Tissue cell processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a tissue cell processing method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a labeling region of each tissue type contained in a target object based on a staining map of the target object, and determining the size of a to-be-processed sub-region corresponding to each labeling region according to the cell size of each tissue type, and further performing gridding processing on each labeled region to obtain gridding coordinates, extracting corresponding data from the gene expression data, and superposing the data to the gridding coordinates to obtain a tissue cell processing result. As the cell sizes of different tissues are inconsistent, the cell sizes corresponding to different tissue types can be distinguished according to the sizes of the to-be-processed sub-regions, so that grid data of different sizes are generated in different marked regions, pseudo cell sizes of different cell tissues are simulated, and tissue-level cell analysis is realized; the obtained analysis result can accurately reflect the tissue information of the sample, the accuracy of the analysis result is improved, and a good analysis effect is achieved.

Description

Tissue cell processing method, device, electronic device and storage medium Technical Field

[0001] The present application relates to the field of transcriptome sequencing technology, and more specifically, to a tissue cell processing method, device, electronic device and storage medium. Background Art

[0002] One of the goals of life science research is to solve the difficult problem of cells and tissues and how these tissues affect function. Sequencing technology can provide gene expression information in certain tissues under specific conditions. It can not only infer the functions of corresponding unknown genes and reveal the mechanism of action of specific regulatory genes, but also identify cell types and heterogeneity, providing a theoretical basis for disease diagnosis. Since cells, as the basic units of the body, cooperate with the microenvironment in a specific spatial location to exert their unique biological functions, the spatial location information of cells is particularly important for studying and understanding the mechanisms of cell biology, tumor biology, developmental biology and other disciplines. Spatial transcriptomics can combine microscopic imaging and sequencing technology to obtain gene expression data while retaining the spatial location information of the sample to the greatest extent, so spatial transcriptome sequencing technology came into being.

[0003] Spatial transcriptome sequencing technology can use gene chips to retain sample location information on the chip, and then use second-generation sequencing technology to sequence the RNA in the sample, and superimpose the read content back on the tissue image, thereby generating a complete gene expression image on the tissue section. This technology can simultaneously obtain the spatial location information and gene expression data of cells, greatly promoting the research and development of gene sequencing technology.

[0004] However, in actual research, the data obtained through spatial transcriptome sequencing technology has certain errors and cannot meet higher accuracy requirements.

[0005] Summary of the Invention

[0006] In view of this, the present application provides a tissue cell processing method, device, electronic device and storage medium to solve the problem that existing spatial transcriptome sequencing technology has certain errors, resulting in poor analysis results.

[0007] In order to achieve the above objectives, the following solutions are proposed:

[0008] A tissue cell processing method, comprising:

[0009] Obtaining gene expression data and a staining map of a target object, wherein the staining map is used to provide cell tissue distribution information of the target object;

[0010] Determining the labeled regions and cell areas corresponding to each tissue type of the target object based on the staining image;

[0011] Determining the area of ​​a to-be-processed sub-region contained in each of the annotated regions based on the cell area, wherein the area of ​​the to-be-processed sub-region is positively correlated with the cell area, and the to-be-processed sub-region is used to represent the minimum processing unit of the corresponding annotated region;

[0012] According to the area of ​​each of the sub-regions to be processed, the corresponding marked region is gridded to obtain grid coordinates corresponding to each of the marked regions;

[0013] Data corresponding to the gridded coordinates are extracted from the gene expression data and superimposed onto the gridded coordinates to obtain a tissue cell processing result of the target object.

[0014] Preferably, the gene expression data includes: gene identifiers, gene coordinate data and gene expression levels.

[0015] Preferably, determining the labeled regions and cell areas corresponding to each tissue type of the target object based on the staining map includes:

[0016] Determining the labeled areas corresponding to the respective tissue types of the target object based on the cell tissue distribution and tissue types of the staining image;

[0017] Determine the corresponding average cell area based on the area of ​​each marked region and the number of cells contained therein, and use the average cell area as the cell area corresponding to the tissue type;

[0018] or,

[0019] A preset reference cell area for each of the tissue types is obtained, and the reference cell area is used as the cell area corresponding to the tissue type.

