An upstream cell-miRNA-downstream cell network construction method and system

Abstract

The application discloses a kind of upstream cell-miRNA-downstream cell network construction method. By identifying primary miRNA in single cell nucleus transcriptome data and its expression data in mRNA cell cluster, upstream cell and miRNA relationship pair can be obtained; by correlating exosome miRNA expression data, single cell mRNA expression data, miRNA and downstream cell type gene / mRNA relationship pair can be obtained; finally, based on the results of the first two methods and single cell transcriptome cell communication data, a complete upstream cell-miRNA-downstream cell functional interaction regulation network can be constructed, which provides data support for identifying specific miRNA source cell type and downstream regulatory cell type, and clarifying the interaction and regulation between different cell types. The complete technical solution of exosome miRNA sequencing analysis and single cell nucleus sequencing analysis from experiment to bioinformatics analysis is realized. The application also discloses a system for realizing the above method and application of the method.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology and relates to a method and system for jointly analyzing exosomal miRNA and single-cell nuclear transcriptome data from the same tissue to obtain miRNA-mediated cell population regulatory networks. Background Technology

[0002] Exosomes are extracellular vesicles secreted by cells, with a diameter ranging from 30 to 150 nm. They are the main carriers for intercellular communication, and miRNAs play important regulatory roles in various physiological and pathological activities. Single-nucleus RNA sequencing (snRNA-seq) is a new technology that sequences the transcriptome at the single-cell level. It can obtain key information such as cellular function and state, providing detailed data for revealing the biological mechanisms behind life phenomena, and has become a core research method and tool in the life sciences.

[0003] Essentially, life phenomena are driven and manifested by the interactions between different cells; that is, life is a network of cellular interactions. Existing research has established a technical approach for simultaneously performing exosomal miRNA sequencing and single-cell nuclear sequencing in the same tissue (application publication number: CN 114350747A). This approach combines communication signals and nodes within the cellular network, enabling in-depth and systematic exploration of detailed intercellular interaction networks. This allows for answering more complex biological questions and exploring deeper biological mechanisms, bringing revolutionary developments to life science research.

[0004] Sequencing data requires appropriate bioinformatics algorithms to analyze specific mechanisms. In the analysis of exosomal miRNA and single-cell nuclear transcriptome sequencing data, the most crucial aspect is identifying the cell types from which specific miRNAs originate and the cell types they regulate downstream, thereby clarifying the interactions and regulation between different cell types. However, current single-cell transcriptome sequencing analysis methods, or miRNA sequencing data analysis methods, lack suitable methods and workflows to achieve this analysis. There is an urgent need to develop a method and workflow capable of jointly analyzing exosomal miRNA and single-cell nuclear transcriptome sequencing data. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a bioinformatics method for the joint analysis of exosomal miRNA data and single-cell nuclear transcriptome data in the same tissue, thereby constructing an exosomal miRNA-mediated cell-miRNA-cell regulatory network and uncovering the interactions and regulatory relationships between different cell types.

[0006] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0007] This invention has prior access to data on existing exosomal miRNAs and single-cell transcriptome mRNAs.

[0008] The main steps of the method of the present invention are as follows:

[0009] Step 1: Based on single-cell nuclear transcriptomics, obtain expression data of primary miRNAs in different cell types;

[0010] Step 2: Based on the expression data of primary miRNAs of different cell types obtained in Step 1, perform differential expression analysis of primary miRNAs of each cell type with all other cell types to obtain upstream cell type-miRNA relationship pairs;

[0011] Step 3: Perform correlation analysis on the pre-obtained exosomal miRNA results and mRNA expression data of different cell types in single cell nuclei to obtain negatively correlated miRNA-gene (mRNA) relationship pairs;

[0012] Step 4: Based on the cell communication ligand gene, receptor gene and corresponding cell type data of mRNA single cells, combined with the miRNA-gene (mRNA) relationship pairs obtained by the correlation analysis in Step 3, and using the gene (mRNA) as the medium, obtain the miRNA-downstream cell type relationship pairs;

[0013] Step 5: Based on the miRNA-downstream cell type relationship pairs obtained in Step 4 and the upstream cell type-miRNA relationship pairs obtained in Step 2, using miRNA as a medium, and combining the ligand cell and receptor cell interaction relationships in cell communication, an upstream cell-miRNA-downstream cell network is obtained.

