Method and device for screening endothelial cell related regulatory factors and pathways in myocardial infarction

Abstract

The invention discloses a method and a device for screening endothelial cell related regulatory factors and pathways in myocardial infarction. The method comprises the following steps: integrating common single-cell RNA sequencing data and a space transcriptome data set of a myocardial infarction mouse and a healthy mouse to form a first data set; analyzing endothelial cells in the first data set, identifying subpopulations of endothelial cells, and inferring a signaling network between different subpopulations of cells; based on the first data set, analyzing cell fate transformation by adopting an RNA rate and a virtual knockout technology; on the basis of the endothelial cell subpopulation, constructing an endothelial cell-immune cell interaction map so as to reveal a signal regulation and control basis of a vascular regeneration microenvironment; based on an endothelial cell-immune cell interaction map, key regulatory factors and signal pathways are obtained through screening. According to the method, the research and development efficiency is remarkably improved, molecular mechanism analysis can be converted into clinical conversion, and a brand new thought and method are provided for precise diagnosis and treatment of myocardial infarction.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of biomedicine, single-cell sequencing technology and computer-aided computing, and specifically relates to a method and device for screening regulatory factors and pathways related to endothelial cell remodeling during myocardial infarction. Background Technology

[0002] Myocardial infarction (MI) is a leading cause of heart failure and cardiovascular death worldwide. [1] Its pathological process involves complex tissue damage and repair mechanisms, in which vascular endothelial cells (ECs) play an important role in regulating key aspects such as inflammatory responses, angiogenesis, and tissue fibrosis. [2] However, traditional research methods such as histological staining and bulk RNA sequencing (bulk RNA-seq) have significant limitations: histological methods can only provide static morphological information, while bulk RNA-seq masks transcriptional differences between different endothelial cell subpopulations and cannot fully reflect the heterogeneity and dynamic changes of endothelial cells.

[0003] In recent years, the development of single-cell RNA sequencing (scRNA-seq) technology has provided a revolutionary tool for elucidating the composition of cardiac cells. Existing studies have used this technology to reveal the heterogeneous changes in cardiomyocytes, fibroblasts, and immune cells during myocardial infarction. However, research on endothelial cells has lagged behind. The single-cell atlas of myocardial infarction published in Nature in 2021 mainly focused on cardiomyocytes and macrophages, with a relatively brief analysis of EC subsets. Recent studies have shown that cardiac endothelial cells exhibit significant spatiotemporal heterogeneity, but systematic research on their subset transition patterns, key regulatory factors, and intercellular interaction networks during the progression of myocardial infarction remains lacking.

[0004] At the analytical methodological level, existing research has the following shortcomings: (1) Most studies use the conventional Seurat process and fail to effectively integrate public data resources across platforms and species; (2) RNA rate analysis often uses steady-state models, which have limited accuracy in predicting dynamic changes in EC. (3) The construction of gene regulatory networks often ignores epigenetic information, affecting the reliability of regulatory relationships. In addition, although studies have found the role of specific EC subsets (such as tip cells) in angiogenesis, their function and regulatory mechanism in myocardial repair are poorly understood.

[0005] It is worth noting that the large amount of public single-cell data accumulated in recent years (such as GEO, EMBL-EBI, and other databases) provides valuable resources for in-depth research, but existing analytical methods have failed to fully tap the potential value of these data. In particular, there are significant research gaps in the following areas: (1) the dynamic evolution of EC subsets at different stages of myocardial infarction; (2) the regulatory mechanisms of key transcription factors (such as the Sox family) on EC fate determination; and (3) the role of the EC-immune cell interaction network in tissue repair. Solving these problems will contribute to the development of precision therapeutic strategies targeting endothelial cells.

[0006] References: [1]Liu, T. et al. Advanced Cardiac Patches for the Treatment ofMyocardial Infarction. Circulation 149, 2002-2020, doi:10.1161 / circulationaha.123.067097 (2024). [2]Thygesen, K. et al. Fourth Universal Definition of MyocardialInfarction (2018). Journal of the American College of Cardiology 72, 2231-2264, doi:10.1016 / j.jacc.2018.08.1038 (2018). Summary of the Invention This invention aims to address the problem of the inability of existing technologies to accurately analyze the dynamic heterogeneity of endothelial cells (ECs) after myocardial infarction, and provides an analysis method based on single-cell transcriptome sequencing (scRNA-seq) to achieve the following objectives: To reveal the dynamic changes of endothelial cell subsets (especially tip-like cells) in myocardial infarction; to identify key regulatory factors (such as Sox17) and cell interaction mechanisms (such as the Mif-Cd44 axis); and to provide new therapeutic strategies targeting specific endothelial subsets.

[0007] Firstly, this application provides a method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction, including: The first dataset was formed by integrating public single-cell RNA sequencing data and spatial transcriptome datasets containing both myocardial infarction mice and healthy mice. Analyze the endothelial cells in the first dataset to identify endothelial cell subpopulations and infer the signal transduction networks between different cell subpopulations; Based on the first dataset, RNA rate and virtual knockout techniques were used to analyze cell fate transitions. Based on endothelial cell subsets, an endothelial cell-immune cell interaction atlas was constructed to reveal the signal regulation basis of the angiogenesis microenvironment. Based on the endothelial cell-immune cell interaction map, key regulatory factors and signaling pathways were screened and identified.

