Method for evaluating immune homeostasis of CD163 gene edited pigs to non-specific pathogens

Abstract

The invention belongs to the technical field of non-specific pathogen immune homeostasis evaluation, and provides a method for evaluating non-specific pathogen immune homeostasis by a CD163 gene edited pig, which comprises the following steps: peripheral blood sample collection, PBMC separation, sample induction, single cell library establishment and sequencing, cell annotation, standardization treatment, difference degree quantitative calculation and interval matching. According to the designed peripheral blood mononuclear cell immune homeostasis evaluation method based on single cell sequencing, directive information can be provided for animal experiments, the experiment cost is effectively reduced, the target performance is improved, and the repeatability of results is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of non-specific pathogen immune homeostasis evaluation, in particular to a method for evaluating the non-specific pathogen immune homeostasis of CD163 gene edited pigs. BACKGROUND

[0002] At present, gene edited animals have been widely prepared and studied in multiple species, especially in major livestock such as pigs, cattle, sheep, etc., which have shown great potential in improving production performance and enhancing disease resistance. However, although some gene edited animals have shown obvious advantages in resistance to specific pathogens, such as CD163 gene edited pigs with resistance to porcine reproductive and respiratory syndrome virus, there is still a lack of systematic understanding of their biological performance under the background of non-specific pathogen infection. Gene editing may trigger complex molecular and immunological cascade effects, such as changes in immune response pathways, metabolic levels or microbial community structure, which may potentially affect the susceptibility of animals to non-specific pathogenic bacteria widely existing in the environment. Such unintended effects not only affect the health of individual animals and the production efficiency of the population, but also may have indirect effects on the stability of the breeding ecosystem and the dynamics of pathogenic bacteria in the environment. Therefore, it is of great significance to evaluate the disease resistance of gene edited animals to non-specific pathogenic bacteria, which not only helps to scientifically evaluate the potential environmental unintended effects of gene editing, but also provides a solid scientific basis for risk management, policy making and safe application of gene edited animals. At present, the research strategy for evaluating the disease resistance of gene edited animals to non-specific pathogenic bacteria usually includes four levels: molecular function verification based on in vitro experiments, cell infection experiments, animal challenge experiments based on in vivo experiments, and population and environmental evaluation. The specific research methods are as follows: 1) multi-omics analysis + molecular function verification to determine whether gene editing changes the expression of immune-related pathways in animals; 2) using primary macrophages, fibroblasts or intestinal epithelial cells of gene edited animals to infect specific pathogens and non-specific pathogens respectively, and comparing the replication ability and host response; 3) selecting common environmental pathogens to detect the susceptibility and immune response of gene edited animals; 4) simulating the breeding environment to observe the performance of gene edited animals under complex pathogens.

[0003] The above methods are currently used to evaluate the disease resistance of gene edited animals to non-specific pathogens, although a multi-level research strategy has been formed, but there are still some problems: 1) multi-omics analysis is costly and data is redundant, and simply comparing the expression before and after editing cannot reflect the potential unintended effects of animals in the non-specific pathogen environment; 2) the success rate of cell infection experiments is low, non-specific pathogens often cause cell death, and the results are not reproducible; 3) evaluating multiple non-specific pathogens one by one requires a large number of animal experiments, which is costly, and the results are limited by the large differences between individual animals. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide a method for evaluating the immune homeostasis of CD163 gene edited pigs to non-specific pathogens, solving the problems of high analysis cost and redundant data and poor repeatability of results of the existing method.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions: A method for evaluating the immune homeostasis of CD163 gene edited pigs to non-specific pathogens, comprising: Collecting samples from target objects to obtain peripheral blood samples; the target objects include CD163 gene edited Duroc pigs and wild type Duroc pigs; Separating PBMCs from the peripheral blood samples to obtain separated samples; Setting a plurality of candidate stimulation conditions, inducing the separated samples under each candidate stimulation condition to obtain induced samples; Performing single cell library construction and sequencing on the induced samples and control group samples to obtain a sample database; Performing cell annotation and expression pattern matching on the sample database to obtain a matching database; Performing standardization processing and average expression calculation on the matching database to obtain a gene expression database; Quantitatively calculating the difference degree of the gene expression database using Z-score to obtain standard score values; According to a preset evaluation rule, interval matching is performed on the standard score values to obtain a non-specific pathogen immune homeostasis evaluation result, and the non-specific pathogen immune homeostasis evaluation result is converted into a visualized graph; the visualized graph includes UMAP, t-SNE and a heat map.