[0020] Preferably, determining the area of ​​the sub-region to be processed contained in each of the marked regions based on the cell area includes:

[0021] Determining parameter values ​​corresponding to the sub-regions to be processed contained in each of the marked regions based on the cell area and the preset capture unit area, and determining the areas of the sub-regions to be processed based on the parameter values;

[0022] or,

[0023] Obtain grid data corresponding to each cell area, wherein the grid data represents the size of a square grid with the cell area as the grid area, and determine the area of ​​the sub-region to be processed contained in the corresponding marked region based on the grid data.

[0024] Preferably, determining the parameter value corresponding to the sub-region to be processed contained in each of the marked regions based on the cell area and the preset capture unit area includes:

[0025] Determining the number of capture units corresponding to each cell area based on the cell area and a preset capture unit area;

[0026] According to the number of capture units corresponding to each cell area, the parameter value of the sub-region to be processed included in the marked region is determined.

[0027] Preferably, gridding the corresponding marked area according to the area of ​​each sub-area to be processed to obtain the grid coordinates corresponding to each marked area includes:

[0028] A grid corresponding to the area of ​​the sub-region to be processed is divided in each of the marked areas, and the coordinates of the center point of each of the grids are obtained to obtain the gridded coordinates corresponding to the marked area.

[0029] Preferably, acquiring the coordinates of the center point of each grid to obtain the gridded coordinates corresponding to the marked area includes:

[0030] Based on the coordinates established by the capture unit, a coordinate interval where each grid is located is obtained, and a center point of the coordinate interval is determined as the gridded coordinate corresponding to the grid.

[0031] Preferably, it also includes:

[0032] Based on the tissue cell processing results, cell cluster analysis is performed on the target object, and a cluster analysis result is output.

[0033] A tissue cell processing device, comprising:

[0034] a sample preprocessing unit, configured to obtain gene expression data and a staining map of a target object, wherein the staining map is configured to provide information on the cell tissue distribution of the target object;

[0035] a data analysis unit, configured to determine, based on the staining image, the labeled regions and cell areas corresponding to the various tissue types of the target object;

[0036] a sub-region determining unit, configured to determine the area of ​​a sub-region to be processed contained in each of the annotated regions based on the cell area, wherein the area of ​​the sub-region to be processed is positively correlated with the cell area, and the sub-region to be processed is used to represent the minimum processing unit of the corresponding annotated region;

[0037] A grid processing unit, configured to perform grid processing on the corresponding marked area according to the area of ​​each sub-area to be processed, so as to obtain grid coordinates corresponding to each marked area;

[0038] A data extraction unit is used to extract data corresponding to the grid coordinates from the gene expression data and superimpose the data onto the grid coordinates to obtain the tissue cell processing result of the target object.

[0039] An electronic device comprises: at least one memory and at least one processor; the memory stores an application, the processor calls the application stored in the memory, and the application is used to implement the tissue cell processing method as described in any one of the above method claims.

[0040] A storage medium, characterized in that the storage medium stores computer program code, and when the computer program code is executed, it implements the tissue cell processing method as described in any one of the above method claims.