[0008] In one possible implementation, the analysis of endothelial cells in the first dataset, identification of endothelial cell subpopulations, and inference of signal transduction networks between different cell subpopulations include: Six endothelial subsets were identified using secondary clustering and UMAP dimensionality reduction, including: arterial endothelial cells, venous endothelial cells, endothelial-mesenchymal transition endothelial cells, lymphoid endothelial cells, capillary endothelial cells, and tip-like endothelial cells. Based on the principle of ligand-receptor co-expression, the signal transduction network between different cell subpopulations was inferred.

[0009] In one possible implementation, the analysis of cell fate transitions based on a first dataset, employing RNA rate and virtual knockout techniques, includes: The simulation analysis of virtual knockout of transcription factors (TF) was performed using single-cell omics data and a gene regulatory network (GRN) model in the first dataset, and the analysis results were obtained. Based on the analysis results, the changes in capillary transformation rate of tip-like endothelial cells after virtual knockout of the Sox17 gene were obtained; by comparing the cell state transition probability after virtual knockout of Sox17 with wild-type data, cell fate transition was analyzed to confirm whether Sox17 is a key switch for angiogenesis.

[0010] Furthermore, the simulation analysis of virtual knockout of transcription factors (TFs) using single-cell omics data and a gene regulatory network (GRN) model from the first dataset yielded the following results: An endothelial-specific gene regulation network (GRN) was constructed based on CellOracle: Single-cell transcriptome data containing raw gene expression counts, clustering information, and developmental trajectory data were converted into anndata format, an Oracle object was constructed, highly variable genes were selected to initialize the gene regulation network, and the network connection was optimized by interpolation using the K-nearest neighbor algorithm. Perform virtual knockout simulation analysis of transcription factors: Based on the constructed GRN, the expression level of a specific transcription factor is set to zero to simulate its loss of function, the change in cell state transition probability is calculated, the analysis results are obtained, and the changes in cell fate trajectory after perturbation are visualized.

[0011] In one possible implementation, the construction of an endothelial cell-immune cell interaction atlas based on endothelial cell subsets to reveal the signal regulation basis of the angiogenesis microenvironment includes: Based on known ligand-receptor interaction databases and single-cell data from the first dataset, we infer possible signal transduction networks between different cell subpopulations. The signal intensity of the signal transduction network was calculated by obtaining gene expression levels and combined expression levels of ligand-receptor complexes. By visualizing the signal transduction networks and signal intensities among different cell subpopulations, an atlas of endothelial cell-immune cell interactions can be obtained.

[0012] In one possible implementation, the screening for key regulatory factors and signaling pathways based on endothelial cell-immune cell interaction maps includes: CellChat was used to identify the most significantly upregulated signaling pathways in the acute phase of myocardial infarction group and healthy control group; among them, the focus was on signaling pathways that were generated or received by tip-like endothelial cells and associated with monocytes or macrophages, including: Mif-Cd44, Mif-Ackr3, Lgals9-Cd45 and Lgals9-P4hb. We used NicheNet to systematically screen and rank ligand-receptor interactions, identifying ligand-receptor pairs that were highly expressed specifically in the acute phase and had biological significance. By combining the most significantly upregulated signaling pathways and highly active ligand-receptor pairs, core ligand-receptors that mediate interactions between tip-like cells and monocytes or macrophages in the acute phase were screened. Based on the core ligand-receptor pair, key regulatory factors and signaling pathways are identified.

[0013] Secondly, it provides a device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction, including: The acquisition module is used to integrate public single-cell RNA sequencing data and spatial transcriptome datasets containing myocardial infarction mice and healthy mice to form the first dataset; The identification module is used to analyze endothelial cells in the first dataset, identify endothelial cell subpopulations, and infer the signal transduction networks between different cell subpopulations. The parsing module is used to analyze cell fate transitions based on the first dataset, employing RNA rate and virtual knockout techniques. A module was built to construct an endothelial cell-immune cell interaction atlas based on endothelial cell subsets, in order to reveal the signal regulation basis of the angiogenesis microenvironment. The screening module is used to screen for key regulatory factors and signaling pathways based on the endothelial cell-immune cell interaction map.

[0014] 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 screening endothelial cell-related regulatory factors and pathways in myocardial infarction as described in the first aspect.

[0015] Fourthly, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction as described in the first aspect.

[0016] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction as described in the first aspect.