[0006] Preferably, the PBMC separation of the peripheral blood sample to obtain the separated sample comprises: Density gradient centrifugation of the peripheral blood sample using Ficoll method under preset centrifugation parameters to obtain a centrifuged solution; the centrifugation parameters include acceleration 8 , deceleration 5 , rotation speed 2000 rpm, and time 40 min; Transferring the second layer of white PBMC solution in the centrifuged solution to a centrifuge tube, washing the PBMC solution twice with PBS to obtain the separated sample.

[0007] Preferably, the candidate stimulation conditions include adding 10 μg / mL lipopolysaccharide for 3.5 hours and adding 10 μg / mL polyinosinic acid for 3.5 hours.

[0008] Preferably, the quality control standards for the sample database include: removing genes expressed in fewer than 3 cells, removing cells with fewer than 200 or more than 5000 expressed genes, and removing cells with a mitochondrial gene ratio exceeding 5%.

[0009] Preferably, the marker genes for cell annotation include: CD3E, CD8A+, GNLY, CD62L, CD45, CCR7, CD19, CD79B, CD20, CD14, CD172a, CD68, CD172a, CST3, and GNLY+; wherein, CD3E corresponds to Helper T cells; CD8A+ and GNLY correspond to Cytotoxic T cells; CD62L, CD45, and CCR7 correspond to Regulatory T cells; CD19, CD79B, and CD20 correspond to B cells; CD14 and CD172a correspond to Monocytes; CD68 and CD172a correspond to Macrophages; CST3 corresponds to Dendritic cells; and GNLY+ corresponds to Natural Killer cells.

[0010] Preferably, the standardization process employs the log-normalization method.

[0011] Preferably, the preset evaluation rule is as follows: the standard score value between [-2, 2] corresponds to gene expression within the normal physiological range; the standard score value greater than 2 corresponds to gene expression exceeding the normal WT range; and the standard score value less than -2 corresponds to gene expression below the normal WT range.

[0012] The present invention discloses the following technical effects: This invention provides a method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens. By designing a peripheral blood mononuclear cell immune homeostasis evaluation method based on single-cell sequencing, it solves the problems of high analysis cost, data redundancy, and poor reproducibility of existing methods, and realizes the provision of directional information for animal experiments and reduces experimental costs. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the evaluation process for the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens, provided in an embodiment of the present invention. Figure 2 This is a classification of different gene states in different cell types within the Control group provided in this embodiment of the invention; Figure 3 This is a classification of different gene states in different cell types within the LPS group provided in this embodiment of the invention. Figure 4 This is a classification of different gene states in different cell types within the Poly(I:C) group provided in this embodiment of the invention; Figure 5 This is a stacked bar graph showing the deviation of gene expression from the WT range in different cell types under different treatment conditions provided in this embodiment of the invention; Figure 6 A heatmap showing the number of genes under different treatment conditions and cell types in three expression states, as provided in the embodiments of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] The purpose of this invention is to provide a method for evaluating the immune homeostasis of CD163 gene-edited pigs against non-specific pathogens, solving the problems of high analysis cost, data redundancy, and poor reproducibility of existing methods.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Figure 1 This is a schematic diagram of the evaluation process for the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens provided in an embodiment of the present invention, as shown below. Figure 1 As shown, this invention provides a method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens, comprising: Step 100: Collect samples from the target subjects to obtain peripheral blood samples; the target subjects include: CD163 gene-edited Duroc pigs and wild-type Duroc pigs; Step 200: Perform PBMC separation on the peripheral blood sample to obtain a separated sample; Step 300: Set several candidate stimulus conditions, and induce the separated sample under each candidate stimulus condition to obtain induced sample; Step 400: Perform single-cell library construction and sequencing on the induced samples and control group samples to obtain a sample database; Step 500: Perform cell annotation and expression pattern matching on the sample database to obtain a matching database; Step 600: Standardize the matching database and calculate the average expression level to obtain a gene expression level database; Step 700: Use Z-score to quantify the degree of difference in the gene expression database and obtain a standard score. Step 800: According to the preset evaluation rules, the standard score values ​​are matched within intervals to obtain the non-specific pathogen immune homeostasis evaluation results, and the non-specific pathogen immune homeostasis evaluation results are converted into visualization graphs; the visualization graphs include: UMAP, t-SNE, and heatmap.