[0041] It can be seen from the above technical solutions that the present application provides a method for processing tissue cells, which obtains the gene expression data and staining map of the target object, determines the annotated area corresponding to each tissue type in the target object based on the staining map, determines the size of the sub-area to be processed of each annotated area according to the cell size of the tissue type, further performs gridding processing on each annotated area to obtain grid coordinates, extracts corresponding data from the gene expression data, and superimposes the data with the grid coordinates to obtain the tissue cell processing result. The present application processes and analyzes cells based on cell tissues. Since the cell sizes of different tissues are inconsistent, the sizes of the sub-areas to be processed are set to be different to distinguish the cell sizes corresponding to different tissue types, and then generates grid data of different sizes in different annotated areas, simulates pseudo-cells of different cell tissues, and realizes cell analysis at the tissue level. It can more accurately reflect the tissue information of the sample and achieve better analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0043] FIG1 is a flow chart of a tissue cell processing method provided in an embodiment of the present application;

[0044] FIG2 is a schematic diagram of tissue cell expression data provided in an embodiment of the present application;

[0045] FIG3 is a schematic diagram of the tissue cell pattern of an Arabidopsis stem sample provided in an example of the present application;

[0046] FIG4 is a histological cell staining diagram of an Arabidopsis stem sample provided in an example of the present application;

[0047] FIG5 is a schematic diagram of the labeling results provided in an embodiment of the present application;

[0048] FIG6a is a schematic diagram of the tissue cell processing results provided in an embodiment of the present application;

[0049] FIG6 b is a schematic diagram of tissue cell processing results obtained using the existing method;

[0050] FIG7a is a schematic diagram of the clustering results of FIG6a;

[0051] FIG7b is a schematic diagram of the clustering results of FIG6b;

[0052] FIG8 is a schematic structural diagram of a tissue cell processing device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] Current spatial transcription technologies can spatially locate and detect single cells and molecular information. For example, Stereo-seq spatial transcriptome sequencing technology can capture RNA mRNA in single cell tissues through spatiotemporal chips. After two sequencing cycles, the spatial position of the mRNA sequence and the corresponding gene expression level are determined, thereby achieving spatial positioning and detection of single cells and molecular information.

[0055] The inventors' research has revealed that, in actual processing, even within the same subject, the sizes of cells in different tissues can vary significantly. For example, the hippocampus in the brain and the meristematic zone in plants are both composed of extremely small cells. Spatial transcription techniques struggle to accurately image the outlines of these tiny cells, hindering the acquisition of effective cell spatial information and reducing the accuracy of the resulting fusion of cellular gene expression information and spatial location.

[0056] To address this issue, one approach is to set the cell size of each tissue to be tested to be consistent, thereby obtaining uniform cell analysis data that reflects the average expression level of each tissue cell in the sample. However, this approach results in analysis results that do not accurately reflect the biological information of the sample, resulting in large errors and poor analysis results.

[0057] In view of this, the inventors have proposed a tissue cell processing method. FIG1 shows a flow chart of a tissue cell processing method provided in an embodiment of the present application. As shown in FIG1 , the process may include:

[0058] Step S10: Obtain gene expression data and staining map of the target object.

[0059] The target object can be a complete tissue slice of any animal or plant sample. The tissue slice can contain multiple cell tissue structures. For example, a plant sample slice can contain cells of epidermal tissue, phloem tissue, parenchyma tissue and other tissue types.

[0060] Specifically, the gene expression data of the target object and the aligned staining map can be obtained by using the Stereo-seq spatial transcriptome sequencing technology.

[0061] The gene expression data of the target object is used to record the gene information of the target object; the registered staining map can provide the distribution of each cell tissue contained in the target object.

[0062] The gene expression data may include a gene identifier ID, gene coordinate data x, y, and a gene expression amount MIDCount.

[0063] Step S11: determining the labeled regions and cell areas corresponding to each tissue type of the target object based on the staining image.

[0064] Specifically, the tissue information provided by the staining map and the prior knowledge related to the target object can be combined to determine the labeled areas corresponding to the various tissue types contained in the target object based on the staining map. The prior knowledge related to the target object can be which cell tissues the target object contains, or the cell morphology of these tissues. In an optional case, the tissue outlines of each cell tissue of the target object can be drawn using a specific manual tool, and the areas corresponding to different tissues can be labeled with different colors. The specific manual tool can be the open source tool 3Dslicer, which supports manual labeling, and can also be combined with the open source tool plant-seg network to label tissue areas.

