Cell boundary information acquisition method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a cell boundary information acquisition method and device, a storage medium and electronic equipment, and the method comprises the steps: carrying out the first registration adjustment of a first cell image and a second cell image, and obtaining a first cell image after the first registration adjustment and a second cell image after the first registration adjustment; obtaining a gene expression graph corresponding to the tissue slice, and performing second registration adjustment on the gene expression graph and the second cell image after the first registration adjustment based on a trace line to obtain an adjustment parameter adopted by the second registration adjustment; and performing third registration adjustment on the first cell image after the first registration adjustment and the gene expression graph according to the adjustment parameter, and determining cell boundary information of the cell boundary according to the first cell image after the third registration adjustment. By adopting the technical scheme, the problem that accurate cell boundary information cannot be obtained in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of biotechnology, and in particular to a method and device for acquiring cell boundary information, a storage medium, and an electronic device. Background Art

[0002] Spatial transcriptome technology (Stereo-seq) is a large-field high-resolution whole transcriptome sequencing technology. It uses two sequencings to confirm the spatial position and corresponding expression of messenger RNA (mRNA) sequences. It has now been applied in many biological and medical fields and has become one of the mainstream methods of spatial transcription sequencing. Stereo-seq technology brings detailed spatial information based on traditional transcriptome sequencing, identifies the location of transcription within the tissue, and is accurate to the cellular level or even higher resolution, which promotes the understanding and interpretation of single cells and cell groups within the entire tissue.

[0003] At present, Stereo-seq technology mainly provides images of cell nucleus staining, such as single-stranded DNA (ssDNA), DNA-associated protein immobilization (DAPI) and hematoxylin and eosin staining (H&E). Cell-level analysis can be achieved by using the track lines provided by Stereo-seq technology itself and the cell nucleus outlines provided by the staining map. However, there is no accurate way to obtain cell boundary information for single cells and entire tissue sections in related technologies.

[0004] There is currently no effective solution to the problem of being unable to obtain accurate cell boundary information in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a method and device for obtaining cell boundary information, a storage medium, an electronic device and a storage medium, so as to at least solve the problem of being unable to obtain accurate cell boundary information.

[0006] According to one embodiment of the present application, a method for acquiring cell boundary information is provided, comprising: performing a first registration adjustment on a first cell image and a second cell image to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image of a cell boundary containing a tissue section, and the second cell image is an image of a cell nucleus containing the tissue section; acquiring a gene expression map corresponding to the tissue section, and performing a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on a track line to acquire adjustment parameters used for the second registration adjustment; performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment.

[0007] Optionally, obtaining the gene expression map corresponding to the tissue section includes: obtaining the genome information of the tissue section; converting the genome information of the tissue section into a gene expression matrix through target software; converting the gene expression matrix into a gene expression map, wherein the value of each pixel point in the gene expression map represents the number of captured molecules at each pixel point.

[0008] Optionally, determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment includes: performing cell segmentation on the first cell image after the third registration adjustment to obtain the cell boundary of each cell included in the tissue section; determining the position information and gene expression information of each cell in the gene expression map based on the cell boundary; and determining the cell boundary information according to the position information and the gene expression information of each cell.

[0009] Optionally, performing a first registration adjustment on the first cell image and the second cell image to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment includes: acquiring a plurality of first cell original images and a plurality of second cell original images of the stained tissue section; splicing the plurality of first cell original images to obtain the first cell image, and splicing the plurality of second cell images through a target technology to obtain the second cell image; performing a first registration adjustment on the first cell image and the second cell image, wherein the first registration adjustment is a registration adjustment based on Fourier transform.

[0010] Optionally, before acquiring a plurality of first cell original images and a plurality of second cell original images of the stained tissue slice, the method further comprises: slicing the tissue sample to obtain the tissue slice; pasting the tissue slice and fixing the pasted tissue slice; and staining the fixed tissue slice.

