Method and device for comprehensively analyzing immune metabolism and ad disease-related genes through multiple omics

Abstract

The application discloses a method and device for comprehensively analyzing immune metabolism and AD disease-related genes by multiple omics. The method comprises the following steps: obtaining single-cell sequencing data of AD patients; obtaining GWAS data of immune cells and metabolites of AD patients; screening independent single nucleotide polymorphisms (SNPs); obtaining causally related immune cell characteristics and metabolite factor characteristics through Mendelian randomization causal inference; analyzing the causally related immune cells and metabolites to identify immune cells and metabolites that have an important impact on the occurrence of AD, and then obtaining metabolic pathways that affect AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain gene analysis results; and screening genes related to AD disease. The method disclosed by the application uses advanced machine learning technology to identify key biomarkers related to Alzheimer's disease and accurately predict the risk of individuals in disease development.

Description

Technical Field

[0001] This invention relates to the interdisciplinary application of bioinformatics, genomics, and machine learning in the biomedical field, and in particular to a method for comprehensive multi-omics analysis of genes related to Alzheimer's disease based on immune metabolic crosstalk. Background Technology

[0002] Gene screening is the process of identifying key genes related to specific biological phenotypes (such as diseases, traits, etc.) from massive gene data. It is of great significance for revealing biological mechanisms, disease diagnosis, and the discovery of therapeutic targets.

[0003] Currently, gene screening methods include: gene screening methods based on transcriptomics data, gene screening methods based on genomics data, gene screening methods based on proteomics data, and gene screening methods based on epigenomics data.

[0004] The existing gene screening methods mentioned above rely on single-omics data (such as GWAS or differential gene expression analysis), lack multi-dimensional data integration, and are difficult to analyze the relationship between gene function and metabolic pathways.

[0005] In addition, the lack of integration of multi-omics data (such as transcriptomics and single-cell sequencing) can lead to the omission of key genes related to immune metabolism. Summary of the Invention

[0006] This application provides a method for comprehensive multi-omics analysis of genes associated with Alzheimer's disease (AD) based on immunometabolic crosstalk. This method can integrate multi-omics data to analyze genes associated with AD and obtain immune cells and metabolites related to AD.

[0007] Firstly, it provides a comprehensive multi-omics approach to analyzing genes related to immune metabolism and Alzheimer's disease, including:

[0008] Obtain single-cell sequencing data from AD patients;

[0009] Obtain genome-wide association study (GWAS) data on immune cells and metabolites in AD patients;

[0010] Based on GWAS data, independent single nucleotide polymorphisms (SNPS) were screened out; through Mendelian randomized causal inference, the characteristics of immune cells and metabolite factors associated with causality were obtained.

[0011] By classifying immune cells with causal relationships and performing enrichment analysis on related metabolites, we can identify immune cells and metabolites that have an important impact on the development of Alzheimer's disease (AD), and thus obtain the metabolic pathways that affect AD. Among them, the identified immune cells include immune cells that have protective factors and risk factors for AD, and the identified metabolites include metabolites that have protective factors and risk factors for AD.

[0012] Single-cell sequencing data from AD patients were analyzed to identify cell populations associated with metabolic pathways, and gene expression data showing differential expression of metabolic pathways in different cell populations were obtained; differential gene analysis was performed to obtain gene analysis results.

[0013] Based on the differential expression of different metabolic pathways in different cell populations in the gene analysis results, genes associated with Alzheimer's disease were screened.

[0014] In one possible implementation, the independent single nucleotide polymorphisms (SNPSs) are screened based on GWAS data; causally associated immune cell characteristics and metabolite factor characteristics are obtained through Mendelian randomized causal inference, including:

[0015] GWAS data were used to screen instrumental variables for AD-related exposure factors to identify candidate single nucleotide polymorphisms (SNPs).

[0016] Based on candidate SNPs, SNPs related to AD are screened out, and linkage disequilibrium calculation is performed to obtain independent SNPs.

[0017] Based on independent SNPs, Mendelian randomization method was used to infer causal relationships, and immune cell characteristics and metabolite factor characteristics of causal associations were obtained.

[0018] Sensitivity analyses were performed on the immune cell characteristics and metabolite factors of causal associations, respectively. The stability and reliability of the analysis results were evaluated using consistency tests, heterogeneity tests, and level pleiotropy tests. Relevant characteristics of stable and reliable causal associations were preserved.

[0019] In one possible implementation, the analysis of causally related immune cells and metabolites identifies immune cells and metabolites that significantly influence the development of AD, thereby revealing metabolic pathways affecting AD, including:

[0020] Analysis of immune cells and metabolites with causal relationships identifies immune cells and metabolites that have a significant impact on the development of Alzheimer's disease (AD).

[0021] By classifying the identified immune cells, we can obtain immune cells that have protective factors against AD and immune cells that have risk factors.

[0022] The identified metabolites were statistically analyzed to identify those that pose risk factors and protective factors against Alzheimer's disease (AD). Enrichment analysis was conducted to identify metabolic pathways that affect AD, including both protective and risk pathways.

[0023] In one possible implementation, the analysis of single-cell sequencing data from AD patients identifies cell populations associated with metabolic pathways, obtains gene expression data showing differential expression of metabolic pathways in different cell populations, and performs differential gene analysis to obtain gene analysis results, including:

[0024] Analyze single-cell sequencing data from AD patients to obtain preprocessed data;

[0025] Dimensionality reduction and cluster analysis were performed on the preprocessed data to obtain cluster analysis results including the main feature vectors, the optimal number of principal components, and the uniform manifold approximation and projection analysis.

[0026] Based on the cluster analysis results, cell clustering and annotation were performed to identify cell populations that have an important impact on the occurrence of AD;

[0027] Cell population regrouping and gene expression levels: Cell populations were regrouped based on immune cell and metabolite information obtained from S400; gene expression levels of metabolite pathways in different cell populations were analyzed.

[0028] T cells, B cells, and monocytes were extracted from the regrouped cell populations; based on the gene expression levels of metabolite pathways, the cells were divided into a high-metabolic immune cell group and a low-metabolic immune cell group.

[0029] Differential gene analysis yields gene analysis results.

[0030] In one possible approach, genes associated with Alzheimer's disease (AD) are screened based on the differential expression of different metabolic pathways in different cell populations revealed by gene analysis, including:

[0031] The differentially expressed genes of high and low metabolomes in various cell types were analyzed using statistical methods; screening criteria were determined to identify genes associated with Alzheimer's disease.

[0032] Furthermore, the genes screened for those associated with Alzheimer's disease include:

[0033] Differential genes between monocytes with high nucleotide metabolism and those with high amino acid metabolism; differential genes between protective T cells and those with high amino acid metabolism; and differential genes between protective B cells and those with high amino acid metabolism.

[0034] Secondly, it provides a device for comprehensively analyzing genes related to immune metabolism and Alzheimer's disease using multi-omics methods, including:

[0035] The first acquisition module is used to acquire single-cell sequencing data from AD patients;

[0036] The second acquisition module is used to acquire genome-wide association study (GWAS) data of immune cells and metabolites in AD patients.