[0014] According to one aspect of the present invention, the present invention provides a method for identifying miRNAs secreted by upstream cell populations based on single-cell nuclear transcriptomics, the identification method comprising the following steps:

[0015] Step a: Based on the pre-obtained single-cell nuclear transcriptome data and the miRNA_primary_transcript gff file of the corresponding species in the mirbase database (https: / / www.mirbase.org / ftp / CURRENT / genomes / ), the expression results of primary miRNAs in each cell were obtained using the single-cell transcriptome Cell Ranger workflow;

[0016] Step b: Further, based on the pre-obtained single-cell nuclear mRNA cell cluster data, the identified cells expressing primary miRNA are regressed to the single-cell mRNA cell type to obtain the expression data of primary miRNA for each cell type.

[0017] Step c: Further, following the single-cell transcriptome bimodal test method, based on the primary miRNA expression data of each cell type, perform primary miRNA differential expression analysis on each cell type and all other cell types to obtain miRNA data specifically expressed by each cell type relative to other cell types, i.e., upstream cell type-miRNA relationship pairs.

[0018] According to another aspect of the present invention, the present invention provides a method for performing correlation analysis on mRNA expression data of various cell types in single nuclei and exosome miRNA expression data, the correlation analysis method comprising the following steps:

[0019] Step 1) Take the average value of gene (mRNA) expression in all cells of each cell type in a single cell nucleus (mRNA) sample, and use it as the gene (mRNA) expression value of that cell type in that sample;

[0020] Step 2) Use the Spearman method of the cor function in R to perform correlation analysis on the pre-obtained exosomal miRNA sequencing expression data and the gene (mRNA) expression data of each cell type obtained in Step 1) to obtain the exosomal miRNA-single-cell transcriptome gene (mRNA) relationship pairs;

[0021] Step 3) Based on the default threshold of correlation coefficient less than or equal to -0.8 and P value less than 0.05, screen the exosome miRNA-single-cell transcriptome gene (mRNA) relationship pairs in Step 2) and extract the negatively correlated miRNA-gene (mRNA) relationship pairs that meet the threshold.

[0022] Since some species may not have database information, alternatively, the miRNA-gene (mRNA) target relationship of species can be studied based on the miRWalk and miRDB databases. The intersection of the exosome miRNA-single-cell transcriptome gene (mRNA) results in step 3) can be extracted to obtain negatively correlated miRNA-gene (mRNA) relationship pairs that meet the target relationship.

[0023] The intersection extraction refers to comparing the miRNA-gene relationship pairs in the database with the miRNA-gene (mRNA) relationship pairs of exosomes and single cells mentioned in this invention, and taking the same values ​​for the miRNA and its corresponding gene (mRNA), so that the result is still a miRNA-gene relationship pair.

[0024] According to a final aspect of the present invention, the present invention provides an upstream cell-miRNA-downstream cell network comprising cell-cell relationships, based on upstream cell type-miRNA relationship pairs, pre-obtained single-cell transcriptome cell communication data, and miRNA-gene (mRNA) relationship pairs, the method comprising the following steps:

[0025] Step 1: Based on the cell communication ligand gene, receptor gene and corresponding cell type data of mRNA single cells, combined with the exosomal miRNA-single cell transcriptome gene (mRNA) correlation relationship pairs obtained in the correlation analysis, using genes (miRNA regulatory target genes and ligand or receptor genes in cell communication) as mediators, obtain miRNA-downstream ligand or receptor cell type relationship pairs respectively (miRNA-downstream ligand cell type relationship pairs and miRNA-downstream receptor cell type relationship pairs together constitute miRNA-downstream cell type relationship pairs);

[0026] Step 2: Based on the miRNA-downstream ligand or receptor cell type relationship pairs and upstream cell type-miRNA relationship pairs in Step 1, obtain upstream cell type-miRNA-downstream ligand cell type and upstream cell type-miRNA-downstream receptor cell type networks respectively using miRNA as the medium.