[0017] The beneficial effects of this application are as follows: This invention, by integrating multi-omics data and innovative analytical methods, systematically identifies six endothelial cell subsets in myocardial infarction (including capillary endothelium, arterial endothelium, venous endothelium, lymphatic endothelium, endothelial-mesenchymal transition cells, and acute-phase-specific enriched tip-like cells with core therapeutic potential), filling a gap in the field; establishes a diagnostic spectrum of characteristic genes of endothelial cell subsets (such as Sox4 / Tfdp1 / Cd44 in tip-like cells and Sox17 / Alox12 in arterial endothelium); reveals that the Sox17 transcription factor drives tip-like cells to differentiate into capillary endothelium by activating downstream target genes Slc6a6 / Stmn2 (its deletion, verified by virtual gene knockout, can block the process of angiogenesis); and that Sox17 promotes angiogenesis in arterial endothelium by regulating target genes such as Alox12 / Ube2j1 through the PI3K / AKT pathway (immunofluorescence confirms that its expression peak is located 3-5 days after MI); and discovers a new mechanism of Mif-Cd44 immune interaction—single MIF ligands secreted by nuclei / macrophages bind to tip-like cell surface CD44 / CD74 receptor complexes, activating pro-angiogenic signals (predicted by CellChat / NicheNet and validated by cross-species spatial transcriptomics). This signaling axis is specifically highly expressed in the acute phase (5-7 days) of the infarcted area. Based on the above mechanism, targeted therapy strategies (Sox17 agonists promote capillary regeneration, anti-MIF / CD44 antibodies regulate immune-endothelial interactions) and clinical diagnostic indicators (serum CD44 level / myocardial CD44+ cell density assesses acute-phase angiogenesis activity, tissue Sox17 expression level predicts arterial repair capacity, and p-AKT / AKT ratio monitors PI3K pathway activation status) are provided. Technological innovations are reflected in the development of multi-omics integrated analysis workflows, endothelial dynamic prediction models (RNA rate / CellRank trajectory inference), and virtual gene knockout technology (such as Sox17 perturbation simulation). In-depth mining of public data (124,229 cells) significantly improves R&D efficiency. These breakthroughs, from the analysis of molecular mechanisms to clinical translation, have provided new ideas and methods for the precision diagnosis and treatment of myocardial infarction. Attached Figure Description

[0018] Figure 1 This is a single-cell atlas of endothelial cells from mice with myocardial infarction, as described in this application. Figure 1 A is a schematic diagram of the research design. Figure 1 B represents the UMAP visualization of the main cell types. Figure 1 C is the UMAP plot, showing the distribution of different groups in the mouse MI atlas. Figure 1 D represents the UMAP visualization of endothelial cell subsets. Figure 1 E, the UMAP plot, shows the distribution of groups included in the mouse endothelial cell atlas. Figure 1F is a heatmap of the expression patterns of the top 10 significantly overexpressed genes in each endothelial cell subset. Figure 1 G represents the cell type composition under different pathological conditions (left) and the number of cell types at different stages of mouse MI (right).

[0019] Figure 2 This figure shows the results of a virtual knockout simulation analysis of the Sox17 gene using CellOracle software, as described in this application. The figure illustrates the simulated effect of blocking the dynamic phenotypic transformation of tip-like cells into capillary endothelial cells when the Sox17 gene expression level is set to zero. This is the first time that computational simulation has demonstrated that Sox17 is a key transcription factor regulating angiogenesis.

[0020] Figure 3 The control group obtained by analyzing the CellChat tool in the embodiments of this application ( Figure 3 A) Acute phase ( Figure 3 B) and subacute phase ( Figure 3 C) A global network diagram of the number of intercellular interactions in the myocardial infarction model. Comparisons show that in the acute phase model, communication connections between tip-like cells and immune cells such as macrophages are significantly enhanced. This analysis ultimately helped identify key signaling pathways that are specifically enhanced in the acute phase, such as the Mif-(Cd74+Cd44) interaction pair between tip-like cells and macrophages.

[0021] Figure 4 The following are the results of systematic screening and ranking of ligand activity using the NicheNet tool in this application embodiment. Figure 4A shows the ligand activity and related information when monocytes are used as the ligand-delivering cells and tip-like endothelial cells are used as the target cells; Figures 4B-C present the activity of the Mif ligand when monocytes are used as the ligand-delivering cells and tip-like endothelial cells are used as the target cells, and predict its potential to regulate target genes such as Cd44.

[0022] Figure 5 This is a summary diagram that combines the analysis results of CellChat and NicheNet to screen and define key signaling pathways for embodiments of this application. Figure 5 A CellChat analysis identified signaling pathways that were specifically upregulated in the acute phase, with the Mif→(Cd74+Cd44) signaling axis between Tip-like cells and macrophages or monocytes being the most significant. Figure 5 B shows the signaling pathways that are downregulated during the acute phase; Figure 5C further used NicheNet analysis to rank ligand activity using monocytes as ligand-sending cells and tip-like cells as target cells, confirming that Mif has the highest regulatory potential in the acute phase, thus jointly defining Mif-Cd44 / Cd74 as the key signaling pathway.

[0023] Figure 6 shows the results of in-situ spatial co-localization verification of the calculated and predicted ligand-receptor interaction relationship using spatial transcriptome data in the embodiments of this application, presenting a closed-loop analysis based on the "CellChat + NicheNet + spatial transcriptome verification" triple strategy. Figure 6A is a time progression diagram of the spatiotemporal expression patterns of Cd44 and Mif genes in cardiac tissue in the sham-operated group and on days 1, 3, 7, and 28 after myocardial infarction; Figure 6B is a spatial distribution analysis result of Cd44 expression signal in representative tissue sections, showing the expression differences in the ischemic area, control group, fibrotic area, and distal area of ​​the human body.

[0024] Figure 7 shows the experimental results of this application embodiment using a mouse myocardial infarction model (LAD ligation method). Immunofluorescence staining was used to explore the expression of related genes and the co-localization of cell markers. Figures 7A and 7B show the expression and co-localization (merge) of Cd31 (green, endothelial cell marker) and Cd44 (red) at different time points after myocardial infarction (MI - day 3, MI - day 7, MI - day 5, MI - day 14), respectively. CD44 protein expression reached its peak on day 7. Figures 7C and 7D show the expression and co-localization (merge) of Cd31 (green) and Sox17 (red) at different time points after myocardial infarction. Sox17 protein expression increased significantly on day 5. Overall, this reveals a coherent biological event chain from "Sox17 transcriptional regulation" to "CD44-mediated immune microenvironment dialogue", which can be used to explore the distribution and interrelationship of related molecules at different stages of myocardial infarction.