[0037] The inference module is used to screen out independent single nucleotide polymorphisms (SNPS) based on GWAS data; and to obtain causal associations of immune cell characteristics and metabolite factor characteristics through Mendelian randomized causal inference.

[0038] The first analysis module is used to classify immune cells with causal relationships and perform enrichment analysis of related metabolites to identify immune cells and metabolites that have an important impact on the occurrence of AD, thereby obtaining the metabolic pathways that affect AD; among them, the identified immune cells include immune cells that have protective factors and risk factors for AD, and the identified metabolites include metabolites that have protective factors and risk factors for AD.

[0039] The second analysis module is used to analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, obtain gene expression data showing differential expression of metabolic pathways in different cell populations, and perform differential gene analysis to obtain gene analysis results.

[0040] The screening module is used to screen for genes associated with Alzheimer's disease based on the differential expression of different metabolic pathways in different cell populations in the gene analysis results.

[0041] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for comprehensively analyzing immune metabolism and AD-related genes as described in the first aspect.

[0042] Fourthly, a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for comprehensively analyzing immune metabolism and AD-related genes using multi-omics as described in the first aspect.

[0043] Fifthly, a computer program product includes a computer program, characterized in that, when the computer program is executed by a processor, it implements the method for comprehensively analyzing immune metabolism and AD-related genes using multi-omics analysis as described in the first aspect.

[0044] The beneficial effects of this application are as follows:

[0045] (1) This application provides a comprehensive multi-omics anatomical Alzheimer's disease prediction method that combines machine learning. It aims to identify key biomarkers related to Alzheimer's disease by integrating genetic, transcriptional, and single-cell transcriptomics data and using advanced machine learning technology, and to accurately predict an individual's risk in the development of the disease.

[0046] (2) The method described in this application, combined with a machine learning model, generates a prediction model from a training dataset that can capture complex nonlinear relationships and high-dimensional data features. Compared with traditional statistical methods, it significantly improves the prediction accuracy of Alzheimer's disease risk. This method comprehensively considers data from multiple biological levels (genome, transcriptome), enabling the model to analyze the biological mechanisms of the disease from a global perspective and provide a more comprehensive biological explanation.

[0047] (3) The method provided in this application analyzes and ultimately identifies immune cells and metabolites associated with AD by using the single-cell sequencing GSE181279 dataset of peripheral blood from normal elderly patients and AD patients in the GEO database.

[0048] (4) The method provided in this application provides technical support for further exploring the characteristics of differentially expressed genes and their association with immune cell metabolism in the AD patient population.

[0049] (5) The method provided in this application provides strong technical support for in-depth research on the role of differentially expressed genes (DEGs) in Alzheimer's disease (AD) populations, especially their association with immune cell metabolism. Attached Figure Description

[0050] Figure 1 To identify SNP-based AD-sensitive factors associated with immune cells and plasma metabolites in Mendelian randomization studies; among them,

[0051] Figure 1 (a, e) represents: (a) Immune cell characteristics (731 phenotypes; screening threshold: P < 5 × 10⁻⁶). -6 (nSNP=8,926) and (e) plasma metabolites (1,400 compounds; screening threshold: P<5×10⁻⁶). -6The diagram shows the Mendelian randomization (MR) analysis workflow for Alzheimer's disease (AD) related factors in the study (nSNP=5,804). Both analyses were based on data from the genome-wide association study (GWAS) of AD (cases=954; controls=487,331; total nSNP=12,321,875), using inverse variance weighted (IVW) regression as the primary analytical method, supplemented by sensitivity analysis (MR-Egger, weighted median method).

[0052] Figure 1 (b, f) is a volcano plot of immune cell characteristics (b) and metabolite factors (f) that are causally associated with AD. Risk-increasing factors (red) and protective factors (blue) are plotted based on effect size (odds ratio) and statistical significance (-log10 P value).

[0053] Figure 1 (c, d) are bubble diagrams of AD-related immune cell types, with bubble size reflecting relative cell abundance. Orange represents risk-related cell types, and blue represents protective cell types.

[0054] Figure 1 (g, h) represents the KEGG pathway enrichment analysis of AD-related metabolites. Orange indicates risk-related pathways, and blue indicates protective pathways. The figure shows the enrichment ratio and the corresponding P-value.

[0055] Figure 2 This describes the cellular distribution of AD-sensitive metabolic factors in human blood; among them,

[0056] Figure 2 (a) Single-cell clustering of immune cells in the blood of AD patients and NC patients using UMAP technology;

[0057] Figure 2 (b) Percentage changes in immune cell types in NC and AD patients;

[0058] Figure 2 (c) Location and distribution of AD risk metabolic pathways in immune cells; the cells with the most distribution indicated by the red arrows are monocytes;

[0059] Figure 2 (d) Location and distribution of AD protective metabolic pathways in immune cells; green arrows point to T cells and B cells, which are more abundant;

[0060] Figure 2 (e) Volcano plots show significant differences in gene expression between high- and low-Namis monocytes;

[0061] Figure 2(f) The bar chart shows the results of functional enrichment analysis of upregulated and downregulated genes in high NamIis monocytes; dark red bars represent upregulated gene function and light red bars represent downregulated gene function.

[0062] Figure 2 Volcano plots (g) and (i) show significant differences in gene expression in high and low Aam T cells and B cells, respectively;

[0063] Figure 2 Bar charts (h) and (j) show the results of gene function enrichment analysis of upregulated and downregulated genes in high Aam T cells and B cells, respectively; dark green bars represent upregulated gene function and light green bars represent downregulated gene function.

[0064] Figure 3 Cluster annotation and cluster analysis diagram of single cells in the blood of AD patients; among which,

[0065] Figure 3 (a) RNA characteristics of different cell populations before batch effect removal;

[0066] Figure 3 (b) UMAP downscaling plot before batch effect removal;

[0067] Figure 3 (c) RNA characteristics of different cell populations after batch effect removal;

[0068] Figure 3 (d) UMAP diagram after batch effect elimination using the harmonization algorithm;

[0069] Figure 3 (e) The relationship between nCount_RNA and percent.mt, and the relationship between nCount_RNA and nFeature_RNA;

[0070] Figure 3 (f) Hierarchical clustering dendritic diagram of cell populations;

[0071] Figure 3 (g) The relationship between standard deviation and principal components (PCs) in principal component analysis (PCA);

[0072] Figure 3 (h) Use parameters such as the number of neighbors (n_neighbours), minimum distance (min_dist), and metric to perform UMAP downscaling plots on NC and AD samples;

[0073] Figure 3 (i) Heatmaps of different cell populations, including marker gene expression and cell population proportions.

[0074] Figure 4 This is a schematic diagram illustrating the method for identifying genes related to immune metabolism and Alzheimer's disease (AD); among them, Figure 4 (a) Identification of differentially expressed genes in single blood cells of Alzheimer's disease (AD); Figure 4 (b) Identification of differentially expressed genes in the blood transcriptome of Alzheimer's disease; Figure 4 (c) To determine the final set of three characteristic genes.