[0011] Optionally, a second registration adjustment is performed on the gene expression map and the second cell image after the first registration adjustment based on the track lines to obtain adjustment parameters used for the second registration adjustment, including: detecting the track lines in the second cell image after the first registration adjustment and the gene expression map through a target algorithm; locating the intersection points of horizontal track lines and vertical track lines in the second cell image after the first registration adjustment and the gene expression map, respectively; performing a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on the intersection points of the track lines, wherein the track lines include: the horizontal track lines and the vertical track lines; and obtaining the adjustment parameters used for the second registration adjustment according to the geometric relationship between the intersection points of the track lines.

[0012] Optionally, performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, including: parsing the adjustment parameters to obtain adjustment values ​​corresponding to the adjustment parameters; performing a third registration adjustment on the first cell image after the first registration adjustment according to the adjustment values ​​corresponding to the adjustment parameters.

[0013] Optionally, after determining the cell boundary information of the cell boundary according to the first cell image adjusted by the third registration, the method further includes: obtaining gene expression molecules of the cell boundary information; and assigning the gene expression molecules to each cell in the tissue section to obtain a spatial single-cell gene expression map of the tissue section.

[0014] According to another embodiment of the present application, a device for acquiring cell boundary information is provided, comprising: a first registration module, used to perform a first registration adjustment on a first cell image and a second cell image to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image of a cell boundary containing a tissue section, and the second cell image is an image of a cell nucleus containing the tissue section; an acquisition module, used to acquire a gene expression map corresponding to the tissue section, and perform a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on a track line to acquire adjustment parameters used for the second registration adjustment; a second registration module, used to perform a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and determine the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment.

[0015] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0016] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0017] In the present application, a first cell image and a second cell image are subjected to a first registration adjustment to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image containing the cell boundary of the tissue section, and the second cell image is an image containing the cell nucleus of the tissue section; a gene expression map corresponding to the tissue section is obtained, and a second registration adjustment is performed on the gene expression map and the second cell image after the first registration adjustment based on the track line to obtain the adjustment parameters used for the second registration adjustment; a third registration adjustment is performed on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and the cell boundary information of the cell boundary is determined according to the first cell image after the third registration adjustment. The second cell image with the track first is used as a bridge to complete the registration adjustment of the first cell image and the gene expression map, and the cell boundary information is obtained based on the first cell image after the third registration adjustment, which solves the problem that accurate cell boundary information cannot be obtained in the prior art, thereby achieving the technical effect of obtaining accurate cell boundary information so as to obtain a more accurate spatial single cell atlas. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for acquiring cell boundary information in an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for obtaining cell boundary information according to an embodiment of the present application;

[0021] Figure 3-1 This is a schematic diagram of the principle of performing registration adjustment using DAPI image as a bridge in an embodiment of the present application;

[0022] Figure 3-2This is another schematic diagram of the principle of an embodiment of the present application in which the DAPI image is used as a bridge for registration adjustment;

[0023] Figure 4-1 This is a schematic diagram of a method for acquiring a spatial single-cell atlas based on a cell boundary image according to an embodiment of the present application;

[0024] Figure 4-2 This is a schematic diagram of another method for obtaining a spatial single-cell atlas based on a cell boundary image according to an embodiment of the present application;

[0025] Figure 5-1 This is a schematic diagram of a spatial single cell atlas obtained based on cell boundary images in an embodiment of the present application;

[0026] Figure 5-2 This is a schematic diagram of a spatial single cell atlas obtained based on cell boundary images in an embodiment of the present application;

[0027] Figure 6 It is a structural block diagram of a device for acquiring cell boundary information according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal for a method for obtaining cell boundary information in an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for obtaining cell boundary information in an embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] In order to solve the above problem, a method for obtaining cell boundary information is provided in this embodiment, which is applied to the above computer terminal. Figure 2 is a flow chart of a method for obtaining cell boundary information in an embodiment of the present application. Figure 2 As shown, the process includes the following steps S202-S206:

[0034] Step S202: performing a first registration adjustment on the first cell image and the second cell image to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image containing a cell boundary of a tissue section, and the second cell image is an image containing a cell nucleus of the tissue section;

[0035] It should be noted that the cell may be an animal cell or a plant cell, and further, the cell boundary is the cell membrane of the animal cell or the cell wall of the plant cell.

