Application of substances for detecting XCL2 gene expression in the preparation of products for detecting Parkinson's disease

Abstract

This application relates to the field of neurodegenerative diseases and discloses the application of substances that detect the expression of human JUN and / or XCL2 genes in the preparation of products for detecting human Parkinson's disease. This application also discloses a product for detecting human Parkinson's disease, said product comprising substances that detect the expression of human JUN and / or XCL2 genes. The product provided by this application can achieve screening, diagnosis, or prediction of Parkinson's disease through blood tests, overcoming the technical obstacle of existing technologies that rely solely on cerebrospinal fluid diagnosis during the asymptomatic phase. This greatly alleviates patient suffering and promotes the treatment strategy of early detection and early treatment of the disease, and has significant implications for the diagnosis, treatment, and research of Parkinson's disease.

Description

Technical Field

[0001] This instruction manual relates to the field of neurodegenerative diseases, and in particular to a product for detecting Parkinson's disease in humans. Background Technology

[0002] Parkinson's disease (PD) is a common neurodegenerative disease characterized by the accumulation of intracellular protein inclusions, known as Lewy bodies (LB), which are pathological markers of both sporadic and familial PD. Due to population aging, it is estimated that up to 12 million people will be diagnosed with PD in the next decade. Currently, the diagnosis of PD is entirely based on clinical observation, which has limited accuracy and a high rate of misdiagnosis, posing a significant challenge to clinical medicine. Increasing evidence suggests that biomarkers may be useful for the diagnosis of neurodegenerative diseases. Cerebrospinal fluid (CSF) biomarkers used for PD diagnosis include CSF Aβ1-42, total tau (T-tau), tau phosphorylated at threonine 181 (P-tau181), neurofilament light chain protein (NFL), cardiac fatty acid-binding protein (HFABP), and alpha-synuclein. However, the use of these biomarkers in early clinical diagnosis and monitoring of treatment efficacy is limited due to the risks associated with invasive lumbar puncture surgery.

[0003] Recent research on neurodegenerative diseases has shifted the focus from the central nervous system to the peripheral blood. Under physiological conditions, peripheral immune cells are difficult to detect in the central nervous system due to the presence of the blood-brain barrier (BBB). However, the pathological process of Parkinson's disease can disrupt the BBB, leading to increased infiltration of peripheral immune cells into the central nervous system, which has been identified as one of the major contributing factors to Parkinson's disease. Recently, many studies have reported the correlation between peripheral immune responses and PD disease progression. It has been proposed that T-cell responses associated with α-synuclein pathology in mouse PD models may impair key areas of the central nervous system. In a recent study, NK cells were shown to be scavengers of α-synuclein, and systemic depletion of NK cells exacerbated synuclein pathology in a preclinical mouse PD model. However, the construction of a peripheral immune response profile for PD progression remains lacking. A better understanding of the host immune response profile during PD is urgently needed to better design prognostic and diagnostic biomarkers and provide appropriate therapeutic interventions for patients with severe disease manifestations. Summary of the Invention

[0004] To address the aforementioned technical challenges, this application utilizes scRNA-seq analysis to map the peripheral immune profile of PD by analyzing PBMCs from patients in early and late stages of PD and matched controls. This application further expands the blood sample size and clinically validates the role of NK cells in numerous immune-related biological processes. To elucidate the connection between the peripheral and central nervous systems, this application also utilizes human brain slices to detect the infiltration of NK cells into the motor cortex of the brain as the disease progresses.

[0005] This application provides the use of substances that detect JUN and / or XCL2 gene expression in the preparation of products for detecting Parkinson's disease.

[0006] This application also provides a product for detecting Parkinson's disease, the product comprising a substance for detecting JUN and / or XCL2 gene expression.

