Method for screening transcriptional regulatory element and application thereof

Abstract

The invention discloses a method for screening a transcriptional regulatory element and application thereof, relates to the technical field of genes, and provides a method for predicting a collaborative transcriptional regulatory element based on combination of single-cell multi-omics and experimental exploration. The potential synergistic effect between transcriptional regulation elements, chromatin remodeling complex interaction and other cross-level regulation events can be revealed, and a method is provided for recognition of disease key regulation nodes. On the other hand, the application systematically reveals key transcriptional regulatory elements involved in disease responses based on single cell multi-omics sequencing techniques. Through integration of single cell chromatin accessibility analysis and transcriptome sequencing data, interaction among a core transcription factor, an epigenetic regulation element and a signal transduction pathway for driving disease generation is clarified, and the mononuclear cell specific epigenetic-transcriptional regulation network is successfully constructed.

Description

Technical Field

[0001] The present application relates to the field of biomedicine technology, and in particular to a method for screening transcriptional regulatory elements and its application. Background Art

[0002] Gene expression regulation is a multi-level precision regulatory process, covering multiple key links from transcription initiation and post-transcriptional processing to translation and post-translational modification. This multi-level regulatory network affects the function and state of cells by precisely controlling gene expression. As the core executors of the regulatory network, trans-acting factors represented by transcription factors, chromatin remodeling complexes and RNA polymerases form dynamic regulatory modules through complex synergistic effects. Although studies have revealed the important functions of these regulatory elements, the construction of their regulatory networks and the mode of interaction remain unclear. Currently, there is a lack of methods to systematically predict their synergistic effects. Analyzing the hierarchical structure and action patterns of gene regulatory networks can not only elucidate the potential mechanisms of transcriptional regulation in eukaryotes, but also provide methods for identifying key regulatory nodes in diseases and in-depth analysis of pathological mechanisms, which is of great significance for the screening of therapeutic targets. Summary of the Invention

[0003] The present application provides a method for screening transcriptional regulatory elements and its application, so as to provide a method for screening transcriptional regulatory elements.

[0004] In a first aspect, the present application provides a method for screening transcriptional regulatory elements, comprising the following steps:

[0005] De-noising the scRNA-seq and scATAC-seq data of the samples to be analyzed to obtain pre-processed scRNA-seq and scATAC-seq data;

[0006] Perform dimensionality reduction, clustering, and cell annotation on the pre-processed scRNA-seq and scATAC-seq data to obtain at least one cell type of the sample to be analyzed;

[0007] Perform dimensionality reduction clustering and cell annotation on at least one type of cells in the sample to be analyzed to obtain a cell subpopulation of that type of cells;

[0008] Perform differentially expressed genes and differentially chromatin accessible peak analysis on the cell subpopulations of the sample to be analyzed, and obtain differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed;

[0009] Perform scRNA-seq and scATAC-seq joint analysis on the differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed to determine the differentially expressed genes regulated by chromatin accessibility in the cell subpopulations of the sample to be analyzed;

[0010] Perform enrichment analysis on the differential chromatin accessible peak data of the cell subpopulations of the analyzed sample to identify transcriptional elements involved in the regulation of gene expression related to chromatin accessible peaks;

[0011] The potential regulatory regions and target genes of transcriptional regulatory elements are analyzed, and transcriptional elements with high overlap between regulatory regions and target genes are screened, which are target regulatory elements.

[0012] This application provides a method for predicting synergistic transcriptional regulatory elements based on the combination of single-cell multi-omics and experimental research, which can reveal cross-level regulatory events such as potential synergy between transcriptional regulatory elements and interactions of chromatin remodeling complexes, and provide a method for identifying key regulatory nodes of the disease. On the other hand, the application is based on single-cell multi-omics sequencing technology, which systematically reveals the key transcriptional regulatory elements involved in disease response. By integrating single-cell chromatin accessibility analysis with transcriptome sequencing data, the interactions between the core transcription factors, epigenetic regulatory elements and signal transduction pathways that drive the generation of the disease are clarified, and a monocyte-specific epigenetic-transcriptional regulatory network is successfully constructed.

[0013] In some embodiments, the samples to be analyzed include public sample data. Typically, the samples to be analyzed are large amounts of scRNA-seq and scATAC-seq sequencing data. ScRNA-seq and scATAC-seq sequencing work requires multi-person teamwork, and public sample repositories are often used to obtain publicly available data for analysis.

[0014] In some embodiments, the public sample data includes scRNA-seq and scATAC-seq sequencing data. Among the public sample data, both scRNA-seq and scATAC-seq sequencing data are generally required for the analysis of this application.

[0015] In some embodiments, the cell type comprises at least one of monocytes, T cells, dendritic cells, natural killer cells, and B cells.

[0016] In some embodiments, the monocyte subpopulation includes at least one of CD14+ monocytes and CD16+ monocytes; and / or,

[0017] The T cell subpopulation includes at least one of CD8+ T cells and CD4+ T cells; and / or,

[0018] The subpopulation of dendritic cells includes at least one of plasmacytoid dendritic cells and classical dendritic cells.

[0019] In some embodiments, performing differentially expressed gene and differential chromatin accessible peak analysis on the cell subpopulations of the sample to be analyzed to obtain differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed includes:

[0020] Grouping of cell subpopulations of the sample to be analyzed;

[0021] The differentially expressed genes and differentially chromatin accessible peaks of the cell subpopulations of the sample to be analyzed after grouping are analyzed to obtain the differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed.

[0022] By grouping the cell subpopulations of the sample to be analyzed and obtaining the differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the analysis sample according to the grouping situation, the differences in differentially expressed genes and differentially chromatin accessible peaks between different groups can be more accurately seen, thereby improving the accuracy of the analysis.

[0023] It should be noted that all cell types may be grouped into subpopulations to achieve full data analysis, or some or specific cell types may be selected for grouping based on empirical values ​​to achieve partial data analysis.

[0024] In some embodiments, grouping the cell subpopulations of the sample to be analyzed comprises:

[0025] Group cell subsets of the analyzed sample based on health status.