[0036] Optionally, the first registration adjustment is a registration adjustment based on Fast Fourier Transform (FFT).

[0037] Step S204: acquiring a gene expression map corresponding to the tissue section, and performing a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on the track line, so as to acquire adjustment parameters used in the second registration adjustment;

[0038] In an optional embodiment of the present application, a gene expression graph is a graph used to describe the expression level of a gene under different conditions. It can show the expression level of a gene in different tissues, at different time points, or under different treatment conditions. A gene expression graph is usually presented in the form of a line graph, a bar graph, or a heat map, wherein the horizontal axis represents the condition and the vertical axis represents the gene expression level.

[0039] Optionally, the second registration adjustment is a registration adjustment based on a track line.

[0040] Step S206: performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment.

[0041] Through the above steps S202-S206, the first cell image and the second cell image are first registered and adjusted to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment, wherein the first cell image is an image containing the cell boundary of the tissue section, and the second cell image is an image containing the cell nucleus of the tissue section; the gene expression map corresponding to the tissue section is obtained, and the gene expression map and the second cell image after the first registration adjustment are second registered and adjusted based on the track line to obtain the adjustment parameters used for the second registration adjustment; the first cell image after the first registration adjustment and the gene expression map are third registered and adjusted according to the adjustment parameters, and the cell boundary information of the cell boundary is determined according to the first cell image after the third registration adjustment. The second cell image with the track line is used as a bridge to complete the registration adjustment of the first cell image and the gene expression map, and the cell boundary information is obtained based on the first cell image after the third registration adjustment, which solves the problem that accurate cell boundary information cannot be obtained in the prior art, thereby achieving the technical effect of obtaining accurate cell boundary information to facilitate the acquisition of a more accurate spatial single cell atlas.

[0042] In an exemplary embodiment, obtaining the gene expression map corresponding to the tissue section can be achieved by following the steps S11-S13:

[0043] Step S11: obtaining genome information of the tissue section;

[0044] It should be noted that the genomic information of the tissue section includes but is not limited to transcriptome information.

[0045] As an optional embodiment, the genomic information of the tissue section can be directly obtained by Stereo-seq technology.

[0046] Among them, Stereo-seq technology is a method for determining RNA sequences, and its main purpose is to determine the stereochemical structure of RNA molecules. Traditional RNA sequencing methods can only obtain the linear sequence of RNA, but cannot determine its stereo configuration, while Stereo-seq technology can determine the stereostructure of RNA molecules while sequencing. The basic principle of Stereo-seq technology is to use a special chemical marker that can selectively react with oxygen atoms in RNA molecules. During the reaction, the marker forms a specific chemical bond to determine the stereochemical structure in the RNA molecule. By introducing this marker during the sequencing process, the stereo information can be encoded into the RNA sequence and read out through sequencing technology.

[0047] Step S12: converting the genomic information of the tissue section into a gene expression matrix by target software;

[0048] As an optional embodiment, the target software may be a Software Analysis Workbench (SAW for short).

[0049] Among them, SAW software is a tool for system biology modeling and analysis. It integrates a variety of different tools and models to help researchers perform biological network analysis, metabolic pathway modeling, signal transduction simulation, etc. SAW software can directly convert the genomic information of tissue sections into a gene expression matrix.

[0050] Step S13: converting the gene expression matrix into a gene expression map, wherein the value of each pixel in the gene expression map represents the number of captured molecules at each pixel.

[0051] It should be noted that the conversion of the gene expression matrix into a gene expression map can also be achieved by SAW software, and the number of captured molecules represents the number of mRNAs in the transcriptome information. The higher the value of the pixel point, the more mRNAs there are.

[0052] Through the above steps S11-S13, the genomic information of the tissue section can be automatically converted into a gene expression map.

