Important pathway and marker screening method for acute posterior polar multifocal squamous epithelial lesion based on single cell sequencing technology and application of important pathway and marker screening method for acute posterior polar multifocal squamous epithelial lesion based on single cell sequencing technology

Abstract

The invention discloses an important pathway and marker screening method for acute posterior multifocal squamous epithelium lesion based on a single cell sequencing technology and application of the important pathway and marker screening method for acute posterior multifocal squamous epithelium lesion based on the single cell sequencing technology. An scRNA-seq technology is adopted, and PBMCs from an acute-attack APMPPE patient and a healthy control person are analyzed; according to the present invention, the immunoreaction change of the PBMC cells of the APMPPE patient is identified by using the single cell sequencing technology, and the monocytes are classified in detail according to the difference of the expression modes, such that the panoramic description of the characteristics of the monocytes of the APMPPE patient is achieved, and the potential biomarkers of the monocytes in the peripheral blood of the APMPPE patient are found so as to research the immunopathogenesis of the APMPPE. Research results provide evidence that immune response and melanogenesis dysfunction are related to APMPPE, and in addition, IFITM3 is identified as an immune response key factor for driving APMPPE pathogenesis. And a new perspective is provided for knowing the proinflammatory property and the key effect of the CD16 mononuclear cell in the APMPPE immune pathogenesis.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and specifically relates to an important pathway and marker screening method and application of acute posterior pole multifocal squamous pigment epithelial lesions based on single-cell sequencing technology. Background Art

[0002] Acute posterior pole multifocal squamous pigment epitheliopathy (APMPPE) is a very rare primary inflammatory choroidopathy. It primarily affects young adults of both sexes, with an estimated annual incidence of 0.15 cases per 100,000 population. The disease was first described in 1968 by Gass, who reported three young women who presented with multiple discrete cream-colored lesions in the posterior pole when they experienced sudden, painless visual loss. Early descriptions suggested that the disease was self-limited, typically resolving spontaneously within 6 weeks. However, subsequent reports have documented recurrent cases and poor visual outcomes. The multimodal imaging features of APMPPE suggest that primary inflammation involves the choriocapillaris, while secondary photoreceptor damage contributes to its unique clinical phenotype.

[0003] The clinical features and diagnostic criteria of APMPPE have been described in several studies, however, its exact underlying pathogenesis remains unknown. Initially, inflammation was thought to occur in the retinal pigment epithelium (RPE), and systemic corticosteroids were recommended for treatment in more than two-thirds of patients, possibly in combination with nonsteroidal immunosuppressive therapy. Over the past few decades, increasing evidence has supported the immune-driven nature of the disease. Case series have highlighted the presence of an influenza-like prodrome before the onset of APMPPE and have suggested an autoimmune or autoinflammatory response. APMPPE has been associated with a variety of preexisting autoimmune diseases, including psoriasis, granulomatous disease, erythema nodosum, eczema, and diabetes. In addition, some patients have been reported to develop these diseases after the diagnosis of APMPPE. A recent genetic study showed that people with susceptibility genes (such as HLA-B7 and HLA-DR2) are more susceptible to APMPPE. Nevertheless, the mechanism of how triggers trigger the innate immune response and subsequent choroidal and RPE inflammation remains unclear. Likewise, it remains unclear why the highly pigmented RPE is the primary target and why fundus hypoautofluorescence is the predominant phenotype of this disease.

[0004] Peripheral blood mononuclear cells (PBMCs) consist of multiple cell subsets, including monocytes, macrophages, dendritic cells (DCs), and B cells, which play important roles as fundamental components of the immune system. These cells play key roles in the development of inflammatory diseases, angiogenesis, and tumor growth. In recent years, single-cell RNA sequencing (scRNA-seq) has enabled comprehensive lineage analysis of the immune system in an unprecedented way. Summary of the invention

[0005] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides an important pathway and marker screening method and application of acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology to solve the problem of unclear immune pathogenesis of APMPPE. The screening method of the present invention analyzes the single-cell atlas of PBMC of APMPPE patients based on single-cell sequencing technology to describe the characteristics of the immune system during the pathogenesis of APMPPE. The complexity of monocytes in the peripheral blood of APMPPE patients was analyzed. According to the changes in the number of new cell subpopulations and gene expression differences found, the single-cell sequencing technology was used to deeply sequence and analyze the immune system and functional changes of APMPPE patients, and the potential biomarkers of monocytes in APMPPE patients were found, which is of great significance for studying the pathogenic mechanism of APMPPE and the development of precise clinical treatment strategies, and provides a reference for the application of single-cell sequencing technology in the exploration of the potential pathogenic mechanism of posterior uveitis and the discovery of biomarkers.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solution: the important pathway and marker screening method of acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology of the present invention comprises the following steps: S1, isolation of peripheral blood mononuclear cells; S2, 10× Genomics single-cell RNA sequencing; S3, alignment and quantification of single-cell RNA sequencing data; S4, dimensionality reduction cluster analysis and cell identification of PBMC from APMPPE patients; S5, identification and enrichment analysis of differentially expressed genes in PBMCs of APMPPE patients; S6, dimensionality reduction cluster analysis and cell identification of monocytes from patients with APMPPE; S7, GSVA analysis of potential functions and important pathways of APMPPE monocytes; S8, GO and KEGG functional enrichment analysis of differentially expressed genes in pro-inflammatory and CD16 monocytes; S9. IFITM3 was screened as an important biomarker of monocytes in APMPPE patients.

[0007] Furthermore, the specific process of step S1 is as follows: a patient with acute multifocal retinochoroiditis and a healthy individual matched with the patient's age and gender were used as research samples; peripheral blood mononuclear cells in the blood samples were purified by Ficoll gradient separation; PBMCs were 1 × 10 6 The density of cells was cryopreserved for subsequent analysis. The specific process of step S2 is as follows: single cells from each sample are independently processed into single-cell suspensions, and libraries are generated on the 10× Genomics system; single-cell libraries are generated using the GEMCode single-cell instrument and the Chromium Next GEM SingleCell 5ʹ GEM, Library&Gel Bead Kit v3.1 kit; the libraries are quality tested and sequenced.