[0027] Step 3: Based on the two upstream cell type-miRNA-downstream ligand / receptor cell type networks obtained in Step 2, and combining the ligand cell and receptor cell interaction relationships in cell communication, an upstream cell-miRNA-downstream cell complete network containing cell-cell relationships is obtained by integrating the common cell types.

[0028] The present invention also provides a system for implementing the above method, the system comprising: a data input module, an upstream cell and miRNA relationship pair acquisition module, a miRNA and downstream cell type gene (mRNA) relationship pair acquisition module, and an upstream cell-miRNA-downstream cell network construction module;

[0029] The data input module is used to input pre-obtained data on existing exosomal miRNAs and single-cell transcriptome mRNAs.

[0030] The upstream cell and miRNA relationship pair acquisition module is used to obtain upstream cell and miRNA relationship pairs by identifying primary miRNAs and their expression data in mRNA cellular groups in single-cell nuclear transcriptome data.

[0031] The miRNA and downstream cell type gene (mRNA) relationship pair acquisition module is used to obtain miRNA and downstream cell type gene (mRNA) relationship pairs by associating exosomal miRNA expression data and single-cell mRNA expression data;

[0032] The upstream cell-miRNA-downstream cell network construction module is used to construct a complete upstream cell-miRNA-downstream cell functional interaction regulatory network based on upstream cell and miRNA relationship pairs, miRNA and downstream cell type gene (mRNA) relationship pairs, and single-cell transcriptome cell communication data.

[0033] This invention also provides the application of the above-described method or system in the construction of cell-miRNA-cell networks.

[0034] The beneficial effects and innovations of this invention lie in the development of a single-cell miRNA bioinformatics analysis method, which can identify primary miRNAs and their expression data in mRNA cellular groups within single-cell nuclear transcriptome data, thereby obtaining upstream cell-miRNA relationship pairs; the development of a combined analysis method, which can correlate exosomal miRNA expression data and single-cell mRNA expression data to obtain miRNA-downstream cell type gene (mRNA) relationship pairs; and the development of a cell network construction method, which can construct a complete upstream cell-miRNA-downstream cell functional interaction regulatory network based on the results of the first two methods and single-cell transcriptome cell communication data, providing data support for identifying the source cell type and downstream regulatory cell type of specific miRNAs, and clarifying the interactions and regulation between different cell types. This provides a complete technical solution for the sequencing analysis of exosomal miRNAs from the same tissue and single-cell nuclear sequencing analysis, from experimental to bioinformatics analysis.

[0035] The implementation of this invention is based on corresponding experimental data, so other methods cannot be implemented at all due to the lack of basic data. Therefore, at the bioinformatics level, it is a completely innovative content that matches the experimental process. Attached Figure Description

[0036] Figure 1 This is the complete flowchart of the present invention.

[0037] Figure 2It is a primary miRNA file in gff3 format from the mirbase website. It contains the start (fourth column), end (fifth column), and strand direction (seventh column) of the primary miRNA on the chromosome (first column).

[0038] Figure 3 The single-cell analysis tool CellRanger requires GTF file format. (Content and...) Figure 2 It is consistent with primarymiRNAgff3, but is compatible with the format required for CellRanger quantification.

[0039] Figure 4 This is a network diagram of cell-miRNA-cell interactions. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to the specific embodiments and accompanying drawings. Except for the contents specifically mentioned below, the processes, conditions, and experimental methods for implementing the present invention are all common knowledge and general knowledge in the art, and the present invention does not have any particular limitations.