[0025] Figure 8 A schematic diagram of the structure of the device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0026] The content of this application will be further described below with reference to specific embodiments, but the content of this application is not limited thereto.

[0027] Existing analytical methods have not yet addressed the following aspects: (1) The dynamic evolution of endothelial cell (EC) subsets at different stages of myocardial infarction; (2) The regulatory mechanisms of key transcription factors (such as the Sox family) on EC fate determination; (3) The role of the EC-immune cell interaction network in tissue repair.

[0028] Therefore, there is an urgent need for a method and device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction, which can help develop precision treatment strategies that target endothelial cells.

[0029] See Figure 1 This application provides a method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction, including: S100. Integrate public single-cell RNA sequencing data and spatial transcriptome datasets containing myocardial infarction mice and healthy mice to form the first dataset.

[0030] In one possible implementation, the integration method includes integrating single-cell RNA sequencing data and spatial transcriptome datasets using the Harmony package. This method can effectively eliminate batch effects.

[0031] Specifically, the public single-cell RNA sequencing data came from the single-cell transcriptome datasets (E-MTAB-9816, E-MTAB-7895) in the European Nucleotide Archive (ENA). The spatial transcriptome datasets came from six independent single-cell transcriptome datasets (GSE214611, GSE269054, etc.) in the GEO database. Both datasets covered seven time points: 1 / 3 / 5 / 7 / 14 / 28 days post-myocardial infarction and healthy controls, with a sample size of 124,229 cells.

[0032] S200. Analyze the endothelial cells in the first dataset, identify endothelial cell subpopulations, and infer the signal transduction networks between different cell subpopulations.

[0033] In one possible implementation, S200 includes: S210. Through secondary clustering and UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction, six endothelial subpopulations were identified, including: arterial endothelial cells, venous endothelial cells, endothelial-mesenchymal transition endothelial cells, lymphoid endothelial cells, capillary endothelial cells, and tip-like endothelial cells.

[0034] It should be noted that tip-like cells were identified as an acute-phase specific subset, and Ro / e value analysis confirmed that they were significantly enriched on day 5 (Ro / e=3.2).

[0035] S220. Based on the principle of ligand-receptor co-expression, the signal transduction network between different cell subpopulations is inferred.

[0036] Furthermore, S220 includes: S220a. Using the CellChat algorithm, candidate signaling pathways with significant differences in the process of myocardial infarction are identified by comparing the expression probabilities of ligand-receptor among different cell subpopulations under different physiological states. S220b uses the NicheNet model, taking the differentially expressed gene set of the recipient cell as input, to calculate the ligand activity score in order to screen out key ligand-receptor pairs with high regulatory potential on the target cell. S220c integrates spatial transcriptome data to analyze the expression correlation between candidate ligands and receptors in situ in tissues, confirming their spatial proximity and forming a closed-loop analysis framework from computational prediction to experimental verification to ensure the biological reliability of the inferred results.

[0037] S300, based on the first dataset, uses RNA rate and virtual knockout techniques to analyze cell fate transitions.

[0038] In one possible implementation, S300 includes: S310. Using the single-cell omics data and gene regulatory network (GRN) model in the first dataset, a simulation analysis of virtual knockout of transcription factors (TF) was performed to obtain the analysis results.

[0039] Furthermore, S310 includes: S310a. Constructing an endothelial-specific gene-regulatory network (GRN) based on CellOracle (v0.10.5): Single-cell transcriptome data containing raw gene expression counts, clustering information, and developmental trajectory data are converted into anndata format, an Oracle object is constructed, highly variable genes are selected to initialize the gene-regulatory network, and the network connection is optimized by K-nearest neighbor algorithm interpolation. S310b, Perform virtual knockout simulation analysis of transcription factors: Based on the constructed GRN, the expression level of a specific transcription factor (such as Sox17) is set to zero to simulate its loss of function, calculate the change in cell state transition probability, obtain the analysis results, and visualize the changes in cell fate trajectory after perturbation.

[0040] S320. Based on the analysis results, the changes in capillary transformation rate of tip-like endothelial cells after virtual knockout of the Sox17 gene were obtained; by comparing the cell state transition probability after virtual knockout of Sox17 with wild-type data, cell fate transition was analyzed to confirm whether Sox17 is a key switch for angiogenesis.

[0041] It is important to note that Sox17 is a crucial endothelial cell transcription factor, and the SOXF family member Sox17 is upregulated in the endothelial progenitor cell population of myocardial infarction patients. Therefore, by simulating the effect of Sox17 gene deletion on endothelial cell differentiation after myocardial infarction, we aim to confirm whether Sox17 is a key switch for angiogenesis. Upregulated expression would confirm Sox17 as a key switch for angiogenesis. Capillary turnover rate refers to the efficiency and proportion of tip-like cells differentiating into mature capillary endothelial cells. This ratio is a key indicator for assessing the smoothness of the angiogenesis process, and the expression level of Sox17 is closely related to this turnover rate.