[0008] Furthermore, the specific process of step S3 is as follows: the raw data generated in the high-throughput sequencing is a fastq format sequence, and the Cell Ranger software provided by 10× Genomics is used to perform data quality statistics on the raw data and compare it to the reference genome. The software quantifies the high-throughput single-cell transcriptome data by identifying the Barcode markers that distinguish cells in the sequence and the UMI markers of different mRNA molecules in each cell, and obtains quality control statistical information such as the number of high-quality cells, gene median value, and sequencing saturation; based on the preliminary quality control of Cell Ranger, data analysis is performed using Seurat The data were further quality-controlled using the R software package; cells were further filtered according to the following threshold parameters: the total number of expressed genes was 500–9000; the number of UMIs / genes exceeded the limit of ±2 standard deviations of the mean, assuming that the number of UMIs / genes per cell was Gaussian distributed; and the proportion of mitochondrial genes was greater than 10%; after applying these quality control criteria, 14,537 single cells were retained in the APMPPE group and 15,588 single cells were retained in the control group and included in subsequent analyses; data from all samples were merged using the merge function of R and standardized using the NormalizeData function of Seurat; data were normalized using global scaling normalization.

[0009] Furthermore, the specific process of step S4 is as follows: use the FindVariableGenes function in Seurat to identify the top 2000 highly variable genes; use the expression profiles of highly variable genes to perform PCA dimensionality reduction analysis through the RunPCA function in Seurat; use the FindClusters function in Seurat to perform graphical clustering based on the gene expression profile, and visualize the results in two-dimensional space through UMAP; use the FindAllMarkers function in Seurat to identify the marker genes of each cluster; for a given cluster, FindAllMarkers identifies the positive markers compared with all other cells; then, use the R package SingleR to annotate the cell types by referring to the transcriptome dataset "Human Primary Cell Atlas"; Seurat was then used for unsupervised cluster analysis, and these clusters were annotated using SingleR in combination with classic marker genes. After removing a small number of unknown cells, most PBMCs were classified into four cell types: B cells, monocytes, natural killer cells, and T cells. Statistical graphs of the number of cells of each cell type and the TOP10 marker gene graphs of each subpopulation were drawn. To fully understand the changes in PBMCs in APMPPE disease, a comparative analysis of PBMC cell subsets was performed between APMPPE patients and healthy controls. The UMAP plots revealed that the distribution patterns of various cell populations were significantly different between APMPPE patients and the control group. It is noteworthy that the changes in monocyte subsets were the most significant among all subtypes. The proportion of cells in each sample was quantified, and it was found that a high density of T cells and monocytes was present in both groups of samples. However, the percentage of monocytes in PBMCs of APMPPE patients was significantly lower than that in the normal control group.

[0010] Furthermore, the specific process of step S5 is as follows: differentially expressed genes are identified by the FindMarkers function in Seurat; GO and KEGG pathway enrichment analysis of significantly differentially expressed genes is performed using R based on the hypergeometric distribution test; A heat map was generated to visualize the differential expression of the top 25 genes upregulated and downregulated between APMPPE patients and normal controls in four specific cells: B cells, T cells, monocytes, and NK cells. KEGG pathway classification analysis was performed on the differentially expressed genes in each cell subtype. The results showed that the upregulated genes in monocytes, B cells, T cells, and NK cells were significantly enriched in the immune system and infectious diseases, with monocytes ranking first, NK cells second, and B cells third. Based on these results, it is suggested that significant changes have occurred in the genes related to the immune system and infectious diseases in the PBMCs of APMPPE patients, especially in monocytes.

[0011] Further, the specific process of step S6 is as follows: Monocytes are considered to be key players in the progression of autoimmune diseases; a recent study revealed abnormalities in monocytes in the peripheral blood of patients with Vogt-Koyanagi-Harada disease, an autoimmune disease that affects the eyes; in order to gain a deeper understanding of the changes in monocytes and their subpopulations in the peripheral blood of APMPPE patients, we focused on extracting monocytes for subpopulation analysis; initially, monocytes were divided into two subpopulations based on the expression of CD14 and CD16, including classical monocytes and non-classical monocytes; in order to more accurately depict the monocyte profile of APMPPE patients, we combined the identified precise marker genes; five different monocyte subpopulations were identified. The dot plots showed the marker genes of each monocyte subset, and NK-like monocytes, CD16 monocytes, S100A12 monocytes, and megakaryocyte-like monocytes were clearly identifiable; however, among the proinflammatory monocytes, most cells had proinflammatory and HLA markers; since the proinflammatory marker genes were more specific, including the unique expression of IFI6, ISG15, IL1B, and IFI44L, they were defined as proinflammatory monocytes; five different monocyte subsets were identified, namely NK-like, CD16, proinflammatory, megakaryocyte-like, and S100A12 monocytes; in addition, each subset showed a unique gene expression signature, indicating its specific function; The UMAP plots demonstrate differences in the distribution of monocyte subsets between APMPPE patients and controls; quantification of the proportions of monocyte subsets in each sample is shown; the most significant expression change among all monocyte subsets in APMPPE patients is the emergence of transcriptional programs associated with a proinflammatory state; APMPPE disease results in subtype shifts between monocyte subsets; with the onset of APMPPE disease, the percentage of proinflammatory monocytes increases, while the percentages of S100A12 monocytes and NK-like monocytes decrease; notably, the percentage of CD16 monocytes decreases slightly, but their pattern in UMAP changes significantly; these findings suggest that proinflammatory monocytes may be actively involved in the pathogenesis of APMPPE disease; the results suggest that the proinflammatory monocyte cluster is a more common state associated with ocular / uveal inflammation.