[0007] The beneficial effects of this application include, but are not limited to: 1. The biomarkers provided in this application for detecting the progression of Parkinson's disease, early screening, or diagnosis are positively correlated with the HY score of Parkinson's disease patients, and their expression is significantly increased in Parkinson's disease patients, with high specificity and accuracy; 2. The kits prepared from the biomarkers and their detection substances provided in this application can achieve screening, diagnosis, or prediction of Parkinson's disease through blood tests, overcoming the technical barrier of existing technologies that can only diagnose through cerebrospinal fluid during the asymptomatic period, greatly reducing the suffering of patients, promoting the treatment policy of early detection and early diagnosis and treatment of the disease, and having great significance for the diagnosis, treatment, and research of Parkinson's disease. Attached Figure Description

[0008] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, wherein:

[0009] Figure 1 (A) An experimental workflow diagram for a study of cell clusters annotated with PBMCs from healthy controls and Parkinson's disease patients using scRNA-seq, as shown in some embodiments of this application. (B) Cell count in each sample obtained by 10×scRNA-seq before and after quality control. (C) UMAP projection map of 12 different cell clusters. (D) Dot plot showing the mean logCPM of classical marker genes for each of the 12 cell clusters. (E) UMAP plot showing the distribution of 6 major cell types: CD4 T cells: clusters 06, 8; CD8 T cells: clusters 1, 4, 10; NK cells: cluster 2; monocytes: clusters 3, 7, and 9; B cells: cluster 5; GMP: cluster 11.

[0010] Figure 2The following are examples illustrating gene expression changes in different cell subpopulations according to some embodiments of this application. (A) A UMAP diagram shows the expression of classic marker genes in six cell subpopulations. (B) A heatmap shows differential gene expression in different cell types at different stages of PD. (C) The intersection of differentially expressed genes that are upregulated or downregulated in different cell subpopulations with genes related to the PD pathway. (D) A cartoon flowchart illustrates the close relationship between peripheral immune cells and PD.

[0011] Figure 3 The following are examples illustrating the changes in the proportion of cell subtypes during PD progression according to some embodiments of this application. (A) A UMAP plot shows the distribution of the six major cell types in each sample. (B) Changes in major cell types in healthy controls, early and late PD patients. (CD) Bar charts show the percentage changes of each cell subtype at different stages of PD.

[0012] Figure 4 To identify differentially expressed cell subsets in human peripheral blood and brain during PD progression, as shown in some embodiments of this application. (AB) Scatter plots and bar charts were used to detect T cells (CD45) in PBMCs of 14 healthy controls, 21 PD-Early patients, and 14 PD-Late patients, respectively. + CD3 + ), B cells (CD45) + CD3 - CD19 + ) and NK cells (CD45) + CD3 - / CD16 + CD56 +(C) Bar chart showing the correlation between the percentage of T cells, B cells, and NK cells in PD patients and the HY score in PD patients. (DE) Immunofluorescence staining images and quantitative bar charts showing NK cell expression in PBMCs of healthy controls (n=6), early (n=4–6), and late (n=5–6). Green indicates CD56 and CD16 positive cells. Blue indicates DAPI-stained nuclei. Scale bar = 50 μm. Data are expressed as mean ± standard deviation and were analyzed by one-way ANOVA and Tukey's post-hoc test. (FG) Immunofluorescence staining and quantification of TH in the amygdala and motor cortex of healthy controls (n=3) and late PD patients (n=5 slides from 2 patients). Green indicates TH positive cells. Blue indicates DAPI-stained nuclei. Scale bar = 50 μm. Data are expressed as mean ± standard deviation and were analyzed by one-way ANOVA and Tukey's post-hoc test. (HI) Immunofluorescence staining and quantification of NK cell expression in the amygdala and motor cortex of brain sections from healthy controls (n=3) and advanced PD patients (n=5 sections from 2 patients). Green indicates CD56-positive cells. Blue indicates DAPI-stained cell nuclei. Scale bar = 50 μm. Data are expressed as mean ± standard deviation and were analyzed by one-way ANOVA and Tukey's post-hoc test. (J) Correlation analysis of NK cell count with TH fluorescence intensity in the amygdala and motor cortex of advanced PD patients. *p<0.05, **p<0.01, ***p<0.001.