[0065] The cell area in this step can be a pre-set reference cell area for the target tissue type. That is, the cell area for each tissue type is pre-set. This pre-set cell area can be based on experience or the average value of the same tissue type across different subjects. Therefore, when executing this step, the pre-set cell area can be directly obtained and used as the cell area corresponding to the marked region for subsequent processing.

[0066] Step S12: determining the area of ​​the sub-region to be processed contained in each marked region according to the cell area.

[0067] Among them, the sub-region to be processed corresponding to the marked area can be used as the minimum processing unit of the marked area to process and analyze tissue cells. When using a gene chip to extract the position information of cells in the target object, a sub-region to be processed is extracted as a unit, and the positional relationship between the sub-regions to be processed is used to characterize the positional relationship between cells in the marked area. Because the area of ​​the sub-region to be processed is positively correlated with the cell area, that is, the larger the cell area of ​​the marked area determined in the previous step, the larger the sub-region to be processed divided by the marked area. Therefore, it can better express the positional relationship between cells in the marked domain corresponding to different tissue types.

[0068] Step S13: gridding the corresponding marked area according to the area of ​​each sub-area to be processed to obtain grid coordinates corresponding to each marked area.

[0069] Specifically, an optional way to perform gridding processing on the marked area is to divide each marked area into grids with an area equal to the area of ​​the corresponding sub-area to be processed, and obtain gridded coordinates.

[0070] Optionally, the center point coordinates of the grid can also be used as the gridded coordinates of the grid, wherein the center point coordinates of the grid can be determined as the center point coordinates of the grid by obtaining the coordinate interval where the grid is located in the coordinates established by the capture unit, calculating the center point coordinates of the coordinate interval, and determining them as the center point coordinates of the grid.

[0071] Step S14: extracting data corresponding to the gridded coordinates from the gene expression data, and superimposing the data onto the gridded coordinates to obtain the tissue cell processing result of the target object.

[0072] Specifically, the data corresponding to each gridded coordinate is extracted from the gene expression data to obtain the tissue cell processing data of the target object, which includes the expression levels of different genes of the target object in cell tissues of various tissue types. The corresponding gridded coordinates are further combined to generate the tissue cell processing results of the target object.

[0073] In one possible implementation, the gene expression data includes a gene identifier ID, gene coordinate data x, y, and gene expression amount MIDCount. Since the grid coordinates have been determined at this time, the coordinate range of each grid can be determined by the grid coordinates. Then compare the gene coordinate data x, y of each data with the coordinate range of each grid. If the gene coordinate data x, y falls within the coordinate range of a grid, it means that the data needs to be superimposed on this grid. At this time, the ID of the corresponding grid can be set for the data in the expression data, that is, the Cell ID. When the data is superimposed subsequently, according to the added Cell ID, it can be clear which grid each data falls in, that is, the center point coordinates of the grid to which the data needs to be superimposed.

[0074] As shown in FIG2 , gene expression data containing 12 data are given. Although these data have different gene identifier IDs, their corresponding Cell IDs are all 13201.0, and they will eventually be superimposed into the grid with Cell ID 13201.0.

[0075] Further optionally, after obtaining the tissue cell processing result, a cell cluster analysis can be performed on the target object based on the result, and the cluster analysis result can be output to visually verify the accuracy of the processing result.

[0076] This application scheme fully considers the inconsistency of cell sizes in different tissues. By determining the size of the sub-region to be processed to reflect the cell size corresponding to different tissue types, grid data of different sizes are generated in different labeled areas, simulating the cell size of different cell tissues, and realizing tissue-level cell analysis. The obtained analysis results can more accurately reflect the tissue information of the sample. When the tissue cell processing results obtained by this scheme are applied to gene sequencing, the accuracy of the analysis results can be improved, thereby achieving better analysis effects.

