissue cDNA fluorescence image according to the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image.

[0072] In this embodiment, the gradient amplitude of each effective permeabilization observation point in each target tissue cDNA fluorescence image is calculated according to the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image, and the calculation formula is as follows:

[0073]

[0074] Where G represents the gradient amplitude of the effective permeabilization observation point; G x G represents the gradient component of the effective transparent observation point in the horizontal direction; y Represents the gradient component of the effective transparent observation point in the vertical direction.

[0075] In this embodiment, a gradient image is first constructed based on the gradient amplitude of each effective permeabilization observation point in each target tissue cDNA fluorescence image. Then, the variance or the sum of the absolute values ​​of the gradient amplitudes of all effective pixels (or a subset of pixels selected as needed) is calculated on this gradient image to obtain the sharpness index value corresponding to each target tissue cDNA fluorescence image. In the target tissue cDNA fluorescence image analysis, the sharpness index value can help identify which images contain clear and recognizable structural information, thereby facilitating subsequent image analysis and interpretation.

[0076] S104: Obtaining an optimal permeabilization time analysis image and a corresponding optimal permeabilization time according to the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image.

[0077] In this embodiment, if Figure 5 As shown, a schematic diagram of a process for determining the optimal permeabilization time in an embodiment of the present invention is shown. According to the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image, the method of obtaining the optimal permeabilization time analysis image and the corresponding optimal permeabilization time includes:

[0078] S1041: Sort the brightness index values ​​corresponding to each target tissue cDNA fluorescence image, and select the target tissue cDNA fluorescence image corresponding to the highest brightness index value and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value; wherein the target tissue cDNA fluorescence image corresponding to the highest brightness index value is used as the first permeabilization time analysis image, and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value is used as the second permeabilization time analysis image.

[0079] In this embodiment, the brightness index values ​​corresponding to each target tissue cDNA fluorescence image are sorted, and the sorting can be ascending or descending. The target tissue cDNA fluorescence image corresponding to the highest brightness index value is selected as the first permeabilization time analysis image, and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value is selected as the second permeabilization time analysis image.

[0080] S1042: Determine whether the difference between the highest brightness index value and the second highest brightness index value is greater than a preset threshold.

[0081] In this embodiment, it is determined whether the difference between the highest brightness index value and the second highest brightness index value is greater than a preset threshold, and the preset threshold includes 5% of the second highest brightness index value.

[0082] S1043: If yes, select the first permeabilization time analysis image as the optimal permeabilization time analysis image, and use the permeabilization time corresponding to the first permeabilization time analysis image as the optimal permeabilization time.

[0083] If the difference between the highest brightness index value and the second highest brightness index value is greater than 5% of the second highest brightness index value, the first permeabilization time analysis image (i.e., the target tissue cDNA fluorescence image corresponding to the highest brightness index value) is selected as the optimal permeabilization time analysis image, and the permeabilization time corresponding to the first permeabilization time analysis image (i.e., the target tissue cDNA fluorescence image corresponding to the highest brightness index value) is used as the optimal permeabilization time.

[0084] S1044: If not, determine the sharpness index values ​​of the first transparency time analysis image and the second transparency time analysis image, select the image with a higher sharpness index value as the optimal transparency time analysis image, and use the transparency time corresponding to the image with a higher sharpness index value as the optimal transparency time.

[0085] In this embodiment, if the difference between the highest brightness index value and the second highest brightness index value is less than or equal to 5% of the second highest brightness index value, it is necessary to judge the sharpness index value, and select the image with a higher sharpness index value and the corresponding permeabilization time. If the sharpness index value of the first permeabilization time analysis image is greater than the sharpness index value of the second permeabilization time analysis image, the first permeabilization time analysis image (i.e., the target tissue cDNA fluorescence image corresponding to the highest brightness index value) is selected as the optimal permeabilization time analysis image, and the permeabilization time corresponding to the first permeabilization time analysis image is used as the optimal permeabilization time. If the sharpness index value of the first permeabilization time analysis image is less than the sharpness index value of the second permeabilization time analysis image, the second permeabilization time analysis image (i.e., the target tissue cDNA fluorescence image corresponding to the second highest brightness index value) is selected as the optimal permeabilization time analysis image, and the permeabilization time corresponding to the second permeabilization time analysis image is used as the optimal permeabilization time.

[0086] In this embodiment, when the brightness index values ​​of the target tissue cDNA fluorescence images are relatively close, a secondary judgment is performed using their sharpness index values, which can provide a more accurate basis for judgment, select the permeabilization time corresponding to the image with higher sharpness as the optimal permeabilization time, optimize the experimental conditions, realize automated interpretation, and avoid the subjectivity and time-consuming nature of manual judgment.