[0075] Figure 5 This is a schematic diagram of the device provided by the present invention for comprehensively analyzing immune metabolism and AD-related genes using multi-omics analysis.

[0076] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0077] The present invention will be further described below with reference to specific embodiments, but the content of the present invention is not limited thereto.

[0078] This application provides a method for comprehensive multi-omics analysis of AD-related genes based on immune metabolic crosstalk, including:

[0079] S100: Obtain single-cell sequencing data from AD patients.

[0080] In one possible implementation, the single-cell sequencing data is obtained by retrieving the GSE181279 dataset of single-cell sequencing data from AD patients, which contains single-cell RNA sequencing data of peripheral blood mononuclear cells (PBMCs) from AD patients. The single-cell sequencing data is intended to reveal AD-related immune characteristics and potential biomarkers through single-cell level analysis.

[0081] S200: Obtain genome-wide association study (GWAS) data on immune cells and metabolites from AD patients.

[0082] S300, based on genome-wide association study data, screened out independent single nucleotide polymorphisms (SNPS); through Mendelian randomized causal inference, obtained causal associations of immune cell characteristics and metabolite factor characteristics.

[0083] For example, aggregated statistics of 731 GWAS related to immune cells were obtained from the GWAS Catalog. The GWAS IDs of these data range from GCST90001391 to GCST90002121, covering a wide range of immune cell-related genetic variations, which helps to reveal the potential role of the immune cell system in the pathogenesis of Alzheimer's disease (AD).

[0084] Furthermore, this application also includes data from a large-scale plasma metabolite GWAS cohort downloaded from peer-reviewed publications. This cohort includes 8,299 participants, 1,091 metabolites, and 309 metabolite ratios, providing important data support for studying the relationship between metabolites and AD. These factors influence specific immune cells and metabolites that affect Alzheimer's disease risk.

[0085] In one possible implementation, S300 includes the following sub-steps:

[0086] S310. GWAS data were used to screen instrumental variables of AD-related exposure factors to identify candidate single nucleotide polymorphisms (SNPs).

[0087] Furthermore, the exposure factors include immune cell characteristics and metabolite characteristics.

[0088] Furthermore, functions from the TwoSampleMR package were used to screen for single nucleotide polymorphisms (SNPs) from the GWAS data. These SNPs will serve as potential instrumental variables (IVs) to systematically identify features with significant causal relationships to Alzheimer's disease, aiming to provide a solid theoretical foundation and data support for future research and clinical applications.

[0089] S320. Based on candidate SNPs, select SNPs related to AD, perform linkage disequilibrium (LD) calculation, and obtain independent SNPs.

[0090] In one possible implementation, S320 includes:

[0091] S321. Based on the candidate SNPs, filter out the SNPs related to AD.

[0092] Specifically, based on candidate SNPs, a strict screening criterion was set during the instrumental variable screening process, relying on the MR Base database. That is, single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) were selected, and their significance level was lower than 5e-8.

[0093] S322. Perform linkage disequilibrium calculations on SNPs associated with AD to identify and retain genetically independent SNPs. This step is crucial because it ensures that there are no significant correlations among the selected SNPs, thus forming a carefully selected dataset of independent SNPs.

[0094] Understandably, through S320, the embodiments of this application can effectively reduce the impact of multicollinearity problems, thereby significantly improving the accuracy and reliability of subsequent analyses. In the process of performing LD independence verification, it is first necessary to exclude those r 2Redundant single nucleotide polymorphisms (SNPs) with a value (coefficient of determination) greater than or equal to 0.001 are selected to ensure that only independent instrumental variables are retained.

[0095] S330. Based on independent SNPs, Mendelian randomization (MR) method is used to infer causal relationships and obtain causal associations in immune cell characteristics and metabolite factor characteristics.

[0096] In one possible implementation, the Mendelian randomization method includes: inverse variance weighted (IVW), MR-Egger regression, weighted median, and weighted mode.

[0097] It should be noted that the MR method aims to reveal the causal relationship between exposure factors and diseases, and to provide estimates of the corresponding statistical significance and effect size. The use of multiple methods is to ensure that the embodiments of this application can obtain reliable results from different perspectives and methodologies, thereby gaining a deeper understanding of the complex relationship between exposure factors and diseases.

[0098] S340. Sensitivity analysis was performed on the immune cell characteristics and metabolite factor characteristics of causal associations, respectively. The stability and reliability of the analysis results were evaluated using the consistency test, heterogeneity test and level pleiotropy test. The relevant characteristics of stable and reliable causal associations were preserved.

[0099] In one possible implementation, S340 includes:

[0100] S341, for the immune cell characteristics and metabolite factors of causal association, instrumental variable elimination method was used to eliminate each single nucleotide polymorphism (SNP) one by one, and the effect value was recalculated on this basis to ensure the comprehensiveness and accuracy of the results.

[0101] S342. Perform a multi-method consistency test on the immune cell characteristics and metabolite factor characteristics of the causal association obtained in S341 to verify whether the results under different methods are consistent; the multi-method consistency test includes comparing the consistency of the results of IVW (inverse variance weighted method), MR-Egger regression and weighted median method.

[0102] S343. Perform a heterogeneity test on the SNP effect size for each exposure factor.

[0103] In one possible implementation, the heterogeneity test method includes: employing Cochran's Q test, setting a p-value greater than 0.05 as the acceptable criterion, while combining it with I... 2The statistical measure is evaluated; a value less than 50% indicates low heterogeneity. This method helps determine the reliability of the results.

[0104] S344: The immune cell characteristics and metabolite factor characteristics of the causal association obtained from S344 were tested for pleiotropic effects using the MR-Egger intercept test. If the p-value is greater than 0.05, it indicates that there is no significant pleiotropic effect.

[0105] S345 eliminates causal association features that fail to meet the standards in consistency tests, heterogeneity tests, and level pleiotropy tests, while retaining stable and reliable features.

[0106] Through the analytical steps of S340, this application strives to ensure the robustness and scientific validity of the research results, providing a solid foundation for subsequent research.

[0107] S400: Classify and analyze the immune cells and metabolites with causal relationships obtained from S345 to identify immune cells and metabolites that have an important impact on AD, and then obtain the metabolic pathways that affect AD; among them, the identified immune cells include immune cells with protective factors against AD and immune cells with risk factors, and the identified metabolites include metabolites with risk factors and protective factors against AD.

[0108] In one possible implementation, step S400 includes:

[0109] S410. Analyze immune cells and metabolites with causal relationships to identify immune cells and metabolites that have an important influence on the occurrence of AD.

[0110] S420. By classifying the identified immune cells, obtain immune cells that have protective factors against AD and immune cells that have risk factors.

[0111] S430. Statistically analyze the identified metabolites to obtain metabolites that pose risk factors and protective factors for AD; conduct enrichment analysis by accessing the MetaboAnalyst website to obtain metabolic pathways that affect AD; among which, metabolic pathways that affect AD include protective pathways and risk pathways.

[0112] Furthermore, S430 includes the following sub-steps:

[0113] S431. Statistically analyze the identified metabolites and classify them into two categories: risk factors and protective factors;

[0114] S432. Perform enrichment analysis on the metabolites:

[0115] Open the MetaboAnalyst website (https: / / www.metaboanalyst.ca / ).