[0013] Figure 5Bioinformatics analysis of key genes in NK cells of PD patients according to some embodiments of this application: (A) Heatmap showing upregulated DEG expression in NK cells during healthy controls, early PD, and late PD stages. (B) Intersection of three sets of fold change values: FC1 (fold change 1), early PD vs. healthy controls; FC2 (fold change 2), late PD vs. early PD; FC3 (fold change 3), late PD vs. healthy controls. (C) GO analysis of the intersection of key genes. (D) Volcano plot showing differential expression of key genes in NK cells. (E) Heatmap showing expression changes of key upregulated DEGs obtained through volcano plot in healthy controls, early PD, and late PD stages. (F) GO analysis of key upregulated DEGs in NK cells identified by volcano plot. (G) Heatmap showing expression changes of downregulated DEGs during healthy controls, early PD, and late PD stages. (H) Intersection of three sets of fold change values. FC1 (fold change 1), early PD vs. healthy control group; FC2 (fold change 2), late PD vs. early PD; FC3 (fold change 3), late PD vs. healthy control group. (I) GO analysis of key gene intersections. (J) KEGG enrichment analysis of key gene intersections.

[0014] Figure 6 To assess the predictive performance of biomarkers for PD severity as shown in some embodiments of this application, the following methods were used: (A) Screening of DEGs in NK cells. (B) NK cell-specific upregulation of relative DEG expression levels. Healthy controls (n=13), PD group (n=36, CCL3; 31, NFKBIA; 35, JUN; 29, IER2; 25, IER2; 38, LY6E; 38, MT2A; 35, XCL2; 35, FOS; 35, IFITM3). (C) AUC values ​​of JUN and XCL2 predicting different stages of PD. (D) Expression of JUN in major cell subsets of PBMCs in healthy controls, early and late PD patients. (E) Expression of XCL2 in major cell subsets of PBMCs in healthy controls, early and late PD patients. (F) Correlation between JUN levels and HY scores in PD patients. (G) Correlation between XCL2 levels and HY scores in PD patients.

[0015] Figure 7To illustrate the characteristics of NK cell subsets according to some embodiments of this application, (A) a UMAP plot showing eight clusters of NK cells. (B) Expression of marker genes used to identify the eight clusters of NK cells. (C) A UMAP plot showing the expression distribution of marker genes in the eight clusters of NK cells. (D) Cell proportions of the eight clusters of NK cells at different stages of PD. (E) A UMAP plot showing the enrichment of XCL2 in each cluster. (F) A dot plot showing the expression of XCL2 in healthy controls, early and late C2 and C5. (G) GO enrichment and enrichment network analysis demonstrating the biological pathways involved in the enrichment of genes specifically upregulated in C2 and C5.

[0016] Figure 8 The differences in XCL2 expression in B cells between the control group and PD2.0 patients, as shown in some embodiments of this application, and its diagnostic efficacy are illustrated. (A) Box plot of XCL2 expression in B cells of patients in the control group (n=9) and PD2.0 group (n=11) showed statistical significance (P<0.05). (B) Independent analysis of the ROC curve for XCL2 diagnosis of PD2.0 showed an AUC of 0.7879, P<0.05.

[0017] Figure 9 The differences in XCL2 expression in B cells between the control group and PD3.5 patients, as shown in some embodiments of this application, and its diagnostic efficacy are investigated. (A) Box plot of XCL2 expression in B cells of the control group (n=9) and PD3.5 group (n=4) patients, showing statistical significance (P<0.05). (B) Independent analysis of the ROC curve of XCL2 in diagnosing PD3.5 showed an AUC of 0.9722, P<0.05.

[0018] Figure 10 This study examines the differences in XCL2 expression in B cells between the control group and PD patients, as well as its diagnostic efficacy, according to some embodiments of this application. (A) Box plots of XCL2 expression in B cells between the control group (n=9) and the PD group (n=15) showed statistical significance (P<0.05). (B) Independent analysis of the ROC curve for XCL2 in diagnosing PD showed an AUC of 0.8370, P<0.05. (C) Combined independent analysis of ROC curves for XCL2, diagnosing PD 2.0, PD 3.5, and PD patients showed AUCs of 0.7879, 0.9722, and 0.8370, respectively, with P values ​​all less than 0.05.