[0026] Usually, the sample data to be analyzed include two health conditions: diseased and healthy. By grouping the cell subpopulations of the sample to be analyzed according to the health condition, the differences in differentially expressed genes and differentially chromatin accessible peaks between diseased and healthy people can be analyzed.

[0027] In some embodiments, the potential regulatory regions and target genes of the transcriptional regulatory elements are analyzed to screen for transcriptional elements with highly overlapping regulatory regions and target genes, i.e., target regulatory elements:

[0028] The core nucleotide sequence of the target transcriptional regulatory element SMARCC1-binding motif includes SEQ ID NO.5.

[0029] In some embodiments, after analyzing the potential regulatory regions and target genes of the transcriptional regulatory elements and screening the transcriptional elements with highly overlapping regulatory regions and target genes, i.e., the target regulatory elements, the method further comprises:

[0030] Validate the interaction between multiple target transcriptional regulatory elements by co-immunoprecipitation; and / or,

[0031] Chromatin immunoprecipitation-qPCR was used to verify the regulation of target genes by target transcriptional regulatory elements.

[0032] The accuracy of the analysis method of the present application can be verified by verifying the interaction between multiple target transcription regulatory elements through co-immunoprecipitation.

[0033] Verifying the regulation of target transcriptional regulatory elements on target genes by chromatin immunoprecipitation qPCR can verify the accuracy of the analysis method of this application.

[0034] In some embodiments, the verification of the regulation of the target transcriptional regulatory element on the target gene by chromatin immunoprecipitation qPCR includes verifying the regulatory specificity of JUND on the TIMP1 gene promoter region, and the primers used to verify the regulatory specificity of JUND on the TIMP1 gene promoter region include upstream primers and downstream primers, wherein:

[0035] The upstream primer sequence is shown in SEQ ID NO.1;

[0036] The downstream primer sequence is shown in SEQ ID NO.2.

[0037] Secondly, this application provides a method for screening transcriptional regulatory elements for use in identifying disease therapeutic targets. Once the target transcriptional regulatory element is identified as an upstream regulatory element associated with the disease being analyzed, targeting the upstream regulatory element associated with the disease being analyzed becomes a target for treating the disease, thereby improving drug development progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 This is a flow chart of a method for screening transcriptional regulatory elements according to one embodiment of the present application.

[0040] Figure 2 This is a flow chart of a method for screening transcriptional regulatory elements according to one embodiment of the present application.

[0041] Figure 3 This is a flow chart of a method for screening transcriptional regulatory elements according to one embodiment of the present application.

[0042] Figure 4 This is a flow chart of a method for screening transcriptional regulatory elements according to one embodiment of the present application.

[0043] Figure 5This is a dimensionality reduction clustering diagram of peripheral blood mononuclear cell scRNA-seq data in the examples of this application.

[0044] Figure 6 This is a dimensionality reduction clustering diagram of the peripheral blood mononuclear cell scATAC-seq data in the examples of this application.

[0045] Figure 7 This is a dimensionality reduction clustering diagram of monocyte scATAC-seq data in the examples of this application.

[0046] Figure 8 This is a dimensionality reduction clustering diagram of monocyte scRNA-seq data in the examples of this application.

[0047] Figure 9 This is a volcano plot of differentially accessible peaks of monocytes from COVID-19 patients and healthy volunteers in the examples of this application.

[0048] Figure 10 This is a volcano plot of differentially expressed genes in monocytes of COVID-19 patients and healthy volunteers in the examples of this application.

[0049] Figure 11 This is a correlation diagram between genes and chromatin accessible peaks in the examples of this application.

[0050] Figure 12 This is a motif enrichment map of highly accessible regions of monocytes from COVID-19 patients in the examples of this application.

[0051] Figure 13 This is a diagram showing the activities of SMARCC1 and JUND in monocyte subsets in the examples of this application.

[0052] Figure 14 This is a nucleotide sequence diagram of the SMARCC1 and JUND binding motif in the examples of this application.

[0053] Figure 15 It is the intersection of the potential regulatory regions of SMARCC1 and JUND in the examples of this application.

[0054] Figure 16 This is a bar chart showing the functional enrichment analysis of target genes in the potential regulatory regions of SMARCC1 and JUND in the examples of this application.

[0055] Figure 17 These are the potential binding sites of SMARCC1 and JUND in the genomic region of the proinflammatory cytokine TIMP1 in the examples of this application.

[0056] Figure 18 This is a Co-IP diagram of SMARCC1 and JUND in the examples of this application.

[0057] Figure 19 This is a ChIP-qPCR diagram of JUND regulating TIMP1 in the examples of this application. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of this application without making any creative efforts shall fall within the scope of protection of this application.

[0059] Gene expression regulation is a multi-level precision regulatory process, covering multiple key links from transcription initiation and post-transcriptional processing to translation and post-translational modification. This multi-level regulatory network affects the function and state of cells by precisely controlling gene expression. As the core executors of the regulatory network, trans-acting factors represented by transcription factors, chromatin remodeling complexes and RNA polymerases form dynamic regulatory modules through complex synergistic effects. Although studies have revealed the important functions of these regulatory elements, the construction of their regulatory networks and the mode of interaction remain unclear. Currently, there is a lack of methods to systematically predict their synergistic effects. Analyzing the hierarchical structure and action patterns of gene regulatory networks can not only elucidate the potential mechanisms of transcriptional regulation in eukaryotes, but also provide methods for identifying key regulatory nodes in diseases and in-depth analysis of pathological mechanisms, which is of great significance for the screening of therapeutic targets.