[0116] Select the enrichment analysis module: Click the "Pathway Analysis" module.

[0117] Upload the metabolite list: Upload the compiled list of metabolite names (including risk factors and protective factors) to MetaboAnalyst. Select "Compound Name" as the input type.

[0118] Metabolite Matching: The system will automatically match the uploaded metabolite names with metabolites in the database. Unmatched metabolites will be displayed in yellow. You can click "View" to see details and manually match them.

[0119] Ensure all metabolites are correctly matched. For any mismatched metabolites, refer to databases such as HMDB for further confirmation.

[0120] Choose the analysis algorithm: In enrichment analysis, you can choose the Fishers Exact Test method.

[0121] Select a species database: Select the appropriate species database (Homosapiens) based on the research subject (e.g., humans).

[0122] Parameter settings: Set other parameters as needed, such as p-value truncation.

[0123] Run the analysis: Click the "Proceed" button to begin the enrichment analysis. The analysis results will display metabolic pathways related to AD, including dangerous and protective pathways.

[0124] The results are usually presented in tabular form, including information such as pathway name, p-value, and adjusted p-value.

[0125] Pathway visualization: Click on the pathway name to view the corresponding pathway diagram, which helps to understand the location and role of metabolites in the pathway.

[0126] S433. Based on the enrichment analysis results, the metabolic pathways affecting AD were obtained.

[0127] S500: Analyze single-cell sequencing data of AD patients to identify cell populations related to metabolic pathways, obtain gene expression data showing differential expression of metabolic pathways in different cell populations; perform differential gene analysis to obtain gene analysis results.

[0128] In one possible implementation, S500 includes the following sub-steps:

[0129] S510. Analyze single-cell sequencing data from AD patients to obtain preprocessed data.

[0130] In one possible implementation, the single-cell sequencing data of the AD patient includes sample clinical information and gene expression profiles.

[0131] In one possible implementation, the preprocessing includes reading single-cell expression profile data, filtering low-expression genes, and normalizing and standardizing.

[0132] Specifically, locate the GSE181279 dataset in the GEO database (download link: https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE181279) and click to download the Series MatrixFile. After downloading and unzipping, you will get a file containing sample clinical information and gene expression profiles. Download its barcodes, features, and matrix data. Use R packages such as Seurat and Harmony to import the downloaded matrix data into the R software system, read the single-cell expression profile data, filter low-expression genes, standardize and normalize, and obtain preprocessed data.

[0133] Standardized data: Standardize the data to ensure that data from different samples are comparable.

[0134] Normalized data: Data is normalized to eliminate technical biases.

[0135] S520. Perform dimensionality reduction and cluster analysis on the preprocessed data to obtain cluster analysis results including the main eigenvectors, the optimal number of principal components, and the uniform manifold approximation and projection analysis.

[0136] Principal Component Analysis (PCA) Analysis: Perform Principal Component Analysis (PCA) on preprocessed data to extract the main eigenvectors.

[0137] Determine the optimal number of principal components: Use the tool ElbowPlot to observe the optimal number of principal components (pcs).

[0138] Uniform Manifold Approximation and Projection (UMAP) Analysis: Based on the PCA results, UMAP analysis is performed to visualize the clustering results and observe the positions between each cluster.

[0139] S530. Based on the cluster analysis results, perform cell clustering and annotation to identify cell populations that have an important impact on AD.

[0140] Cluster analysis: Based on the UMAP analysis results, the cells were clustered.

[0141] Cell annotation: Clusters were annotated using the Seurat package to identify cell populations that have a significant impact on AD development.

[0142] S540, Cell Population Regrouping and Gene Expression Levels: Combining the immune cell and metabolite information obtained from S400, the cell population was regrouped; the gene expression levels of metabolite pathways in different cell populations were analyzed.

[0143] S550. Extract T cells, B cells, and monocytes from the regrouped cell populations; based on the gene expression levels of metabolite pathways, divide the cells into a high-metabolic immune cell group and a low-metabolic immune cell group.

[0144] It should be noted that the T cells include: T cells obtained from amino acid metabolism enriched by protective metabolite pathways and T cells obtained from amino acid metabolism enriched by dangerous metabolite pathways; the B cells include: B cells obtained from amino acid metabolism enriched by protective metabolite pathways and B cells obtained from amino acid metabolism enriched by dangerous metabolite pathways; the monocytes include monocytes obtained from amino acid metabolism enriched by protective metabolite pathways and monocytes obtained from amino acid metabolism enriched by dangerous metabolite pathways.

[0145] S560, differential gene analysis, to obtain gene analysis results.

[0146] Comparing differentially expressed genes between high and low metabolites: Using Seurat's FindMarkers function, differentially expressed genes were compared between the high and low metabolites.

[0147] Visualizing differentially expressed genes: The expression of differentially expressed genes is shown through volcano plots and heatmaps.

[0148] S600. Based on the differential expression of different metabolic pathways in different cell populations in the gene analysis results, genes associated with AD disease were screened out.

[0149] In one possible implementation, S600 includes:

[0150] The differentially expressed genes of high and low metabolites in each cell type (T / B / monocytes) were analyzed using statistical methods; screening criteria were determined to obtain genes associated with Alzheimer's disease.

[0151] Specifically, the screening criteria were as follows: significance threshold: adjusted p-value (adj.P.Val) < 0.05; expression change threshold: average log2-fold change > 0.5.

[0152] The methods described above will be explained below with reference to detailed embodiments.

[0153] A comprehensive multi-omics analysis method based on immune metabolic crosstalk and AD-related genes includes the following steps:

[0154] S1. Obtain single-cell sequencing data from AD patients.

[0155] Download single-cell sequencing data from blood of AD patients:

[0156] Download publicly available blood single-cell sequencing data related to Alzheimer's disease (AD) from the GEO (Gene Expression Omnibus) database (https: / / www.ncbi.nlm.nih.gov / geo / info / datasets.html), including single-cell data of blood from AD patients GSE181279, which includes sample data of complete single-cell expression profiles from 2 healthy patients and 3 AD patients for single-cell analysis.

[0157] The specific methods for obtaining transcriptome data from the blood of AD patients are as follows:

[0158] (1) Accessing the GEO database: Open the GEO database website (<https: / / www.ncbi.nlm.nih.gov / geo / info / datasets.html> Enter the dataset number (e.g., GSE63060, GSE63061, etc.) in the search box. Click the search button to find the corresponding dataset. After entering the dataset page, click the "Download" button. Select the download format (e.g., SOFT file or MINiML file). Save the data to your local machine. On the dataset page, find the "Annotation SOFT file" link. Download the annotation file (e.g., GPL6947).

[0159] (2) Data Preprocessing - Reading Data: Use R to read the downloaded SOFT file. Filtering Low-Expression Genes: Filter out low-expression genes to reduce noise. Standardization and Normalization: Standardize and normalize the data to ensure comparability between different samples.