[0019] Figure 11This invention relates to the differences in XCL2 expression in B cells between male and female PD patients according to some embodiments of this application, and its diagnostic efficacy. (A) Box plot of XCL2 expression in B cells between male (n=17) and female (n=15) patients showed no statistically significant difference (P>0.05). (B) Independent analysis of ROC curves of XCL2 in PD patients of different genders showed an AUC of 0.5686, P>0.05. Detailed Implementation

[0020] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0021] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0023] Although the focus of research on Parkinson's disease (PD) has shifted from the central nervous system (CNS) to the peripheral blood, a significant knowledge gap remains regarding the relationship between PD severity and peripheral blood immune responses. This study aimed to map the peripheral blood immune atlas of peripheral blood mononuclear cells (PBMCs) from PD patients and healthy individuals using single-cell RNA sequencing (scRNA-seq) technology.

[0024] Recently, single-cell RNA sequencing (scRNA-seq) has emerged as an effective strategy for obtaining a global view of disease-related changes at unprecedented resolution. scRNA-seq has the capability to identify disease-specific genetic variations to reveal disease-specific characteristics in particular cell types, thereby enabling the identification of a variety of candidate biomarkers for diagnosis and prognosis in preclinical and clinical studies. In this application, scRNA-seq analysis identified differentially expressed gene subsets in peripheral blood mononuclear cells (PBMCs) from PD patients and healthy individuals, as well as differentially expressed genes in NK cells. The essential functions of NK cells and NK cell-specific molecular biomarkers highlight the significance of peripheral immune responses in PD progression. The identification of potential pathogenic cell types and molecular biomarkers for potential Parkinson's disease reveals the feasibility of identifying disease biomarkers in specific cell types rather than whole blood, and will encourage researchers to develop more effective diagnostic methods for Parkinson's disease based on scRNA-seq results.

[0025] This application provides the use of substances that detect JUN and / or XCL2 gene expression in the preparation of products for detecting Parkinson's disease.

[0026] In some embodiments, the sample tested by the product may be peripheral blood. Further, in some embodiments, the product may be used to detect the expression levels of JUN and / or XCL2 genes in NK cells of peripheral blood. In some embodiments, the Parkinson's disease detection product may be selected from Parkinson's disease progression monitoring products or Parkinson's disease risk assessment products. In some embodiments, the NK cells may be XCL2 cells. + GZMK - NK and XCL2 + GZMK + NK cells.

[0027] In some embodiments, the XCL2 gene is specifically expressed in NK cell subtypes C2 and C5. Preferably, in some embodiments, the XCL2 gene is expressed only in XCL2 cells. + GZMK - NK and XCL2 + GZMK + It is expressed in NK cells.

[0028] In some embodiments, the substance for detecting JUN or XCL2 gene expression includes at least one of primer pairs, probes, or antibodies. In some embodiments, the nucleotide sequences of the primer pairs for detecting JUN may be as shown in SEQ ID NO. 11 and SEQ ID NO. 12.

[0029] In some embodiments, the primer pair nucleotide sequences for detecting XCL2 may be as shown in SEQ ID NO.19 and SEQ ID NO.20.

[0030] In some embodiments, the product may be any of a kit, chip, membrane strip, protein array, composition, or detection system. In some embodiments, preferably, the product may include one or more of DNA polymerase, a mixture of deoxyribonucleotides (dNTPs), a buffer solution, a positive control, or a negative control.

[0031] In some embodiments, the product may further include substances for detecting the expression of CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA genes. In some embodiments, the substances for detecting the expression of CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA genes may include at least one of primer pairs, probes, or antibodies.

[0032] In some embodiments, the method of using the product includes the following steps: (1) extracting genomic DNA from NK cells in a peripheral blood sample to be tested; (2) performing an amplification reaction to obtain the expression level of at least one gene, JUN or XCL2, in the sample to be tested; preferably, it further includes detecting the expression level of at least one gene, CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA; (3) determining the progression of Parkinson's disease in the donor patient based on the gene expression level.