[0042] Understandably, by comparing the cell state transition probabilities after virtual Sox17 knockout with wild-type data, the fate transition process of tip-like cells to capillary endothelial cells can be analyzed. The simulation results of this application's embodiments show that Sox17 deletion significantly reduces the transition probability of this differentiation trajectory and decreases the capillary transformation rate, thus confirming that Sox17 is a key switch gene regulating this cell fate transition.

[0043] S400, based on endothelial cell subsets, constructs an endothelial cell-immune cell interaction atlas to reveal the signal regulation basis of the angiogenesis microenvironment.

[0044] It should be noted that S400, based on the classification of endothelial cell subsets (such as tip-like cells and capillary endothelial cells) and immune cells (such as monocytes / macrophages) identified by S200, systematically analyzes their communication networks during myocardial infarction, aiming to elucidate the potential regulatory role of the immune microenvironment on endothelial cell fate (such as the tip-like cell differentiation process revealed by S300).

[0045] In one possible implementation, S400 includes: S410. Based on the known ligand-receptor interaction database and single-cell data in the first dataset, infer possible signal transduction networks between different cell subpopulations. S420. Calculate the signal intensity of the signal transduction network by obtaining gene expression levels and the combined expression levels of ligand-receptor complexes. S430. Visualize the signal transduction network and signal intensity between different cell subpopulations to obtain an endothelial cell-immune cell interaction atlas.

[0046] It should be noted that the endothelial cell-immune cell interaction atlas aims to systematically depict the interaction network between specific endothelial cell subsets (such as tip cells) and key immune cell types (such as monocytes and macrophages) at different stages of myocardial infarction (MI), especially the acute phase. Based on this interaction network, significant and dynamically changing interactions between certain endothelial cell subsets and their corresponding immune cells were found after MI. Key ligand-receptor signaling pathways that are significantly upregulated or downregulated under MI conditions (especially the acute phase) and mediate endothelial-immune cell communication were identified from the complex interaction network.

[0047] S500, based on the endothelial cell-immune cell interaction map, screened and obtained key regulatory factors and signaling pathways.

[0048] In one possible implementation, S500 includes: S510. Using CellChat, identify the most significantly upregulated signaling pathways in the acute phase of myocardial infarction group and healthy control group; with a focus on signaling pathways generated or received by tip-like endothelial cells and associated with monocytes or macrophages, including: Mif-Cd44, Mif-Ackr3, Lgals9-Cd45, and Lgals9-P4hb.

[0049] Furthermore, the acute phase of myocardial infarction group refers to mice 1-7 days after myocardial infarction; the healthy control group refers to mice that have not undergone myocardial infarction surgery.

[0050] S520. Using the NicheNet tool, ligand-receptor interactions were systematically screened and sorted to identify ligand-receptor pairs that were specifically highly expressed in the acute phase and had biological significance.

[0051] In one possible implementation, S520 includes: based on single-cell data in the first dataset, defining monocytes or macrophages as ligand-sending cell populations and tip-like endothelial cells as target cell populations; and comprehensively ranking all predicted ligand-receptor pairs by calculating ligand activity scores (based on the enrichment of ligand target genes) and receptor expression levels, thereby screening out ligand-receptor pairs that are specifically highly expressed in the acute phase and have biological significance. S530, combined with the most significantly upregulated signaling pathways and highly active ligand-receptor pairs, screened out the core ligand-receptor that mediates the interaction between tip-like cells and monocytes or macrophages in the acute phase.

[0052] S540, based on the core ligand-receptor pair, defines key regulatory factors and signaling pathways.

[0053] Furthermore, S540 includes: identifying Mif as a key regulatory ligand expressed by monocytes or macrophages; and identifying Cd44 and Cd74 as key receptor complexes formed on tip-like endothelial cells. Thus, Mif-(Cd44 / Cd74) is defined as a key signaling pathway mediating interactions between tip-like endothelial cells and monocytes or macrophages during the acute phase of myocardial infarction.

[0054] In one possible implementation, the key regulatory factor and signaling pathway is the Mif-Cd44 / Cd74 signaling axis; wherein the ligand is the macrophage migration inhibitor Mif, which is highly expressed by monocytes / macrophages, especially in the acute phase; the receptor is a complex receptor formed by Cd44 and Cd74, which is highly expressed on tip-like cells, especially in the acute phase, where Cd74 is a high-affinity receptor for Mif, and Cd44 participates in signal transduction as a co-receptor.

[0055] It should be noted that this signaling pathway was identified as a key upregulated pathway mediating the interaction between monocytes / macrophages and tip-like endothelial cells in the acute phase following myocardial infarction (MI). CellChat analysis showed that Mif-(Cd74+Cd44) was a significantly enhanced interaction pair in the acute phase compared to healthy controls. NicheNet prediction confirmed that Mif is one of the top-ranking ligands from monocytes acting on tip-like cells, and its activity is significantly enriched in a set of target genes involving genes such as Cd44. Spatial expression analysis and immunofluorescence staining further confirmed at the protein level the spatiotemporally specific upregulation of Cd44 and Mif in the infarct region after MI (especially in the acute phase).