[0160] S2. Obtain genome-wide association study (GWAS) data of immune cells and metabolites from AD patients.

[0161] Download GWAS data on immune cells and metabolites in AD patients:

[0162] Download GWAS data of blood from AD patients from the GWAS summary datasets (https: / / gwas.mrcieu.ac.uk / ) (GWAS ID: ieu-b-5067, including 488,285 samples and 12,321,875 SNPs).

[0163] In addition, summary statistics of 731 immune cell-related GWAS were obtained from the GWAS catalog (GWAS IDs GCST90001391 to GCST90002121).

[0164] In addition, a large GWAS cohort of serum metabolites was downloaded, including 1,091 metabolites and 309 metabolite ratios from 8,299 individuals. All three datasets were derived from European populations, ensuring the consistency of the analysis in this study.

[0165] Data preprocessing: Use the R package "TwoSampleMR" to read the downloaded GWAS data. Filter out low-quality SNPs.

[0166] S3. Based on genome-wide association study data, independent single nucleotide polymorphisms (SNPS) were screened out; through Mendelian randomized causal inference, the characteristics of immune cells and metabolite factors associated with causality were obtained.

[0167] Specifically, S3 includes:

[0168] S3.1 Filtering relevant SNPs: Filter out SNPs related to AD based on p-values.

[0169] S3.2 Constructing Instrumental Variables: Construct instrumental variables using the IVW (Inverse Variance Weighted) method.

[0170] S3.3 Perform Mendelian randomization analysis: Use MR analysis tools (such as MR Base) to analyze and identify immune cells and metabolites associated with AD.

[0171] S3.4 Result Validation: Use sensitivity analysis (such as MR-PRESSO) to validate the reliability of the results.

[0172] Among them, S3.3 Mendelian Randomization (MR) analysis includes:

[0173] Access the MR Base database (http: / / app.mrbase.org / ) to obtain a large amount of aggregated statistics containing hundreds of GWAS studies. The result IDs filtered through the MR Base database are extracted from the GWAS aggregated data (https: / / gwas.mrcieu.ac.uk / ). SNPs related to each gene at each locus are matched for significance threshold (P<1e-5) as potential IVs (Instrumental Variables), and linkage disequilibrium (LD) between SNPs is calculated. 2 For SNPs <0.001 (clumping window size=10,000kb), only P is retained. 2 <5e-8 SNPs. Causal effects are estimated sequentially using inverse variance weighted (IVW, which uses meta-analysis combined with Wald estimates for each SNP to calculate the causal effect), MR-Egger (based on the assumption that instrument strength is independent of direct effects), and MR-Egger regression. MR-Egger allows estimation in the presence of horizontal pleiotropy. Weighted median (which allows for correct estimation of causality even with 50% IV invalidity) is robust to outliers. Weighted model (which provides a stronger ability to detect causality) is also used.

[0174] The sensitivity analysis in section S3.4 includes:

[0175] This application employs Mendelian randomization (MR) sensitivity analysis to assess the impact of specific gene variants on the risk of Alzheimer's disease (AD). By systematically eliminating each SNP and recalculating its effect, variants significantly affecting the overall estimate are identified, generating new estimate points and their 95% confidence intervals to assess the unique contribution and robustness of each SNP. The estimate after eliminating individual SNPs is compared with the overall estimate including all SNPs to check the robustness of the analysis. Furthermore, the statistical heterogeneity among SNPs is assessed using the Mendelian heterogeneity test, calculating the Q value; a P-value greater than 0.05 indicates consistent SNP effects. MR-Egger regression is used to test for horizontal multivariates; a P-value greater than 0.05 indicates no significant horizontal multivariates.

[0176] The results showed that none of the 31 immune cell phenotypes used in AD MR analysis exhibited heterogeneity (Q test P>0.05) or horizontal multipotentimetry (MR-Egger P>0.05), demonstrating the robustness of the causal relationship (Table 2). Leave-one-out and funnel plot analyses also indicated the reliability of the data.

[0177] S4. Classify immune cells with causal relationships and perform enrichment analysis on related metabolites to identify immune cells and metabolites that have an important impact on AD, thereby obtaining the metabolic pathways that affect AD; among them, the identified immune cells include immune cells that have protective factors and risk factors for AD, and the identified metabolites include metabolites that have protective factors and risk factors for AD.

[0178] Specifically, S4 includes the following sub-steps:

[0179] S4.1 Analyze immune cells and metabolites with causal relationships to identify immune cells and metabolites that have an important impact on the occurrence of AD;

[0180] S4.2. By classifying the identified immune cells, we can obtain immune cells that have protective factors against AD and immune cells that have risk factors.

[0181] S4.3 Statistically analyze the identified metabolites to obtain metabolites that pose risk factors and protective factors against AD; conduct enrichment analysis by accessing the MetaboAnalyst website to obtain metabolic pathways that affect AD; among them, metabolic pathways that affect AD include protective pathways and risk pathways.

[0182] Specifically, MetaboAnalyst 6.0 was used in S4.3 for metabolite enrichment analysis (MSEA).

[0183] Metabolomics enrichment analysis (MSEA) was performed using the MetaboAnalyst 6.0 platform, a comprehensive web-based tool designed for analyzing and interpreting metabolomics data. The MSEA module of MetaboAnalyst 6.0 offers a variety of enrichment analysis methods. In this embodiment, overrepresentation analysis (ORA) was selected to identify metabolomics significantly associated with the dataset. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Small Molecule Pathways Database (SMPDB) were used as reference libraries for pathway-based enrichment.

[0184] Specifically, the method in S4.3 includes the following sub-steps:

[0185] S4.3.1. Classify metabolites associated with Alzheimer's disease (AD) into two categories: risk factors and protective factors. Ensure the metabolite names are accurate for subsequent matching.

[0186] S4.3.2 Data Upload

[0187] S4.3.2a. Access the MetaboAnalyst website: Open the official MetaboAnalyst website (<https: / / www.metaboanalyst.ca / > ).

[0188] S4.3.2b Select the enrichment analysis module: Click the “Pathway Analysis” module to enter the enrichment analysis page.

[0189] S4.3.2c, Upload the metabolite list: Upload the compiled list of metabolite names (including risk factors and protective factors) to MetaboAnalyst. Select "Compound Name" as the input type.

[0190] S4.3.3, Data Matching

[0191] Automatic metabolite matching: The system will automatically match the uploaded metabolite names with metabolites in the database. Unmatched metabolites will be displayed in yellow.

[0192] Manually match metabolites: Click "View" to see details of unmatched metabolites. For metabolites that cannot be automatically matched, manually confirm and match them by referring to databases such as HMDB.

[0193] S4.3.4 Setting Analysis Parameters

[0194] S4.3.4a. Selecting the analysis algorithm: In enrichment analysis, select Fisher's Exact Test method.

[0195] S4.3.4b. Select a species database: Select the appropriate species database (Homosapiens) based on the research subject (e.g., humans).

[0196] S4.3.4c, Set other parameters: Set p-value truncation (e.g., p<0.05) and other necessary parameters.

