Method for determining refractory ITP pathogenesis of children

Abstract

The embodiment of the invention provides a method for determining children refractory ITP pathogenesis, at least part of pathogenesis of RITP is determined, and the method comprises the following steps: S1, collecting a bone marrow sample; s2, carrying out sequencing treatment by utilizing the bone marrow sample; the sequencing treatment comprises single cell transcriptome sequencing; s3, obtaining cell type information of RITP and CITP according to a sequencing result; s4, comparing the cell type information of RITP and CITP, and determining the main destroying cell type causing immune destruction to the target cell in the multiple cell types of intractable ITP; s5, performing communication interaction analysis by using the bone marrow sample, and constructing a first cell communication network and a second cell communication network corresponding to the RITP and the CITP respectively; s6, comparing the first cell communication network with the second cell communication network, and determining a receptor which is not included in the second cell communication network in the first cell communication network as an undetermined receptor; and S7, determining the specific receptor between the main destroyed cell type of the RITP and the target cell according to the expression condition of the to-be-determined receptor in the first cell communication network.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method for determining the pathogenesis of refractory ITP in children. Background Art

[0002] Primary Immune Thrombocytopenia (ITP) is the most common acquired immune bleeding disease in childhood, and its clinical manifestations include thrombocytopenia, prolonged hemostasis time, spontaneous bleeding of the skin and mucous membranes, and megakaryocyte maturation disorder in the bone marrow. Chronic ITP (CITP for short) refers to thrombocytopenia that lasts for more than 12 months, while refractory ITP (RITP) refers to splenectomy that is ineffective or relapses after effective treatment and requires hormone treatment and other diseases that cause thrombocytopenia need to be excluded. In most children with ITP, the platelet count can gradually return to normal within 12 months after diagnosis, while about 5% to 10% of children will develop chronic or refractory ITP. In recent years, people have gradually discovered that the pathogenesis of ITP is a highly complex process that mainly includes two aspects: the decline of self-immune tolerance and the increase of autoimmune activation. Although current studies have found that the pathogenesis of chronic refractory ITP in children may be related to immune-related gene mutations, the pathogenesis of RITP is still not fully understood, and there is a lack of a more in-depth description of the bone marrow immune microenvironment. Therefore, it is an important challenge for pediatric hematologists to find the true cause of bone marrow immune disorders in children with RITP through new technologies and methods to reduce nonspecific immunotherapy and achieve precise targeted treatment for children with RITP. Summary of the invention

[0003] The purpose of the present invention is to provide a method for determining the pathogenesis of refractory ITP in children, so as to determine at least part of the pathogenesis of RITP and provide guidance for achieving precise targeted treatment of children with RITP.

[0004] One embodiment of the present invention provides a method for determining the pathogenesis of refractory ITP in children, wherein the pathogenesis includes specific receptor information associated with target cells, and the method comprises the following steps:

[0005] S1: Collect bone marrow samples, including bone marrow samples from children with refractory ITP and chronic ITP;

[0006] S2: performing sequencing processing using the bone marrow sample; the sequencing processing includes single-cell transcriptome sequencing;

[0007] S3: Obtaining cell type information of the refractory ITP and the chronic ITP according to the results of the sequencing process; the cell type information includes cell type, cell subtypes of each cell type, and the number of each cell type;

[0008] S4: comparing the cell type information of the refractory ITP and the chronic ITP, and determining the main destructive cell type that causes immune destruction to the target cells among the multiple cell types of the refractory ITP;

[0009] S5: performing communication interaction analysis using the bone marrow sample to construct a first cell communication network corresponding to the refractory ITP and a second cell communication network corresponding to the chronic ITP;

[0010] S6: comparing the first cell communication network and the second cell communication network, and determining in the first cell communication network a receptor not included in the second cell communication network as a pending receptor according to the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the first cell communication network being less than the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the second cell communication network;

[0011] S7: Determine the specific receptor between the main damaging cell type of the refractory ITP and the target cell according to the expression of the undetermined receptor in the first cell communication network in multiple cell subtypes of the main damaging cell type.

[0012] In some embodiments, S7 specifically includes S71: determining that the receptor among the multiple receptors to be determined, which is expressed in greater than or equal to a first preset number of cell subtypes in multiple cell subtypes of the main destructive cell type, is a specific receptor between the main destructive cell type of the childhood refractory ITP and the target cell.

[0013] In some embodiments, S7 specifically includes S72: determining that the multiple pending receptors whose total expression levels in multiple cell subtypes of the main destructive cell type are greater than or equal to a second preset number are specific receptors between the main destructive cell type of the childhood refractory ITP and the target cells.

[0014] In some embodiments, the pathogenesis also includes targeted immune gene information, and the method also includes: S8: detecting the expression levels of multiple immune-related genes in multiple cell types in the bone marrow sample; S9: determining the targeted immune genes of the refractory ITP based on the expression levels of the multiple immune-related genes.

[0015] In some embodiments, step S9 specifically includes: determining that the immune-related genes among the multiple immune-related genes have a higher expression level in the main destroyed cell type than in other cell types among the multiple cell types, and the immune-related genes whose expression level in the main destroyed cell type in the bone marrow sample of the child with refractory ITP is higher than the expression level in the main destroyed cell type in the bone marrow sample of the child with chronic ITP are the targeted immune genes.

[0016] In some embodiments, after the sequencing process is performed in step S2, step S10 is further included: constructing a sequencing library of the refractory ITP and the chronic ITP according to the sequencing process.

[0017] In some embodiments, the pathogenesis also includes hub gene information; the method further includes step S11: using the sequencing library to perform protein interaction network analysis of the refractory ITP to determine multiple hub genes of the refractory ITP.

[0018] In some embodiments, step S12 is also included: performing qRT-PCR processing on the bone marrow sample to obtain a first sample corresponding to the refractory ITP and a second sample corresponding to the chronic ITP, and detecting the expression levels of the multiple hub genes in the first sample and the second sample, respectively; determining the hub gene among the multiple hub genes whose expression level difference between the first sample and the second sample is greater than a preset difference value as the target hub gene.

[0019] In some embodiments, the sequencing process also includes immune group sequencing.

[0020] In some embodiments, S3 further includes S13: constructing a bone marrow cell type composition map of the refractory ITP based on the results of the sequencing process.

[0021] The above embodiments of the present invention have at least one or more of the following beneficial effects: The method for determining the pathogenesis of refractory ITP in children provided by the above embodiments of the present invention can use the bone marrow samples of children with refractory ITP and chronic ITP to perform single-cell transcriptome sequencing to obtain the cell type information of the refractory ITP and the chronic ITP, breaking through the prior art's understanding of the RITP bone marrow immune microenvironment, and can track immune cell subpopulations more comprehensively and accurately, providing a more accurate basis for analyzing the bone marrow immune microenvironment of refractory ITP in children. And the main destructive cell type can be determined based on the cell type information, and the specific receptors between the main destructive cell type and the target cell can be determined based on the communication interaction analysis, which can help determine the potential molecular functions related to RITP-specific immunotherapy, and provide a new strategy for targeted immunotherapy of children with RITP. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The specific implementation modes of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Figure 1 A schematic flow chart of a method for determining the pathogenesis of refractory ITP in children provided in an embodiment of the present invention.