[0033] In some embodiments, the primer pair nucleotide sequences for detecting CCL3 may be as shown in SEQ ID NO.3 and SEQ ID NO.4.

[0034] In some embodiments, the primer pair nucleotide sequences for detecting FOS may be as shown in SEQ ID NO.5 and SEQ ID NO.6.

[0035] In some embodiments, the primer pair nucleotide sequences for detecting IER2 may be as shown in SEQ ID NO.7 and SEQ ID NO.8.

[0036] In some embodiments, the primer pair nucleotide sequences for detecting IFITM3 may be as shown in SEQ ID NO.9 and SEQ ID NO.10.

[0037] In some embodiments, the primer pair nucleotide sequences for detecting LY6E may be as shown in SEQ ID NO.13 and SEQ ID NO.14.

[0038] In some embodiments, the primer pair nucleotide sequences for detecting MT2A may be as shown in SEQ ID NO.15 and SEQ ID NO.16.

[0039] In some embodiments, the primer pair nucleotide sequences for detecting NFKBIA may be as shown in SEQ ID NO.17 and SEQ ID NO.18.

[0040] In some embodiments, when the expression level of the JUN or XCL2 gene in the peripheral blood sample to be tested is increased relative to that in a healthy person, preferably, the expression level of at least one of the genes CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA is also increased relative to that in a healthy person, it is determined that the person corresponding to the peripheral blood sample to be tested has Parkinson's disease, and / or, it is predicted that the person corresponding to the sample to be tested has an increased risk of developing Parkinson's disease, and / or, it is determined that the progression of Parkinson's disease in the donor patient is assessed. In some embodiments, the progression of Parkinson's disease can be divided into 5 stages according to the HY score, with a higher number indicating more severe clinical symptoms and a more serious condition. Wherein, 0 = no signs; 1.0 = unilateral disease; 1.5 = unilateral disease affecting axial muscles; 2.0 = bilateral disease without impaired balance; 2.5 = mild bilateral disease with slightly impaired postural reflexes, but able to correct themselves; 3.0 = bilateral disease with postural balance disorder and a positive pull-back test; 4.0 = severe disability, but able to stand or walk independently; 5.0 = unable to get out of bed or living in a wheelchair. In some embodiments, when the expression level of the XCL2 gene in the human NK cells corresponding to the peripheral blood sample being tested is increased relative to that of a healthy person, it is determined that the person corresponding to the sample has Parkinson's disease, and / or, it is predicted that the person corresponding to the peripheral blood sample being tested has an increased risk of developing Parkinson's disease, and / or, it is determined that the progression of Parkinson's disease in the donor patient is assessed. In some embodiments, preferably, when the expression level of the XCL2 gene in the human NK cells corresponding to the peripheral blood sample being tested is increased ... peripheral blood sample being tested has Parkinson's disease, and / or, it is predicted that the person corresponding to the peripheral blood sample being tested has an increased risk of developing Parkinson's disease, and / or, it is determined that the progression of Parkinson's disease in the donor patient is assessed. + GZMK - NK and XCL2 + GZMK + When the expression level of the XCL2 gene in NK cells is increased relative to that of healthy individuals, it can be used to determine whether the person corresponding to the peripheral blood sample being tested has Parkinson's disease, and / or predict an increased risk of developing Parkinson's disease in the person corresponding to the peripheral blood sample being tested, and / or determine the progression of Parkinson's disease in the donor patient.

[0041] This application also provides a product for detecting Parkinson's disease, the product comprising a substance for detecting JUN and / or XCL2 gene expression.

[0042] In some embodiments, the sample tested by the product may be peripheral blood. Further in some embodiments, the product may be used to detect the expression levels of the JUN and / or XCL2 genes in NK cells of peripheral blood. In some embodiments, the product for detecting Parkinson's disease may be selected from Parkinson's disease progression monitoring products or Parkinson's disease risk assessment products.

[0043] In some embodiments, the XCL2 gene is primarily expressed in NK cells. Preferably, in some embodiments, the XCL2 gene is expressed only in XCL2 cells. + GZMK - NK and XCL2 + GZMK + It is expressed in NK cells.