[0019] Specifically, this embodiment uses wild-type animal peripheral blood mononuclear cells (PBMCs) as the research object, simulating bacterial and viral infections through stimulation methods such as lipopolysaccharide (LPS) and polyinosinic acid (poly I:C). Under different stimulation conditions, single-cell transcriptome sequencing was performed on PBMCs to construct a reference database of wild-type animal immune cells, and the normal expression range (homeostasis) of specific genes of various immune cells under different infection backgrounds was systematically defined. This database can not only be used to compare and evaluate whether there are deviations or abnormalities in the immune response of gene-edited animals under non-specific pathogen stimulation, but also to further trace changes in related signaling pathways through differences in gene expression levels, thereby providing a clear direction and scientific basis for subsequent animal experiments.

[0020] Furthermore, the experimental subjects and materials: Experimental materials: porcine peripheral blood mononuclear cell isolation kit (Solarbio), ACK lysis buffer (Solarbio), RMPI-1640 (Gibco), fetal bovine serum (Gibco), recombinant porcine granulocyte-macrophage colony-stimulating factor (rpGM-CSF); Experimental subjects: CD163 gene-edited Duroc pigs (male, 12 months old, n=4) and wild-type Duroc pigs (male, 12 months old, n=4). Sample type: anticoagulated whole blood, PBMCs were separated using Ficoll density gradient centrifugation; Stimulants: LPS (O55:B5) (InvivoGen), Poly (I:C) (HMW) (Sigma).

[0021] Preferably, the experimental method is as follows: 1) PBMC separation steps: PBMCs were isolated using the Ficoll density gradient centrifugation method. Fresh peripheral blood was diluted 1:1 with sterile PBS and slowly spread into 50 mL centrifuge tubes containing 15 mL of Ficoll separation solution (composed of Ficoll, hydroxyethyl starch 550, and diatrizoate meglumine). The tubes were then centrifuged horizontally at 2000 rpm for 40 min.

[0022] After centrifugation, aspirate the second white PBMC layer and transfer it to a new centrifuge tube. Add RPMI 1640 medium (containing 10% FBS), mix well, centrifuge at 2000 rpm for 10 min, and discard the supernatant. Resuspend the cells in RPMI 1640 medium (containing 10% FBS), centrifuge at 1500 rpm for 10 min, and wash once more. Finally, resuspend the PBMCs in an appropriate amount of medium for later use.

[0023] 2) Total cell count: 14 × 10⁻⁶ cells per sample. 6 Up to 4×10 6 Cell viability greater than 90%, and the number of high-quality cells in a single sample greater than 8000; 3) Optimization of stimulation conditions: The concentrations of LPS (O55:B5) and Poly (I:C) were both 10 μg / mL, and the induction time was 3.5 hours.

[0024] 4) Single-cell transcriptome sequencing: In the single-cell transcriptome sequencing experiment, the cell suspension was first adjusted to an appropriate concentration (700 cells / μL to 1200 cells / μL). Then, following the standard operating procedure of the MobiCube High-Throughput Single-Cell 3' Transcriptome Library Kit V2.1 (PN-S050200301), droplet generation, single-cell labeling, reverse transcription, and cDNA pre-amplification were completed on the MobiNova-100 microfluidic platform. The resulting full-length cDNA was fragmented by enzyme digestion, end repaired, adapter ligation, and PCR amplified to construct a high-throughput transcriptome sequencing library. The library was sequenced using high-throughput sequencing to obtain raw sequence data in FASTQ format, and preprocessed using MobiVision software (v3.2). This software identifies cell barcodes and molecular tags (UMIs) to compare and quantify raw reads, with the Sscrofa11.1 genome as the reference genome. It also outputs quality control indicators such as high-quality cell count, gene median, and sequencing saturation.