[0056] Biological significance of this signaling pathway: Activation of this pathway may regulate the function of tip-like endothelial cells (such as migration, proliferation, and survival), thereby affecting early angiogenesis and tissue repair processes after myocardial infarction. Therefore, the Mif-Cd44 / Cd74 signaling axis is a potential therapeutic target for intervening in vascular remodeling and promoting repair after myocardial infarction.

[0057] The following describes a more detailed example.

[0058] I. Screening Method A method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction, comprising: Step 1: Integrate public single-cell data and spatial transcriptome datasets containing myocardial infarction mice and healthy mice to form the first dataset.

[0059] Specifically, the study integrated datasets from the European Nucleotide Archive (ENA) (E-MTAB-9816, E-MTAB-7895) and six independent datasets from the GEO database (GSE214611, etc.). These ENA datasets and the GEO database covered seven time points: 1 / 3 / 5 / 7 / 14 / 28 days post-myocardial infarction and healthy controls, with a sample size of 124,229 cells. Figure 1 AC).

[0060] Step 2: Analyze the endothelial cells in the first dataset, identify endothelial cell subpopulations, and infer the signal transduction networks between different cell subpopulations.

[0061] 2.1 Sorting criteria: Based on the classic marker gene Cdh5 / Pecam1 (expression level >1 logCPM), 24,829 endothelial cells were extracted from the whole-cell atlas.

[0062] 2.2. Through secondary clustering (resolution=0.8) and UMAP dimensionality reduction, six endothelial subsets were identified: including arterial endothelial cells, venous endothelial cells, endothelial-mesenchymal transition endothelial cells, lymphoid endothelial cells, capillary endothelial cells, and the newly discovered tip-like cells. Figure 1 DE).

[0063] 2.3. Based on the principle of ligand-receptor co-expression, infer the signal transduction network between different cell subpopulations.

[0064] Step 3: Based on the first dataset, RNA rate and virtual knockout techniques are used to analyze cell fate transitions.

[0065] 3.1. Constructing an endothelial-specific gene regulatory network (GRN) based on CellOracle v0.10.5: 3,000 hypervariable genes were selected to initialize the GRN, and kNN interpolation was used to optimize network connections.

[0066] 3.2 Simulated Sox17 loss: Setting the expression level of the Sox17 gene to 0 showed that the dynamic phenotypic transformation of tip-like cells into capillary endothelial cells was blocked. Figure 2 This study is the first to demonstrate that Sox17 is a key transcription factor regulating angiogenesis.

[0067] 3.3 Analyzing Cellular Dynamics: In-depth analysis of the cell trajectories after virtual knockout revealed that Sox17 deficiency led to a decreased probability of state transition from tip-like cells to capillary endothelium. The fundamental reason for this lies in the increased transcriptomic disorder and significantly slowed directional migration speed along the differentiation pathway. This analysis, from a computational simulation perspective, reveals the specific dynamics of Sox17 as a key switch regulating cell fate transition. Figure 2 ).

[0068] Step 4: Construct an endothelial cell-immune cell interaction atlas.

[0069] Specifically, this step constructs a dynamic evolution map of myocardial infarction endothelial cells, revealing the changing patterns of subpopulation proportions from the acute phase to the repair phase, such as the peak proportion of tip-like cells on day 5. Figure 1 (G). Analysis revealed that this peak (peak percentage on day 5) pattern has three key implications: First, it clarifies the golden intervention window for intrinsic vascular repair after myocardial infarction (days 3-7); second, it establishes tip-like cells as key targets of the core repair unit; and third, it provides an indispensable spatiotemporal biological context for the regulatory role of Sox17 and the function of signaling pathways such as Mif-Cd44. This not only reveals the behavioral patterns of core repair cells but also provides a core basis for developing precise pro-angiogenic therapies targeting specific cells and specific time windows.

[0070] Step 5: Based on the endothelial cell-immune cell interaction map, key regulatory factors and signaling pathways are screened and obtained.

[0071] 5.1 CellChat Global Network: Grouped by disease course (acute / subacute phase), it was found that tip-like cells-Mφ Mif-(Cd74+Cd44) interaction pairs ( Figure 3 AC).

[0072] 5.2 NicheNet ligand activity ranking: Using monocytes as the sender, the Mif ligand activity score was 0.38 (Top 1), regulating target genes such as Cd44. Figure 4 AC).

[0073] 5.3. Combining the most significantly upregulated pathways identified by CellChat tools and the highly active ligand-receptor pairs predicted by NicheNet, core ligand-receptor pairs mediating interactions between tip-like cells and monocytes or macrophages in the acute phase were screened. Figure 5 AC).

[0074] 5.4. Based on the aforementioned core ligand-receptor pairs, identify key regulatory factors and signaling pathways.

[0075] Specifically, the core ligand-receptor pair Mif-(Cd74+Cd44) selected through steps 5.1 to 5.3 is defined as a key signaling pathway mediating communication between tip-like endothelial cells and monocytes / macrophages during the acute phase of myocardial infarction. Mif is defined as a key regulatory ligand derived from immune cells, and Cd44 and Cd74 are defined as key receptor complexes formed on tip-like endothelial cells, together constituting the Mif-Cd44 / Cd74 signaling axis.

[0076] In one possible implementation, the key regulatory factor and signaling pathway is the Mif-Cd44 / Cd74 signaling axis; wherein the ligand is the macrophage migration inhibitor Mif, which is highly expressed by monocytes / macrophages, especially in the acute phase; the receptor is a complex receptor formed by Cd44 and Cd74, which is highly expressed on tip-like cells, especially in the acute phase, where Cd74 is a high-affinity receptor for Mif, and Cd44 participates in signal transduction as a co-receptor.