[0024] Figure 2 Schematic diagram of the cell communication network of 8 cell types in CITP included in the second cell communication network.

[0025] Figure 3 Schematic diagram of the cell communication network of 8 cell types in RITP included in the first cell communication network.

[0026] Figure 4 Schematic diagram of the cell communication network of the second cell communication network including megakaryocytes and macrophages in CITP, type 1 conventional dendritic cells, type 2 conventional dendritic cells, atypical monocytes and classical monocytes 1-5.

[0027] Figure 5 Schematic diagram of the cell communication network of the first cell communication network including megakaryocytes and macrophages, type 1 conventional dendritic cells, type 2 conventional dendritic cells, atypical monocytes and classical monocytes 1-5 in RITP.

[0028] Figure 6 Schematic diagram of the expression levels of six potential receptors in megakaryocytes during RITP.

[0029] Figure 7 Schematic diagram of the expression levels of seven specific ligands in mononuclear phagocytes during RITP.

[0030] Figure 8 Schematic diagram of the expression level of IFH1 in eight cell types of RITP.

[0031] Fig. 9 Schematic representation of the expression levels of NLRP1 in eight cell types during RITP.

[0032] Fig.10 The figure is a comparison of the expression levels of IFIH1 in RITP and CITP.

[0033] Fig.11 The figure is a comparison of the expression levels of NLRP1 in RITP and CITP.

[0034] Fig.12 Schematic diagram of the visualized protein interaction network.

[0035] Fig.13Schematic diagram of the top 100 genes in the protein interaction network.

[0036] Fig.14 Schematic diagram of the expression levels of the above five hub genes verified by qRT-PCR.

[0037] Fig.15 Schematic diagram of the bone marrow cell type composition map in children with refractory ITP. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] It should also be noted that the division of multiple embodiments in the present invention is only for the convenience of description and should not constitute a special limitation. The features in various embodiments can be combined and referenced to each other without contradiction.

[0042] The embodiment of the present invention provides a method for determining the pathogenesis of refractory ITP in children, wherein the pathogenesis includes specific receptor information associated with target cells, and the method includes the following steps:

[0043] S1: Collect bone marrow samples, including bone marrow samples from children with refractory ITP and chronic ITP;

[0044] S2: performing sequencing processing using the bone marrow sample; the sequencing processing includes single-cell transcriptome sequencing;

[0045] S3: obtaining cell type information of the refractory ITP and the chronic ITP according to the results of the sequencing process, wherein the cell type information includes cell type, cell subtypes of each cell type, and the quantity of each cell type;

[0046] S4: comparing the cell type information of the refractory ITP and the chronic ITP, and determining the main destructive cell type that causes immune destruction to the target cells among the multiple cell types of the refractory ITP;

[0047] S5: performing communication interaction analysis using the bone marrow sample to construct a first cell communication network corresponding to the refractory ITP and a second cell communication network corresponding to the chronic ITP;

[0048] S6: comparing the first cell communication network and the second cell communication network, and determining in the first cell communication network a receptor not included in the second cell communication network as a pending receptor according to the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the first cell communication network being less than the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the second cell communication network;

[0049] S7: Determine the specific receptor between the main damaging cell type of the refractory ITP and the target cell according to the expression of the undetermined receptor in the first cell communication network in multiple cell subtypes of the main damaging cell type.

[0050] Specifically, MKs (megakaryocytes) are the target cells of ITP. In step S1, bone marrow samples of children with refractory ITP and chronic ITP are collected according to clinical diagnostic criteria. According to the clinical classification and diagnostic criteria (National Health Commission, Diagnosis and Treatment Guidelines for Primary Immune Thrombocytopenia in Children, 2019), the criteria for diagnosing refractory ITP in children must meet the following conditions: I. Ineffective or relapse after splenectomy; II. Drug treatment is still required after splenectomy to reduce the risk of bleeding; III. Other causes of thrombocytopenia need to be excluded. The diagnostic criteria for chronic ITP refer to repeated thrombocytopenia with a course of more than 1 year. The clinical data of each child were recorded, including the child's age, gender, duration of illness, disease type, non-specific immunotherapy methods and reactions. ② Exclusion criteria: Other diseases that may cause thrombocytopenia, such as pseudothrombocytopenia, secondary thrombocytopenia, and hereditary thrombocytopenia, need to be excluded.

[0051] The sequencing process in step S2 may also include immune group sequencing. Step S2 may be followed by step S10 to construct a sequencing library for refractory ITP and chronic ITP based on the sequencing process. The sequencing library may include a transcriptome sequencing library and an immune group sequencing library. Single cell transcriptome immune receptor (TCR & BCR) kit. The chip is a portable microfluidic chip that efficiently integrates the different steps of single-cell transcriptome sequencing pre-processing, including single-cell capture, cell lysis, molecular labeling, and cell mRNA capture. The microfluidic chip captures single cells and adds millions of TCR&BCR Barcoding Beads carrying unique cell labels (Cell Barcode) into the microwells of the chip to ensure that only one TCR&BCR Barcoding Bead falls into each microwell. After cell lysis, TCR&BCR Barcoding Beads with unique cell labels (TCR&BCR Barcoding Beads) and molecular labels (UMI) mark cells and mRNA by capturing the poly (A) tail and TCR&BCR region on mRNA. The TCR&BCR Barcoding Beads in the chip are collected, and the mRNA captured by the TCR&BCR Barcoding Beads is reverse transcribed into cDNA and amplified. A portion of the obtained cDNA is fragmented, connected to adapters, and other steps to construct a sequencing library (transcriptome sequencing library) suitable for the Illumina sequencing platform. The remaining cDNA will be enriched for immune receptors (TCR & BCR) using multiplex PCR technology, and the enriched products will be amplified by PCR to construct a sequencing library (immune group sequencing library) suitable for the Illumina sequencing platform.

[0052] In step S3, the cell type information of the refractory ITP and the chronic ITP can be obtained through steps such as S31: quality control, dimensionality reduction and clustering (seurat); 32: differentially expressed gene analysis; and S33: cell type annotation.

[0053] Specifically in S31, cells can be screened according to gene counts less than 200, gene counts ranked in the top 2%, and UMI counts ranked in the top 2%. Cells with mitochondrial content exceeding 20% ​​were removed. Dimensionality reduction and clustering were performed using the functions provided by Seurat v3.1.2. Afterwards, all gene expressions were normalized and scored using the NormalizeData and ScaleData functions, and the top 2000 variable genes were found for PCA analysis using the FindVariableFeautres function using the top 20 principal components. Cells were divided into multiple clusters using the FindClusters function. Batch effects between samples were removed using Harmony. Finally, the UMAP algorithm was used to visualize the cells in two-dimensional space.