[0060] Breakthrough developments in single-cell multi-omics technologies have provided unprecedented resolution for analyzing gene expression regulation. SCENIC et al. have constructed inference models of transcriptional regulation based on single-cell scRNA-seq co-expression networks, enabling systematic prediction of core transcription factors driving gene expression and their targets in cell subpopulations. Li Hao et al. have developed a method and device for predicting single-cell transcription factor regulatory networks using scATAC-seq data, addressing the problem of predicting transcription factor regulatory networks at the single-cell level. Furthermore, Zhang Yongqing et al. have developed a method for predicting single-cell transcription factors based on deep learning and attention mechanisms. While existing research has explored single-omics data or specific methods, effectively integrating multiple omics data to comprehensively capture the spatiotemporal characteristics of transcription factor regulation remains a major technical challenge. By integrating single-cell transcriptomics (scRNA-seq), single-cell chromatin accessibility (scATAC-seq), and epigenetic modification data, we can better capture the spatiotemporal coupling between transcription factor binding activity, chromatin remodeling status, and target gene expression at the individual cell level, revealing the specific combination of regulatory elements in the context of cellular heterogeneity. However, such methods are currently scarce, and there are also relatively few methods for systematically analyzing cross-level regulatory events such as transcription factor synergy and chromatin remodeling complex interactions.

[0061] Cells precisely regulate chromatin topology through ATP-driven chromatin remodeling complexes to overcome the physical limitations of higher-order three-dimensional structures on the access of genetic information. Four distinct families of chromatin remodeling complexes have been identified in humans: the SWI / SNF, ISWI, CHD, and INO80 families. The SWI / SNF subfamily (pBAF, cBAF, and ncBAF) is composed of shared subunits such as SMARCC1, SMARCA2 / 4, and SMARCD1, as well as subfamily-specific subunits such as BRD9, BRD7, and ARID1A / B. SMARCC1 is the core subunit of the SWI / SNF complex and contains three structural domains: the SWIRM, SANT, and CC domains. The CC domain is a chromatin-targeting region that binds DNA. The SWI / SNF complex dynamically regulates gene transcription through nucleosome remodeling (sliding / ejection / histone variant exchange). The SWI / SNF complex not only collaborates with transcription factors to achieve specific regulation of target genes (e.g., activation of inflammation-related genes) but also forms an epigenetic cooperative network through histone modifications. Targeting the dynamic assembly or functional modules of the SWI / SNF complex may become a new therapeutic strategy for regulating pathological inflammation.

[0062] Immune dysregulation triggered by SARS-CoV-2 infection has become a key feature of the COVID-19 pathological process. Studies have shown that SARS-CoV-2 can induce an abnormal cytokine storm, characterized by excessive release of proinflammatory cytokines such as IL-6, TNF-α, and CCL2. This uncontrolled inflammatory response can lead to lung tissue damage and systemic multi-organ dysfunction and failure. Notably, this persistent disruption of immune homeostasis may underlie the long-term sequelae of COVID-19 pneumonia (Long COVID) through chronic activation of immune response mechanisms. Immune cells such as alveolar macrophages and monocytes produce and release inflammatory cytokines such as IL-1β, IL-6, and TNF-α, actively participating in the inflammatory response and promoting the development and progression of inflammatory responses in COVID-19 patients. Currently, there are still gaps in our understanding of the key regulatory nodes in the overactivation of immune cells such as monocytes. In particular, the interaction network between the core transcription factors, epigenetic regulatory elements, and signaling pathways that drive the production of inflammatory genes has not been fully elucidated. In-depth analysis of the transcriptional regulatory elements involved in the production of inflammatory genes in monocytes during SARS-CoV-2 infection is expected to provide new targets for the prevention and treatment of inflammatory responses.

[0063] In view of this, the present application provides a method for screening transcriptional regulatory elements and its application, so as to provide a method for screening transcriptional regulatory elements.

[0064] First, as Figure 1 As shown, the present application provides a method for screening transcriptional regulatory elements, comprising the following steps:

[0065] S100, denoising the scRNA-seq and scATAC-seq data of the sample to be analyzed to obtain pre-processed scRNA-seq and scATAC-seq data;

[0066] S200, performing dimensionality reduction clustering and cell annotation on the pre-processed scRNA-seq and scATAC-seq data to obtain at least one cell type of the sample to be analyzed;

[0067] S300, performing dimensionality reduction clustering and cell annotation on at least one type of cells in the sample to be analyzed to obtain a cell subpopulation of the cell type;

[0068] S400, performing differentially expressed gene and differential chromatin accessible peak analysis on the cell subpopulations of the sample to be analyzed, to obtain differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed;

[0069] S500, performing scRNA-seq and scATAC-seq joint analysis on the differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed, to determine the differentially expressed genes regulated by chromatin accessibility in the cell subpopulations of the sample to be analyzed;

[0070] S600: Perform enrichment analysis on the differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed to identify transcriptional elements involved in the regulation of gene expression related to chromatin accessible peaks;

[0071] S700. Analyze the potential regulatory regions and target genes of the transcriptional regulatory elements, and screen the transcriptional elements whose regulatory regions and target genes highly overlap, which are the target regulatory elements.

[0072] This application provides a method for predicting synergistic transcriptional regulatory elements based on the combination of single-cell multi-omics and experimental research, which can reveal cross-level regulatory events such as potential synergy between transcriptional regulatory elements and interactions of chromatin remodeling complexes, and provide a method for identifying key regulatory nodes of the disease. On the other hand, the application is based on single-cell multi-omics sequencing technology, which systematically reveals the key transcriptional regulatory elements involved in disease response. By integrating single-cell chromatin accessibility analysis with transcriptome sequencing data, the interactions between the core transcription factors, epigenetic regulatory elements and signal transduction pathways that drive the generation of the disease are clarified, and a monocyte-specific epigenetic-transcriptional regulatory network is successfully constructed.

[0073] In conjunction with the first aspect, in some embodiments provided herein, the samples to be analyzed include public sample data. Typically, the samples to be analyzed are large amounts of scRNA-seq and scATAC-seq sequencing data. ScRNA-seq and scATAC-seq sequencing work requires multi-person teamwork, and public sample repositories are typically used to obtain publicly available data for analysis.

[0074] In conjunction with the first aspect, in some embodiments provided herein, the public sample data includes scRNA-seq and scATAC-seq sequencing data. Among public sample data, scRNA-seq and scATAC-seq sequencing data are generally required for the analysis of this application.

[0075] In combination with the first aspect, in some embodiments provided in the present application, the cell type includes at least one of monocytes, T cells, dendritic cells, natural killer cells and B cells.