[0087] It is worth noting that the spatial transcriptome permeation time analysis method of the present invention can combine image brightness index and sharpness index, quickly and automatically judge the clearest permeation conditions of internal tissue structure, greatly improve the accuracy and efficiency of judgment, do not need to observe the cDNA fluorescence intensity of different permeation times by fluorescence microscope, do not rely on manual judgment, avoid the subjectivity and time-consuming of manual judgment, can significantly reduce experimental cost and time cost, quickly and accurately determine the optimal permeation time, enhance the repeatability of the experiment and the consistency of the results. In addition, there is no need to carry out complex fast Fourier transform (FFT) and image processing, the computational complexity is relatively low, greatly saves computing resources, reduces the difficulty of operation and threshold, and enables more researchers to use it easily.

[0088] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first permeation time analysis image and the second permeation time analysis image are only used to distinguish different permeation time analysis images, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0089] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0090] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0091] Figure 6 is a schematic block diagram of the spatial transcriptome permeabilization time analysis system provided in the embodiments of the present application. Figure 6 As shown, the spatial transcriptome permeabilization time analysis system 600 includes:

[0092] The preprocessing module 601 is used to obtain and preprocess a plurality of biological tissue slice permeation images; wherein each of the biological tissue slice permeation images is generated based on different permeabilization times.

[0093] The cDNA fluorescence image acquisition module 602 is used to acquire the target tissue cDNA fluorescence image corresponding to each of the biological tissue slice penetration images according to the preprocessed biological tissue slice penetration images and based on an edge detection algorithm.

[0094] The brightness and sharpness index calculation module 603 is used to respectively calculate the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image.

[0095] The optimal permeabilization time analysis module 604 is used to obtain the optimal permeabilization time analysis image and the corresponding optimal permeabilization time according to the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image.

[0096] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0097] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0098] Figure 7 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. The electronic terminal includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the spatial transcriptome permeabilization time analysis method as described above. Figure 7 As shown, the electronic terminal 700 includes: at least one processor 701, a memory 702, at least one network interface 703 and a user interface 705. The various components in the device are coupled together through a bus system 704. It can be understood that the bus system 704 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 704 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 7 In the specification, various buses are labeled as bus systems.

[0099] The user interface 705 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0100] It is understood that the memory 702 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of exemplary but not limiting explanation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0101] The memory 702 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 700. Examples of these data include: any executable program for operating on the electronic terminal 700, such as an operating system 7021 and an application 7022; the operating system 7021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 7022 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The spatial transcriptome permeability time analysis method provided in the embodiment of the present invention can be included in the application 7022.

[0102] The method disclosed in the above embodiment of the present invention can be applied to the processor 701, or implemented by the processor 701. The processor 701 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 701 or the instruction in the form of software. The above processor 701 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 701 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 701 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0103] In an exemplary embodiment, the electronic terminal 700 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0104] According to the method provided in the embodiment of the present application, the present application also provides a computer program product, which includes: a computer program code, when the computer program code is run on a computer, the computer executes Figures 1 to 5 A method according to any one of the embodiments shown.

[0105] According to the method provided in the embodiment of the present application, the present application also provides a computer-readable storage medium, which stores a program code, and when the program code is run on a computer, the computer executes Figures 1 to 5 A method according to any one of the embodiments shown.

[0106] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0107] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0112] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When loading and executing computer program instructions (programs) on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integrations. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs), etc.).

[0113] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0114] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0115] In summary, the present application provides a spatial transcriptome permeation time analysis method, system, medium, product and terminal, can be combined with image brightness index and sharpness index, quickly and automatically judge the clearest permeation condition of internal tissue structure, greatly improve the accuracy and efficiency of judgment, do not need to carry out fluorescence microscope observation of different permeation times of cDNA fluorescence intensity, do not rely on manual judgment, avoid the subjectivity and time-consuming of manual judgment, can significantly reduce experimental cost and time cost, quickly and accurately determine the optimal permeation time, enhance the repeatability of experiment and the consistency of result. And, without the need to carry out complex fast Fourier transform (FFT) and image processing, computational complexity is relatively low, greatly saves computing resources, reduces operational difficulty and threshold, and enables more researchers to use easily. So, the present application effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0116] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A spatial transcriptome permeabilization time analysis method, characterized in that: include: Acquire a plurality of biological tissue slice permeation images and perform preprocessing; wherein each of the biological tissue slice permeation images is generated based on different permeabilization times; According to the preprocessed biological tissue slice penetration images and based on an edge detection algorithm, a target tissue cDNA fluorescence image corresponding to each biological tissue slice penetration image is acquired; Calculate the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image respectively; According to the brightness index value and sharpness index value corresponding to the cDNA fluorescence image of each target tissue, the optimal permeabilization time analysis image and the corresponding optimal permeabilization time are obtained.