[0077] Furthermore, the cell area in step S11 can also be obtained by other methods. Specifically, the area of ​​each marked region in the staining image and the number of cells contained in each marked region can be counted first, and then the average cell area of ​​each marked region can be calculated, and the obtained average cell area can be used as the cell area corresponding to the cell tissue in the marked region.

[0078] This method directly uses the information expressed by the image in the staining map as a basis, thereby making the calculated cell area more accurate and avoiding the problem of reduced accuracy caused by individual differences in the target objects.

[0079] In this embodiment, the imagej image processing tool can be used to calculate the number of pixels in the marked area, and the corresponding area of ​​the marked area can be further obtained. At the same time, the number of cells contained in the marked area can also be counted, and the average cell area can be calculated.

[0080] When performing the step of determining the area of ​​the sub-region to be processed contained in each of the annotated regions based on the cell area, considering that the annotated regions will subsequently need to be gridded, the area of ​​the sub-region to be processed can be determined using grid attributes. The grid data corresponding to the cell area of ​​each annotated region can be calculated to obtain the area of ​​the corresponding sub-region to be processed. The grid data can be the size of the square grid occupied by the cell area. A square grid with an area equal to the corresponding cell area can be constructed, and the size of the square grid can be used as the corresponding grid data. The size and area of ​​the corresponding sub-region to be processed can then be further determined based on the grid data.

[0081] Furthermore, considering that the spatial transcriptome sequencing process needs to rely on gene chips to complete, the area of ​​the sub-region to be processed contained in each of the marked regions can be determined based on the cell area, and the parameters of the gene chip can also be used for determination.

[0082] The capture units of a gene chip directly extract information from cellular tissues, and their extraction efficiency directly impacts the final processing outcome. Therefore, one alternative approach is to combine the area of ​​the capture units on the gene chip to determine the size of the subregion to be processed. First, the cell area corresponding to each labeled region is determined. Combined with the preset capture unit area, a parameter value for the corresponding subregion to be processed can be obtained. This parameter value can be the number of capture units corresponding to the cell area—that is, the number of capture units required to capture the information in a subregion to be processed, for example, 4900.

[0083] Furthermore, the capture units are arranged in the form of a chip array on the gene chip, so the parameter value may also be the arithmetic square root of the number of capture units, that is, the number multiplied by the number of capture units, for example, 70*70.

[0084] Next, we will use an Arabidopsis stem sample as an example to further describe a tissue cell processing method provided in the present embodiment. The tissue cell pattern of the Arabidopsis stem sample is shown in Figure 3, which includes cell tissue structures such as the stele, phloem, and epidermis. As can be seen from Figure 3, the cell sizes of different tissue types vary significantly.

[0085] First, the gene expression data of the target object and the aligned staining map were obtained through the Stereo-seq spatial transcriptome sequencing method.

[0086] The steps of the Stereo-seq sequencing method include embedding, sectioning, mounting, fixation, permeabilization, reverse transcription, tissue removal, cDNA release, and magnetic bead recovery. In this example, the target object is a cross-section sample of an Arabidopsis stem. After the above steps, the gene expression data and staining map of the Arabidopsis stem sample can be obtained. The staining map is shown in Figure 4.

[0087] Combined with the tissue distribution provided in the staining map shown in Figure 4, based on the tissue and cell information such as the relevant tissue types and cell structures of the Arabidopsis stem, the biological structure of the Arabidopsis stem sample was obtained, including regions of cell tissue structures such as the stele, phloem, and epidermis. Using the plant-seg network and the 3Dslicer tool, the regions corresponding to the above cell tissues were annotated with different colors or different depths of the same color to determine the annotated regions corresponding to each tissue type. The annotation results are shown in Figure 5. In Figure 5, regions of the same color represent the same cell tissue, and a total of five annotated regions corresponding to cell tissue types were divided. If the same color is annotated with different depths, the same depth regions represent the same cell tissue, and different depths represent different cell tissues.

[0088] In this embodiment, the area of ​​each marked region and the number of cells contained therein were counted using the ImageJ tool to calculate the average cell area corresponding to each marked region.