[0076] In combination with the first aspect, in some embodiments provided herein, the subpopulation of monocytes includes at least one of CD14+ monocytes and CD16+ monocytes.

[0077] In combination with the first aspect, in some embodiments provided herein, the subpopulation of T cells includes at least one of CD8+ T cells and CD4+ T cells.

[0078] In combination with the first aspect, in some embodiments provided in the present application, the subpopulation of dendritic cells includes at least one of plasmacytoid dendritic cells and classical dendritic cells.

[0079] Combined with the first aspect, such as Figure 2 As shown, in some embodiments provided in the present application, the differentially expressed genes and differentially chromatin accessible peaks analysis is performed on the cell subpopulations of the sample to be analyzed, and the differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed are obtained, including:

[0080] S401, grouping cell subpopulations of the sample to be analyzed;

[0081] S402 , performing differentially expressed genes and differentially chromatin accessible peak analysis on the grouped cell subpopulations of the sample to be analyzed to obtain differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed.

[0082] By grouping the cell subpopulations of the sample to be analyzed and obtaining the differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the analysis sample according to the grouping situation, the differences in differentially expressed genes and differentially chromatin accessible peaks between different groups can be more accurately seen, thereby improving the accuracy of the analysis.

[0083] It should be noted that all cell types may be grouped into subpopulations to achieve full data analysis, or some or specific cell types may be selected for grouping based on empirical values ​​to achieve partial data analysis.

[0084] Usually, the sample data to be analyzed include two health conditions: diseased and healthy. By grouping the cell subpopulations of the sample to be analyzed according to the health condition, the differences in differentially expressed genes and differentially chromatin accessible peaks between diseased and healthy people can be analyzed.

[0085] In combination with the first aspect, in some embodiments provided in the present application, the potential regulatory regions and target genes of the transcriptional regulatory elements are analyzed to screen transcriptional elements with highly overlapping regulatory regions and target genes, that is, in the target regulatory elements: the core nucleotide sequence of the motif to which the target transcriptional regulatory element SMARCC1 binds includes SEQ ID NO.5, and the base composition of the nucleotide sequence of SEQID NO.5 is specifically TGACTCA.

[0086] Combined with the first aspect, such as Figure 3As shown, in some embodiments provided in the present application, the analysis of potential regulatory regions and target genes of transcriptional regulatory elements, screening of transcriptional elements with highly overlapping regulatory regions and target genes, i.e., target regulatory elements, further includes:

[0087] S800, verify the interaction between multiple target transcriptional regulatory elements by co-immunoprecipitation.

[0088] The accuracy of the analysis method of the present application can be verified by verifying the interaction between multiple target transcription regulatory elements through co-immunoprecipitation.

[0089] Combined with the first aspect, such as Figure 4 As shown, in some embodiments provided in the present application, the analysis of potential regulatory regions and target genes of transcriptional regulatory elements, screening of transcriptional elements with highly overlapping regulatory regions and target genes, i.e., target regulatory elements, further includes:

[0090] S900, verify the regulation of target genes by target transcriptional regulatory elements through chromatin immunoprecipitation qPCR method.

[0091] Verifying the regulation of target transcriptional regulatory elements on target genes by chromatin immunoprecipitation qPCR can verify the accuracy of the analysis method of this application.

[0092] In conjunction with the first aspect, in some embodiments provided herein, the verification of the regulation of the target transcriptional regulatory element on the target gene by chromatin immunoprecipitation qPCR includes verifying the regulatory specificity of JUND on the TIMP1 gene promoter region, and the primers used to verify the regulatory specificity of JUND on the TIMP1 gene promoter region include upstream primers and downstream primers, wherein:

[0093] The upstream primer sequence is shown in SEQ ID NO.1, and the base composition of the nucleotide sequence of SEQ ID NO.1 is specifically CAGGGAGAGGGAGGAGGAGG;

[0094] The downstream primer sequence is shown in SEQ ID NO.2, and the base composition of the nucleotide sequence of SEQ ID NO.2 is specifically CGGAAACCACAGGCCTCC.

[0095] In the second aspect, the present application provides an application of a method for screening transcriptional regulatory elements in determining disease treatment targets. When the target transcriptional regulatory element is identified as an upstream regulatory element associated with the disease to be analyzed, acting on the upstream regulatory element associated with the disease to be analyzed becomes a target for treating the disease, thereby improving drug research and development progress. The diseases to be analyzed include, but are not limited to, infectious diseases (such as COVID-19, sepsis) and inflammatory diseases.

[0096] The technical solution provided by this application is described in detail below with reference to the examples. Taking the peripheral blood mononuclear cell data of healthy and sick people with COVID-19 as an example, the data source is the announced datasets GSE206283 and GSE206455.

[0097] Example 1

[0098] scRNA-seq and scATAC-seq identify cell types in peripheral blood mononuclear cells

[0099] (1) Cell quality control of scRNA-seq data

[0100] scRNA-seq data were processed using the Seurat software package. scRNA-seq data were integrated using an anchor integration approach. To remove low-quality cells, cells meeting the criteria of mitochondrial expression less than 10%, gene expression counts between 200 and 4,000, and UMI counts less than 30,000 were used to screen for qualified cells. In addition, genes with expression in fewer than three cells were also excluded.

[0101] (2) Dimensionality reduction and cell annotation of scRNA-seq data

[0102] The scRNA-seq data were normalized and scaled using the Seurat function “Normalization”. Principal component analysis was performed based on the top 2000 most variable features determined by the vst method. Appropriate principal component analysis dimensions were selected, and the “FindNeighbors” and “FindClusters” functions were used to identify cell clusters. Uniform manifold approximation and projection were used to visualize cell subpopulations. A dual strategy was used to annotate the cell subpopulations of scRNA-seq. First, the SingleR software package was used to automatically annotate the cell subpopulations, and then the specific types of cell subpopulations were further determined based on the highly expressed genes in each cell subpopulation. The cell annotation results are shown in Figure 5 .