[0012] Furthermore, the specific process of step S7 is as follows: select the GSEABase package to load the gene set file, which is downloaded and processed from the KEGG database to perform GSVA; further estimate the signal pathway activity score and assign it to the single cell, and use the GSVA package for analysis; perform differential pathway analysis through the Limma package; To investigate the potential functions of the monocyte clusters, the Gene Set Variant Analysis package was used to extract KEGG pathways from the molecular signature database and perform pathway enrichment analysis. GSVA data revealed significant enrichment of signaling pathways between monocyte subpopulations, indicating that each subpopulation exerts specific functions. Proinflammatory monocytes showed high expression of several immune pathways, such as natural killer cell-mediated cytotoxicity, type 1 diabetes, and viral infectious diseases. CD16 monocytes showed significant enrichment in melanogenesis, human papillomavirus infection, Wnt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. Compared with the normal control group, the expression of various immune pathways in the monocytes of APMPPE patients was increased, while the melanogenesis pathway was significantly inhibited. Compared with other monocyte subsets, all these melanogenesis pathway genes were particularly highly expressed in CD16 monocytes, suggesting that the melanogenesis pathway plays a key and unique role with CD16 in the pathogenesis of APMPPE. The human eye is composed of multiple layers of pigmented tissue, mainly composed of melanin; the role of melanocortical signaling in melanogenesis in uveal melanocytes is still unclear; in the melanogenesis pathway, CTNNB1, GNAI3, calmodulin CALM2, CALM3 and CALML4 were found to be upregulated in monocytes of APMPPE patients, while GNAI2, PRKCB, PLCB2, PRKACA and CREBBP were downregulated; the regulatory changes of the above genes in CD16 monocytes observed suggested that most pathways involved in CD16 monocyte melanogenesis are associated with the pathogenesis of APMPPE; GSVA analysis revealed the importance of melanogenesis in CD16 monocytes in the pathogenesis of APMPPE.

[0013] Furthermore, the specific process of step S8 is as follows: In order to study the function of each subtype in monocytes, the differentially expressed genes of each subtype were further analyzed; In order to study the function of each subtype in monocytes, the differentially expressed genes (DEGs) of each subtype were further analyzed; A volcano plot of DEGs in monocyte subtypes was drawn; Obviously, in most monocyte subtypes, IFI6, LY6E and IFITM3 were upregulated, while EEF1A1 and FOLR3 were downregulated in APMPPE patients; In order to elucidate the potential functional changes of proinflammatory monocytes in APMPPE patients, GO and KEGG enrichment analysis of DEGs in this subtype was performed; DEGs in proinflammatory monocytes were enriched in a variety of biological processes, including regulation of dendritic cell differentiation, immune response, negative regulation of viral genome replication, type I interferon signaling, response to viruses, and immune system processes; These findings suggest that the occurrence of inflammation and immune responses in proinflammatory monocytes may play an important role in the pathogenesis of APMPPE; Based on the GSVA results, it was hypothesized that infection with influenza-like virus pathogens may trigger an innate immune response, ultimately leading to an inflammatory response in APMPPE patients; in addition, the study found that the pattern of CD16 monocytes in APMPPE patients changed significantly; in order to further verify the key role of CD16 monocytes in innate immune and inflammatory responses, functional enrichment analysis of DEGs in CD16 monocytes was performed; GO enrichment analysis of DEGs in CD16 monocytes showed that biological processes such as type I interferon signaling, negative regulation of viral genome replication, response to viruses, response to interferon-β, and viral defense response were changed in CD16 monocytes of APMPPE patients; the results of the study showed that proinflammatory monocytes and CD16 monocytes play a key role in promoting inflammation and immune responses, which supports the mainstream hypothesis that viral infection may trigger the occurrence of APMPPE disease.

[0014] Further, the specific process of step S9 is as follows: IFITM3 was identified as the most significantly differentially expressed gene in monocytes of APMPPE patients compared with healthy controls, and was particularly highly expressed in monocytes in PBMC subpopulations; IFITM3 is an interferon-induced transmembrane protein that has been previously identified as an endogenous protein that blocks viral infection; in order to investigate the role of IFITM3 in APMPPE disease, the expression of IFITM3 in monocytes of APMPPE patients was examined at the single-cell transcriptome level; the results showed that IFITM3 was significantly upregulated in proinflammatory and CD16 monocyte subpopulations compared with healthy monocytes; in addition, scVelo analysis showed strong expression of IFITM3 unspliced ​​mRNA, which was expressed as a positive velocity, in the direction of proinflammatory and CD16 monocytes, indicating that IFITM3 Induction of mRNA transcription; these findings suggest that IFITM3 activation may drive the differentiation of S100A12 into proinflammatory and CD16 monocyte subsets; in addition, the most consistently and significantly upregulated genes in monocytes from APMPPE patients were interferon-induced gene products and other genes associated with acute inflammation; therefore, IFITM3 plays an important role in the immune response in the pathogenesis of APMPPE and becomes a potential biomarker for APMPPE patients.

[0015] The application of important pathways and marker screening methods for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology includes the following: According to the changes in the number of cell subpopulations between groups and the differences in immune gene expression of each subpopulation of cells in healthy patients and APMPPE patients, the expression level of marker genes is detected to find marker genes with large expression abnormalities in the healthy group and APMPPE patients. This is used as a biomarker to detect immune changes in APMPPE patients, and is used to study the pathogenic mechanism of APMPPE and the development of precise clinical treatment strategies, providing a reference for the application of single-cell sequencing technology in the exploration of the potential pathogenic mechanism of posterior uveitis and the discovery of biomarkers.

[0016] By adopting the above-mentioned technical scheme, compared with the prior art, the present invention has the following technical effects: the present application identifies the changes in the immune response of PBMC cells of APMPPE patients through single-cell sequencing technology, and classifies monocytes in detail according to the differences in their expression patterns, thereby achieving a panoramic characterization of the monocyte characteristics of APMPPE patients and discovering potential biomarkers of monocytes in the peripheral blood of APMPPE patients, which is of great significance for studying the pathogenic mechanism of APMPPE and the development of precise clinical treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A The figure shows the workflow of the experimental strategy of the present invention.

[0018] Figure 1B Integrated UMAP projection of scRNA-seq data from PBMCs of APMPPE patients and healthy controls. A total of 28,704 cells were clustered into 4 clusters based on their expression patterns. The cluster identities based on expression markers are annotated on the right. Different color markers are unbiasedly classified by graph-based clustering. Each dot represents an individual cell.

[0019] Figure 1C The violin plots show the expression of cell type-specific marker genes for each cluster. The distribution of cell type-specific marker genes in all clusters reflects their cell identities.

[0020] Figure 1D is the proportion of each subpopulation in each sample.

[0021] Figure 1E The cell numbers of each cell type in APMPPE patients and healthy controls are shown.