[0054] Specifically, in S32, the differentially expressed genes (DEGs) can be determined based on the Wilcox likelihood ratio test using the Seurat FindMarkers function with default parameters. If a gene is expressed in more than 10% of cells in a cluster and its log (fold ratio) value is greater than 0.25, then the gene is a DEG. For each cell type annotation of a cluster, the expression of classic markers found in these DEGs and the information recorded in the literature are comprehensively considered, and the Seurat DoHeatmap / DotPlot / Vlnplot function is used to generate a heat map / dot plot / violin plot to display the expression of each cell type marker. Cells expressing different cell type markers are identified as bimodal cells, and these cells are manually excluded.

[0055] Specifically, in S33, the cell type consistency in each cluster can be determined using the SynEcoSys database based on the expression of classic markers found in DEGs. Seurat v3.1.2 DoHeatmap / DotPlot / Vlnplot was used to generate heat maps / dot plots / violin plots, which were used to display marker expression to determine each cell type.

[0056] For example, after step S2 and after step S3, for example, pathway enrichment analysis, pseudo-time series analysis, transcription factor regulatory network analysis and TCR / BCR analysis can also be performed.

[0057] Pathway enrichment analysis Gene ontology (GO) and Kyoto Gene and Genome Database (KEGG) enrichment analysis can be performed using the “clusterProfiler” R package 3.16.1 to investigate the potential functions of DEGs. Pathways with p_adj values ​​less than 0.05 were considered to be significantly enriched. Gene ontology gene sets including molecular function (MF), biological process (BP), and cellular component (CC) categories were used as references.

[0058] Pseudo-sequential analysis can reconstruct cell differentiation trajectories using Monocle2. Highly variable genes (HVG) are used to sort cells in a spatiotemporal differentiation order. FindVairableFeatures and dimensionality reduction are performed using DDRTree. Finally, the trajectory is visualized using the plot_cell_trajectory function.

[0059] Transcription factor regulatory network analysis can be constructed using pyscenic v0.11.0 based on the scRNA expression matrix and transcription factors in AnimalTFDB. First, GRNBoost2 is used to predict the regulatory network based on the co-expression of regulators and target factors. After that, CisTarget is used to exclude indirect target factors and find transcription factor binding motifs. After that, AUCell is used to quantify the activity of the regulators in each cell. The top-ranked TF regulators with high RSS (regulator specificity score) are visualized using pheatmap in R.

[0060] TCR / BCR analysis can be performed using GRCh38 as a reference and the Cell Ranger (v4.0.0) vdj pipeline for identification of TCR clonotypes. In short, a TCR / BCR diversity metric is obtained, which includes clonotype frequency and barcode information. Each unique TRA-TRB pair is defined as a clonotype. If a clonotype appears in at least two cells, the cell with the clonotype is considered to be clonally generated and the number of such cells is used to represent the clonality of the clonotype. Using GRCh38 as a reference, the Cell Ranger (v4.0.0) vdj pipeline is used for identification of BCR clonotypes. In short, a TCR / BCR diversity metric is obtained, which includes clonotype frequency and barcode information. Each unique IGH-IGK / IGL pair is defined as a clonotype. If a clonotype appears in at least two cells, the cell with the clonotype is considered to be clonally generated and the number of such cells is used to represent the clonality of the clonotype.

[0061] In step S4, cell types with significantly different numbers in refractory ITP and chronic ITP can be specifically identified as the main damaged cell types.

[0062] In step S5, cell interaction analysis was performed using CellPhoneDB v2.1.0, specifically based on known receptor-ligand interactions between two cell types / subtypes. The cluster labels of all cells were randomly permuted 1000 times to calculate the null distribution of the average ligand-receptor expression levels of the interaction clusters. A cutoff value was used to set a threshold for the expression of each individual ligand or receptor based on the average log gene expression distribution of all genes in all cell types. When the p value < 0.05 and the average log expression > 0.1, the interaction of the cells was defined as a significant interaction, which was visualized using the circlize v0.4.10R package.

[0063] In step S6, the number of receptors to be determined may be multiple.

[0064] In some embodiments, step S7 may specifically include S71: determining that among the multiple undetermined receptors, the undetermined receptor expressed in a plurality of cell subtypes greater than or equal to a first preset number of cell subtypes in the multiple cell subtypes of the main destructive cell type is a specific receptor between the main destructive cell type of the pediatric refractory ITP and the target cell. The first preset number can be designed according to actual needs, and step S71 can be understood as if a certain undetermined receptor among the multiple undetermined receptors is expressed in most of the cell subtypes of the main destructive cell, then the undetermined receptor can be determined to be a specific receptor between the main destructive cell type of the pediatric refractory ITP and the target cell.

[0065] In some embodiments, the S7 specifically includes S72: determining that the receptors to be determined, whose total expression amount in the multiple cell subtypes of the main destructive cell type is greater than or equal to the second preset number among the multiple receptors to be determined, are specific receptors between the main destructive cell type of the pediatric refractory ITP and the target cells. The second preset number can be designed according to actual needs, and step S72 can be understood as that if a certain receptor to be determined is only expressed in a small number of cell subtypes in the main destructive cell type, but its expression amount is large, then the receptor to be determined can be determined as a specific receptor between the main destructive cell type of the pediatric refractory ITP and the target cell.

[0066] In the past, there have been few studies on the pathogenesis of refractory ITP in children, and there is still a gap in the study of the bone marrow immune microenvironment of RITP in children from the level of single-cell transcriptome and immune group sequencing. The method for determining the pathogenesis of refractory ITP in children provided in the above embodiment of the present invention can use the bone marrow samples of children with refractory ITP and chronic ITP to perform single-cell transcriptome sequencing to obtain the cell type information of the refractory ITP and the chronic ITP, breaking through the prior art's understanding of the RITP bone marrow immune microenvironment, and can track immune cell subpopulations more comprehensively and accurately, providing a more accurate basis for analyzing the bone marrow immune microenvironment of refractory ITP in children. And determining the main destructive cell type based on cell type information, and determining the specific receptors between the main destructive cell type and the target cell based on communication interaction analysis, can help determine the potential molecular functions related to RITP-specific immunotherapy, and provide a new strategy for targeted immunotherapy of children with RITP.

[0067] In some embodiments, the pathogenesis further includes targeted immune gene information, and the method for determining the pathogenesis of refractory ITP in children further includes:

[0068] S8: detecting the expression levels of multiple immune-related genes in multiple cell types in the bone marrow samples of the refractory ITP and the chronic ITP;

[0069] S9: Determine the targeted immune gene for the refractory ITP according to the expression levels of the multiple immune-related genes.

[0070] In step S8, the gene capture strategy and the GenCap custom enrichment kit (MyGenostics, China) can be used to select target genes related to thrombocytopenia and / or autoimmune diseases. The immune-related genes (a total of 23) are as follows: TNFRSF13B, CARD11, CBL, RAG2, AIRE, C1R, LAT, LRBA, CASP10, CD40LG, FASLG, CATA2, IFIH1, IL2KG, JAK2, LZTR1, NHEJ1, NLRP1, NLRP2, NLRP12, PIK3CD, PIK3R1, PLCG2.