[0025] Quality control criteria include: removing genes expressed in fewer than 3 cells; removing cells expressing fewer than 200 or more than 5000 genes; and removing cells with more than 5% mitochondrial genes. The overall sequencing depth is approximately 14,000 to 35,000 raw reads per cell, and the cell capture per sample is approximately 8,000 to 15,000.

[0026] Downstream analysis was performed using Seurat (v4.3) in the R environment for data standardization, dimensionality reduction, batch effect correction and data integration, cell type annotation and differential expression analysis.

[0027] Preferably, the database is constructed based on single-cell transcriptome sequencing results, primarily including analysis results under different experimental treatment conditions. The database content includes: UMAP dimensionality reduction distribution of cell types across different treatment groups, comparison of the proportion and absolute number of different immune cell types, dot plots of expression patterns of representative marker genes, three-dimensional volcano plots of differentially expressed genes, and gene expression distribution, differential analysis tables, and functional annotation results for specific cell populations (such as monocytes / macrophages). Furthermore, gene set enrichment analysis of differentially expressed genes and KEGG signaling pathway annotation and pathway maps are also included to demonstrate differences at specific pathway levels.

[0028] Optionally, the raw and processed data from the database are stored in CSV and RData file formats, and an R package is built based on this data. This R package contains data structures and calling functions that can be used to directly load and process the database content within the R environment. The database can be used in two ways: first, by directly browsing (URL) and querying visualizations through an online interactive website; second, by loading the data through the R package and combining it with external data for further analysis. Query method: Entering a gene name will output its expression mean ± standard deviation under different cell populations and stimulation conditions.

[0029] Example 1: Isolation of PBMCs and LPS stimulation.

[0030] Experimental animals: Four healthy Duroc animals aged 12 months were selected, and no clinical abnormalities were found after veterinary examination; Sample collection: 10 mL of peripheral blood was collected from each pig using blood collection tubes containing an anticoagulant (EDTA); PBMC separation: The Ficoll method was used. The separation solution (Ficoll, hydroxyethyl starch 550, and meglumine diatrizoate) was subjected to density gradient centrifugation at 2000 rpm for 40 min (centrifuge setting: acceleration 8). deceleration 5 Transfer the second layer of white PBMCs to a new centrifuge tube. Collect the middle layer of mononuclear cells and wash twice with PBS. Cell treatment: Cell concentration was 2 × 10⁻⁶ 6 cells / mL to 3×10 6 cells / mL; Stimulation conditions: Lipopolysaccharide (LPS, derived from Escherichia coli O55:B5, 10 μg / mL) was added, and induction was performed for 3.5 hours; Control group: Add an equal volume of PBS; Single-cell library construction and sequencing: Cells were collected and libraries were constructed using a 10x Genomics Chromium Single Cell 3'V3.1 library, capturing approximately 8,000 to 10,000 cells per group; sequencing was performed on the Illumina NovaSeq 6000 platform, with a target depth of approximately 50,000 reads / cell.

[0031] Example 2: Poly(I:C) stimulation of PBMCs.

[0032] Isolation of laboratory animals from PBMCs: Same as in Example 1; Stimulation conditions: Poly(I:C), 10 μg / mL, was added and induced for 3.5 hours; Control group: Add an equal volume of PBS; Single-cell library construction and sequencing: The method is the same as in Example 1.

[0033] Example 3: Construction of an immune cell reference database and definition of gene expression range.

[0034] Data integration: The single-cell sequencing results of Examples 1 and 2 were combined with the control group.