[0022] Figure 1F UMAP plots showing the different distributions of the 4 clusters in APMPPE patients and healthy controls. Patient cells are colored in the left panel and healthy control cells are colored in the right panel. Cell types and their respective colors are labeled on the right.

[0023] Figure 1G For the heatmap reported in Figure 1B Normalized expression of the discriminating gene set for each cluster identified in Figure 2. The top 10 marker genes for each cluster are shown.

[0024] Figure 2A Heatmap showing single-cell gene expression profiles of the top 25 up- and down-regulated genes in monocytes from APMPPE patients and normal controls.

[0025] Figure 2B Pathway classification analysis of differentially expressed genes (DEGs) in monocyte subtypes based on KEGG.

[0026] Figure 3A Integrated UMAP projection of scRNA-seq data of monocytes from APMPPE patients and healthy controls. A total of 6,532 monocytes were Figure 3A The subpopulations defined in the Figure 1 were divided into five subclusters. Different colors are used to distinguish each cluster. The number of single cells successfully analyzed for each subpopulation is as follows: S100A12 monocytes (n = 3,117), proinflammatory monocytes (n = 2,135), CD16 monocytes (n = 848), NK-like monocytes (n = 251), and megakaryocyte-like monocytes (n = 181). Each dot represents an individual cell.

[0027] Figure 3B UMAP plots of monocytes from APMPPE patients and healthy controls. Patient monocytes are colored in the left panel and healthy control monocytes are colored in the right panel. Monocyte subsets and their respective colors are labeled on the right.

[0028] Figure 3C The dot plots show the marker genes for the monocyte subsets defined in 3A.

[0029] Figure 3D Bar graphs highlight the cellular abundance of monocyte subsets (n=5) in patients and healthy controls.

[0030] Figure 4A The GSVA results showed differential enrichment of signaling pathways between monocyte subsets.

[0031] Figure 4B Differentially regulated signaling pathways in APMPPE patients and healthy controls were analyzed for GSVA.

[0032] Figure 4C The dot plots show the expression changes of five specific up-regulated genes and down-regulated genes (p<0.05) in the monocyte melanogenesis pathway.

[0033] Figure 4D The UMAP graph and dot plot show the expression changes of five specific up-regulated genes and down-regulated genes (p<0.05) in the monocyte melanogenesis pathway.

[0034] Figure 5A Volcano plot showing differentially expressed genes between monocyte subsets.

[0035] Figure 5B The top 10 gene ontologies (GO) for biological processes in proinflammatory monocytes.

[0036] Figure 5C The top 10 gene ontology (GO) for biological processes in CD16 monocytes.

[0037] Fig. 6A The integrated UMAP image shows the expression of IFITM3 in PBMCs of APMPPE patients.

[0038] Figure 6B Dot plots show the expression of IFITM3 in monocyte subsets in APMPPE patients and healthy subjects.

[0039] Figure 6C Ci: Heatmap highlights genes highly correlated with velocity pseudotime. Cii: Gene parsing velocity for IFITM3. Ciii: Expression of highest likelihood gene IFITM3 (driver gene) along time.

[0040] Fig.6D The UMAP plot shows the expression of six significantly upregulated genes in monocytes. DETAILED DESCRIPTION

[0041] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and examples.

[0042] like Figure 1A The present invention provides a method for screening important pathways and markers of acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology, comprising the following steps: S1. Isolation of peripheral blood mononuclear cells: A patient with acute multifocal retinochoroiditis (APMPPE) and an age- and sex-matched healthy individual were included in the study. Peripheral blood mononuclear cells (PBMCs) were purified from the blood samples by Ficoll gradient separation (Ficoll-Paque Plus, GE Healthcare, Sweden). PBMCs were isolated at 1 × 10 6 The cells were cryopreserved at a density of 100 cells per well for subsequent analysis.

[0043] S2, 10× Genomics single-cell RNA sequencing: The specific process of step S2 is as follows: individual cells from each sample are independently processed into single-cell suspensions, and libraries are generated on the 10× Genomics system; single-cell libraries are generated using the GEMCode single-cell instrument and the Chromium Next GEM SingleCell 5ʹ GEM, Library&Gel Bead Kit v3.1 kit; the libraries are quality tested (FragmentAnalyzer 2100, Agilent Technologies) and sequenced (platform: Illumina NovaSeq6000; read length: 150 bp, paired-end).

[0044] S3. Alignment and quantification of single-cell RNA sequencing data: The raw data (raw reads) generated in high-throughput sequencing are fastq format sequences. The Cell Ranger software (version 5.0.0) provided by 10×Genomics is used to perform data quality statistics on the raw data and compare it to the reference genome (human: GRCh38). The software quantifies high-throughput single-cell transcriptome data by identifying the Barcode markers that distinguish cells in the sequence and the UMI markers of different mRNA molecules in each cell, and obtains quality control statistical information such as high-quality cell number, gene median, and sequencing saturation. Based on the initial quality control of Ranger, the data analysis used the SeuratR (version 3.1.1) software package for further quality control of the data; cells were further filtered according to the following threshold parameters: the total number of expressed genes was 500–9000; the number of UMI / gene exceeded the limit of mean ± 2 times standard deviation, assuming that the number of UMI / gene per cell was Gaussian distributed; and the proportion of mitochondrial genes was greater than 10%; after applying these quality control (QC) criteria, 14,537 single cells were retained in the APMPPE group (3860 cells were filtered out of a total of 18,397 cells), and 15,588 single cells were retained in the control group (3808 cells were filtered out of a total of 19,396 cells) and included in subsequent analysis; data of all samples were merged using the merge function of R and standardized using the NormalizeData function of Seurat; data were normalized using global scaling normalization (method: LogNormalize, scale. factor = 10.000).

[0045] S4. Dimensionality reduction clustering analysis and cell identification of PBMC from APMPPE patients: Use the FindVariableGenes function in Seurat to identify the Top 2000 highly variable genes. The expression profile of highly variable genes was used to perform PCA (principal component) dimensionality reduction analysis through the RunPCA function in Seurat. Graphical clustering was performed using the FindClusters function in Seurat based on the gene expression profile, and the results were visualized in two-dimensional space by UMAP (non-linear dimensionality reduction). The FindAllMarkers function in Seurat was used to identify the marker genes for each cluster. For a given cluster, FindAllMarkers identifies positive markers compared to all other cells. Then, the R package SingleR was used to annotate the cell types by referring to the transcriptome data set "Human Primary Cell Atlas".