[0041] This invention provides a method for jointly analyzing exosomal miRNAs and single-cell nuclear transcriptome data from the same tissue to obtain miRNA-mediated cell population regulatory networks. By identifying primary miRNAs and their expression data in mRNA cellular groups within the single-cell nuclear transcriptome data, upstream cell-miRNA relationship pairs can be obtained. By correlating exosomal miRNA expression data and single-cell mRNA expression data, downstream cell type gene (mRNA) relationship pairs can be obtained. Finally, based on the upstream cell-miRNA relationship pairs, the miRNA-downstream cell type gene (mRNA) relationship pairs, and single-cell transcriptome cell communication data, a complete upstream cell-miRNA-downstream cell functional interaction regulatory network can be constructed. This provides data support for identifying the source cell type and downstream regulatory cell type of specific miRNAs, and clarifying the interactions and regulation between different cell types. This invention provides a complete technical solution for the sequencing and analysis of exosomal miRNAs from the same tissue and single-cell nuclear sequencing, from experimental to bioinformatics analysis.

[0042] The complete flowchart of this invention can be found here. Figure 1 The specific implementation steps are as follows:

[0043] 1. Identification, expression, and specific expression analysis of primary miRNAs in single-cell nuclei

[0044] 1) Based on the project species information, download the primary miRNAgff file from the mirbase website (https: / / www.mirbase.org / ftp / CURRENT / genomes / ). The file format is as follows: Figure 2 .

[0045] 2) Extract the row in the primary miRNAgff file where the third column is miRNA_primary_transcript. Modify the second column to miRNA and the third column to exon. Extract the miRNAid after Name= in the ninth column and edit it. Convert it to GTF format, which is compatible with the single-cell analysis tool CellRayr. Figure 3 .

[0046] 3) Based on the primary miRNA gtf file from step 2) and the previously obtained single-cell nuclear transcriptome data, routine single-cell transcriptome analysis was performed using CellRanger software.

[0047] 4) Based on the single-cell transcriptome mRNA cell clustering results and the miRNA-cell correspondence obtained by routine single-cell transcriptome analysis in step 3) using CellRanger software, the miRNAs of each cell are mapped back to the cell type of mRNA, and the average expression file of miRNAs in each cell population is obtained (as shown in Table 1). This table can show the expression level of miRNAs in each cell population, so that whether a cell population secretes a miRNA can be identified by whether the miRNA is expressed in a certain cell population.

[0048] In the table, the first column is the miRNA ID, and the following columns are the miRNA expression levels in each cell type. B_cell represents B lymphocytes, Chondrocytes represent chondrocytes, and T_cells represent T lymphocytes.

[0049] Table 1. Examples of average miRNA expression values ​​in different cell populations.

[0050] miRNA_id B_cell Chondrocytes T_cells hsa-let-7a-2-3p 0.08219630161850683 0.0 2.107880845711553 hsa-let-7a-3p 3.49334281878654 3.755384231117122 71.70927975116757 hsa-let-7a-5p 783.5773433292254 946.1527292724325 9826.775178719365 hsa-let-7b-3p 3.0412631598847524 2.2450666599069757 47.57197751792154

[0051] 5) The Bimod test was used to examine the differences in miRNAs between each cell population and all other cell populations. The specific marker miRNAs of each cell population were screened out (as shown in Table 2). This obtained the upstream cell type-miRNA relationship pairs, indicating that the cell population specifically expresses these marker miRNAs, which are then used to establish a relationship network with the miRNA-downstream cell type.

[0052] The bimod test method refers to the bimod function in the seurat package (R language), a common method for analyzing differential gene expression among cell populations in single-cell studies to identify marker genes. The screening criteria define a gene as a marker gene for that cell cluster if the number of cells expressing the gene is >10% in the current cluster and <10% in other clusters. The identified marker miRNAs are specifically expressed by that cell population and may be closely related to the research context.

[0053] In Table 2, miRNA_id is the miRNA name, p_val is the significance p-value obtained by the bimod test, avg_logFC is the average value after removing the logarithm of the fold change, pct.1 is the proportion of the marker miRNA in the current cell population, pct.2 is the proportion of the marker miRNA in other cell populations, p_val_adj is the corrected p-value, cluster is the current cell population, and gene_diff is the ratio of pct.1 to pct.2.