[0077] II. Relevant Verification 1. Spatial Co-localization Validation: To verify the calculated and predicted ligand-receptor interactions in situ within a two-dimensional tissue space, this application employs a triple strategy of 'CellChat + NicheNet + Spatial Transcriptome Validation'. First, CellChat identifies the most significantly upregulated signaling pathways after myocardial infarction (e.g., the Mif signaling pathway). Then, NicheNet is used to screen for high-ranking ligand-receptor pairs (e.g., Mif-(Cd44 / Cd74)) that act on specific receptor cells (tip-like cells) at the ligand activity level. Finally, the spatial transcriptome dataset (GSE214611) is integrated for in situ validation. Spatial expression analysis confirms that the expression of both Cd44 and Mif is significantly upregulated in the myocardial infarction region, and their expression patterns exhibit a highly consistent co-localization trend in space. Figure 6 (AB), thus supporting the reliability of the aforementioned computational predictions in two-dimensional tissues. This strategy effectively overcomes the limitation of single-cell data lacking spatial information, achieving a closed-loop analysis from computational prediction to morphological verification.

[0078] 2. Experimental verification and transformation Validation of a mouse model of myocardial infarction (LAD ligation): C57 mice were used for LAD ligation. Embedded sections of the mouse heart were taken at different time points after myocardial infarction and stained with fluorescence. Immunofluorescence showed that the expression level of Sox17 increased significantly on day 5, and CD44 reached its peak on day 7. Figure 7A). These results indicate that Sox17, a key transcription factor for arterial endothelial cells, is rapidly activated in the early stages of angiogenesis (day 5), and its upregulation closely coincides with the emergence and expansion of tip-like cells, suggesting that it may initiate the vascular budding process. CD44, as a co-receptor for Mif, shows a peak expression that lags behind ligand expression, reaching its peak on day 7. This signifies that the cell-cell interaction established between tip-like cells and monocytes / macrophages via the Mif-CD44 signaling axis has entered an active phase, thereby synergistically regulating the formation and stability of the acute-phase angiogenesis microenvironment. This time sequence clearly reveals a coherent biological event chain from "transcriptional regulation (Sox17) → cell differentiation (tip-like cells) → immune microenvironment dialogue (CD44-mediated interaction)".

[0079] Human sample validation: Analysis of the public dataset (HCA) confirms that CD44 has high regional specificity in human heart infarction.

[0080] Tip-like specific cell markers (Aplnr, Kcne3, which can be used to accurately obtain tip-like cells in tissue staining or cell extraction) and the Mif-Cd44 signal axis identified based on single-cell datasets can be used to assess acute-phase angiogenesis activity, for example, by evaluating factors such as serum CD44 levels and tissue Sox17 expression.

[0081] The following describes the device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction provided by the present invention. The device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction described below can be referred to in correspondence with the method for screening endothelial cell-related regulatory factors and pathways in myocardial infarction described above.

[0082] Figure 8 This is a schematic diagram of the structure of the device for screening endothelial cell-related regulatory factors and pathways in myocardial infarction provided in an embodiment of the present invention, as shown below. Figure 8 As shown, it includes: an acquisition module 81, an identification module 82, a parsing module 83, a construction module 84, and a filtering module 85, wherein: The acquisition module 81 is used to integrate public single-cell RNA sequencing data and spatial transcriptome datasets containing myocardial infarction mice and healthy mice to form the first dataset; The identification module 82 is used to analyze endothelial cells in the first dataset, identify endothelial cell subpopulations, and infer the signal transduction network between different cell subpopulations. Module 83 is used to analyze cell fate transitions based on the first dataset, employing RNA rate and virtual knockout techniques. Module 84 is used to construct an endothelial cell-immune cell interaction atlas based on endothelial cell subsets to reveal the signal regulation basis of the angiogenesis microenvironment. The screening module 85 is used to screen for key regulatory factors and signaling pathways based on the endothelial cell-immune cell interaction map.

[0083] Figure 9 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 9 As shown, the electronic device may include a processor 910, a communications interface 920, a memory 930, and a communications bus 940, wherein the processor 910, communications interface 920, and memory 930 communicate with each other via the communications bus 940. The processor 910 can call logical instructions in the memory 930 to execute methods for screening endothelial cell-related regulatory factors and pathways in myocardial infarction.

[0084] Furthermore, the logical instructions in the aforementioned memory 930 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, 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.

[0085] 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 execute the screening methods for endothelial cell-related regulatory factors and pathways in myocardial infarction provided by the above methods.

[0086] 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 screening methods for endothelial cell-related regulatory factors and pathways in myocardial infarction provided by the methods described above.

[0087] 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.

[0088] 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.