[0103] (3) Cell quality control of scATAC-seq data

[0104] scATAC-seq data were processed using the ArchR package. To remove low-quality cells and doublets, fragment files from the scATAC-seq data were first imported into R Studio. Cells were then filtered using the criteria of >1000 unique fragments per cell and >4 transcription start site enrichment. After filtering, Arrow files were created and merged to form an ArchRProject for downstream analysis.

[0105] (4) Dimensionality reduction of scATAC-seq data cells

[0106] The "addGroupCoverages," "addReproduciblePeakSet," and "addPeakMatrix" functions were used to create a peak matrix and add it to the ArchRProject project. The peak matrix and gene expression matrix were extracted for subsequent analysis. A Seurat object was created from the peak matrix. Dimensionality reduction and clustering were performed using scopen. Harmony was used to correct for batch effects between samples. After removing batch effects, cell clusters were identified using the "FindNeighbors" and "FindClusters" functions. The cell clusters, uniform manifold approximation, and projection results were then added to the ArchRProject project.

[0107] (5) Cell annotation and cell subpopulation extraction of scATAC-seq data

[0108] Use the "getMarkerFeatures" function to obtain the specific genes expressed by each cell cluster. Then, the cell type is annotated based on the specific genes expressed by each cell cluster. For example, the cell cluster with high expression of MS4A1, CD79A and CD79B is annotated as B cells, and the cell cluster with high expression of FCGR3A, LYZ, CD14 and MS4A7 is annotated as monocytes. The cell annotation results are shown in Figure 6 .

[0109] Example 2

[0110] Combined analysis of differentially chromatin accessible peaks and differentially expressed genes to identify genes regulated by chromatin accessibility (using monocytes from COVID-19 patients as an example)

[0111] (1) Dimensionality reduction, clustering, and annotation of monocyte subsets using scATAC-seq and scRNA-seq

[0112] For scATAC-seq data, the "subset" function was used to extract monocytes from the ArchRProject project and perform dimensionality reduction clustering. First, the "addIterativeLSI" function was used to reduce the dimensionality based on the first 25,000 variable features and the first 30 dimensions. Subsequently, the cells were clustered at an appropriate resolution (such as 0.8) and the cells were visualized in low dimensions using uniform manifold approximation and projection. The results are shown in Figure 7 .

[0113] For scRNA-seq data, the "subset" function was also used to extract monocyte-related information from the Seurat object and re-cluster it for dimensionality reduction. First, the data was re-standardized and rescaled. Then, principal component analysis was performed on the 2000 most variable genes identified by the vst method. The appropriate principal component analysis dimensions were selected, and the "FindNeighbors" and "FindClusters" functions were used to identify cell clusters. Uniform manifold approximation and projection were used to visualize cell subpopulations. The results are shown in Figure 8 .

[0114] (2) scATAC-seq differential chromatin accessible peak analysis

[0115] Add group labels to the ArchRProject project to divide the data into two groups, COVID-19 and Healthy, for differential chromatin accessible peak analysis. To achieve chromatin accessibility analysis between different groups, use "addGroupCoverages" to call reproducible peaks according to the group, and use the "addReproduciblePeakSet" function of the Macs2 algorithm to create a reproducible peak matrix by group. After the peak matrix is ​​created, the Wilcoxon rank sum test is used as the test method, and the "getMarkerFeatures" function is used to filter differentially accessible peaks that meet the conditions (such as FDR < 0.1 and |Log2FC|> 0.5). Among them, inflammatory cytokines such as TIMP1, IL21R, and CCRL2 are expressed at increased levels in monocytes of COVID-19 patients. The results are shown in Figure 9 .

[0116] (3) scRNA-seq differentially expressed gene analysis

[0117] Add grouping labels to the monocyte seruat object to divide the data into two groups, COVID-19 and Healthy, for differentially expressed gene analysis. Use the grouping results as cell labels and use the "FindAllMarkers" function to obtain differentially expressed genes between the COVID-19 group and the Healthy group. Figure 10 , among which inflammatory cytokines such as TIMP1, IL15, IL21R, CCRL2, and IFI30 were expressed at increased levels in monocytes of COVID-19 patients.

[0118] (4) Joint analysis of scATAC and scRNA-seq data

[0119] To perform joint analysis of scATAC-seq and scRNA-seq data, the cell-independent gene score matrix (from scATAC-seq data) and the gene expression matrix (from scRNA-seq data) were aligned using the "FindTransferAnchor" function (Seurat function) and the "addGeneIntegrationMatrix" function (ArchR function). All monocytes from scATAC-seq were integrated with the monocytes from scRNA-seq. Gene integration scores were then assigned to each cell to create a gene integration matrix.

[0120] (5) Peak co-accessibility analysis and determination of peak-to-gene linkage

[0121] The “addCoAccessibility” function was used to calculate the co-accessibility correlation between peaks in scATAC-seq monocyte data. In addition, the “addPeak2GeneLinks” function was used to calculate the correlation between peak accessibility and gene expression in monocytes. Peak-to-gene links with CoAccessibility > 0.2 and P < 0.05 were considered to have potential regulatory links between peaks and genes. Figure 11 .

[0122] Example 3

[0123] Motif enrichment analysis to screen transcriptional elements that collaboratively participate in gene expression regulation

[0124] (1) Motif enrichment analysis of highly accessible chromatin regions

[0125] To analyze the transcriptional regulatory elements potentially involved in gene expression in monocytes of COVID-19 patients. After completing the identification of chromatin differential accessibility peaks, we first systematically annotated the transcription factor binding motifs in the highly open chromatin regions of monocytes of COVID-19 patients based on the "CIS-BP" regulatory element database using the "addmotifAnnotations" function of the ArchR software package, and generated motif-Matches-In-Peaks objects. Subsequently, the "peakAnnoEnrichment" function was used to quantitatively analyze the significantly enriched regulatory motifs in the differentially open regions, and the "addDeviationsMatrix" function of the HOMER software was used to calculate the deviation of all motifs in each cell. At the same time, the ChromVAR algorithm was used to calculate the motif accessibility deviation value in each sample and measure the transcription element activity. The enrichment pattern of key transcription factors was presented using the ggplot2 visualization tool. The results are shown in Figure 12 and Figure 13Motif enrichment analysis showed that motifs binding to transcriptional regulatory elements such as SMARCC1 and JUND were significantly enriched in highly accessible chromatin regions of monocytes from COVID-19 patients. In addition, the activity of SMARCC1 and JUND in monocyte clusters showed significant consistency, especially in C4 and C7 clusters, where their activity was significantly upregulated.