[0071] The step S9 specifically includes: determining that the immune-related gene whose expression level in the main destroyed cell type is higher than that in other cell types among the multiple cell types, and whose expression level in the main destroyed cell type in the bone marrow sample of the child with refractory ITP is higher than that in the main destroyed cell type in the bone marrow sample of the child with chronic ITP is the targeted immune gene.

[0072] In this embodiment, by comparing the expression levels of immune-related genes in refractory ITP and chronic ITP, targeted immune genes with a high correlation with refractory ITP are further determined from multiple immune-related genes, which can provide guidance for specific targeted treatment of refractory ITP.

[0073] The pathogenesis also includes hub gene information. In some embodiments, the method for determining the pathogenesis of refractory ITP in children also includes step S11: using the sequencing library to perform protein interaction network analysis of the refractory ITP to determine multiple hub genes of the refractory ITP.

[0074] In step S11, a protein interaction network can be constructed with the help of the STRING database. By setting the confidence greater than 0.9 and hiding unconnected nodes, a visual protein interaction network is constructed with Cytoscape. Hub genes can be determined with CytoHubba. CytoHubba is a Cytoscape plug-in that supports the use of topological analysis algorithms such as MCC, DMNC, MNC, Degree, EPC, BottleNeck, EcCentricity, Closeness, Radiality, Betweenness and Stress. These methods can be used to predict and explore important nodes in protein interaction networks. The scores of the topological algorithms are assigned to each node in the protein interaction network. According to the gene score, the top-ranked genes can be used as hub genes. In the present embodiment, 10 topological analysis algorithms (MCC, DMNC, MNC, Degree, EPC, BottleNeck, EcCentricity, Closeness, Radiality and Betweenness) are used to obtain the top 100 genes in the protein interaction network. Its intersection is the hub gene.

[0075] In some embodiments, the hub gene determined in step S11 can also be verified, and the target hub gene with greater impact on RITP can be determined among the hub genes. Step S11 also includes step S12: performing qRT-PCR on the bone marrow sample to obtain a first sample corresponding to the refractory ITP and a second sample corresponding to the chronic ITP, detecting the expression levels of the multiple hub genes in the first sample and the second sample respectively; determining the hub gene whose expression difference between the first sample and the second sample is greater than the preset difference value among the multiple hub genes as the target hub gene. The qRT-PCR (Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction) treatment in step S12 includes, for example, RNA detection, mRNA reverse transcription and other specific steps, wherein the mRNA reverse transcription can be reverse transcribed using the SureScript-First-strand-cDNA-synthesis-kit of Sevier Company. Verifying the hub gene through step S12 can further narrow the scope of the confirmed hub gene, obtain the target hub gene, and provide more accurate guidance.

[0076] In some embodiments, S3 further includes S11: constructing a bone marrow cell type composition map of refractory ITP according to the results of the sequencing process. The map can be used to provide a theoretical basis for subsequent research on other pathogenesis of RITP.

[0077] The specific steps of the method for determining the pathogenesis of refractory ITP in children provided in the embodiment of the present invention are illustrated below in combination with some specific experiments.

[0078] 1. Collection of bone marrow samples. Bone marrow samples were collected from one child with RITP and two children with CITP.

[0079] 2. Cell type information based on sequencing: After data preprocessing and quality assessment, a total of 18,712 cells were collected. Each cell has an average of 5,474 UMIs and 1,591 genes. Unsupervised cell clustering shows 8 parallel groups based on lineage-specific marker genes (corresponding to the 8 cell types identified in step 3 above), including T cells (specifically expressing CD3D, CD2, and TRBC2), B cells (specifically expressing MS4A1 and CD79A), mononuclear phagocytes (MPs, specifically expressing CD14 and VCAN), neutrophils (specifically expressing CAMP and LCN2), granulocyte-monocyte progenitors (GMPs, specifically expressing MPO, ELANE, and AZU1), hematopoietic stem cells (HSCs, specifically expressing MSI2, CD34, and HOPX), erythrocytes (specifically expressing HBA1, ALAS2, and SNCA), and megakaryocytes (MKs, specifically expressing PF4, PPBP, and ITGA2B). Mononuclear phagocytes, neutrophils, granulocyte-monocyte progenitor cells, and erythrocytes accounted for the majority of all samples. In RITP samples, the mononuclear phagocyte subset accounted for 55.39%, the erythrocyte subset accounted for 21.09%, the granulocyte-monocyte progenitor subset accounted for 8.55%, and the neutrophil subset accounted for 5.55%. In CITP samples, the mononuclear phagocyte subset accounted for 25.38%, the erythrocyte subset accounted for 17.09%, the granulocyte-monocyte progenitor subset accounted for 14.35%, and the neutrophil subset accounted for 29.71%. The number and ratio of each cell subset (that is, each cell type) in the bone marrow samples are shown in Table 1:

[0080] Table 1:

[0081] Cell Type RITP CITP Chi-square test MPs 4088(55.39％) 2876(25.38％) P<0.01 Neutrophils 410(5.55％) 3305(29.71％) P<0.01 Red blood cells 1557(21.09％) 1937(17.09％) P<0.01 B cells 174(2.36％) 741(6.54％) P<0.01 Hematopoietic stem cells 294(3.98％) 504(4.45％) P<0.01 T cells 190(2.57％) 294(2.59％) P<0.01 GMP 631(8.55％) 1626(14.35％) P<0.01 MKs 37(0.5％) 48(0.42％) P<0.01 total 7381(100％) 11331(100％) -

[0082] It can be seen from the data in Table 1 that the number of MPs in the RITP sample is significantly greater than the number of MPs in the CITP sample, and the number of MPs accounts for a large proportion of the total number of cells. According to this result, MPs can be determined as the main destructive cell type that causes immune damage to MKs (corresponding to the result of step S3). Considering that in RITP, genes involved in immunity-related diseases cause immune dysfunction due to incorrect splicing, resulting in immune damage to MKs or platelets. In addition, MKs are the cell subpopulation with the smallest proportion in the RITP group. According to the above results, it was found that MKs in the RITP group were significantly lower than those in the CITP group, so it is considered that reduced production or increased destruction of MKs may be one of the main pathogenesis of RITP.