[0054] Table 2. Examples of differential expression of specific expression marker miRNAs

[0055] miRNA_id p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene_diff hsa-mir-1295a 1.2e-26 3.87 0.266 0.004 4.5e-24 Hepatocytes 66.5 hsa-mir-4477a 1.9e-23 0.37 0.973 0.912 6.6e-21 Endothelial_cells 1.067 hsa-mir-10401 5.6e-13 0.57 0.729 0.524 1.9e-10 Hepatocytes 1.391 hsa-mir-4477b 1.3e-05 0.21 0.946 0.857 0.0 Endothelial_cells 1.104

[0056] 2. Correlation analysis between single-cell mRNA and exosomal miRNA expression

[0057] 1) Take the average mRNA expression of all cells under each cell type in a single-cell nucleus sample as the gene expression value of that cell type in that sample.

[0058] 2) Based on the one-to-one correspondence between exosomes and single-cell transcriptome samples, Spearman correlation analysis was performed on significantly differentially expressed miRNAs from exosomes and genes from various single-cell populations. The correlation results between genes from each cell population and exosome miRNAs were obtained (as shown in Table 3). The higher the correlation coefficient and the smaller the p-value, the more correlated the differentially expressed miRNAs from exosomes are with genes from a particular single-cell population. This expression correlation is likely derived from functional correlation. Through correlation analysis, miRNA-gene pairs that are expression-correlated and may also have functional correlations can be identified.

[0059] In Table 3, Category 1 represents category 1, which is generally miRNA; Category 2 represents category 2, which is generally gene; Correlation is the correlation coefficient, with a larger value indicating a higher correlation; Pvalue is the significance p-value, with a smaller value indicating greater significance; and AdjPvalue is the corrected p-value, with a smaller value indicating greater significance.

[0060] Table 3. Examples of correlation results between genes and exosomal miRNAs in different cell populations

[0061]

[0062]

[0063] 3) Using a correlation coefficient less than or equal to -0.8 and a p-value less than 0.05 as thresholds, the miRNA-gene (mRNA) correlation results from step 2) are screened, and negatively correlated miRNA-gene (mRNA) relationship pairs that meet the thresholds are extracted.

[0064] 4) Optionally, based on the known miRNA-gene target regulatory relationships (miRNA-gene relationship pairs) of species in the database, take the intersection with the results obtained in step 3 (format as shown in Table 3, which are miRNA-gene relationship pairs) (extract the results where the miRNA and the corresponding gene are completely identical) to obtain negatively correlated miRNA-gene (mRNA) relationship pairs that conform to the target regulatory relationship.

[0065] 3. Construction of upstream cell-miRNA-downstream cell network

[0066] 1) Cell communication refers to the process by which information emitted by a signaling cell is transmitted to a target cell through a medium (i.e., a ligand: a bioactive molecule recognized and bound by a receptor) and interacts with its corresponding receptor (a macromolecule that can recognize and selectively bind a certain ligand). This interaction then leads to a series of physiological and biochemical changes within the target cell through cell signal transduction, ultimately manifesting as a biological effect on the target cell as a whole. Based on the cell communication ligand or receptor genes and corresponding cell type data of mRNA-based single cells (as shown in Table 4, representing cell communication results in single-cell transcriptomics. Taking the first row of the table as an example, i.e., the receptor gene PVR of B_cell and the ligand gene CD96 of Tissue_stem_cells have a cell communication relationship), combined with the correlation pairs between exosomal miRNAs and single-cell transcriptomics genes (mRNAs) screened in step 2, using genes (miRNA-regulated target genes and ligand or receptor genes in cell communication) as mediators, we obtained miRNA-downstream ligand or receptor cell type relationship pairs (as shown in Table 5).

[0067] In Table 4, receptor is the receptor gene, ligand is the ligand gene, receptor_cell is the receptor cell type, ligand_cell is the ligand cell type, secreted indicates whether it is secreted, is_integrin indicates whether it is an integrin, pval is the significance p-value, receptor_expr is the receptor expression value, and ligand_expr is the ligand expression value.