[0126] (2) Analysis of transcriptional element target regions

[0127] To visualize the specific nucleotide sequences of the binding motifs of regulatory elements such as SMARCC1 and JUND, the PPM matrix information corresponding to regulatory elements such as SMARCC1 and JUND was extracted from the peakAnnotation object in the ArchRProject project and converted into a PWM matrix. The seqLogo package was used for visualization to screen transcriptional regulatory elements with highly consistent motif nucleotide sequences. The results are shown in Figure 14 The nucleotide sequence logo of the motif shows that the core nucleotide sequence of the motif to which JUND and SMARCC1 bind is "TGACTCA" (SEQ ID NO. 5).

[0128] To obtain regions potentially regulated by regulatory elements such as JUND and SMARCC1, we extracted chromatin regions containing binding motifs for transcriptional regulatory elements such as SMARCC1 and JUND from the generated motif-matches-in-peaks objects, and obtained the intersection of these regions to screen for transcriptional regulatory elements with highly overlapping regulatory regions. Figure 15 At the same time, the target genes in the potential regulatory regions of transcriptional regulatory elements were analyzed, and the functions of the target genes were analyzed by GO and KEGG enrichment analysis. Figure 16 The results showed that the regions potentially regulated by SMARCC1 and JUND had a 68.1% overlap, and the targeted genes of the two were highly correlated with the production of cytokines and cytokines. In addition, SMARCC1 and JUND had binding sites in the promoter regions of proinflammatory cytokines such as TIMP1, IL21R, and CCRL2. Figure 17 These results suggest that JUND and SMARCC1 may synergistically participate in the expression of inflammatory genes such as TIMP1, IL21R, and CCRL2.

[0129] Example 4

[0130] Co-IP and ChIP-qPCR verification of the interaction between transcription elements and the regulation of transcription elements on potential target genes

[0131] Co-IP validates interactions between transcriptional elements

[0132] (1) Take 1×10 7 THP-1 cells were plated in pre-chilled IP lysis buffer containing 1× protease inhibitor PMSF. After cell lysis, the cell suspension was collected in a 1.5 mL centrifuge tube and centrifuged at 12,000–16,000 × g in a 4°C ultracentrifuge for 15 minutes. The supernatant was collected. Protein concentration was determined using the BCA assay.

[0133] (2) Take 100 μL of the extracted protein solution (supernatant) as the forward and reverse input controls (50 μL each). Add 3-5 μL of SMARCC1 and JUND antibodies to the remaining protein solution, respectively, and incubate at 4°C overnight. After overnight antibody incubation, add 0.25 mg of Protein A / G Magnetic Beads (the beads have been washed and resuspended in lysis buffer) and incubate on a shaker at room temperature for 60 min.

[0134] (3) Place the centrifuge tube in a magnetic rack, allow the magnetic beads to gather on one side of the centrifuge tube, and aspirate the supernatant. Add lysis buffer to the centrifuge tube, vortex to mix, collect the magnetic beads using a magnetic rack, discard the supernatant, and repeat the wash 2 to 3 times. To perform protein denaturation and elution, add 100 μL of loading buffer (1×) to the centrifuge tubes containing the input and sample respectively. Then place the sample in a 100°C metal bath and heat for 10 minutes. The magnetic beads are separated by the magnetic rack. Finally, aspirate the supernatant containing the target protein and store it in a -20°C refrigerator for use.

[0135] (4) Preparation of SDS-PAGE protein gel: First, check the sealing of the gel plate. Add distilled water to the assembled gel plate to ensure that the gel plate does not leak. Then pour out the water and dry the gel plate. Take 2 mL of sterile water and place it in a 15 mL centrifuge tube. Add 1.25 mL of 1.5 M Tris-HCl (pH 8.8), 100 μL of 10% ammonium persulfate solution, 100 μL of 10% SDS, and 1.65 mL of 30% acrylamide. Then add 5 μL of TEMED solution in a fume hood, mix well, and gently and evenly add it to the electrophoresis tank to avoid bubbles. Prepare a 10% separation gel. After adding 4 mL of separation gel to each gel plate, add a water seal layer and let it stand at room temperature for 20 to 30 minutes. After the separation gel solidifies, discard the water. Place 1.1 mL of pure water in a 15 mL centrifuge tube, and add 250 μL of 1 M Tris-HCl solution (pH 6.8), 330 μL of 30% acrylamide solution, 20 μL of 10% SDS solution, and 20 μL of 10% APS solution. In a fume hood, add 7 μL of TEMED solution to the centrifuge tube. Finally, add about 1.5 mL of concentrated gel to the gel plate and insert a tooth comb into the concentrated gel. Let it stand at room temperature again for 20 to 30 minutes. After the gel solidifies, pull out the tooth comb vertically for use.

[0136] (5) Assemble the prepared gel plate and electrophoresis tank and pour in 1× electrophoresis buffer. Slowly add approximately 3-5 μL of the prepared protein sample to the corresponding wells in sequence. Add 2-3 μL of reference protein marker to the wells on both sides of the lane to facilitate the subsequent determination of the target protein size. Load 1× loading buffer into the blank well. The electrophoresis program is divided into two stages: the first stage is a constant voltage of 80V for 30 minutes; the second stage is a constant voltage of 120V for 60 minutes.

[0137] (6) Cut the PVDF membrane of appropriate size (0.22 μm or 0.45 μm) according to the needs and activate it by soaking in methanol. After the electrophoresis is completed, remove the separation gel and trim off the excess part. Then assemble and place it into the transfer system in the following order: negative electrode (black splint) - sponge - filter paper (three layers) - separation gel - PVDF membrane - filter paper (three layers) - sponge - positive electrode (transparent splint). Replenish the pre-cooled transfer solution and transfer the membrane in an ice bath according to the program of constant current 200 mA for 60 minutes (the duration needs to be adjusted according to the size of the target protein).