[0089] 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 screening of endothelial cell-related regulators and pathways in myocardial infarction, characterized in that, The application relates to a method for constructing an endothelial cell-immune cell interaction atlas to reveal the signal regulation basis of a blood vessel regeneration microenvironment. The method comprises the following steps: integrating public single-cell RNA sequencing data and spatial transcriptome data sets containing myocardial infarction mice and healthy mice to form a first data set; analyzing endothelial cells in the first data set, identifying subgroups of endothelial cells, and inferring signal transmission networks between different cell subgroups; based on the first data set, RNA rate and virtual knockout technology are used to analyze cell fate transition; based on the subgroups of endothelial cells, an endothelial cell-immune cell interaction atlas is constructed to reveal the signal regulation basis of the blood vessel regeneration microenvironment; 2. The method of screening for endothelial cell-related regulators and pathways in myocardial infarction according to claim 1, wherein, based on the endothelial cell-immune cell interaction atlas, key regulatory factors and signal pathways are screened. The analysis of endothelial cells in the first data set, the identification of endothelial cell subgroups, and the inference of signal transmission networks between different cell subgroups comprise: six endothelial subgroups are identified through secondary clustering and UMAP dimension reduction, including: arterial endothelial cells, venous endothelial cells, endothelial mesenchymal transition endothelial cells, lymphatic endothelial cells, capillary endothelial cells, and tip-like endothelial cells; 3. The method of claim 1, wherein the method is for screening of endothelial cell related regulators and pathways in myocardial infarction. based on the ligand-receptor co-expression principle, the signal transmission network between different cell subgroups is inferred. The RNA rate and virtual knockout technology are used to analyze cell fate transition based on the first data set, which comprises the following steps: using single-cell omics data in the first data set and a gene regulatory network (GRN) model to perform simulation analysis of transcription factor (TF) virtual knockout, and obtaining analysis results; 4. The method of screening for endothelial cell-related regulators and pathways in myocardial infarction according to claim 3, wherein, based on the analysis results, the change of capillary transformation rate of tip-like endothelial cells after virtual knockout of Sox17 gene is obtained; by comparing the cell state transition probability after virtual knockout of Sox17 with the wild type data, the cell fate transition is analyzed to confirm whether Sox17 is a key switch for blood vessel regeneration. The simulation analysis of transcription factor (TF) virtual knockout using single-cell omics data in the first data set and a gene regulatory network (GRN) model to obtain analysis results comprises the following steps: based on CellOracle, an endothelial-specific gene regulatory network GRN is constructed: single-cell transcriptome data containing gene expression raw counts, clustering information and developmental trajectory data are converted into anndata format, an Oracle object is constructed, high-variable genes are selected to initialize the gene regulatory network, and K-nearest neighbor algorithm is used to interpolate and optimize network connections; 5. The method of screening for endothelial cell-related regulators and pathways in myocardial infarction according to claim 1, wherein, transcription factor virtual knockout simulation analysis: based on the constructed GRN, the functional loss state is simulated by setting the expression amount of a specific transcription factor to zero, the change of cell state transition probability is calculated, and the analysis results are visualized to show the change of cell fate trajectory after perturbation. Based on the subgroups of endothelial cells, an endothelial cell-immune cell interaction atlas is constructed to reveal the signal regulation basis of the blood vessel regeneration microenvironment, which comprises the following steps: based on the known ligand-receptor interaction database and the single-cell data in the first data set, the possible signal transmission network between different cell subgroups is inferred; the signal intensity of the signal transmission network is calculated by obtaining the gene expression amount and the combined expression level of the ligand-receptor complex; the signal transmission network and the signal intensity between different cell subgroups are visualized to obtain the endothelial cell-immune cell interaction atlas.

6. The method of screening for endothelial cell-related regulators and pathways in myocardial infarction according to claim 1, wherein, The key regulatory factors and signal pathways are screened based on the endothelial cell-immune cell interaction map, including: The most significantly up-regulated signal pathways in the acute myocardial infarction group and the healthy control group are identified using the CellChat tool; among them, the signal pathways sent or received by the tip-like endothelial cells and related to monocytes or macrophages are focused on, including: Mif-Cd44, Mif-Ackr3, Lgals9-Cd45 and Lgals9-P4hb; The ligand-receptor interactions are systematically screened and sorted using the NicheNet tool, and the ligand-receptor pairs that are specifically highly expressed in the acute phase and have biological significance are screened out; The core ligand-receptor that mediates the interaction between tip-like cells and monocytes or macrophages in the acute phase is screened out by combining the most significantly up-regulated signal pathways and the high-activity ligand-receptor pairs; Based on the core ligand-receptor pair, the key regulatory factors and signal pathways are defined.

7. An apparatus for screening of endothelial cell-related regulators and pathways in myocardial infarction, characterized by, It includes: The acquisition module is used to integrate the public single-cell RNA sequencing data and spatial transcriptome data set containing myocardial infarction mice and healthy mice to form a first data set; The identification module is used to analyze the endothelial cells in the first data set, identify the subgroups of endothelial cells, and infer the signal transmission network between different cell subgroups; The analysis module is used to analyze the endothelial cells in the first data set, identify the subgroups of endothelial cells, and infer the signal transmission network between different cell subgroups; The construction module is used to construct an endothelial cell-immune cell interaction map based on the subgroups of endothelial cells to reveal the signal regulation basis of the vascular regeneration microenvironment; The screening module is used to screen the key regulatory factors and signal pathways based on the endothelial cell-immune cell interaction map.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the endothelial cell-related regulatory factor and pathway screening method in myocardial infarction according to any one of claims 1-6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the endothelial cell-related regulatory factor and pathway screening method in myocardial infarction according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the endothelial cell-related regulatory factor and pathway screening method in myocardial infarction according to any one of claims 1-6.

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