[0053] In an exemplary embodiment, determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment can be achieved by the following steps S21-S23:

[0054] Step S21: performing cell segmentation on the first cell image after the third registration adjustment to obtain the cell boundary of each cell included in the tissue section;

[0055] It should be noted that the cell may be a plant cell or an animal cell, and the corresponding cell boundary may be a cell wall or a cell membrane.

[0056] Step S22: determining the position information and gene expression information of each cell in the gene expression map based on the cell boundary;

[0057] It should be noted that the gene expression information includes the position information, gene expression level and cell type of each site on the chip carrying the tissue section.

[0058] Step S23: determining the cell boundary information according to the position information of each cell and the gene expression information.

[0059] In an exemplary embodiment, performing a first registration adjustment on the first cell image and the second cell image to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment can be achieved by the following steps S31-S33:

[0060] Step S31: acquiring a plurality of first cell original images and a plurality of second cell original images of the stained tissue section;

[0061] It should be noted that the first original cell image may be collected through a microscope. Compared with tissue sections, the field of view of a microscope is limited, so it is necessary to obtain multiple first original cell images and multiple second original cell images.

[0062] Step S32: splicing the plurality of first cell original images to obtain the first cell image, and splicing the plurality of second cell images by the target technology to obtain the second cell image;

[0063] As an optional embodiment, StereoCell may be used to stitch multiple first cell original images and multiple second cell images to obtain a stitched large image, namely, the first cell image and the second cell image.

[0064] Step S33: performing a first registration adjustment on the first cell image and the second cell image, wherein the first registration adjustment is a registration adjustment based on Fourier transform.

[0065] It should be noted that when performing the first alignment adjustment based on Fourier transform (Fast Fourier Transform, referred to as FFT), the core is to calculate the offset of the two images. Specifically, the following steps are included: First, the first cell image and the second cell image can be aligned in the upper left corner, cropped or padded with zeros to make them the same size. In order to speed up the operation, the image is downsampled to 3000*3000, and the offset is obtained by FFT alignment. According to the downsampling ratio, the value of the offset is restored and applied to the original image to complete the first alignment adjustment.

[0066] It should be further explained that before the first registration adjustment, a quality check will be performed first, that is, the offset will be calculated. If the calculated offset is less than 2 pixels, it is considered that there is no offset and no registration adjustment will be performed. Similarly, if the offset is too large (for example, more than 20 pixels), the experiment is considered abnormal and needs to be repeated.

[0067] In an exemplary embodiment, before staining the tissue section, the following steps S41-S43 are further performed:

[0068] Step S41: Slicing the tissue sample to obtain the tissue slice;

[0069] Step S42: applying a patch to the tissue slices, and fixing the applied tissue slices;

[0070] Step S43: staining the fixed tissue sections.

[0071] It should be noted that when using Stereo-seq technology for analysis, tissue sections need to be attached to special chips for fixation.

[0072] It should be noted that the staining methods used for tissue sections of plant cells and animal cells are different. When staining animal cell membranes, multiplex immunofluorescence assay (MLF) is used, and when staining plant cell walls, Coumarin Fluorescent White (CFW) is used.

[0073] In an exemplary embodiment, performing a second registration adjustment on the gene expression graph and the second cell image after the first registration adjustment based on the track line to obtain adjustment parameters used in the second registration adjustment can be achieved by the following steps S51-S54:

[0074] Step S51: detecting the second cell image after the first registration adjustment and the track line in the gene expression map by using a target algorithm;

[0075] As an optional embodiment, the target algorithm can be a line search algorithm. The line search algorithm is a basic optimization algorithm. Commonly used line search algorithms include the Kalman filter algorithm. The Kalman filter algorithm is a recursive, adaptive estimation algorithm that estimates the state at the current moment by taking a weighted average of the observed value at the current moment and the predicted value at the previous moment. In cell tracking, the position of the cell can be used as the state. By observing the position change of the cell within a certain time interval, the Kalman filter algorithm is used to predict and estimate the position of the cell at the next moment. The cell's track line is the track line.