[0138] (7) Blocking and antibody incubation: Place the transferred PVDF membrane in a box containing 5% skim milk and soak it. Place the box on a shaker and block it at room temperature for 1 hour. Dilute the SMARCC1 and JUND antibodies in proportion according to the reagent manufacturer's instructions in advance. After the membrane is blocked and washed, cut the bands as needed according to the position of the protein band size (provided by the protein marker) and the molecular size of the target protein. After cutting, place the bands in the antibody incubation box, add the corresponding antibody diluent, and incubate at 4°C overnight. After incubation for 16 hours, recover the antibody diluent in the antibody incubation box, and add 1×PBST to wash the membrane three times, each washing for 5 minutes. Dilute the secondary antibody with the antibody diluent according to the reagent manufacturer's instructions, then place the bands washed in the previous step again in the antibody incubation box, add the corresponding secondary antibody diluent, and incubate on a shaker at room temperature for 60 to 90 minutes. After incubation, collect the secondary antibody diluent and wash the membrane again with 1×PBST. The washing time and number are as above. Finally, 1× PBST was added to soak the strips.

[0139] (8) Prepare ECL solution according to the reagent manufacturer's instructions and use it immediately. Soak the strips in ECL solution for 1 to 2 minutes before placing them in the chemiluminescence imaging system for imaging. Set different exposure times to take pictures and export the corresponding picture results. Co-IP results show that there is an interaction between SMARCC1 and JUND, see Figure 18 .

[0140] ChIP-qPCR transcriptional elements regulate potential target genes

[0141] (1) Take a bottle of THP-1 cells in good growth condition, transfer the cell suspension into a 1.5 mL centrifuge tube, centrifuge at 500 × g for 5 min, discard the culture medium, add 1 mL of 1% formaldehyde solution, and let it stand at room temperature for 10 min to allow the cell DNA and protein to crosslink.

[0142] (2) Aspirate the solution and add 100 μL of 10× glycine solution to the centrifuge. Mix by briefly rotating the tube and incubate at room temperature for 5 min.

[0143] (3) Collect the cells by centrifugation at 1200 × g for 5 min at 4°C and wash twice with 1 mL of pre-chilled 1× PBS buffer. Collect the cells by centrifugation at 1200 × g for 5 min at 4°C and remove the supernatant.

[0144] (4) Resuspend the cells in 1 mL of pre-cooled 1× Cell Swelling Buffer and incubate on ice for 10 minutes. Mix by inversion every 3 minutes. Centrifuge at 5000×g for 5 minutes at 4°C to precipitate the nuclei. Remove the solution and resuspend the precipitate in 1 mL of ChIP ultrasonic buffer and incubate on ice for 10 minutes. Use a 3 mm diameter micro-probe of a Xinzhi probe ultrasonic instrument with a power of 150W. Ultrasonicate for 5 seconds, let it stand for 5 seconds, and ultrasonicate for about 20 minutes. Centrifuge at 12000×g for 10 minutes at 4°C to remove nuclear fragments and other impurities in the sample. Transfer the supernatant as the cross-linked chromatin sample and measure the sample concentration.

[0145] (5) Add an equal amount of cross-linked chromatin sample to each sample tube and fill it up to a total volume of 500 μL with 1× ultrasonic buffer. Pipette 25 μL of sample from each sample tube into a new centrifuge tube as a 5% sample input control. No immunoprecipitation reaction is performed and the samples are temporarily stored at -20°C.

[0146] (6) Add the corresponding antibody (2-5 μg) to the target protein sample tube and add normal isotype IgG to the negative reference tube. Incubate overnight at 4°C. Add protein A / G magnetic beads, reseal the sample tube, and incubate at 4°C for 2 h. Place the centrifuge tube on a magnetic separation rack to allow the protein A / G magnetic beads to adhere to the tube wall. Allow the solution to clear for 1-2 min, then aspirate the supernatant.

[0147] (7) Add 1 mL of low-salt rinse buffer and incubate at 4°C for 5 min. Place the sample back on the magnetic separation rack. After the solution has clarified for 2 min at room temperature, carefully aspirate the solution and add 1 mL of high-salt rinse buffer to wash again. Add 1 mL of TE buffer, vortex, and place the sample back on the magnetic separation rack. After the solution has clarified, aspirate the supernatant and repeat the TE buffer wash.

[0148] (8) Add 125 μL of ChIP elution buffer to the 5% input tube, add 150 μL of ChIP elution buffer to each sample tube, incubate in a 65°C metal bath for 30 min, and gently shake with a vortex mixer every 5 min to elute the chromatin from the antibody-protein A / G magnetic beads. Adsorb the protein A / G magnetic beads using a magnetic separation rack. Wait for the solution to clarify, carefully transfer the supernatant and mark it. Add 6 μL of 5 M NaCl solution and 2 μL of RNase A solution to all tubes, incubate at 37°C for 30 min, then add 2 μL of proteinase K solution and incubate at 65°C for 2 h. After returning to room temperature, proceed to the next step.

[0149] (9) The resulting solution was added vertically to the adsorption column (the adsorption column was placed in a collection tube). After standing for 2 minutes, the column was centrifuged at 12,000 × g for 30-60 seconds. The waste liquid in the tube was discarded and the liquid along the wall was aspirated. The adsorption column was placed back in the collection tube, 600 μL of anhydrous ethanol rinse solution was added, the waste liquid was discarded by centrifugation, and the adsorption column was placed in the collection tube for washing twice. After the adsorption column was dried, it was placed in a clean centrifuge tube. 30-60 μL of elution buffer was added dropwise to the middle of the adsorption membrane. After standing for 2 minutes, the column was centrifuged at 12,000 × g for 2 minutes to obtain the DNA solution.