[0068] In Table 5, the first column is the miRNA name, the second column is the cell type, and the third column is the ligand-receptor type of the cell type in cell communication (the example is the ligand). Taking the first row of Table 5 as an example, through the correlation pair between hsa-miR-204-3p and gene A (from Chondrocytes), and the B_cell ligand gene A from Table 4, using gene A as the medium, hsa-miR-204-3p is associated with the cell type Chondrocyte to which gene A belongs, thus obtaining the hsa-miR-204-3p-Chondrocytes relationship pair.

[0069] Table 4. Examples of single-cell transcriptome cell communication results

[0070]

[0071]

[0072] Table 5. Examples of miRNA-downstream cell relationships

[0073] mirna down lr hsa-miR-204-3p Chondrocytes ligand hsa-miR-204-3p Bcell ligand

[0074] 2) Based on the cell communication ligand or receptor gene and corresponding cell type data of mRNA single cells, combined with the relationship pair between exosomal miRNA and single-cell transcriptome gene (mRNA) obtained in step 2, the relationship pair between miRNA and downstream ligand or receptor cell type is obtained using gene (mRNA) as the medium.

[0075] 3) Combine the miRNA-receptor cell relationship pairs from steps 1) and 2) to obtain miRNA-downstream cell type relationship pairs based on cell communication.

[0076] 4) Based on the upstream cell type and miRNA relationship pair in step 5) of step 1 and the miRNA and downstream cell type relationship pair in step 3) above, upstream cell-miRNA-downstream cell relationship pairs are obtained using miRNA as a medium (as shown in Table 6).

[0077] In Table 6, up represents the upstream cell type, miRNA represents the miRNA name, down represents the downstream cell type, lrtype represents the downstream cell ligand / recipient type, and lr represents the ligand / recipient cell corresponding to the downstream cell in cell communication. Taking the first row of Table 6 as an example, the relationship pairs between hsa-miR-204-3p and downstream cells (Chondrocytes) obtained in the previous step, and the relationship pairs between upstream cells (Hepatocytes) and hsa-miR-204-3p) in step 1 (step 5), are combined using miRNA hsa-miR-204-3p as the medium to obtain the relationship network between upstream cells (Hepatocytes) and downstream cells (Chondrocytes).

[0078] Table 6. Examples of miRNA-downstream cell relationships

[0079] up mirna down lrtype lr Hepatocytes ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​

[0080] By incorporating cell-cell relationships in single-cell transcriptome gene communication into the upstream cell-miRNA-downstream cell network, a cell-miRNA interaction network including cell-cell interactions is obtained, such as... ​ It is a visual network of upstream cells-miRNA-downstream cells, with circles representing miRNAs, squares representing upstream and downstream cells, and lines representing the relationship between two elements.

[0081] The scope of protection of this invention is not limited to the above embodiments. Any variations and advantages that can be conceived by those skilled in the art without departing from the spirit and scope of this invention are included in this invention and are protected by the appended claims.