[0076] Step S52: locating the intersection of the horizontal track line and the vertical track line in the second cell image and the gene expression map after the first registration adjustment respectively;

[0077] It should be noted that the track lines can be divided into horizontal track lines and vertical track lines according to the cell's trajectory, and the intersection of the horizontal track line and the vertical track line can be directly located using the line search algorithm.

[0078] Step S53: performing a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on the intersection of the track lines, wherein the track lines include: the horizontal track lines and the vertical track lines;

[0079] Step S54: obtaining adjustment parameters used in the second registration adjustment according to the geometric relationship between the intersection points of the track lines.

[0080] It should be noted that, since the intersections of the second cell image after the first registration adjustment and the track lines of the gene expression graph are offset, the adjustment parameters can be calculated by the geometric relationship between the intersections, and the geometric relationship includes but is not limited to the length of the line segments between the intersections.

[0081] In an exemplary embodiment, performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters can be achieved by the following steps S61-S62:

[0082] Step S61: parsing the adjustment parameter to obtain an adjustment value corresponding to the adjustment parameter;

[0083] Step S62: performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment value corresponding to the adjustment parameter.

[0084] It should be noted that for the offset between the intersection points of the track lines, the adjustment parameters can be determined by the geometric relationship between the intersection points of the track lines. At the same time, since the pattern of the track lines of the cells is periodic, the center of gravity of the first cell image and the gene expression map after the first registration adjustment can be calculated based on the adjustment parameters, and then the adjustment parameters and the track lines can be used to complete the registration adjustment of the first cell image and the gene expression map after the first registration adjustment.

[0085] In an exemplary embodiment, after determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment, the following steps S71-S72 are further included:

[0086] Step S71: obtaining gene expression molecules of the cell boundary information;

[0087] It should be noted that gene expression molecules refer to molecules involved in the process of regulating gene expression in cells. Gene expression refers to the process by which genes produce corresponding proteins through transcription and translation, as well as other regulatory RNA processes. In this process, a series of molecules are involved, including nucleic acids, proteins and other auxiliary molecules, which are all called gene expression molecules.

[0088] Step S72: assigning the gene expression molecules to each cell in the tissue slice to obtain a spatial single-cell gene expression map of the tissue slice.

[0089] It should be noted that, according to the gene expression molecules, the cells are divided into different groups or categories using a clustering algorithm. Unsupervised learning algorithms, including but not limited to hierarchical clustering, can be used to classify and categorize cells. Further, the classified cells are spatially positioned to obtain a single-cell gene expression map in space. The position information provided by spatial transcriptomics data can be used, or the spatial information of tissue sections can be obtained by methods such as immunohistochemistry. Based on the spatial information of tissue sections, suitable visualization tools are used to visualize the cell classification results and spatial positioning results. Atlases in the form of heat maps, scatter plots, bar graphs, etc. can be drawn according to information such as cell type and gene expression level to better understand and explain the functions and relationships of cells.

[0090] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0091] The embodiment of the present application analyzes the newly generated cell boundary image based on the framework of StereoCell, and provides a method for obtaining a spatial single cell atlas. While obtaining accurate information on cell boundaries, a more accurate and reliable spatial single cell atlas is provided. The specific implementation steps are as follows:

[0092] Step 1. Obtain DAPI nucleus images of tissue sections through Stereo-seq technology, and obtain animal cell membrane images after multiplex immunofluorescence assay or plant cell wall images after Coumarin Fluorescent White (CFW), as well as transcriptome information of tissue sections. It should be noted here that the mIF or CFW images and DAPI images of Stereo-seq technology are stained without moving the sample, the camera setting parameters are the same, and the images are taken twice or more times. Generally, there is only a small offset. If it is a multi-channel microscope, multiple channel images do not need to be aligned and adjusted.

[0093] Step 2. The transcriptome sequence information is converted into a gene expression matrix using SAW software, and the expression matrix is ​​converted into a gene expression map, where each position represents a pixel and the pixel value represents the number of captured molecules at that site.