[0046] Seurat was then used for unsupervised cluster analysis, and these clusters were annotated using SingleR in combination with classical marker genes. After removing a small number of unknown cells, most PBMCs were classified into four cell types: B cells (CD19, MS4A1, CD79A, CD79B), monocytes (CD14, S100A12, CLEC12A), natural killer cells (NK cells) (FCGR3A, NKG7, KLRB1) and T cells (CD3E, CD3D, CD3G) ( Figure 1B , Figure 1C ). Draw a statistical graph of the number of cells of each cell type and a graph of the TOP10 marker genes of each subpopulation. The number of cells of each cell type has been counted and Figure 1D , Figure 1E The TOP10 marker genes of each subgroup are shown in the figure Figure 1G shown.

[0047] In order to fully understand the changes in PBMCs in APMPPE disease, a comparative analysis of PBMC cell subsets was performed between APMPPE patients and healthy controls. Through the UMAP map, it was observed that the distribution patterns of each cell population were significantly different between APMPPE patients (left) and the control group (right) ( Figure 1F ). It is worth noting that the changes in monocyte subsets were the most significant among all subtypes ( Figure 1F ). The proportion of cells in each sample was quantified, and it was found that high density of T cells and monocytes were present in both groups of samples ( Figure 1D and Figure 1E However, the percentage of monocytes in PBMCs of APMPPE patients was significantly lower than that in normal controls ( Figure 1D ).

[0048] S5. Identification and enrichment analysis of differentially expressed genes in PBMC of APMPPE patients: Differentially expressed genes (DEGs, P value < 0.05, |log2foldchange|> 0.58) were identified by the FindMarkers function in Seurat. GO and KEGG pathway enrichment analysis of significantly differentially expressed genes was performed using R based on the hypergeometric distribution test.

[0049] Generate a heat map to visualize the differences in the expression of the top 25 genes upregulated and downregulated in four specific cells: B cells, T cells, monocytes, and NK cells between APMPPE patients and normal controls. The TOP25 differentially expressed genes of monocytes are as follows: Figure 2A The differentially expressed genes (DEGs) in each cell subtype were analyzed by KEGG pathway classification, as shown in Figure 2. Figure 2B As shown. The results showed that the upregulated genes in monocytes, B cells, T cells, and NK cells were significantly enriched in the immune system and infectious diseases, among which monocytes ranked first, NK cells ranked second, and B cells ranked third. Based on these results, it is suggested that the genes related to the immune system and infectious diseases in the PBMCs of APMPPE patients have undergone significant changes, especially in monocytes.

[0050] S6. Dimensionality reduction clustering analysis and cell identification of monocytes in patients with APMPPE: Monocytes are considered key players in the progression of autoimmune diseases; a recent study revealed abnormalities in monocytes in the peripheral blood of patients with Vogt-Koyanagi-Harada (VKH) disease, an autoimmune disease that affects the eye. To gain a deeper understanding of the changes in monocytes and their subsets in the peripheral blood of APMPPE patients, we focused on extracting monocytes for subset analysis. Figure 3A The integrated UMAP projection of monocyte scRNA-seq data from APMPPE patients and healthy controls is shown. Initially, monocytes can be divided into two subpopulations based on the expression of CD14 and CD16, including classical monocytes (CD14 + CD16 dim) and non-classical monocytes (CD14 dim CD16 +). In order to more accurately portray the monocyte profile of APMPPE patients, the precise marker genes identified by Hu et al. were combined. Five different monocyte subsets were identified and their gene expression profiles were analyzed. The dot plot shows the marker genes for each monocyte subset ( Figure 3C ).like Figure 3CAs shown, NK-like monocytes, CD16 monocytes, S100A12 monocytes, and megakaryocyte-like monocytes can be clearly identified. However, among the proinflammatory monocytes, most cells have proinflammatory and HLA markers. Since the proinflammatory marker genes are more specific, including the unique expression of IFI6, ISG15, IL1B, and IFI44L, they are defined as proinflammatory monocytes. Five different monocyte subsets were identified, namely NK-like, CD16, proinflammatory, megakaryocyte-like, and S100A12 monocytes. In addition, each subset showed a unique gene expression signature, indicating its specific function.

[0051] UMAP images show the differences in the distribution of monocyte subsets between APMPPE patients (left) and controls (right) ( Figure 3B ). Quantification results of the proportion of monocyte subsets in each sample are shown ( Figure 3D The most significant expression change among all monocyte subsets in patients with APMPPE was the emergence of transcriptional programs associated with a proinflammatory state. Figure 3B and 3D As shown, APMPPE disease causes subtype shifts between monocyte subsets. With the development of APMPPE disease, the percentage of proinflammatory monocytes increased, while the percentages of S100A12 monocytes and NK-like monocytes decreased. Notably, the percentage of CD16 monocytes decreased slightly, but their pattern in UMAP changed significantly. These findings suggest that proinflammatory monocytes may be actively involved in the pathogenesis of APMPPE disease. The findings suggest that the proinflammatory monocyte cluster is a more prevalent state associated with ocular / uveal inflammation.

[0052] S7. GSVA analysis of potential functions and important pathways of APMPPE monocytes: The GSEABase (version 1.44.0) package was selected to load the gene set file, which was downloaded and processed from the KEGG database (https: / / www.kegg.jp / ) to perform GSVA. Signaling pathway activity scores were further estimated and assigned to single cells, and analyzed using the GSVA package (version 1.30.0). Differential pathway analysis was performed using the Limma package (version 3.38.3).

[0053] To investigate the potential functions of monocyte clusters, the Gene Set Variant Analysis (GSVA) ​​package was used to extract KEGG pathways from the molecular signature database and perform pathway enrichment analysis. GSVA data revealed significant enrichment of signaling pathways between monocyte subpopulations, indicating that each subpopulation plays a specific function ( Figure 4A). Proinflammatory monocytes showed high expression of several immune pathways, such as natural killer cell-mediated cytotoxicity, type 1 diabetes, and viral infectious diseases (such as Epstein-Barr virus infection). CD16 monocytes showed significant enrichment in melanogenesis, human papillomavirus infection, Wnt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway ( Figure 4A ).