[0089] Then, the average cell area is divided by the area of ​​the capture unit of the gene chip to obtain the number of capture units required for the cells in the corresponding marked area. The area of ​​the corresponding sub-area to be processed is the total area of ​​the captured units occupied. The arithmetic square root of the number of capture units is further calculated as the parameter value of the sub-area to be processed.

[0090] In this embodiment, the parameter values ​​of the sub-regions to be processed obtained by dividing the five regions are 70, 28, 33, 44, and 38 respectively. Taking the marked region with a parameter value of 70 as an example, one region to be processed in the region requires 70*70, that is, 4900 capture units.

[0091] Next, according to the area of ​​the sub-region to be processed corresponding to each marked region, each marked region is divided into grids of corresponding sizes, and the coordinates of the center points corresponding to the grids are calculated to obtain grid coordinate data.

[0092] In this step, the parameter values ​​of the sub-region to be processed corresponding to each marked region are input into a preset gridding program, and processed by the gridding program to obtain gridded coordinate data.

[0093] Next, data corresponding to the grid coordinates were extracted from the gene expression data of the Arabidopsis stem slice sample and superimposed onto the grid coordinates to obtain the tissue cell processing results of the Arabidopsis stem sample, as shown in FIG6 a.

[0094] In Figure 6a, the data points corresponding to the annotated areas with smaller cells in Figure 2 are denser, while the data points corresponding to the annotated areas with larger cells are sparser. This means that the amount of data extracted from gene expression data is directly related to the density and sparseness of the grid. A denser grid has a greater number of center point coordinates, resulting in a greater amount of extracted data and a better understanding of the biological information of densely populated tissues.

[0095] If the existing processing method is used, the gridded result is shown in FIG6b . Regardless of the region, the grid size is the same, and the biological information of densely celled tissues will be omitted, affecting the final result.

[0096] In order to better illustrate the effect of the tissue cell processing method disclosed in the present application, FIG. 6a and FIG. 6b are clustered to obtain FIG. 7a and FIG. 7b respectively.

[0097] For comparison, see Figure 7a and Figure 7b. The clustering results after clustering the processing results obtained by the tissue cell processing method provided in the embodiment of the present application can more accurately reflect the tissue information of the Arabidopsis stem sample and restore the biological information of the sample and the spatial position information of the tissue cells.

[0098] From this, it can be seen that the present application processes and analyzes cells based on cell tissues. Taking into account the inconsistent cell sizes of different tissues, the sizes of the sub-areas to be processed are set to be different to distinguish the cell sizes corresponding to different tissue types, and then grid data of different sizes are generated in different labeled areas to simulate pseudo-cells of different cell tissues, thereby realizing tissue-level cell analysis, which can more accurately reflect the tissue information of the sample and achieve better analysis results.

[0099] A tissue cell processing device provided in an embodiment of the present application is described below. The tissue cell processing device described below and the tissue cell processing method described above can be referenced to each other.

[0100] In conjunction with FIG8 , a tissue cell processing device is introduced. As shown in FIG8 , the device may include:

[0101] The sample preprocessing unit 100 is used to obtain gene expression data and a staining map of a target object, wherein the staining map is used to provide cell tissue distribution information of the target object;

[0102] A data analysis unit 110 is configured to determine the labeled regions and cell areas corresponding to each tissue type of the target object based on the staining image;

[0103] a sub-region determining unit 120, configured to determine the area of ​​a sub-region to be processed contained in each of the annotated regions based on the cell area, wherein the area of ​​the sub-region to be processed is positively correlated with the cell area, and the sub-region to be processed is used to represent the minimum processing unit of the corresponding annotated region;

[0104] A grid processing unit 130 is configured to perform grid processing on the corresponding marked area according to the area of ​​each sub-area to be processed, so as to obtain grid coordinates corresponding to each marked area;

[0105] The data extraction unit 140 is configured to extract data corresponding to the grid coordinates from the gene expression data and superimpose the data onto the grid coordinates to obtain the tissue cell processing result of the target object.