[0197] S4.3.5, Run-through enrichment analysis

[0198] S4.3.5a. Start Analysis: Click the “Proceed” button to start enrichment analysis.

[0199] S4.3.5c, View Results: The analysis results will be displayed in a table format, including information such as pathway name, p-value, and adjusted p-value.

[0200] The results will show metabolic pathways associated with AD, including both dangerous and protective pathways.

[0201] S5. Analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, and obtain gene expression data showing differential expression of metabolic pathways in different cell populations; perform differential gene analysis to obtain gene analysis results.

[0202] Specifically, S5 includes:

[0203] First, the Seurat software package was used to read the expression profiles and filter out low-expression genes. Then, the data were standardized, normalized, and analyzed using PCA and UMAP. Elbow plots were used to observe the optimal number of cells, and UMAP analysis was used to determine the positions between each cluster. Next, the Seurat software package was used to annotate the clusters, identifying some cell populations that significantly influence AD ​​development. Combined with Mendelian randomization analysis of immune cells and metabolites, the cell populations were further subdivided to explore the expression of metabolite pathways within the cell populations. Finally, specific cell populations were extracted and divided into high-metabolic and low-metabolic immune cell groups based on the expression levels of metabolite pathway genes. Differential gene expression analysis was performed between the two groups to obtain the gene analysis results.

[0204] S5 includes the following sub-steps:

[0205] S5.1 Data Quality Control: Perform quality control on single-cell RNA sequencing data, filtering out cells with gene signatures exceeding a specified range or with an excessively high proportion of mitochondrial genes. Use the `subset` function to filter the data and ensure data quality.

[0206] S5.2 Standardization: Standardize the filtered data using logarithmic standardization to ensure data comparability.

[0207] S5.3, Dimensionality Reduction and Clustering, Batch Effect Correction:

[0208] S5.3a. Finding hypervariable genes: Use the `FindVariableFeatures` function to identify hypervariable genes.

[0209] S5.3b, Data Standardization: Standardize the highly variable genes.

[0210] S5.3c, Principal Component Analysis (PCA): Use the `RunPCA` function to perform PCA analysis and extract the principal eigenvectors.

[0211] S5.3d Visualizing PCA Results: Use the `DimPlot` function to visualize the PCA results and observe the distribution of different samples.

[0212] S5.3e. Use the `Harmony` package to perform batch effect correction to ensure that data from different batches are comparable.

[0213] S5.4 Cluster Analysis

[0214] S5.4a Determine the optimal number of principal components: Use the `ElbowPlot` function to determine the optimal number of principal components.

[0215] S5.4b, Constructing an Adjacency Graph: Use the `FindNeighbors` function to construct an adjacency graph.

[0216] S5.4c, Cluster Analysis: Use the `FindClusters` function to perform cluster analysis.

[0217] S5.4d, UMAP dimensionality reduction: Use the `RunUMAP` function to perform UMAP dimensionality reduction and visualize the clustering results.

[0218] S5.5, Cell Type Annotation

[0219] S5.5a. Loading Reference Data: Use the `SingleR` package to load reference data.

[0220] S5.5b, Cell type prediction: Use the `SingleR` function to predict cell types on the test data.

[0221] S5.5c, Save prediction results: Save the prediction results as a CSV file.

[0222] S5.5d, Annotate cell types: Annotate the predicted cell types into the metadata.

[0223] S5.5e. Use the `plotScoreHeatmap` function to draw a heatmap to display the cell type prediction results. S5.6. Identification of metabolic-related differentially expressed genes:

[0224] S5.6a, Risk-M (High Nucleotide Metabolism Monocytes) - Screening of Differential Genes in High Amino Acid Metabolism Monocytes: This step loads several R packages, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree, and clusterProfiler, for data processing, visualization, and analysis. The Seurat package is used first. The monocyte population is extracted using `scRNA_M=subset(scRNA,celltype=='monocyte')`, and the `read.gmt` function is used to read the `Pathways of nucleic acid metabolism and innate immunesensing.gmt` gene set file. The `DotPlot` function is used to display the expression of these genes in different cell clusters (`seurat_clusters`). The `AddModuleScore` function is used to calculate a module score for each gene set and add the score results to a Seurat object. The module score is used to assess the activity of a specific gene set in each cell. Data generated by `DotPlot` was extracted and visualized using `ggplot2`, creating a bubble chart to show the expression of different gene sets in cell clusters. Specific cell types (monocytes) were selected from Seurat objects, and the activity of specific metabolic pathways (such as nucleotide metabolism) was assessed. Cells were divided into high-activity and low-activity groups based on module scores, and the `FindMarkers` function was used to find differentially expressed genes between the two groups. The found differentially expressed genes were then filtered using criteria such as expression percentage, p-value, and mean log2-fold change. Finally, differentially expressed genes in monocytes with high nucleotide metabolism were obtained.

[0225] Section S5.6b, Screening for Differential Genes in Protective T Cells (High-Amino Acid T Cells), utilizes several R packages, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree, and clusterProfiler, for data processing, visualization, and analysis. The Seurat package is used first. T cell populations are extracted using `scRNA_T=subset(scRNA, celltype==T-cell)`. The `read.gmt` function reads the `Pathways of nucleic acid metabolism and innate immune sensing.gmt` gene set file. The `DotPlot` function displays the expression of these genes in different cell clusters (`seurat_clusters`). The `AddModuleScore` function calculates a module score for each gene set and adds the score results to a Seurat object. The module score is used to assess the activity of a specific gene set in each cell. Data generated by `DotPlot` was extracted and visualized using `ggplot2`, creating a bubble chart to show the expression of different gene sets in cell clusters. Specific cell types (T cells) were selected from Seurat objects, and the activity of specific metabolic pathways (such as amino acid metabolism) was assessed. Cells were divided into high-activity and low-activity groups based on module scores, and the `FindMarkers` function was used to find differentially expressed genes between the two groups. The found differentially expressed genes were then filtered using criteria such as expression percentage, p-value, and mean log2-fold change. Finally, differentially expressed genes in high-amino acid metabolism T cells were obtained.

[0226] S5.6c, Screening for Differential Genes in Protective B Cells (High-Amino Acid B Cells): This step loaded several R packages, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree, and clusterProfiler, for data processing, visualization, and analysis. The Seurat package was used first. The B cell population was extracted using `scRNA_B=subset(scRNA, celltype==Bcell)`, and the `read.gmt` function was used to read the Amino acid metabolism.gmt gene set file. The `DotPlot` function was used to display the expression of these genes in different cell clusters (`seurat_clusters`). The `AddModuleScore` function was used to calculate a module score for each gene set, and the score results were added to a Seurat object. The module score was used to assess the activity of a specific gene set in each cell. The data generated by `DotPlot` was extracted and visualized using `ggplot2`, creating a bubble plot to show the expression of different gene sets in cell clusters. Specific cell types (B cells) were selected from the Seurat object, and the activity of specific metabolic pathways (amino acid metabolism) was assessed. Cells were divided into high-activity and low-activity groups based on module scores, and the `FindMarkers` function was used to find differentially expressed genes between the two groups. The found differentially expressed genes were screened using criteria such as expression percentage, p-value, and mean log2-fold change. Finally, differentially expressed genes in high-amino acid metabolism B cells were obtained.