[0150] (10) Use an ultra-micro spectrophotometer to detect the concentration and purity of the DNA solution

[0151] (11) Find the promoter sequence of the target gene from the NCBI database. It is generally believed that the 2kb region upstream of the gene is the promoter region of the gene. Download the 2kb promoter sequence upstream of the gene. Find the binding site of the target transcription factor from the JASPAR database. The reaction prototype is human. Input the 2kb promoter sequence of the target gene to obtain the predicted result of the binding of the transcription factor to the target gene. According to the predicted binding site, use the Primer3plus online tool to design the primers for the corresponding fragments of each site. The designed product length is about 300bp. Try to make each site in the middle position of the corresponding product. The ChIP primers P1 (upstream primer sequence is shown in SEQ ID NO.1, downstream primer is shown in SEQ ID NO.2) and P2 (upstream primer sequence is shown in SEQ ID NO.3, downstream primer is shown in SEQ ID NO.4) of the target gene TIMP1 designed in the present invention are

[0152] SEQ ID NO.1: CAGGGAGAGGGAGGAGG;

[0153] SEQ ID NO.2: CGGAAACCACAGGCCTCC;

[0154] SEQ ID NO.3: GGCACACAGTAGATGCACAATA;

[0155] SEQ ID NO.4: CCAGTCCCCTAAAGTCTCCAG;

[0156] (12) Add samples including the corresponding antibody sample group, 0.5% Input group, IgG sample group, and blank control group without DNA template to PCR tubes and mark them. Add 2 μL of the corresponding DNA sample as a template to each tube. The solution system in each tube is 20 μL (10 μL 2×PCR Mix + 2 μL primers + 1 μL DNA + 5 μL ddH2O). Set up the program according to the standard qPCR two-step program (including melting curve reaction time). After the program is completed, calculate the ChIP enrichment efficiency based on the experimental results and use the following formula to calculate it yourself:

[0157] Percentage of Input=0.5%×2 (C[T]Input Sample-C[T]IP Sample)

[0158] Where C[T]Input Sample——the copy number of the target gene in the input sample group;

[0159] Where C[T]IP Sample corresponds to the copy number of the target gene in the antibody sample group.

[0160] See the results Figure 19 , ChIP-qPCR showed that the transcription factor JUND could regulate the expression of the pro-inflammatory cytokine TIMP1.

[0161] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or equipment. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0162] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0163] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0164] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0165] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0166] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for screening transcriptional regulatory elements, characterized in that: The following steps are involved: De-noising the scRNA-seq and scATAC-seq data of the samples to be analyzed to obtain pre-processed scRNA-seq and scATAC-seq data; Perform dimensionality reduction, clustering, and cell annotation on the pre-processed scRNA-seq and scATAC-seq data to obtain at least one cell type of the sample to be analyzed; Perform dimensionality reduction clustering and cell annotation on at least one type of cells in the sample to be analyzed to obtain a cell subpopulation of that type of cells; Perform differentially expressed genes and differentially chromatin accessible peak analysis on the cell subpopulations of the sample to be analyzed, and obtain differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed; Perform scRNA-seq and scATAC-seq joint analysis on the differentially expressed gene data and differential chromatin accessible peak data of the cell subpopulations of the sample to be analyzed to determine the differentially expressed genes regulated by chromatin accessibility in the cell subpopulations of the sample to be analyzed; Perform enrichment analysis on the differential chromatin accessible peak data of the cell subpopulations of the analyzed sample to identify transcriptional elements involved in the regulation of gene expression related to chromatin accessible peaks; Analyze the potential regulatory regions and target genes of transcriptional regulatory elements, and screen transcriptional elements with high overlap between regulatory regions and target genes, which are target regulatory elements.

2. The method for screening transcriptional regulatory elements according to claim 1, wherein The samples to be analyzed include public sample data.

3. The method for screening transcriptional regulatory elements according to claim 2, wherein The public sample data include scRNA-seq and scATAC-seq sequencing data.

4. The method for screening transcriptional regulatory elements according to claim 1, wherein The cell type includes at least one of monocytes, T cells, dendritic cells, natural killer cells and B cells.

5. The method for screening transcriptional regulatory elements according to claim 4, wherein: The monocyte subpopulation includes at least one of CD14+ monocytes and CD16+ monocytes; and / or, The T cell subpopulation includes at least one of CD8+ T cells and CD4+ T cells; and / or, The subpopulation of dendritic cells includes at least one of plasmacytoid dendritic cells and classical dendritic cells.

6. The method for screening transcriptional regulatory elements according to claim 1, wherein The differentially expressed genes and differentially chromatin accessible peaks analysis of the cell subpopulations of the sample to be analyzed to obtain differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed includes: Grouping of cell subpopulations of the sample to be analyzed; The differentially expressed genes and differentially chromatin accessible peaks of the cell subpopulations of the sample to be analyzed after grouping are analyzed to obtain the differentially expressed gene data and differentially chromatin accessible peak data of the cell subpopulations of the sample to be analyzed.

7. The method for screening transcriptional regulatory elements according to claim 1, wherein The potential regulatory regions and target genes of the transcriptional regulatory elements are analyzed to screen the transcriptional elements whose regulatory regions and target genes highly overlap, which are the target regulatory elements: The core nucleotide sequence of the target transcriptional regulatory element SMARCC1-binding motif includes SEQ ID NO.

5.

8. The method for screening transcriptional regulatory elements according to claim 1, wherein After analyzing the potential regulatory regions and target genes of the transcriptional regulatory elements and screening the transcriptional elements with highly overlapping regulatory regions and target genes, i.e., the target regulatory elements, the method further includes: Validate the interaction between multiple target transcriptional regulatory elements by co-immunoprecipitation; and / or, Chromatin immunoprecipitation-qPCR was used to verify the regulation of target genes by target transcriptional regulatory elements.

9. The method for screening transcriptional regulatory elements according to claim 8, wherein The verification of the regulation of the target transcriptional regulatory element on the target gene by the chromatin immunoprecipitation qPCR method includes verifying the regulatory specificity of JUND on the TIMP1 gene promoter region, and the primers used to verify the regulatory specificity of JUND on the TIMP1 gene promoter region include upstream primers and downstream primers, wherein: The upstream primer sequence is shown as SEO D NO.1; The downstream primer sequence is shown in SEO D NO.

2.

10. Use of the method for screening transcriptional regulatory elements according to any one of claims 1 to 9 in determining disease treatment targets.

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