[0054] Compared with the normal control group, the expression of various immune pathways in monocytes of APMPPE patients was increased, while the melanogenesis pathway was significantly inhibited ( Figure 4B ).like Figure 4C As shown, all of these melanogenesis pathway genes were particularly highly expressed in CD16 monocytes compared with other monocyte subsets, suggesting that the melanogenesis pathway plays a critical and unique role with CD16 in the pathogenesis of APMPPE.

[0055] The human eye is composed of multiple layers of pigmented tissue, mainly composed of melanin. The role of melanocortical signaling in melanogenesis in uveal melanocytes is still unclear. In the melanogenesis pathway, CTNNB1, GNAI3, calmodulin (CaM) proteins (CALM2, CALM3, and CALML4) were found to be upregulated in monocytes from APMPPE patients, while GNAI2, PRKCB, PLCB2, PRKACA, and CREBBP were downregulated (p<0.05) ( Figure 4C and 4D The observed regulatory changes of the above genes in CD16 monocytes indicated that most pathways involved in melanogenesis in CD16 monocytes were associated with the pathogenesis of APMPPE. GSVA analysis revealed the importance of melanogenesis in CD16 monocytes in the pathogenesis of APMPPE.

[0056] S8, GO and KEGG functional enrichment analysis of differentially expressed genes (DEGs) in pro-inflammatory and CD16 monocytes: To investigate the function of each subtype in monocytes, the differentially expressed genes (DEGs) of each subtype were further analyzed. To investigate the function of each subtype in monocytes, the differentially expressed genes (DEGs) of each subtype were further analyzed. A volcano plot of DEGs in monocyte subsets was drawn ( Figure 5A ). Obviously, IFI6, LY6E, and IFITM3 were upregulated in most monocyte subtypes, while EEF1A1 and FOLR3 were downregulated in APMPPE patients. To elucidate the potential functional changes of proinflammatory monocytes in APMPPE patients, GO and KEGG enrichment analysis of DEGs in this subtype was performed. Figure 5BAs shown, DEGs in proinflammatory monocytes were enriched in multiple biological processes, including regulation of dendritic cell differentiation, immune response, negative regulation of viral genome replication, type I interferon signaling, response to viruses, immune system processes, etc. These findings suggest that the occurrence of inflammation and immune responses in proinflammatory monocytes may play an important role in the pathogenesis of APMPPE.

[0057] Based on the GSVA results, it was hypothesized that infection with influenza-like virus pathogens may trigger an innate immune response, ultimately leading to an inflammatory response in APMPPE patients. In addition, the study found that the pattern of CD16 monocytes in APMPPE patients was significantly changed. In order to further verify the key role of CD16 monocytes in innate immune response and inflammatory response, functional enrichment analysis of DEGs in CD16 monocytes was performed. GO enrichment analysis of DEGs in CD16 monocytes showed that biological processes such as type I interferon signaling, negative regulation of viral genome replication, response to viruses, response to interferon-β, and viral defense response were changed in CD16 monocytes of APMPPE patients ( Figure 5C The results of this study suggest that pro-inflammatory monocytes and CD16 monocytes play a key role in promoting inflammation and immune responses, supporting the mainstream hypothesis that viral infection may trigger the development of APMPPE disease.

[0058] S9. Screening of IFITM3 as an important biomarker of monocytes in patients with APMPPE: IFITM3 was identified as the most significantly differentially expressed gene in monocytes from APMPPE patients compared with healthy controls and was particularly highly expressed in monocytes among PBMC subsets ( Fig. 6A ). IFITM3 is an interferon-induced transmembrane protein that has been previously identified as an endogenous protein that blocks viral infection. To investigate the role of IFITM3 in APMPPE disease, the expression of IFITM3 in monocytes from APMPPE patients was examined at the single-cell transcriptome level. The results showed that IFITM3 was significantly upregulated in pro-inflammatory and CD16 monocyte subsets compared with healthy monocytes ( Figure 6B Furthermore, scVelo analysis revealed a strong expression of IFITM3 unspliced ​​mRNA, expressed as a positive rate, in the pro-inflammatory and CD16 monocyte direction, indicating an induction of IFITM3 mRNA transcription ( Figure 6C). These findings suggest that IFITM3 activation may drive the differentiation of S100A12 into proinflammatory and CD16 monocyte subsets. In addition, the most consistently and significantly upregulated genes in monocytes from patients with APMPPE were interferon-induced gene products (such as IFITM3, IFITM2, IFITM1, IFI6, and ISG15) and other genes associated with acute inflammation (such as LY6E and TYMP) ( Fig.6D ). Therefore, IFITM3 plays an important role in the immune response in the pathogenesis of APMPPE and can be a potential biomarker for APMPPE patients.

[0059] The application of the important pathways and markers screening method for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology includes the following: According to the changes in the number of cell subpopulations between groups and the differences in the expression of immune genes in each subpopulation of cells in healthy patients and APMPPE patients, the changes in important pathways of melanin production are screened out, and the expression level of marker genes is detected to find marker genes with large expression abnormalities in the healthy group and APMPPE patients. This is used as a biomarker to detect immune changes in APMPPE patients, and is used to study the pathogenic mechanism of APMPPE and the development of precise clinical treatment strategies, providing a reference for the application of single-cell sequencing technology in the exploration of the potential pathogenic mechanism of posterior uveitis and the discovery of biomarkers.

[0060] The above embodiments illustrate the basic principles and features of the present invention, but the above only illustrates the preferred embodiments of the present invention and is not limited to the embodiments. Under the inspiration of this patent, a person skilled in the art can make many forms of deformation and improvement without departing from the scope of protection of the present invention and the claims, which are all within the protection scope of the present invention. Therefore, the patent and protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology, characterized by: The following steps are involved: S1, isolation of peripheral blood mononuclear cells; S2, 10× Genomics single-cell RNA sequencing; S3, alignment and quantification of single-cell RNA sequencing data; S4, dimensionality reduction cluster analysis and cell identification of PBMC from APMPPE patients; S5, identification and enrichment analysis of differentially expressed genes in PBMCs of APMPPE patients; S6, dimensionality reduction cluster analysis and cell identification of monocytes from patients with APMPPE; S7, GSVA analysis of potential functions and important pathways of APMPPE monocytes; S8, GO and KEGG functional enrichment analysis of differentially expressed genes in pro-inflammatory and CD16 monocytes; S9. IFITM3 was screened as an important biomarker of monocytes in APMPPE patients.

2. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 1, characterized in that: The specific process of step S1 is as follows: a patient with acute multifocal retinochoroiditis and a healthy individual matched with the patient in age and gender were selected as research samples; peripheral blood mononuclear cells were purified from the blood samples by Ficoll gradient separation; PBMCs were isolated at 1 × 10 6 The density of cells was cryopreserved for subsequent analysis. The specific process of step S2 is as follows: single cells from each sample are independently processed into single-cell suspensions, and libraries are generated on the 10×Genomics system; single-cell libraries are generated using the GEMCode single-cell instrument and the Chromium Next GEM Single Cell 5ʹGEM, Library & Gel Bead Kit v3.1 kit; the libraries are quality tested and sequenced.

3. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 2, characterized in that: The specific process of step S3 is as follows: the raw data generated in high-throughput sequencing is a fastq format sequence. The Cell Ranger software provided by 10× Genomics is used to perform data quality statistics on the raw data and compare it to the reference genome. The software quantifies the high-throughput single-cell transcriptome data by identifying the Barcode markers that distinguish cells in the sequence and the UMI markers of different mRNA molecules in each cell, and obtains quality control statistical information such as the number of high-quality cells, gene median value, and sequencing saturation. Based on the preliminary quality control of Cell Ranger, the data analysis uses the Seurat R software package to perform further quality control processing on the data; Cells were further filtered according to the following threshold parameters: the total number of expressed genes was 500–9000; the number of UMI / gene exceeded the limit of ±2 standard deviations of the mean, assuming that the number of UMI / gene per cell was Gaussian; and the proportion of mitochondrial genes was greater than 10%; After applying these quality control criteria, 14,537 single cells were retained in the APMPPE group and 15,588 single cells were retained in the control group and included in subsequent analyses; data from all samples were merged using the merge function of R and normalized using the NormalizeData function of Seurat; data were normalized using global scaling normalization.

4. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 3, characterized in that: The specific process of step S4 is as follows: use the FindVariableGenes function in Seurat to identify the top 2000 highly variable genes; use the expression profiles of highly variable genes to perform PCA dimensionality reduction analysis through the RunPCA function in Seurat; use the FindClusters function in Seurat to perform graphical clustering based on gene expression profiles, and visualize the results in two-dimensional space through UMAP; use the FindAllMarkers function in Seurat to identify marker genes for each cluster; for a given cluster, FindAllMarkers identifies positive markers compared with all other cells; then, use the R package SingleR to annotate cell types by referring to the transcriptome dataset "Human Primary Cell Atlas"; Then, unsupervised cluster analysis was performed using Seurat, and these clusters were annotated using SingleR in combination with classical marker genes; After removing a small amount of unknown cells, most PBMCs were classified into four cell types: B cells, monocytes, natural killer cells, and T cells; statistical graphs of the number of cells of each cell type and the TOP10 marker genes of each subset were drawn; To comprehensively understand the changes in PBMCs in APMPPE disease, a comparative analysis of PBMC cell subsets between APMPPE patients and healthy controls was performed; The UMAP images showed that the distribution patterns of various cell populations were significantly different between APMPPE patients and the control group. It was noteworthy that the changes in the monocyte subpopulation were the most significant among all subtypes. The proportion of cells in each sample was quantified, and it was found that high densities of T cells and monocytes were present in both groups of samples. However, the percentage of monocytes in PBMCs of APMPPE patients was significantly lower than that of normal controls.

5. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 4, characterized in that: The specific process of step S5 is as follows: identify differentially expressed genes through the FindMarkers function in Seurat; use R based on the hypergeometric distribution test to perform GO and KEGG pathway enrichment analysis of significantly differentially expressed genes; A heat map was generated to visualize the differential expression of the top 25 genes upregulated and downregulated between APMPPE patients and normal controls in four specific cells: B cells, T cells, monocytes, and NK cells. KEGG pathway classification analysis was performed on the differentially expressed genes in each cell subtype. The results showed that the upregulated genes in monocytes, B cells, T cells, and NK cells were significantly enriched in the immune system and infectious diseases, with monocytes ranking first, NK cells second, and B cells third. Based on these results, it is suggested that significant changes have occurred in the genes related to the immune system and infectious diseases in the PBMCs of APMPPE patients, especially in monocytes.

6. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 5, characterized in that: The specific process of step S6 is as follows: Monocytes are considered to be key players in the progression of autoimmune diseases; a recent study revealed abnormalities in monocytes in the peripheral blood of patients with Vogt-Koyanagi-Harada disease, an autoimmune disease that affects the eyes; in order to gain a deeper understanding of the changes in monocytes and their subpopulations in the peripheral blood of APMPPE patients, we focused on extracting monocytes for subpopulation analysis; initially, monocytes were divided into two subpopulations based on the expression of CD14 and CD16, including classical monocytes and non-classical monocytes; in order to more accurately depict the monocyte profile of APMPPE patients, we combined the identified precise marker genes ; Five different monocyte subsets were identified and their gene expression profiles were analyzed; The dot plots showed the marker genes for each monocyte subset, and NK-like monocytes, CD16 monocytes, S100A12 monocytes, and megakaryocyte-like monocytes were clearly identifiable; However, among the proinflammatory monocytes, most cells had proinflammatory and HLA markers; Since the proinflammatory marker genes were more specific, including the unique expression of IFI6, ISG15, IL1B, and IFI44L, they were defined as proinflammatory monocytes; Five different monocyte subsets were identified, namely NK-like, CD16, proinflammatory, megakaryocyte-like, and S100A12 monocytes; Furthermore, each subpopulation exhibited a unique gene expression signature, suggesting its specific function; The UMAP images showed the differences in the distribution of monocyte subsets between APMPPE patients and controls; The quantification results of the proportions of monocyte subsets in each sample are shown; The most significant expression change among all monocyte subsets in APMPPE patients was the emergence of a transcriptional program associated with a proinflammatory state; APMPPE disease resulted in a subtype shift between monocyte subsets; with the onset of APMPPE disease, the percentage of proinflammatory monocytes increased, while the percentages of S100A12 monocytes and NK-like monocytes decreased; notably, the percentage of CD16 monocytes decreased slightly, but their pattern in UMAP changed significantly; These findings suggest that proinflammatory monocytes may be actively involved in the pathogenesis of APMPPE disease; the findings suggest that proinflammatory monocyte clustering is a more general state associated with ocular / uveal inflammation.

7. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 6, characterized in that: The specific process of step S7 is as follows: select the GSEABase package to load the gene set file, which is downloaded and processed from the KEGG database to perform GSVA; further estimate the signal pathway activity score and assign it to the single cell, and use the GSVA package for analysis; perform differential pathway analysis through the Limma package; To investigate the potential functions of the monocyte clusters, the Gene Set Variant Analysis package was used to extract KEGG pathways from the molecular signature database and perform pathway enrichment analysis. GSVA data revealed significant enrichment of signaling pathways between monocyte subpopulations, indicating that each subpopulation exerts specific functions. Proinflammatory monocytes showed high expression of several immune pathways, such as natural killer cell-mediated cytotoxicity, type 1 diabetes, and viral infectious diseases. CD16 monocytes showed significant enrichment in melanogenesis, human papillomavirus infection, Wnt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. Compared with the normal control group, the expression of various immune pathways in the monocytes of APMPPE patients was increased, while the melanogenesis pathway was significantly inhibited. Compared with other monocyte subsets, all these melanogenesis pathway genes were particularly highly expressed in CD16 monocytes, suggesting that the melanogenesis pathway plays a key and unique role with CD16 in the pathogenesis of APMPPE. The human eye is composed of multiple layers of pigmented tissue, mainly composed of melanin; the role of melanocortical signaling in melanogenesis in uveal melanocytes is still unclear; in the melanogenesis pathway, CTNNB1, GNAI3, calmodulin, CALM2, CALM3, and CALML4 were found to be upregulated in monocytes of APMPPE patients, while GNAI2, PRKCB, PLCB2, PRKACA, and CREBBP were downregulated; the regulatory changes of the above genes in CD16 monocytes observed suggest that most pathways involved in CD16 monocyte melanogenesis are associated with the pathogenesis of APMPPE; GSVA analysis revealed the importance of melanogenesis in CD16 monocytes in the pathogenesis of APMPPE.

8. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 7, characterized in that: The specific process of step S8 is as follows: To study the function of each subtype in monocytes, the differentially expressed genes of each subtype were further analyzed; To study the function of each subtype in monocytes, the differentially expressed genes (DEGs) of each subtype were further analyzed; A volcano plot of DEGs in monocyte subtypes was drawn; Obviously, IFI6, LY6E, and IFITM3 were upregulated in most monocyte subtypes, while EEF1A1 and FOLR3 were downregulated in APMPPE patients; To elucidate the potential functional changes of proinflammatory monocytes in APMPPE patients, GO and KEGG enrichment analysis of DEGs in this subtype was performed; DEGs in proinflammatory monocytes were enriched in multiple biological processes, including regulation of dendritic cell differentiation, immune response, negative regulation of viral genome replication, type I interferon signaling, response to viruses, and immune system processes; These findings suggest that the occurrence of inflammatory and immune responses in proinflammatory monocytes may play an important role in the pathogenesis of APMPPE; Based on the GSVA results, it was hypothesized that infection with influenza-like virus pathogens may trigger an innate immune response, ultimately leading to an inflammatory response in APMPPE patients; in addition, the study found that the pattern of CD16 monocytes in APMPPE patients changed significantly; in order to further verify the key role of CD16 monocytes in innate immune and inflammatory responses, functional enrichment analysis of DEGs in CD16 monocytes was performed; GO enrichment analysis of DEGs in CD16 monocytes showed that biological processes such as type I interferon signaling, negative regulation of viral genome replication, response to viruses, response to interferon-β, and viral defense response were changed in CD16 monocytes of APMPPE patients; the results of the study showed that proinflammatory monocytes and CD16 monocytes play a key role in promoting inflammation and immune responses, which supports the mainstream hypothesis that viral infection may trigger the occurrence of APMPPE disease.

9. The method for screening important pathways and markers for acute multifocal squamous pigment epithelial lesions of the posterior pole based on single-cell sequencing technology according to claim 7, characterized in that: The specific process of step S9 is as follows: IFITM3 was identified as the most significantly differentially expressed gene in monocytes of APMPPE patients compared with healthy controls, and was particularly highly expressed in monocytes in PBMC subpopulations; IFITM3 is an interferon-induced transmembrane protein that has been previously identified as an endogenous protein that blocks viral infection; To investigate the role of IFITM3 in APMPPE disease, the expression of IFITM3 in monocytes of APMPPE patients was examined at the single cell transcriptome level; The results showed that IFITM3 was significantly upregulated in pro-inflammatory and CD16 monocyte subpopulations compared with healthy monocytes; Furthermore, scVelo analysis revealed robust expression of IFITM3 unspliced ​​mRNA, expressed as a positive rate, in the pro-inflammatory and CD16 monocyte direction, indicating induction of IFITM3 mRNA transcription; these findings suggest that IFITM3 activation may drive S100A12 differentiation toward pro-inflammatory and CD16 monocyte subsets; Furthermore, the most consistently and significantly upregulated genes in monocytes from patients with APMPPE were interferon-induced gene products and other genes associated with acute inflammation; Therefore, IFITM3 plays an important role in the immune response in the pathogenesis of APMPPE and becomes a potential biomarker for APMPPE patients.

10. Use of the important pathway and marker screening method according to any one of claims 1 to 9, characterized in that: Based on the changes in the number of cell subpopulations between groups and the differences in immune gene expression of each subpopulation of cells in healthy patients and APMPPE patients, changes in important pathways of melanin production were screened out, and the expression levels of marker genes were detected to find marker genes with significant expression abnormalities in the healthy group and APMPPE patients. These were used as biomarkers to detect immune changes in APMPPE patients, and were used to study the pathogenic mechanism of APMPPE and the development of precise clinical treatment strategies, providing a reference for the application of single-cell sequencing technology in the exploration of the potential pathogenic mechanism of posterior uveitis and the discovery of biomarkers.

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