[0035] A method for assessing whether a test sample falls within the Wt range. To accurately evaluate whether gene expression in a test sample is within the normal range, the Z-score method is used to standardize its expression level. The Z-score measures the deviation of a data point from the mean of its distribution, expressed as standard deviation. The specific steps are as follows: 1) Cell annotation and data preprocessing: Before performing Z-score evaluation on the test samples, cell annotation is required to identify the different cell types in the samples. This step uses cell characteristic marker genes to classify cells in single-cell samples. By identifying predefined marker genes in single-cell transcriptome data, each cell type can be classified into its corresponding category. The following are the marker genes used for cell annotation: Helper T cells: CD3E; Cytotoxic T cells (CTLs): CD8A+, GNLY; Regulatory T cells: CD62L, CD45, CCR7; B cells: CD19, CD79B, CD20; Monocytes: CD14, CD172a; Macrophages: CD68, CD172a; Dendritic cells (DC): CST3; Natural Killer cells (NK): GNLY+; Here, + indicates high expression.

[0036] Cell annotation matches gene expression profiles with the expression patterns of marker genes, assigning each cell to a corresponding cell population.

[0037] 2) Extracting log-normalized gene expression levels from cell types: After cell annotation is completed, the next step is to extract log-normalized gene expression data for each cell type. This process involves the following steps: Data standardization: Single-cell data are standardized, typically using log-normalization, to eliminate differences in gene expression levels among different cells and the influence of sequencing depth. After standardization, the data presents a form closer to a normal distribution.

[0038] Calculate the average expression level for each cell type: For each annotated cell type, extract the log-normalized expression level of its corresponding genes and calculate the average expression level of each gene in that cell type. This step ensures that the evaluation focuses only on the gene expression levels of each cell population, unaffected by individual cell-specific differences. For example, for Helper T cells, relevant cells are extracted using the gene marker CD3E, and the average expression level of all genes in that cell population is calculated to obtain a log-normalized value. Similarly, the log-normalized gene expression levels for other cell types (such as CTLs, Regulatory T cells, etc.) are calculated using the same method.

[0039] 2) Z-score calculation method: After the above data preprocessing, log-normalized gene expression data of the test samples were obtained. To assess whether the gene expression of the test samples was within the normal range, the Z-score method was used to standardize the gene expression levels of the test samples. The Z-score is used to quantify the difference in gene expression between the test samples and the control group (WT group), and the specific calculation method is as follows: in, This indicates the expression value of the sample in a specific cell type. This is the mean of the entire WT group (all processed data combined). This represents the standard deviation of the entire WT group. The Z-score value is calculated using this formula. It can indicate the degree of deviation of the expression level of the test sample from that of the WT group.

[0040] 4) Evaluation criteria and scope: To ensure that the gene expression values ​​of the tested samples are within the normal range, the effective range of the Z-score is set to the interval [-2, 2]. The specific judgment criteria are as follows: Within WT range: If the Z-score value is between [-2, 2], the gene expression of the sample under test is considered to be within the normal physiological range.

[0041] Above WT range: If the Z-score value is greater than 2, the gene expression of the sample under test is considered to be outside the normal WT range.

[0042] Below WT range: If the Z-score is less than -2, the gene expression of the sample is considered to be below the normal WT range.

[0043] Example 4: Following the methods in Examples 1 and 2, CD163 gene-edited Duroc (n=4, 12 months old) underwent single-cell sequencing according to the procedures in Examples 1 and 2, and the results were analyzed using the same methods. The analysis was compared with that in Example 3. Reference Figures 2 to 4The horizontal axis of the figure represents the Z-score of different genes after log2 transformation, used to compress extreme values ​​while preserving the directionality of expression deviation; the vertical axis represents the average log-normalized expression level of genes in the corresponding cell type. Genes with Z-scores between -2 and 2 are defined as being within the WT range (WithinWT range) and are represented in gray; genes with Z-scores greater than 2 are defined as being above the WT range (AboveWT range), and genes with Z-scores less than -2 are defined as being below the WT range (BelowWT range). These two categories of genes are distinguished by color according to cell type. The dashed lines in the figure represent the Z-score of 0 and the expression baseline, respectively. The statistical results of the number of genes in the three states are marked above the legend.