Claims

1. A method for constructing an upstream cell-miRNA-downstream cell network based on the combination of exosomal miRNA and single-cell nuclear transcriptome, characterized in that, The method includes the following steps: Step 1: Based on single-cell nuclear transcriptomics, obtain expression data of primary miRNAs in different cell types; Step 2: Based on the expression data of primary miRNAs of different cell types obtained in Step 1, perform differential expression analysis of primary miRNAs for each cell type with all other cell types to obtain upstream cell type-miRNA relationship pairs; Step 3: Perform correlation analysis on the pre-obtained exosomal miRNA results and mRNA expression data of different cell types in single nuclei to obtain negatively correlated miRNA-mRNA relationship pairs; Step three includes: Step 3.1: Take the average value of the mRNA expression of all cells under each cell type in a single-cell nucleus mRNA sample, and use it as the mRNA expression value of that cell type in that sample; Step 3.2: Perform correlation analysis on the pre-obtained exosomal miRNA sequencing expression data and the mRNA expression data for each cell type obtained in Step 3.1 to obtain exosomal miRNA-single-cell transcriptome mRNA relationship pairs; Step 3.3: Based on the default threshold of correlation coefficient ≤ -0.8 and P value less than 0.05, screen the exosome miRNA-single-cell transcriptome mRNA relationship pairs in Step 3.2 and extract the negatively correlated miRNA-mRNA relationship pairs that meet the threshold. Step 3.3 also includes: studying the miRNA-mRNA target relationship of species based on the miRWalk and miRDB databases, extracting the intersection of the exosomal miRNA-single-cell transcriptome mRNA results from step 3.2, and obtaining negatively correlated miRNA-mRNA relationship pairs that match the target relationship; The intersection extraction refers to comparing the miRNA-gene relationship pairs in the database with the miRNA-gene relationship pairs of exosomes and single cells obtained in step 3, and taking the same values ​​for miRNAs and corresponding genes to obtain the screened miRNA-gene relationship pairs. Step 4: Based on the cell communication ligand gene, receptor gene and corresponding cell type data of mRNA single cells, combined with the miRNA-mRNA correlation relationship pairs obtained in step 3 under each cell type, and using miRNA regulatory target gene and ligand or receptor gene in cell communication as mediators, obtain miRNA-downstream cell type relationship pairs. Step 5: Based on the miRNA-downstream cell type relationship pairs obtained in Step 4 and the upstream cell type-miRNA relationship pairs obtained in Step 2, using miRNA as a medium, and combining the ligand cell and receptor cell interaction relationships in cell communication, an upstream cell-miRNA-downstream cell network containing cell-cell communication relationships is obtained.

2. The method as described in claim 1, characterized in that, Step one includes: Step 1.1: Based on the pre-obtained single-cell nuclear transcriptome data and the miRNA_primary_transcript gff file of the corresponding species in the mirbase database, the Cell Ranger workflow is used to obtain the expression results of primary miRNAs in different cells; Step 1.2: Based on the pre-obtained single-cell nuclear mRNA cell cluster data, the identified cells expressing primary miRNA are regressed to the single-cell mRNA cell type to obtain the expression data of primary miRNA for each cell type.

3. The method as described in claim 1, characterized in that, Step two is performed using the single-cell transcriptome bimod test method; the upstream cell type-miRNA relationship refers to the miRNA data specifically expressed by each cell type relative to other cell types.

4. The method as described in claim 1, characterized in that, Step five includes: Step 5.1: Based on the miRNA-downstream ligand and receptor cell type relationship pairs obtained in Step 4 and the upstream cell type-miRNA relationship pairs obtained in Step 2, use miRNA as a medium to obtain the upstream cell type-miRNA-downstream ligand cell type and upstream cell type-miRNA-downstream receptor cell type networks, respectively. Step 5.2: Based on the upstream cell type-miRNA-downstream ligand cell type and upstream cell type-miRNA-downstream receptor cell type networks obtained in Step 5.1, and combined with the ligand cell and receptor cell interaction relationship in cell communication, an upstream cell-miRNA-downstream cell complete network containing cell-cell relationships is obtained by integrating according to the common cell types.

5. A system for implementing the method as described in any one of claims 1-4, characterized in that, The system includes: a data input module, an upstream cell and miRNA relationship pair acquisition module, a miRNA and downstream cell type mRNA relationship pair acquisition module, and an upstream cell-miRNA-downstream cell network construction module; The data input module is used to input pre-obtained data on existing exosomal miRNAs and single-cell transcriptome mRNAs. The upstream cell and miRNA relationship pair acquisition module is used to obtain upstream cell and miRNA relationship pairs by identifying primary miRNAs and their expression data in mRNA cellular groups in single-cell nuclear transcriptome data. The miRNA and downstream cell type mRNA relationship pair acquisition module is used to obtain miRNA and downstream cell type mRNA relationship pairs by associating exosomal miRNA expression data and single-cell mRNA expression data. The upstream cell-miRNA-downstream cell network construction module is used to construct a complete upstream cell-miRNA-downstream cell functional interaction regulatory network based on upstream cell and miRNA relationship pairs, miRNA and downstream cell type mRNA relationship pairs, and single-cell transcriptome cell communication data.

6. The application of the method as described in any one of claims 1-4, or the system as described in claim 5, in the construction of cell-miRNA-cell networks.

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