[0094] Step 3. Each image is stitched together through StereoCell to obtain a stitched large image.

[0095] Step 4. Using the DAPI image as a bridge, first align the mIF or CFW image to the DAPI image. Here, the Fast Fourier Transform (FFT) is mainly used for alignment and adjustment to calculate the offset of the two images. First, align the upper left corners of the two large images, crop or fill with zeros to make them the same size. To speed up the calculation, downsample the image to 3000*3000, use FFT alignment to get the offset, restore the offset value according to the downsampling ratio and apply it to the original image to complete the alignment of the mIF or CFW image and the DAPI image. Here we will first perform quality control (QC). If the offset calculation is less than 2 pixels, we will assume that there is no offset and no alignment adjustment will be performed. If it exceeds 20 pixels, the experiment is considered abnormal and no subsequent processing will be performed.

[0096] Step 5. Use StereoCell to complete the alignment adjustment of the DAPI image and the gene expression map (fixed). Here, the "track line" is mainly used to complete the alignment adjustment; because the microscope takes pictures and the chip collects molecular information maps, the data of these two modalities are obtained in different forms, but for the same tissue, the movement of the chip during the operation will cause a large change in the overall rigidity, that is, rotation, scaling and translation, and smaller deformation, which will be ignored here.

[0097] Step 6. Apply the registration adjustment parameters obtained in step 5 directly to the mIF or CFW image, thus completing the registration adjustment between the mIF or CFW image and the gene expression map. Figure 3-1 and Figure 3-2 The figure is a schematic diagram of the principle of performing registration adjustment using the DAPI image as a bridge according to an embodiment of the present application.

[0098] Step 7. Use cellpose2.0 to segment the mIF or CFW image and obtain cell boundary information.

[0099] Step 8. Classify the gene expression molecules according to the cell boundaries (cell mask) obtained in step 7 to obtain the spatial single-cell gene expression map of the tissue section, such as Figure 4-1 and Figure 4-2 The figure is a schematic diagram of a method for acquiring a spatial single-cell atlas based on a cell boundary image according to an embodiment of the present application.

[0100] Combining the above steps, take mouse liver and Arabidopsis seed data as examples:

[0101] 1. After embedding and slicing the tissue and staining it (according to the standard operating procedure (SOP) of Stereo-seq technology), take pictures using a microscope to obtain a large mosaic image.

[0102] 2. Through sequencing technology, the transcriptome sequencing data of mouse liver is obtained and converted into a visual expression map.

[0103] 3. The FFT image registration adjustment method can be used to obtain the mIF (CFW) and DAPI image registration adjustment results.

[0104] 4. Using the TRACK line alignment adjustment technology, the DAPI and expression level alignment adjustment results can be obtained.

[0105] 5. Using the cellpose2.0 cell segmentation algorithm, accurate cell contours can be obtained through mIF (CFW) images.

[0106] 6. Finally, obtain spatial single-cell level analysis data based on the cell outline.

[0107] Mouse liver (animal cell membrane) and Arabidopsis seeds (plant cell wall) are mainly different in the acquisition of these two types of data, which are reflected in the difference in the SOP of Stereo-seq technology. After acquiring the data, the processing flow is the same, such as Figure 5-1 and Figure 5-2 Shown is a schematic diagram of a spatial single-cell atlas obtained based on cell boundary images according to an embodiment of the present application.

[0108] In combination with the above steps and embodiments, it can be seen that in the embodiments of the present application, images with track lines are used as a bridge to achieve high-precision image registration adjustment, obtain more accurate and intuitive spatial single-cell data, and are more conducive to downstream bioinformatics analysis.

[0109] In addition, the processing flow in the embodiment of the present application is not limited to a single or multiple cell boundary images. At the same time, the registration and segmentation methods in the embodiment of the present application can be any automatic or manual method.