[0106] Optionally, the data analysis unit 110 may include:

[0107] a tissue region division unit, configured to determine, based on the cell tissue distribution and tissue type of the staining image, a marked region corresponding to each tissue type of the target object;

[0108] a cell area determination unit, configured to determine the corresponding average cell area based on the area of ​​each marked region and the number of cells contained therein, and use the average cell area as the cell area corresponding to the tissue type;

[0109] The cell area acquisition unit is used to acquire a preset reference cell area for each of the tissue types and use the reference cell area as the cell area corresponding to the tissue type.

[0110] Optionally, the sub-region determining unit 120 may include:

[0111] a parameter value determining unit, configured to determine a parameter value corresponding to a sub-region to be processed contained in each of the marked regions based on the cell area and a preset capture unit area, and determine the area of ​​the sub-region to be processed based on the parameter value;

[0112] The sub-region size determination unit is used to obtain grid data corresponding to each cell area, wherein the grid data represents the size of a square grid with the cell area as the grid area, and determine the area of ​​the sub-region to be processed contained in the corresponding marked area based on the grid data.

[0113] Optionally, the parameter value determining unit may include:

[0114] a capture unit quantity determination unit, configured to determine the quantity of capture units corresponding to each cell area according to the cell area and a preset capture unit area;

[0115] The parameter value calculation unit is used to determine the parameter value of the sub-region to be processed contained in the marked area according to the number of the capture units corresponding to each cell area.

[0116] Optionally, the grid processing unit 130 may include:

[0117] The grid coordinate determining unit is used to divide each of the marked areas into grids corresponding to the area of ​​the sub-area to be processed, and obtain the coordinates of the center point of each of the grids to obtain the grid coordinates corresponding to the marked area.

[0118] Optionally, the gridded coordinate determining unit may include:

[0119] The center point coordinate calculation unit is used to obtain the coordinate interval of each grid based on the coordinates established by the capture unit, and determine the center point of the coordinate interval as the gridded coordinate corresponding to the grid.

[0120] Optionally, the tissue cell processing device provided in the present application may further include:

[0121] A cluster analysis unit is used to perform cell cluster analysis on the target object based on the tissue cell processing result and output the cluster analysis result.

[0122] The workflow of the above modules and units can be referred to the aforementioned method embodiment and will not be repeated here.

[0123] The tissue cell processing device disclosed in this embodiment processes and analyzes cells based on cell tissue. Taking into account the inconsistent cell sizes of different tissues, the sizes of the sub-areas to be processed are set to different to distinguish the cell sizes corresponding to different tissue types, and then grid data of different sizes are generated in different labeled areas to simulate pseudo-cells of different cell tissues, thereby realizing tissue-level cell analysis, which can more accurately reflect the tissue information of the sample and achieve better analysis results.

[0124] The present application also discloses an electronic device, which includes: at least one memory and at least one processor; the memory stores an application program, and the processor calls the application program stored in the memory to execute the tissue cell processing method disclosed in the aforementioned method embodiment.

[0125] At the same time, the present application also discloses a storage medium, which stores computer program code, and when the computer program code is executed, it implements the tissue cell processing method disclosed in the above method embodiment.

[0126] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0127] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0128] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for treating tissue cells, characterized in that: include: Obtaining gene expression data and a staining map of a target object, wherein the staining map is used to provide cell tissue distribution information of the target object; Determining the labeled regions and cell areas corresponding to each tissue type of the target object based on the staining image; Determining the area of ​​a to-be-processed sub-region contained in each of the annotated regions based on the cell area, wherein the area of ​​the to-be-processed sub-region is positively correlated with the cell area, and the to-be-processed sub-region is used to represent the minimum processing unit of the corresponding annotated region; According to the area of ​​each of the sub-regions to be processed, the corresponding marked region is gridded to obtain grid coordinates corresponding to each of the marked regions; Data corresponding to the gridded coordinates are extracted from the gene expression data and superimposed onto the gridded coordinates to obtain a tissue cell processing result of the target object.