[0083] Specifically, the characteristics of each cell type are analyzed as follows:

[0084] Characteristics of B cells: In this experiment, B cells were divided into 10 subtypes, including BCells (marker genes: VPREB1, VPREB3), plasma cells (effector B cells, marker genes: JCHAIN, MZB1, IGH G1) and 8 progenitor B cells (ProB1-8). Among them, progenitor B cells are the most important B cell subtype, accounting for more than 70% of B cells in both RITP and CITP samples. In addition, ProB2 is the highest B cell subpopulation in RITP (31%), which is significantly negatively correlated with cell aging, while ProB1 is the highest B cell subpopulation in CITP (20%), which is significantly positively correlated with DNA replication. BCells accounted for 8.62% of B cells in RITP samples and were significantly positively correlated with coronavirus disease COVID-19, Epstein-Barr virus infection, and viral myocarditis. Plasma cells accounted for 14.4% of B cells in RITP samples and were significantly positively correlated with protein processing, protein export, N-glycan biosynthesis, and various types of N-glycan biosynthesis in the endoplasmic reticulum. In addition, in RITP progenitor B cells, there were 506 up-regulated genes (top 10 up-regulated genes: IGHE, PAXBP1, FOS, SPI NK2, CST3, AIF1, NFKBIA, S100A4, LGALS1, TPT1) and 814 down-regulated genes (top 10 down-regulated genes: DEFA3, CMTM8, CD79B, CD81, CAMP, LTF, TC L1A, LCN2, HIFX, IGHM). The results of differential expression analysis showed that in RITP B cells, there were 683 up-regulated genes (the top 10 up-regulated genes were: PAXBP1, IGHE, RHOB, S100A10, VCAN, CST3, CCL3, FOS, CCL3L1, S100A4) and 745 down-regulated genes (the top 10 down-regulated genes were: CAMP, LTF, DEFA3, LCN2, PGLYRP1, MMP9, ELANE, AZU1, PRTN3, CTSG). In RITP plasma cells, there were 817 upregulated genes (top 10 upregulated genes: HBB, LYZ, HBA1, PAXBP1, HBA2, IGHV3-15, CST3, HBD, S100A9, AH SP) and 187 downregulated genes (top 10 downregulated genes: IGHV4-61, LCN2, DEFA3, LTF, CAMP, IGKV4-1, IGHA2, IGLV6-57, IGHV3-33, IGHGP).

[0085] Characteristics of erythrocytes: erythrocytes are divided into 8 subtypes, including erythrocytes (marker genes: BPGM, DCAF12, BCL2L1, MKRN1) and 7 erythrocyte progenitors (ProEryth1-7). Among them, ProEryth1 is the highest proportion of erythrocyte subpopulation in RITP (24.1%), which is significantly positively correlated with oxidative phosphorylation; erythrocytes are the highest proportion of erythrocyte subpopulation in CITP (44.8%), which is significantly negatively correlated with the function of ProEryth1. In addition, in RITP erythroid progenitor cells, there were 1405 upregulated genes (top 10 upregulated genes: PAXBP1, PRL23, RPS21, RPS23, RPS16, RPL38, RPS11, RPS27A, RPS12, TP T1) and 106 downregulated genes (top 10 downregulated genes: DEFA3, CAMP, LTF, LCN2, TRIM58, BPGM, IFIT1B, SNCA, HBG2, HBA2). In RITP erythrocytes, there were 169 upregulated genes (top 10 upregulated genes: PAXBP1, HIST1H1C, MT2A, GPX4, SMOX, TPT1, EIF1, IGHE, IGKC, RPS12) and 79 downregulated genes (top 10 downregulated genes: HBG2, GPX1, S100A8, HEMGN, DEFA3, MT-CO1, CAMP, GSPT1, SLC4A1, LTF).

[0086] Characteristics of granulocyte-monocyte progenitor cells: Granulocyte-monocyte progenitor cells are divided into 7 subtypes (GMP1-7). Among them, GMP1 is the most common granulocyte-monocyte progenitor cell subpopulation in RITP (39.5%), which is significantly negatively correlated with protein processing in the endoplasmic reticulum; GMP2 is the most common granulocyte-monocyte progenitor cell subpopulation in CITP (28.4%), which is significantly positively correlated with protein processing in the endoplasmic reticulum and ribosomes. In addition, there were 294 upregulated genes (top 10 upregulated genes: PAXBP1, IGHE, ID2, HEXIM1, JUN, S100A10, CXCL8, CD52, RPL38, S100A9) and 293 downregulated genes (top 10 downregulated genes: CAMP, LTF, DEFA3, DEFA 4, CEACAM6, PRTN3, ELANE, CST7, CTSG, AZU1) in RITP granulocyte-monocyte progenitor cells.

[0087] Characteristics of hematopoietic stem cells: Hematopoietic stem cells are divided into 5 subtypes (HSCs1-5). Among them, HSCs1 is the hematopoietic stem cell subpopulation with the highest proportion in RITP (63.3%), which is significantly negatively correlated with DNA replication; HSC s3 is the hematopoietic stem cell subpopulation with the highest proportion in CITP (29.6%), which is significantly positively correlated with carbon metabolism, protein export and endoplasmic reticulum protein processing. In addition, in RITP hematopoietic stem cells, there are 276 up-regulated genes (the top 10 up-regulated genes: IGHE, PAXBP1, GADD45B, HEXIM1, CXCL8, ID2, NFKBIA, NFKBIZ, CXCL2, TPT1) and 473 down-regulated genes (the top 10 down-regulated genes: DEFA3, LTF, CAMP, ELANE, AZU1, MPO, CTSG, CALR, PRTN3, SRG N).

[0088] Characteristics of megakaryocytes: In RITP megakaryocytes, there were 657 up-regulated genes (top 10 up-regulated genes: PAXBP1, IGHE, ID2, CXCL2, RPL38, RPL23, RPS12, MT-NR2, TPT1, MT-NR2) and 999 down-regulated genes (top 10 down-regulated genes: DEFA3, LTF, CAMP, ACRBP, NRGN, TUBB1, TUBA4A, G0S2, CLC, CCL5). The results of GO analysis included three categories: BP, CC, and MF. In terms of BP, the up-regulated DEGs were mainly enriched in co-translational protein targeting membrane, SRP-dependent co-translational protein targeting membrane, nuclear transcribed mRNA decomposition process, and nonsense-mediated decay. In terms of CC, the up-regulated genes were enriched in cytoplasmic ribosomes, ribosomal subunits, and ribosomes. In the case of MF, the upregulated genes were enriched in structural components of ribosomes, rRNA binding, and ubiquitin-protein ligase binding. In addition, the most enriched GO BP terms in the downregulated genes were neutrophil-mediated immunity, neutrophil degranulation, and neutrophil activation involved in immune response. In the case of CC, the downregulated genes were enriched in the secretory granule lumen, cytoplasmic vesicle lumen, and vesicle lumen. In the case of MF, the downregulated genes were enriched in cadherin binding, GTPase activity, and NAD binding. The above results indicate that the robust DEGs are mostly related to RITP.

[0089] Characteristics of mononuclear phagocytes: Mononuclear phagocytes are divided into 10 subtypes (mesenchymal stem cells, macrophages, type 1 conventional dendritic cells, type 2 conventional dendritic cells, atypical monocytes, and classical monocytes 1-5). Among them, classical monocyte 2 is the most common mononuclear phagocyte subpopulation in RITP and CITP (22.1%), which is significantly negatively correlated with focal adhesions, while classical monocyte 1 is the most common mononuclear phagocyte subpopulation in CITP (22.2%), which is significantly positively correlated with the IL-17 signaling pathway and the epithelial cell signaling pathway in Helicobacter pylori infection. Mesenchymal stem cells account for 0.2% of the mononuclear phagocytes in RITP samples, which is significantly positively correlated with the TGF-beta signaling pathway; macrophages account for 0.8% of the mononuclear phagocytes in RITP samples, which is significantly negatively correlated with focal adhesions. Among the mononuclear phagocytes in RITP samples, atypical monocytes account for 2%, which is significantly negatively correlated with protein processing in the endoplasmic reticulum. In addition, in RITP mononuclear phagocytes, there were 179 upregulated genes (top 10 upregulated genes: PAXBP1, IGHE, IER3, TPT1, CCL3, CC L3L1, TRAC, RPL23, RHOB, RPL38) and 314 downregulated genes (top 10 downregulated genes: LTF, CAMP, DEFA3, HLA-DRB6, TMEM176B, LCN2, BRI3, CEBPD, TMEM176A, MMP9).