[0110] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0111] In this embodiment, a device for acquiring cell boundary information is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0112] Figure 6 is a structural block diagram of a device for acquiring cell boundary information in an embodiment of the present application, such as Figure 6 As shown, the device comprises:

[0113] A first registration module 62 is used to perform a first registration adjustment on the first cell image and the second cell image to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image containing the cell boundary of the tissue section, and the second cell image is an image containing the cell nucleus of the tissue section;

[0114] an acquisition module 64, configured to acquire a gene expression map corresponding to the tissue section, and perform a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on a track line, so as to acquire adjustment parameters used in the second registration adjustment;

[0115] The second registration module 66 is used to perform a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and determine the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment.

[0116] In the above device, the first cell image and the second cell image are first registered and adjusted to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment, wherein the first cell image is an image containing the cell boundary of the tissue section, and the second cell image is an image containing the cell nucleus of the tissue section; the gene expression map corresponding to the tissue section is obtained, and the gene expression map and the second cell image after the first registration adjustment are second registered and adjusted based on the track line to obtain the adjustment parameters used for the second registration adjustment; the first cell image after the first registration adjustment and the gene expression map are third registered and adjusted according to the adjustment parameters, and the cell boundary information of the cell boundary is determined according to the first cell image after the third registration adjustment. The second cell image with the track line is used as a bridge to complete the registration adjustment of the first cell image and the gene expression map, and the cell boundary information is obtained based on the first cell image after the third registration adjustment, which solves the problem that accurate cell boundary information cannot be obtained in the prior art, thereby achieving the technical effect of obtaining accurate cell boundary information so as to obtain a more accurate spatial single cell atlas.

[0117] In an exemplary embodiment, the acquisition module 64 is also used to obtain the genomic information of the tissue section; convert the genomic information of the tissue section into a gene expression matrix through the target software; and convert the gene expression matrix into a gene expression map, wherein the value of each pixel point in the gene expression map represents the number of captured molecules at each pixel point.

[0118] In an exemplary embodiment, the second registration module 66 is further used to perform cell segmentation on the first cell image after the third registration adjustment to obtain the cell boundaries of each cell included in the tissue section; based on the cell boundaries, determine the position information and gene expression information of each cell in the gene expression map; and determine the cell boundary information based on the position information and the gene expression information of each cell.

[0119] In an exemplary embodiment, the first registration module 62 is also used to obtain multiple first cell original images and multiple second cell original images of the stained tissue section; stitching the multiple first cell original images to obtain the first cell image, and stitching the multiple second cell images through the target technology to obtain the second cell image; performing a first registration adjustment on the first cell image and the second cell image, wherein the first registration adjustment is a registration adjustment based on Fourier transform.

[0120] In an exemplary embodiment, the device further includes a first processing module, which is used to slice the tissue sample to obtain the tissue slice before staining the tissue slice; to patch the tissue slice and fix the patched tissue slice; and to stain the fixed tissue slice.

[0121] In an exemplary embodiment, the acquisition module 64 is also used to detect the track lines in the second cell image after the first registration adjustment and the gene expression map through a target algorithm; locate the intersection points of the horizontal track lines and the vertical track lines in the second cell image after the first registration adjustment and the gene expression map respectively; perform a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on the intersection points of the track lines, wherein the track lines include: the horizontal track lines and the vertical track lines; and obtain the adjustment parameters used for the second registration adjustment according to the geometric relationship between the intersection points of the track lines.

[0122] In an exemplary embodiment, the second registration module 66 is further configured to analyze the adjustment parameters to obtain adjustment values ​​corresponding to the adjustment parameters; and perform a third registration adjustment on the first cell image after the first registration adjustment according to the adjustment values ​​corresponding to the adjustment parameters.

[0123] In an exemplary embodiment, the device also includes a second processing module, which is used to obtain gene expression molecules of the cell boundary information after determining the cell boundary information of the cell boundary based on the first cell image adjusted by the third registration; and assign the gene expression molecules to each cell in the tissue section to obtain a spatial single-cell gene expression map of the tissue section.