2. The method according to claim 1, characterized in that The gene expression data includes: gene identifiers, gene coordinate data and gene expression levels.

3. The method according to claim 2, characterized in that Determining the labeled regions and cell areas corresponding to the various tissue types of the target object based on the staining map includes: Determining the labeled areas corresponding to the respective tissue types of the target object based on the cell tissue distribution and tissue types of the staining image; Determine the corresponding average cell area based on the area of ​​each marked region and the number of cells contained therein, and use the average cell area as the cell area corresponding to the tissue type; or, A preset reference cell area for each of the tissue types is obtained, and the reference cell area is used as the cell area corresponding to the tissue type.

4. The method according to claim 3, characterized in that The step of determining the area of ​​the sub-region to be processed contained in each of the marked regions based on the cell area includes: Determining parameter values ​​corresponding to the sub-regions to be processed contained in each of the marked regions based on the cell area and the preset capture unit area, and determining the areas of the sub-regions to be processed based on the parameter values; or, Obtain grid data corresponding to each cell area, wherein the grid data represents the size of a square grid with the cell area as the grid area, and determine the area of ​​the sub-region to be processed contained in the corresponding marked region based on the grid data.

5. The method according to claim 4, characterized in that Determining the parameter value corresponding to the sub-region to be processed contained in each marked region based on the cell area and the preset capture unit area includes: Determining the number of capture units corresponding to each cell area based on the cell area and a preset capture unit area; According to the number of capture units corresponding to each cell area, the parameter value of the sub-region to be processed included in the marked region is determined.

6. The method according to claim 2, characterized in that The gridding of the corresponding marked area according to the area of ​​each sub-area to be processed to obtain the grid coordinates corresponding to each marked area includes: A grid corresponding to the area of ​​the sub-region to be processed is divided in each of the marked areas, and the coordinates of the center point of each of the grids are obtained to obtain the gridded coordinates corresponding to the marked area.

7. The method according to claim 6, characterized in that The acquiring the center point coordinates of each grid to obtain the grid coordinates corresponding to the marked area includes: Based on the coordinates established by the capture unit, a coordinate interval where each grid is located is obtained, and a center point of the coordinate interval is determined as the gridded coordinate corresponding to the grid.

8. The method according to claim 2, characterized in that Also includes: Based on the tissue cell processing results, cell cluster analysis is performed on the target object, and a cluster analysis result is output.

9. A tissue cell processing device, characterized in that: include: a sample preprocessing unit, configured to obtain gene expression data and a staining map of a target object, wherein the staining map is configured to provide information on the cell tissue distribution of the target object; a data analysis unit, configured to determine, based on the staining image, the labeled regions and cell areas corresponding to the various tissue types of the target object; a sub-region determining unit, configured to determine the area of ​​a sub-region to be processed contained in each of the annotated regions based on the cell area, wherein the area of ​​the sub-region to be processed is positively correlated with the cell area, and the sub-region to be processed is used to represent the minimum processing unit of the corresponding annotated region; A grid processing unit, configured to perform grid processing on the corresponding marked area according to the area of ​​each sub-area to be processed, so as to obtain grid coordinates corresponding to each marked area; A data extraction unit is used to extract data corresponding to the grid coordinates from the gene expression data and superimpose the data onto the grid coordinates to obtain the tissue cell processing result of the target object.

10. An electronic device, characterized in that: The electronic device includes: at least one memory and at least one processor; the memory stores an application program, and the processor calls the application program stored in the memory, wherein the application program is used to implement the tissue cell processing method according to any one of claims 1 to 7.

11. A storage medium, characterized in that: The storage medium stores computer program code, and when the computer program code is executed, the tissue cell processing method according to any one of claims 1 to 7 is implemented.

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