2. The spatial transcriptome permeabilization time analysis method according to claim 1, characterized in that: Methods for calculating the brightness index values ​​corresponding to the cDNA fluorescence images of each target tissue include: Screening out a number of effective permeabilization observation points in each target tissue cDNA fluorescence image, and obtaining characteristic signal values ​​corresponding to each effective permeabilization observation point; According to the characteristic signal value corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image, the brightness index value corresponding to each target tissue cDNA fluorescence image is calculated.

3. The spatial transcriptome permeabilization time analysis method according to claim 2, characterized in that: Methods for screening out several effective permeabilization observation points in each target tissue cDNA fluorescence image include: Screen out several original permeabilization observation points in the cDNA fluorescence image of each target tissue, and calculate the characteristic signal value corresponding to each original permeabilization observation point; According to the characteristic signal values ​​corresponding to the original permeabilization observation points in each target tissue cDNA fluorescence image and based on a preset value range, a number of effective permeabilization observation points in each target tissue cDNA fluorescence image are screened out.

4. The spatial transcriptome permeabilization time analysis method according to claim 2, characterized in that: The method of calculating the brightness index value corresponding to each target tissue cDNA fluorescence image includes: calculating the average value of the characteristic signal values ​​corresponding to each effective permeabilization observation point in each target tissue cDNA fluorescence image to obtain the brightness index value corresponding to each target tissue cDNA fluorescence image.

5. The spatial transcriptome permeabilization time analysis method according to claim 2, characterized in that: Methods for calculating the sharpness index values ​​corresponding to the cDNA fluorescence images of each target tissue include: The Sobel operator is used to perform convolution operations on each effective permeabilization observation point in each target tissue cDNA fluorescence image, so as to obtain the gradient component in the horizontal direction and the gradient component in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image; According to the gradient components in the horizontal direction and the gradient components in the vertical direction of each effective permeabilization observation point in each target tissue cDNA fluorescence image, the sharpness index value corresponding to each target tissue cDNA fluorescence image is calculated.

6. The spatial transcriptome permeabilization time analysis method according to claim 1, characterized in that: Methods for obtaining the optimal permeabilization time analysis image and the corresponding optimal permeabilization time according to the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image include: The brightness index values ​​corresponding to each target tissue cDNA fluorescence image are sorted, and the target tissue cDNA fluorescence image corresponding to the highest brightness index value and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value are selected; wherein the target tissue cDNA fluorescence image corresponding to the highest brightness index value is used as the first permeabilization time analysis image, and the target tissue cDNA fluorescence image corresponding to the second highest brightness index value is used as the second permeabilization time analysis image; Determine whether the difference between the highest brightness index value and the second highest brightness index value is greater than a preset threshold; If yes, then selecting the first permeabilization time analysis image as the optimal permeabilization time analysis image, and taking the permeabilization time corresponding to the first permeabilization time analysis image as the optimal permeabilization time; If not, determine the sharpness index values ​​of the first transparency time analysis image and the second transparency time analysis image, select the image with a higher sharpness index value as the optimal transparency time analysis image, and use the transparency time corresponding to the image with a higher sharpness index value as the optimal transparency time.

7. A spatial transcriptome permeabilization time analysis system, characterized in that: include: A preprocessing module, used for acquiring and preprocessing a plurality of biological tissue slice penetration images; wherein each of the biological tissue slice penetration images is generated based on different permeabilization times; A cDNA fluorescence image acquisition module, for acquiring a target tissue cDNA fluorescence image corresponding to each of the biological tissue slice penetration images according to the preprocessed biological tissue slice penetration images and based on an edge detection algorithm; A brightness and sharpness index calculation module is used to respectively calculate the brightness index value and sharpness index value corresponding to each target tissue cDNA fluorescence image; The optimal permeabilization time analysis module is used to obtain the optimal permeabilization time analysis image and the corresponding optimal permeabilization time according to the brightness index value and sharpness index value corresponding to the cDNA fluorescence image of each target tissue.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the spatial transcriptome permeabilization time analysis method according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes computer program codes, and when the computer program codes are executed on a computer, the computer is enabled to implement the spatial transcriptome permeabilization time analysis method according to any one of claims 1 to 6.

10. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the spatial transcriptome permeabilization time analysis method according to any one of claims 1 to 6.

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