[0124] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0125] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0126] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0127] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0128] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0129] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0130] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for obtaining cell boundary information, characterized in that: include: Performing a first registration adjustment on the first cell image and the second cell image to obtain a first cell image after the first registration adjustment and a second cell image after the first registration adjustment, wherein the first cell image is an image containing a cell boundary of a tissue section, and the second cell image is an image containing a cell nucleus of the tissue section; Acquire a gene expression map corresponding to the tissue section, and perform a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on the track line to obtain adjustment parameters used in the second registration adjustment; A third registration adjustment is performed on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and cell boundary information of the cell boundary is determined according to the first cell image after the third registration adjustment.

2. The method according to claim 1, characterized in that Obtaining a gene expression map corresponding to the tissue section, comprising: Acquiring genome information of the tissue section; converting the genomic information of the tissue section into a gene expression matrix by target software; The gene expression matrix is ​​converted into a gene expression map, wherein the value of each pixel in the gene expression map represents the number of captured molecules at each pixel.

3. The method according to claim 1, characterized in that Determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment includes: Performing cell segmentation on the first cell image after the third registration adjustment to obtain the cell boundary of each cell included in the tissue section; Based on the cell boundaries, determining the position information and gene expression information of each cell in the gene expression map; The cell boundary information is determined according to the position information of each cell and the gene expression information.

4. The method according to claim 1, characterized in that Performing a first registration adjustment on the first cell image and the second cell image to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment includes: Acquire a plurality of first cell original images and a plurality of second cell original images of the stained tissue section; splicing the plurality of first cell original images to obtain the first cell image, and splicing the plurality of second cell images by a target technology to obtain the second cell image; Perform a first registration adjustment on the first cell image and the second cell image, wherein the first registration adjustment is a registration adjustment based on Fourier transform.

5. The method according to claim 1, characterized in that Performing a second registration adjustment on the gene expression graph and the second cell image after the first registration adjustment based on the track line to obtain adjustment parameters used in the second registration adjustment includes: Detecting the second cell image after the first registration adjustment and the track line in the gene expression graph by a target algorithm; Locate the intersection of the horizontal track line and the vertical track line in the second cell image and the gene expression map after the first registration adjustment respectively; Performing a second registration adjustment on the gene expression graph and the second cell image after the first registration adjustment based on the intersection of the track lines, wherein the track lines include: the horizontal track lines and the vertical track lines; The adjustment parameters used in the second registration adjustment are obtained according to the geometric relationship between the intersection points of the track lines.

6. The method according to claim 1, characterized in that Performing a third registration adjustment on the first cell image after the first registration adjustment and the gene expression graph according to the adjustment parameters includes: Parsing the adjustment parameter to obtain an adjustment value corresponding to the adjustment parameter; A third registration adjustment is performed on the first cell image and the gene expression map after the first registration adjustment according to the adjustment value corresponding to the adjustment parameter.

7. The method according to claim 1, characterized in that After determining the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment, the method further includes: Gene expression molecules for obtaining the cell boundary information; The gene expression molecules are assigned to each cell in the tissue section to obtain a spatial single-cell gene expression map of the tissue section.

8. A device for acquiring cell boundary information, characterized in that: include: A first registration module is used to perform a first registration adjustment on the first cell image and the second cell image to obtain the first cell image after the first registration adjustment and the second cell image after the first registration adjustment, wherein the first cell image is an image containing the cell boundary of the tissue section, and the second cell image is an image containing the cell nucleus of the tissue section; an acquisition module, configured to acquire a gene expression map corresponding to the tissue section, and perform a second registration adjustment on the gene expression map and the second cell image after the first registration adjustment based on a track line, so as to acquire adjustment parameters adopted by the second registration adjustment; The second registration module is used to perform a third registration adjustment on the first cell image after the first registration adjustment and the gene expression map according to the adjustment parameters, and determine the cell boundary information of the cell boundary according to the first cell image after the third registration adjustment.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for obtaining cell boundary information according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for obtaining cell boundary information as described in any one of claims 1 to 7.

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