[0044] In some embodiments, the substance used to detect JUN or XCL2 gene expression includes at least one of primer pairs, probes, or antibodies.

[0045] In some embodiments, the primer pair nucleotide sequences for detecting JUN may be as shown in SEQ ID NO.11 and SEQ ID NO.12.

[0046] In some embodiments, the primer pair nucleotide sequences for detecting XCL2 may be as shown in SEQ ID NO.19 and SEQ ID NO.20.

[0047] In some embodiments, the product may be any of a kit, chip, membrane strip, protein array, composition, or detection system. In some embodiments, preferably, the product may include one or more of DNA polymerase, a mixture of deoxyribonucleotides (dNTPs), a buffer solution, a positive control, or a negative control.

[0048] The term "kit" refers to a packaged collection of related components, such as one or more polynucleotides or compositions, and one or more related materials, such as delivery devices (e.g., syringes), solvents, solutions, buffers, instructions, or desiccants.

[0049] The term "membrane strip" refers to a diagnostic tool that utilizes the principle of specific biomolecular recognition. It involves immobilizing biomolecules such as antigens or antibodies on a membrane, allowing them to specifically bind to the analyte in a sample, and then using visualization or other signal detection methods to qualitatively or quantitatively analyze the target substance in the sample.

[0050] The term "chip" typically refers to a miniature device that integrates biosensors and microfluidics technology. It can perform various operations such as sample preparation, reaction, and detection in biological, chemical, and medical analysis processes at the microscopic level to achieve rapid and accurate detection of disease-related biomarkers.

[0051] Protein arrays, also known as protein microarrays, are high-throughput biotechnology tools that allow for the simultaneous analysis and study of large numbers of proteins. This technology enables rapid analysis of protein expression, protein-protein interactions, and protein-small molecule binding by arranging thousands of different proteins or protein-protein interaction probes in an orderly manner on a solid surface.

[0052] In some embodiments, the product may further include substances for detecting the expression of CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA genes. In some embodiments, the substances for detecting the expression of CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA genes may include at least one of primer pairs, probes, or antibodies.

[0053] As used in this application, the term "primer" refers to a naturally occurring oligonucleotide (e.g., a restriction fragment) or a synthetically produced oligonucleotide that can be used as a starting point for the synthesis of a primer extension product, which, under appropriate conditions (e.g., buffer, salt, temperature, and pH) and in the presence of nucleotides and reagents for nucleic acid polymerization (e.g., DNA-dependent or RNA-dependent polymerases), is complementary to the nucleic acid strand (template or target sequence). Typically, a primer set will consist of at least two primers, an "upstream primer" and a "downstream primer," which together define the amplicon (the sequence to be amplified using the primers).

[0054] The term "probe" refers to any molecule capable of selectively binding to a target biomolecule (e.g., a nucleic acid sequence that hybridizes with the probe). In some embodiments, the probe may be labeled, for example, with a fluorescent group and a quencher group. In some embodiments, the probe may be a Taqman probe with a fluorescent reporter group added to the 5' end and a fluorescent quencher group added to the 3' end.

[0055] Antibodies are protective proteins produced by the body in response to antigen stimulation. In some embodiments, antibodies can be used as diagnostic reagents to detect gene expression.

[0056] In some embodiments, the primer pair nucleotide sequences for detecting CCL3 may be as shown in SEQ ID NO.3 and SEQ ID NO.4.

[0057] In some embodiments, the primer pair nucleotide sequences for detecting FOS may be as shown in SEQ ID NO.5 and SEQ ID NO.6.

[0058] In some embodiments, the primer pair nucleotide sequences for detecting IER2 may be as shown in SEQ ID NO.7 and SEQ ID NO.8.

[0059] In some embodiments, the primer pair nucleotide sequences for detecting IFITM3 may be as shown in SEQ ID NO.9 and SEQ ID NO.10.

[0060] In some embodiments, the primer pair nucleotide sequences for detecting LY6E may be as shown in SEQ ID NO.13 and SEQ ID NO.14.