[0090] Characteristics of neutrophils: Neutrophils are divided into 8 subtypes, including neutrophils 1-4 (marker genes: CAMP, LCN2) and neutrophil progenitors 1-4 (marker genes: DEFA3, DEFA4). Among them, neutrophil 3 is the highest proportion of neutrophil subpopulation in RITP (41%), which is significantly positively correlated with NOD-like receptor signaling pathway and Epstein-Barr virus infection; neutrophil 1 is the highest proportion of neutrophil subpopulation in CITP (22%), which is significantly negatively correlated with spliceosome. In addition, the results of differential expression analysis showed that in RITP neutrophils, there were 1006 upregulated genes (top 10 upregulated genes: VCAN, CST3, S100A10, JUN, CD52, PAXBP1, MS4A6A, LGALS1, CXCL8, TMSB10) and 588 downregulated genes (top 10 downregulated genes: LTF, PGLYRP1, ANXA3, LCN2, CAMP, CRISP3, CEACAM8, CD177, MMP9).

[0091] Characteristics of T cells: T cells are divided into 6 subtypes, including CD8MK167 (marker genes: HMGN 2, NUSAP1, STMN1), naive T cell CCR7 (marker genes: LDHB, PCED1B-AS1, NOSIP), naive T cell SELL (marker genes: IL2RG, CD99, CCL5), naive T cell TCF7 (marker genes: ALOX5AP, CAMP, S100P), NKT KLRD1 (marker genes: VCAN, FGL2) and NKT NKG7 (marker genes: CPVL, CST3). Among them, NKT KLRD1 is the highest proportion of T cell subpopulation in RITP (45.5%), which is significantly negatively correlated with primary immunodeficiency; naive T cell TCF7 is the highest proportion of T cell subpopulation in CITP (29.1%), which is significantly positively correlated with endocytosis and negatively correlated with ribosomes. In addition, in RITP T cells, there were 433 up-regulated genes (top 10 up-regulated genes: PAXBP1, IGHE, CST3, GADD45B, CXCL8, RHOB, S100A10, CCL3, GPX1, RPL23) and 476 down-regulated genes (top 10 down-regulated genes: CAMP, LTF, DEFA3, LCN2, PGLYRP1, MMP8, MMP9, CRISP3, AZU1, DEFA4).

[0092] 3. Transcription factor (TF) regulation analysis

[0093] This experiment also explored potential transcription factors that may govern various myeloid cell phenotypes. SOX5 can positively regulate B cells and negatively regulate other cells. ZNF594, FOXO3, NFIX, and ZNF577 can positively regulate red blood cells and negatively regulate other cells. GTF2B, SP7, ZNF418, ZBTB16, MEIS1, and SIX2 can positively regulate megakaryocytes and negatively regulate other cells. AR, RUNX2, and BCL11A can positively regulate plasma cells and negatively regulate other cells. NR2E1, TFF3, and IKZF1 can positively regulate neutrophils and negatively regulate other cells. MYBL1, HIVEP2, RORA, GATA3, and TCF7 can positively regulate T cells and negatively regulate other cells. In addition, there was a significant negative correlation between erythrocytes and MAZ, BRCA1, MYB, YBX1, HMG B1, and HLF, between neutrophils and YBX1, between erythrocyte progenitors and FOSB, JUNB, and BAC H2, between progenitor B cells and FOSB and RXRA, and between megakaryocytes and BACH2. In terms of B cell subtypes, ProB3 is positively regulated by ZNF467 and negatively regulated by ZBTB49, and ProB4 is negatively regulated by MAFF and POU2F2. ProB5 is positively regulated by VENTX, AHR, and ZNF528, and negatively regulated by ZBTB49. B cells and plasma cells are negatively regulated by LEF1, EBF1, and HMGB1. Plasma cells are positively regulated by TP73, MNT, NR1D1, PRDM1, and XBP1. B cells are positively regulated by SOX5. In terms of erythroid subtypes, the regulatory patterns of erythroid and ProEryth7 are similar. HOXA5, ZNF17, ZNF382, ZNF708, BCL6, HBP1, ARID3A, FOXO3, and TFE3 can positively regulate erythroid and ProEryth7, and negatively regulate other cells. It is worth noting that NR1I2, ERF, and RUNX1 can positively regulate ProEryth3 and negatively regulate other cells; ZNF398 can positively regulate ProEryth5 and negatively regulate other cells; FOXP4 can positively regulate ProEryth6 and negatively regulate other cells. In terms of granulocyte-monocyte progenitor cell subtypes, the regulatory patterns of granulocyte-monocyte progenitor 2 and granulocyte-monocyte progenitor 4 are similar. E2F8, CBFB, and CTCF can positively regulate granulocyte-monocyte progenitor 3. FOSL1, KLF5, and PRDM1 can positively regulate granulocyte-monocyte progenitor 7 and negatively regulate other cells. TFDP1 and RUNX2 can negatively regulate granulocyte-monocyte progenitor cells 6. In terms of hematopoietic stem cell subtypes, BRCA1, MYBL2, E2F1, EZH2, and SAP30 can negatively regulate hematopoietic stem cells 1.MAFF can negatively regulate hematopoietic stem cells 3. NFATC2 can negatively regulate hematopoietic stem cells 5. On the other hand, BCL6, HES1, CREB5, and MAFB can positively regulate hematopoietic stem cells 4 and negatively regulate other cells. POU2F2, FOXJ1, HES6, and ZBTB1 can positively regulate hematopoietic stem cells 5 and negatively regulate other cells. In terms of mononuclear phagocyte subtypes, ZNF135, TWIST2, NFIB, NR2F2, SIX2, and FOXC1 can positively regulate mesenchymal stem cells and negatively regulate other cells. MAF and PRD M1 can positively regulate macrophages and negatively regulate other cells. ZNF283 and ZNF90 can positively regulate type 1 conventional dendritic cells and negatively regulate other cells. FOXO1, RARA, KLF3, CUX1, and TCF7L2 can positively regulate atypical monocytes and negatively regulate other cells. In terms of neutrophil subtypes, the regulatory patterns between neutrophils 1-4 and neutrophil progenitors 1-4 are basically opposite. Neutrophil 3, neutrophil progenitor 3, and neutrophil progenitor 4 are positively regulated by ATF3, ZNF711, and ETV5, respectively. In terms of T cell subtypes, the regulatory patterns between NKT NKG7 and NKT KLRD1 are similar.