[0227] The above describes the detailed implementation steps; the following section presents the relevant test results.

[0228] 1. There is a statistically significant causal relationship between blood cells and plasma metabolites and Alzheimer's disease (AD).

[0229] This application uses Mendelian randomization (MR) knockout sensitivity analysis to assess the impact of specific gene variants on the risk of Alzheimer's disease (AD). The genetic prediction of AD by immune cells was obtained using a forward inductively coupled plasma atomic wave (IVW) method, and the results are as follows: Figure 1 As shown in b and Table 1, the volcano plot results indicate a positive correlation between 22 types of immune cells and the occurrence of Alzheimer's disease (AD). Further classification of these 22 immune cells revealed that the cells most positively correlated with the risk of AD are: myeloid cells, monocytes, and T cells. Figure 1d). In addition, 12 types of immune cells were negatively correlated with the incidence of AD, with T cells and B cells accounting for the largest proportion. Figure 1 e). This indicates a causal relationship or accompanying effect between changes in immune cell mass and the pathogenesis of Alzheimer's disease (AD). Genetic predictions of AD from 1400 plasma metabolites were obtained using a forward inductively coupled plasma microscopy (IVW) method, with results as follows: Figure 1 As shown in c and Table 1, a total of 42 metabolites were found to be associated with AD, including 19 risk-related metabolites and 23 protective factor metabolites. Enrichment analysis of the categorized metabolites revealed the following results for AD risk factor metabolites: Figure 1 f) suggests that these metabolites are mainly enriched in the nucleotide metabolism and innate immune sensing (NMIS) pathway, as well as other pathways and diseases related to nucleotide metabolism; while metabolites of AD protective factors are enriched in the amino acid metabolism pathway. These results indicate that plasma metabolites do indeed have a certain causal relationship or accompanying effect with the pathogenesis of AD.

[0230]

[0231] 2. Processing and Cell Type Analysis of Single-Cell Data from Blood Tissue of AD Patients

[0232] Plasma metabolites reflect the overall metabolic characteristics of the body. Based on this, it can be determined whether immune cells are also related to AD-sensitive metabolite characteristics. Therefore, this application analyzes single-cell transcriptomic data from blood samples of AD patients and healthy controls, focusing on the metabolic state of immune cells under AD pathological conditions. First, quality control was performed on single cells from two different types of tissues. 36,750 cells were retained from the blood single-cell transcriptomic data for analysis (…). Figure 3 a, b). The mRNA / UMI / aggregate / rRNA content of these two types of cells is uniformly distributed and has been normalized to reduce batch effects. Figure 3 b, d).

[0233] Blood single-cell transcriptome clustering analysis identified four cell types: NK cells, T cells, B cells, and monocytes. Figure 3 In AD patients, the proportion of T cells increased by 12.1%, while the proportion of NK cells decreased by 52.5%. Figure 2 a, b). Furthermore, the risk metabolic pathways sensitive to AD are primarily expressed in monocytes (a, b). Figure 2 c), while protective metabolic pathways are mainly expressed in T cells and B cells ( Figure 2 d).

[0234] 3. Analysis of the relationship between metabolic state and monocytes in AD risk

[0235] In this application, cells were divided into a high-metabolic group and a low-metabolic group based on the average expression level of NamIis in the risk metabolic pathway. The differences in gene expression and function between the two groups were explored, and differentially expressed genes in nucleotide metabolism and immune pathways were displayed using volcano plots. Figure 2 e), and GO enrichment analysis was performed on these differentially regulated genes. The results showed that genes enriched in the high-nucleotide-metabolism monocyte group were mainly related to viral genome replication and regulation of innate immune responses; while genes enriched in the high-nucleotide-metabolism monocyte group were mainly involved in biological processes such as regulation of neuronal apoptosis and cytoplasmic translation. Figure 2 f). The homology between monocytes and Micro / Macro cells suggests that these cells play a crucial role in the pathogenesis of AD, which is also consistent with... Figure 1 The presence of monocytes in the middle basal cells is consistent with the fact that AD is a risk factor.

[0236] 4. Analysis of the relationship between AD protective metabolic state and B and T immune cells

[0237] This application's embodiments tested the expression of the AD protective metabolic pathway in human blood cells. It was found that the high amino acid metabolism (Aam) group was mainly concentrated in T cells and B cells. After dividing the cells into high Aam and low Aam groups, the gene expression differences of T cells and B cells in the two groups were displayed using volcano plots. Figure 2 g, i). GO enrichment analysis of T cell Aam metabolism showed that high Aam levels in T cells were related to amino acid metabolism and ribosome synthesis, while low Aam levels in T cells involved regulation of immune cell function. Figure 2 h). Gene ontology analysis of B cell Aam metabolism genes showed that high Aam processes in B cells are related to amino acid metabolism and ribosome synthesis, while low Aam processes in B cells involve immune cell function (h). Figure 2 j). These findings suggest that the immune function of T cells and B cells is antagonized when they are in a protective metabolic state against Alzheimer's disease (AD). Figure 1 The protective effects of T cells and B cells in AD are not truly beneficial because their own immune function is antagonized.

[0238] 5. Screening of key genes in Alzheimer's disease (AD)

[0239] The embodiments of this application are based on Figure 2 The results of e involved the identification of differentially expressed genes in the blood transcriptome. Considering the genetic differences between European and Asian populations, this application included blood transcriptome data from Chinese clinical AD patients and controls to reduce inter-racial variations. By merging the GSE63060 and GSE63061 Chinese self-test datasets, differential analysis was performed on genes related to risk metabolic status and monocytes, identifying a total of 8 differentially expressed genes between AD blood samples and normal blood samples. The same method was used to perform differential analysis on AD-related genes in high-Aam and low-Aam T cells and B cells, identifying 60 differentially expressed genes in ADAam B cells and 840 differentially expressed genes in AD Aam T cells.

[0240] See Figure 4 The optimal differential gene set was selected by analyzing the intersection of the two gene sets. Figure 4 (a) Identification of differentially expressed genes in blood single cells for AD: Differential analysis of blood single cells yielded genes related to metabolic state (high metabolic activity group and low metabolic activity group in blood single cells). Figure 4 (b) Identification of differentially expressed genes in blood transcriptomes for AD: differential expression genes in AD and normal control groups were identified by differential analysis of blood transcriptome data. Figure 4 (c) To ultimately obtain three characteristic gene sets, the following methods are used: Figure 4 (a) and Figure 4 The results in (b) were subjected to intersection analysis, and the optimal differential gene set was finally selected: (Risk-M, high NamIis monocytes), protective T cell group (Protective-T, high AamT cells) and protective B cell group (Protective-B, high AamB cells).

[0241] The apparatus for comprehensively analyzing immune metabolism and AD-related genes provided by the present invention will be described below. The apparatus for comprehensively analyzing immune metabolism and AD-related genes described below can be referred to in correspondence with the method for comprehensively analyzing immune metabolism and AD-related genes described above.