[0044] Specifically, Table 1 shows the statistical distribution of gene expression deviations from the WT range for each cell type under different treatment conditions. The table illustrates the distribution of gene expression relative to the WT reference range in each major immune cell type under three treatment conditions (Control, LPS, and Poly(I:C)). Genes are categorized into three classes based on their Z-score: Above, Below, or Within the WT range. The numerical values ​​represent the number of genes in each category, used to compare the degree of expression deviation for each cell type under different treatment conditions. Figure 5 The diagram illustrates the distribution of gene expression relative to the WT reference range in each major immune cell type under three treatment conditions (Control, LPS, and Poly(I:C)). The bars are grouped by cell type and displayed in a stacked manner as three expression states: Above, Within, and Below. Each facet represents a treatment group, used to compare the expression shift patterns of each cell type under different stimulus conditions. Figure 6 This diagram displays the number of genes under different treatment conditions (Control, LPS, Poly(I:C)) and the main immune cell types in three expression states. The intensity of the color indicates the relative number of genes, with darker colors representing higher numbers. The graph is faceted by expression state (Above, Within, Below) to visually compare the degree of expression deviation among different cell types under various treatment conditions.

[0045] Table 1

[0046] The beneficial effects of this invention are as follows: This invention provides a peripheral blood mononuclear cell immune homeostasis evaluation method based on single-cell sequencing, which can provide directional information for animal experiments, effectively reducing experimental costs, improving targeting, and enhancing the reproducibility of results.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0048] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens, characterized in that, include: Peripheral blood samples were collected from the target subject. The target groups include: CD163 gene-edited Duroc pigs and wild-type Duroc pigs; The peripheral blood sample was subjected to PBMC separation to obtain the separated sample; Several candidate stimulus conditions are set, and the separated sample is induced under each candidate stimulus condition to obtain an induced sample; Single-cell library construction and sequencing were performed on the induced samples and control group samples to obtain a sample database; Cell annotation and expression pattern matching are performed on the sample database to obtain a matching database; The matching database is standardized and the average expression level is calculated to obtain a gene expression level database. The Z-score was used to quantify the degree of difference in the gene expression database to obtain a standard score. According to the preset evaluation rules, the standard score values ​​are matched within intervals to obtain the non-specific pathogen immune homeostasis evaluation results, and the non-specific pathogen immune homeostasis evaluation results are converted into visualization graphs; the visualization graphs include: UMAP, t-SNE, and heatmap.

2. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The peripheral blood sample was subjected to PBMC separation to obtain a separated sample, including: The peripheral blood sample was subjected to density gradient centrifugation using the Ficoll method under preset centrifugation parameters to obtain a centrifuged solution; the centrifugation parameters included: acceleration 8... Deceleration 5 2000 rpm, 40 min; The second white PBMC solution in the centrifuged solution was transferred to a centrifuge tube, and the PBMC solution was washed twice with PBS to obtain the separated sample.

3. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The candidate stimulation conditions include: induction with 10 μg / mL lipopolysaccharide for 3.5 hours and induction with 10 μg / mL polyinosinic acid for 3.5 hours.

4. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The quality control standards for the sample database include: removing genes expressed in fewer than 3 cells, removing cells with fewer than 200 or more than 5000 expressed genes, and removing cells with more than 5% mitochondrial genes.

5. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The marker genes annotated for the cells include: CD3E, CD8A+, GNLY, CD62L, CD45, CCR7, CD19, CD79B, CD20, CD14, CD172a, CD68, CD172a, CST3, and GNLY+. Among them, CD3E corresponds to HelperT cells; CD8A+ and GNLY correspond to Cytotoxic T cells; CD62L, CD45, and CCR7 correspond to Regulatory T cells; CD19, CD79B, and CD20 correspond to B cells; CD14 and CD172a correspond to Monocytes; CD68 and CD172a correspond to Macrophages; CST3 corresponds to Dendritic cells; and GNLY+ corresponds to Natural Killer cells.

6. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The standardization process employs the log-normalization method.

7. The method for evaluating the immune homeostasis of CD163 gene-edited pigs against nonspecific pathogens according to claim 1, characterized in that, The preset evaluation rule is as follows: the standard score value between [-2, 2] corresponds to gene expression within the normal physiological range; the standard score value greater than 2 corresponds to gene expression exceeding the normal WT range; and the standard score value less than -2 corresponds to gene expression below the normal WT range.

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