[0061] In some embodiments, the primer pair nucleotide sequences for detecting MT2A may be as shown in SEQ ID NO.15 and SEQ ID NO.16.

[0062] In some embodiments, the primer pair nucleotide sequences for detecting NFKBIA may be as shown in SEQ ID NO.17 and SEQ ID NO.18.

[0063] This application also discloses a method for determining the stage of Parkinson's disease, wherein the method uses the above-mentioned product to detect the increase in the expression level of at least one of the JUN or XCL2 genes from peripheral blood samples of donor patients relative to healthy individuals; preferably, it further includes detecting the increase in the expression level of at least one of the CCL3, FOS, IER2, IFITM3, LY6E, MT2A or NFKBIA relative to healthy individuals.

[0064] In some embodiments, the method may include the following steps: (1) extracting genomic DNA from NK cells in a peripheral blood sample to be tested; (2) performing an amplification reaction to obtain the expression level of at least one gene, JUN or XCL2, in the sample to be tested; preferably, it may also include detecting the expression level of at least one gene, CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA; (3) determining the progression of Parkinson's disease in the donor patient based on the gene expression level.

[0065] This application also discloses a method for predicting the risk of developing Parkinson's disease in a subject, wherein the method uses the product described above to detect whether the expression level of at least one gene, JUN or XCL2, from the sample to be tested is increased relative to that of a healthy person; preferably, it further includes detecting whether the expression level of at least one gene, CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA, is increased relative to that of a healthy person.

[0066] In some embodiments, the method may include the following steps: (1) extracting genomic DNA from NK cells in a peripheral blood sample to be tested; (2) performing an amplification reaction to obtain the expression level of at least one gene, JUN or XCL2, in the sample to be tested; preferably, it may also include detecting the expression level of at least one gene, CCL3, FOS, IER2, IFITM3, LY6E, MT2A, or NFKBIA; (3) predicting the risk of Parkinson's disease in the person corresponding to the sample to be tested based on the gene expression level.

[0067] In some embodiments, the subject may be a mammal, such as a human.

[0068] In some embodiments, the amplification reaction can be polymerase chain reaction (PCR). In some embodiments, the amplification reaction can be real-time quantitative PCR. Polymerase chain reaction (PCR) is a molecular biology technique used to amplify specific DNA fragments. Real-time quantitative PCR (qPCR), also known as quantitative PCR, is a method that detects the total amount of product after each polymerase chain reaction (PCR) cycle by using fluorescent chemicals during DNA amplification. It can be used to quantitatively analyze specific DNA sequences in the test sample using internal or external controls.

[0069] The term "sample" refers to any composition containing nucleic acids isolated from a subject. In some embodiments, the sample may be selected from at least one of blood, tissue, blood cells, bone marrow, ascites, fine-needle biopsy samples, cellular body fluids, free-floating nucleic acids, sputum, saliva, urine, semen, cerebrospinal fluid, peritoneal fluid, pleural fluid, feces, lymph, skin swabs, oral swabs, nasal swabs, or lavage fluid. In some embodiments, preferably, the sample may be blood or brain slices. In some embodiments, tissue samples may include fresh tissue samples, formalin-fixed paraffin-embedded tissue (FFPET), etc. In some embodiments, the sample may be fresh or frozen (e.g., stored in liquid nitrogen, -80°C, or -20°C). Fresh samples can be obtained from the subject and processed directly (e.g., RNA extraction), or the samples can be frozen and processed later when needed.

[0070] This application, through scRNA-seq analysis, identified differentially expressed cell subsets and genes in NK cells from peripheral blood micromotor (PBMCs) of PD patients and healthy individuals, encompassing six major immune cell subsets, with NK cells decreasing as PD progresses. This application further clinically validated the reduction of NK cells in expanded blood samples. NK cells are involved in many immune-related biological processes, and with disease progression, they infiltrate the cerebral motor cortex, demonstrating close communication between peripheral immune responses and the CNS. Notably, NK cell-specific XCL2 is specifically expressed in NK cell subtypes C2 and C5 and is positively correlated with PD severity, exhibiting good predictive value for PD.