[0094] 4. Trajectory analysis (pseudo-series analysis): The pseudo-time analysis results of hematopoietic stem cell subtypes showed that their evolution trend started from hematopoietic stem cell 3 and hematopoietic stem cell 4, gradually became hematopoietic stem cell 2, and finally, the main hematopoietic stem cell subtype gradually differentiated into megakaryocytes and hematopoietic stem cell 1. The pseudo-time analysis results of mononuclear phagocyte subtypes showed that their evolution trend started from classical monocytes, gradually became macrophages and atypical monocytes, and then the main mononuclear phagocyte subtype gradually differentiated into type 2 conventional dendritic cells and finally became mesenchymal stem cells.

[0095] 5. Single cell V(D)J(TCR / BCR) clonal type analysis:

[0096] Single-cell TCR sequencing captured a total of 55 T cells, accounting for 28.9% compared to T cells captured by scRNA-seq. In RITP, there were 1 intermediate clonotype of naive T cells and 1 NKT, and monoclonotypes of 30 naive T cells, 19 NKT, and 4 proliferating T cells. In addition, in CITP, there were 3 intermediate clonotypes of naive T cells, 3 NKT, and 3 proliferating T cells. Obviously, the cellular immune response in CITP is stronger than that in RITP. Single-cell BCR sequencing captured a total of 41 B cells, accounting for 23.6% compared to B cells captured by scRNA-seq. In RITP, there were 2 intermediate clonotypes of plasma cells, 7 B cells, 21 plasma cells, and monoclonotypes of 11 B cell progenitors. In addition, in CITP, there were 16 intermediate clonotypes of plasma cells and 1 B cell progenitor. Obviously, CITP has stronger humoral immunity than RITP. The results of V(D)J clonotype analysis showed that the percentages of T cells and B cells in RITP were significantly lower than those in single-cell transcriptional sequencing, while the percentages of naive T cells, NKT cells, proliferating T cells, plasma cells, and ProB cells in the RITP group were significantly lower than those in the CITP group.

[0097] 6. Constructing a cell communication network (corresponding to the above step S5 communication interaction analysis):

[0098] Figure 2 It is a schematic diagram of a cell communication network corresponding to eight cell types in CITP included in the second cell communication network in step S5. Figure 3 It is a schematic diagram of a cell communication network corresponding to the 8 cell types in the RITP included in the first cell communication network in step S5. Figure 4 It is a schematic diagram of a cell communication network corresponding to megakaryocytes and macrophages, type 1 conventional dendritic cells, type 2 conventional dendritic cells, atypical monocytes and classical monocytes 1-5 in CI TP included in the second cell communication network in step S5. Figure 5 It is a schematic diagram of the cell communication network corresponding to the first cell communication network in step S5, including megakaryocytes and macrophages, type 1 conventional dendritic cells, type 2 conventional dendritic cells, atypical monocytes and classical monocytes 1-5 in RITP.

[0099] The overall results showed that there were a large number of ligand-receptor pairs between B cells, T cells and mononuclear phagocytes, according to Figure 2 and Figure 3The results of the comparison showed that the number of ligand-receptor pairs in RITP was less than that in CITP. In addition, in RITP samples, the cell communication between megakaryocytes and mononuclear phagocytes, neutrophils, erythrocytes, and granulocyte-monocyte progenitor cells was weakened, respectively. In addition, the cell communication between megakaryocytes (MKs) and macrophages (Ma crophages), type 1 conventional dendritic cells (cCD1), type 2 conventional dendritic cells (cCD2), atypical monocytes (NOnClassical MO) and classical monocytes 1-5 (Classical MO1-Classica l MO5) in RITP was also significantly weakened compared with that in CITP. All these results indicate that the cell communication between B cells, T cells and mononuclear phagocytes in CITP is stronger than that in RITP ( Figures 2 to 5 The thickness of the middle line indicates the strength of communication. The thicker the line, the stronger the strength.) In addition, in the megakaryocytes of the RITP group, there are 6 specific receptors (also known as unidentified receptors) (refer to Figure 6 ) including CD40_TNFSF13B, PG RMC2_CCL4L2, F11R_aLb2 complex, TFRC_TNFSF13B, FCGR2A_CXCL9, LA MP1_FAM3C (corresponding to the undetermined receptor determined in step S6), and 7 specific ligands in mononuclear phagocytes (refer to Figure 7 ) including RCL5_CCR1, BDNF_SORT1, MIF_TNFRSF14, MDK_LRP1, MDK_SORL1, FAM3C_CLEC2D, RCL3L1_DPP4. It is worth mentioning that TFRC_TNFSF13B is significantly overexpressed in atypical monocytes, classical monocyte 5, classical monocyte 4, classical monocyte 3, classical monocyte 2, classical monocyte 1, type 1 conventional dendritic cells, type 2 conventional dendritic cells and megakaryocytes. CD40_TNFSF13B is significantly overexpressed in classical monocyte 5, classical monocyte 4, classical monocyte 3 and megakaryocytes (see Table 2). MDK_SORL1 is significantly overexpressed in atypical monocytes, classical monocyte 5, classical monocyte 4, classical monocyte 3 and megakaryocytes. MIF_TNFRSF14 was significantly overexpressed in atypical monocytes, classical monocytes 5 and megakaryocytes.

[0100] Table 2:

[0101]

[0102]

[0103] according to Figure 2 , Figure 3 , Figure 4 and Figure 5 The results showed that the interactions between B cells, T cells, MPs and MKs in the CITP group were significantly stronger than those in the RITP group. Therefore, we speculated that there may be a small number of specific ligand-receptor pairs between MKs and MPs, but they played an important role. On this basis, we screened out CD40_TNFSF13B and TFRC_TNFSF13B as specific receptor pairs between MKs and MPs in the RITP group. Specifically, according to Table 2, CD40_TNFSF13B is expressed in 6 of the 12 subtypes of MKs and MPs, so CD40_TNFSF13B is determined to be a specific receptor between MKs and MPs (corresponding to step S71). In addition, according to Figure 6 The total expression level of TFRC_TNFSF13B in each subtype is also high, so TFRC_TNFSF13B is determined to be a specific receptor between MKs and MPs (corresponding to step S72).

[0104] 7. Identify target immune-related genes of mononuclear phagocytes in RITP (corresponding to the aforementioned steps S8 and S9):

[0105] Figure 8 Schematic diagram of the expression level of IFH1 in eight cell types of RITP. Fig. 9 Schematic representation of the expression levels of NLRP1 in eight cell types during RITP. Fig.10 The figure is a comparison of the expression levels of IFIH 1 in RITP and CITP. Fig.11 The figure is a comparison of the expression levels of NLRP1 in RITP and CITP.

[0106] according to Figure 8 and Fig. 9 IFIH1 and NLRP1 were confirmed to have target immune-related genes (monocytes and neutrophils, erythrocytes, B cells, hematopoietic stem cells, T cells, granulocyte-monocyte progenitor cells, and megakaryocytes) in mononuclear phagocytes. Subsequently, IFIH1 and NLRP1 expression in mononuclear phagocytes was shown between RITP and CITP. Fig.10 and Fig.11 Obviously, the expression levels of IFIH1 and NLRP 1 in mononuclear phagocytes in RITP samples were significantly higher than those in CITP samples.