[0242] Figure 5 This is a schematic diagram of the device for comprehensively analyzing immune metabolism and AD-related genes provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it includes: a first acquisition module 51, a second acquisition module 52, an inference module 53, a first analysis module 54, a second analysis module 55, and a filtering module 56, wherein:

[0243] The first acquisition module 51 is used to acquire single-cell sequencing data from AD patients;

[0244] The second acquisition module 52 is used to acquire genome-wide association study (GWAS) data of immune cells and metabolites in AD patients.

[0245] Inference module 53 is used to screen out independent single nucleotide polymorphisms (SNPS) based on GWAS data; and to obtain causal associations of immune cell characteristics and metabolite factor characteristics through Mendelian randomized causal inference.

[0246] The first analysis module 54 is used to classify immune cells with causal relationships and perform enrichment analysis of related metabolites, identify immune cells and metabolites that have an important impact on the occurrence of AD, and then obtain the metabolic pathways that affect AD; among them, the identified immune cells include immune cells that have protective factors and risk factors for AD, and the identified metabolites include metabolites that have protective factors and risk factors for AD.

[0247] The second analysis module 55 is used to analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, obtain gene expression data showing differential expression of metabolic pathways in different cell populations, and perform differential gene analysis to obtain gene analysis results.

[0248] The screening module 56 is used to screen for genes associated with Alzheimer's disease based on the expression differences of different metabolic pathways in different cell populations in the gene analysis results.

[0249] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute a method for comprehensively analyzing genes related to immune metabolism and Alzheimer's disease using multi-omics approaches.

[0250] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0251] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to perform the method provided by the above methods for comprehensive multi-omics analysis of immune metabolism and AD-related genes.

[0252] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the method provided by the above methods for comprehensive multi-omics analysis of immune metabolism and AD-related genes.

[0253] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0254] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0255] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for comprehensively analyzing genes related to immune metabolism and Alzheimer's disease using multi-omics approaches, characterized in that... include: Obtain single-cell sequencing data from AD patients; Obtain genome-wide association study (GWAS) data on immune cells and metabolites in AD patients; Based on GWAS data, independent single nucleotide polymorphisms (SNPS) were screened out; through Mendelian randomized causal inference, the characteristics of immune cells and metabolite factors associated with causality were obtained. Analysis of causally related immune cells and metabolites identifies immune cells and metabolites that significantly influence the development of Alzheimer's disease (AD). Classification of the identified immune cells reveals those with protective and risk factors against AD. Statistical analysis of the identified metabolites identifies those with both protective and risk factors for AD. Enrichment analysis identifies metabolic pathways influencing AD, including both protective and risk pathways. Single-cell sequencing data from AD patients were analyzed to identify cell populations associated with metabolic pathways, and gene expression data showing differential expression of metabolic pathways in different cell populations were obtained; differential gene analysis was performed to obtain gene analysis results. Based on the differential expression of different metabolic pathways in different cell populations in the gene analysis results, genes associated with Alzheimer's disease were screened.

2. The method according to claim 1, characterized in that, The independent single nucleotide polymorphisms (SNPS) were screened based on GWAS data. Through Mendelian randomized causal inference, the characteristics of immune cells and metabolite factors associated with causal relationships were obtained, including: GWAS data were used to screen instrumental variables for AD-related exposure factors to identify candidate single nucleotide polymorphisms (SNPs). Based on candidate SNPs, SNPs related to AD are screened out, and linkage disequilibrium calculation is performed to obtain independent SNPs. Based on independent SNPs, Mendelian randomization method was used to infer causal relationships, and immune cell characteristics and metabolite factor characteristics of causal associations were obtained. Sensitivity analyses were performed on the immune cell characteristics and metabolite factors of causal associations, respectively. The stability and reliability of the analysis results were evaluated using consistency tests, heterogeneity tests, and level pleiotropy tests. Relevant characteristics of stable and reliable causal associations were preserved.

3. The method according to claim 1, characterized in that, The analysis of single-cell sequencing data from AD patients identifies cell populations associated with metabolic pathways and obtains gene expression data showing differences in the expression of metabolic pathways in different cell populations. Differential gene analysis yields gene analysis results, including: Analyze single-cell sequencing data from AD patients to obtain preprocessed data; Dimensionality reduction and cluster analysis were performed on the preprocessed data to obtain cluster analysis results including the main feature vectors, the optimal number of principal components, and the uniform manifold approximation and projection analysis. Based on the cluster analysis results, cell clustering and annotation were performed to identify cell populations that have an important impact on the occurrence of AD; Cell population regrouping and gene expression level analysis: Based on the obtained information on immune cells and metabolites, the cell population was regrouped; the gene expression levels of metabolite pathways in different cell populations were analyzed. T cells, B cells, and monocytes were extracted from the regrouped cell populations; based on the gene expression levels of metabolite pathways, the cells were divided into a high-metabolic immune cell group and a low-metabolic immune cell group. Differential gene analysis yields gene analysis results.

4. The method according to claim 1, characterized in that, Based on the differential expression of different metabolic pathways in different cell populations revealed by gene analysis, genes associated with Alzheimer's disease were screened, including: The differentially expressed genes of high and low metabolomes in various cell types were analyzed using statistical methods; screening criteria were determined to identify genes associated with Alzheimer's disease.

5. The method according to claim 4, characterized in that, The genes selected from those associated with Alzheimer's disease include: Differential genes between monocytes with high nucleotide metabolism and those with high amino acid metabolism; differential genes between protective T cells and those with high amino acid metabolism; and differential genes between protective B cells and those with high amino acid metabolism.

6. A device for comprehensively analyzing genes related to immune metabolism and Alzheimer's disease using multi-omics methods, characterized in that, include: The first acquisition module is used to acquire single-cell sequencing data from AD patients; The second acquisition module is used to acquire genome-wide association study (GWAS) data of immune cells and metabolites in AD patients. The inference module is used to screen out independent single nucleotide polymorphisms (SNPS) based on GWAS data; and to obtain causal associations of immune cell characteristics and metabolite factor characteristics through Mendelian randomized causal inference. The first analysis module is used to analyze immune cells and metabolites with causal relationships, identify immune cells and metabolites that have an important impact on the occurrence of Alzheimer's disease (AD); by classifying the identified immune cells, it obtains immune cells that have protective factors against AD and immune cells that have risk factors against AD; by statistically analyzing the identified metabolites, it obtains metabolites that have risk factors and protective factors against AD; and by enrichment analysis, it obtains metabolic pathways that affect AD; among which, metabolic pathways that affect AD include protective pathways and risk pathways. The second analysis module is used to analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, obtain gene expression data showing differential expression of metabolic pathways in different cell populations, and perform differential gene analysis to obtain gene analysis results. The screening module is used to screen for genes associated with Alzheimer's disease based on the differential expression of different metabolic pathways in different cell populations in the gene analysis results.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for comprehensively analyzing immune metabolism and AD-related genes as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for comprehensively analyzing immune metabolism and AD-related genes as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for comprehensively analyzing immune metabolism and AD-related genes as described in any one of claims 1 to 5.

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