[0107] 8. Confirmation of hub genes (corresponding to the result of the above step S11): A visual protein interaction network was constructed using Cytoscape ( Fig.12The network finally had 498 nodes and 2859 edges, including 173 up-regulated genes and 325 down-regulated genes. The MCODE plug-in was used to screen out three key modules from the entire network ( Fig.12 B, C and D). Module 1 (corresponding to Fig.12 In B), the robustly upregulated DEGs were mainly enriched in the electron transport chain of aerobic respiration. Fig.12 The BPs of up-regulated genes in C) were mainly enriched in cytoplasmic translation and ribosome biogenesis. Fig.12 The up-regulated genes in D) are mainly enriched in RNA splicing through esterification reactions with the protruding adenosine as the nucleophile.

[0108] Ten topological analysis algorithms (MCC, DMNC, MNC, Degree, EPC, BottleNec k, EcCentricity, Closeness, Radiality and Betweenness) were used to obtain the top 100 genes in the protein interaction network (refer to Fig.13 ). The intersection is the hub gene, and 5 hub genes were identified, namely EEF2, RPS3, RPL23, UBA52, and RPS27A.

[0109] 9. Verify the hub gene (corresponding to the result of the above step S12):

[0110] Three frozen bone marrow fluid samples were divided into two groups, two CITP bone marrow samples were used as the control group, and one RITP bone marrow sample was used as the experimental group. 500ul of bone marrow fluid was taken from the control group and the experimental group, 1ml of TRIzol reagent was added, and the mixture was fully homogenized and allowed to stand on ice for 10 minutes to fully lyse the cells. Subsequently, 300ul of chloroform was added and shaken for 30 seconds. After standing at room temperature for 10 minutes, the liquid was separated into layers. After centrifugation at 12000g and 4℃ for 15 minutes, RNA was retained in the colorless upper aqueous phase. Then, the upper aqueous phase was carefully drawn into another EP tube, and an equal volume of ice isopropanol was added. After fully mixing and standing for 10 minutes, white RNA precipitate was observed at the bottom of the tube after centrifugation at 12000g and 4℃ for 10 minutes. Then discard the supernatant, keep the precipitate, and add 1ml of 75% ethanol to the precipitate, invert it to make the precipitate float, let it stand for 2 minutes, centrifuge it at 7500g and 4℃ for 5 minutes, and let the precipitate adhere to the bottom of the tube again, and repeat this step twice. Then discard the supernatant, keep the precipitate, dry it naturally for 20 minutes or put it in the clean bench to blow dry, so that the ethanol and water evaporate as much as possible, and the RNA precipitate becomes transparent. Add 20-50ul of RNase-free water to the dried RNA precipitate to completely dissolve the RNA, and take 1ul for concentration detection with Nano drop, record the RNA concentration, calculate the loading amount of the subsequent reverse transcription step, and reverse transcribe the remaining RNA immediately.

[0111] Fig.14 The figure is a schematic diagram of the expression levels of the above five hub genes verified by qRT-PCR. The qRT-PCR results showed that the expression level of RPL23 in RITP samples was significantly higher than that in CITP samples, and the expression level of EEF2 in CITP samples was significantly higher than that in RITP samples.

[0112] Based on the above experiments, a composition map of bone marrow cell types in children with refractory ITP can be constructed (corresponding to step 13). Fig.15 shown.

[0113] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for determining the pathogenesis of refractory ITP in children, characterized in that: The pathogenesis includes specific receptor information associated with target cells, and the method includes the following steps: S1: Collect bone marrow samples, including bone marrow samples from children with refractory ITP and chronic ITP; S2: performing sequencing processing using the bone marrow sample; the sequencing processing includes single-cell transcriptome sequencing; S3: Obtaining cell type information of the refractory ITP and the chronic ITP according to the results of the sequencing process; the cell type information includes cell type, cell subtypes of each cell type, and the number of each cell type; S4: comparing the cell type information of the refractory ITP and the chronic ITP, and determining the main destructive cell type that causes immune destruction to the target cell among the multiple cell types of the refractory ITP; S5: performing communication interaction analysis using the bone marrow sample to construct a first cell communication network corresponding to the refractory ITP and a second cell communication network corresponding to the chronic ITP; S6: comparing the first cell communication network and the second cell communication network, and determining in the first cell communication network a receptor that is not included in the second cell communication network as a pending receptor according to the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the first cell communication network being less than the total number of ligand-receptor pairs between the target cells and each cell subtype of the main destroyed cell type in the second cell communication network; S7: Determine the specific receptor between the main damaging cell type of the refractory ITP and the target cell according to the expression of the undetermined receptor in the first cell communication network in multiple cell subtypes of the main damaging cell type.

2. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: The S7 specifically includes S71: Determine that the receptor among the multiple receptors to be determined, which is expressed in greater than or equal to a first preset number of cell subtypes in the multiple cell subtypes of the main destructive cell type, is a specific receptor between the main destructive cell type of the childhood refractory ITP and the target cell.

3. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: The S7 specifically includes S72: Determine that the receptors among the plurality of the pending receptors whose total expression amount in the plurality of cell subtypes of the main damaging cell type is greater than or equal to the second preset number are specific receptors between the main damaging cell type of the pediatric refractory ITP and the target cells.

4. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: The pathogenesis also includes targeted immune gene information, and the method also includes: S8: detecting the expression levels of multiple immune-related genes in multiple cell types in the bone marrow sample; S9: Determine the targeted immune gene for the refractory ITP according to the expression levels of the multiple immune-related genes.

5. The method for determining the pathogenesis of refractory ITP in children according to claim 4, characterized in that: The step S9 specifically includes: determining that the immune-related gene whose expression level in the main destroyed cell type is higher than that in other cell types among the multiple cell types, and whose expression level in the main destroyed cell type in the bone marrow sample of the child with refractory ITP is higher than that in the main destroyed cell type in the bone marrow sample of the child with chronic ITP is the targeted immune gene.

6. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: After the sequencing process is performed in step S2, the method further includes step S10: constructing a sequencing library of the refractory ITP and the chronic ITP according to the sequencing process.

7. The method for determining the pathogenesis of refractory ITP in children according to claim 6, characterized in that: The pathogenesis also includes hub gene information; the method also includes step S11: using the sequencing library to perform protein interaction network analysis of the refractory ITP to determine multiple hub genes of the refractory ITP.

8. The method for determining the pathogenesis of refractory ITP in children according to claim 7, characterized in that: The method further includes step S12: performing qRT-PCR processing on the bone marrow sample to obtain a first sample corresponding to the refractory ITP and a second sample corresponding to the chronic ITP, and detecting the expression levels of the plurality of hub genes in the first sample and the second sample respectively; A hub gene among the plurality of hub genes whose expression difference between the first sample and the second sample is greater than a preset difference value is determined as a target hub gene.

9. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: The sequencing process also includes immune group sequencing.

10. The method for determining the pathogenesis of refractory ITP in children according to claim 1, characterized in that: The S3 also includes S13: constructing a bone marrow cell type composition map of the refractory ITP according to the results of the sequencing process.

Images