[0071] NK cell-mediated peripheral immune responses play a crucial role in the pathogenesis of Parkinson's disease (PD), and NK cell-specific XCL2 holds promise as a PD-specific diagnostic marker for its treatment. The indispensable functions of NK cells and NK cell-specific molecular markers underscore the significance of peripheral immune responses in PD progression.

[0072] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent companies. All quantitative experiments in the following examples were performed in triplicate, and the results were averaged.

[0073] The relevant experimental methods in this embodiment of the invention are as follows:

[0074] 1. Patient Information

[0075] Based on available clinical data, we initially enrolled four Parkinson's disease (PD) patients from West China Hospital of Sichuan University (Sichuan, China) diagnosed with different stages of PD between September 2017 and March 2018. Inclusion criteria were as follows: clinical diagnosis of early PD (Hoehn-Yahr (HY) stages I-II) or late PD (HY stages III-IV), Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) motor severity score (early PD, 24.96±4.88; late PD, 44.15±4.16), age 60-90 years, and stable medication adherence. All patients had no significant underlying medical conditions such as cancer, hypertension, diabetes, autoimmune diseases, chronic diseases, or comorbid mental illnesses, including mild cognitive impairment (MCI) and dementia. No family history of genetic factors was indicated. Two age-matched healthy controls without neurodegenerative diseases, neurogenetic disorders, or motor disorders were also included. All participants were informed of the study's content and significance and signed informed consent forms. Peripheral blood mononuclear cells (PBMCs) were collected from these six participants for single-cell RNA sequencing (scRNA-seq) analysis to obtain cell subsets and differentially expressed gene information. Following these inclusion criteria and MedSci sample size calculation methods, we subsequently expanded the sample size, collecting venous blood from 42 PD patients (28 early-stage and 14 late-stage) and 14 healthy subjects between September 2017 and January 2021 for fluorescence activated cell sorting (FACS), its correlation with patient HY scores, and further PCR testing to validate changes in differentially expressed cell populations and candidate molecules in PD patients' PBMCs. In addition, human brain tissue samples from histologically confirmed late-stage PD cases and age-matched healthy individuals without dementia were used. All research procedures involving human subjects were approved by the Clinical Trials and Biomedical Ethics Committee of West China Hospital, Sichuan University (Approval No. 20150236; Chinese Clinical Trial Registration No. ChiCTR1900023975). Furthermore, this study was conducted in accordance with the guidelines of the Declaration of Helsinki.

[0076] 2. Isolation and culture of PBMCs

[0077] Blood was drawn from patients at the same time each morning. PBMCs were stored at 4°C, transported to the laboratory, and isolated within 4 hours. PBMCs were isolated from blood samples of healthy controls and PD patients (PD-early and PD-late) using Ficoll gradient separation (Ficoll-Parquetm Plus, GE Healthcare, Sweden). Simply put, 3 mL of Ficoll-Parquetm was aspirated into two 15 mL centrifuge tubes. Blood samples were diluted 1:1 with phosphate-buffered saline (PBS), then layered on the Ficoll-Parquetm gradient, followed by centrifugation at 400×g for 30 min. Cells were carefully collected from the interfacial layer between plasma and Ficoll-Parquetm medium (8 mL / tube) and washed twice in PBS (100×g for 10 min). Immediately after PBMC isolation, cells were cryopreserved in DMEM high-glucose medium containing 20% ​​serum and 15% DMSO freezing solution. Cells were counted using a trypan blue hematology analyzer and purified at 3×10⁻⁶ cells / tube. 6 Cell / tube density is frozen in liquid nitrogen tanks.

[0078] 3. Packaging and library preparation of 10×Genomics single cells

[0079] As previously stated, following the manufacturer's protocol (OEbiotech, Shanghai), this application used the 10×Genomics Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (10×Genomics, 1000268) to generate a single-cell library. PBMCs were resuspended in 0.04% BSA-PBS, and then... II. Automated cell counter counting. This application adjusts the cell count to 1×10⁻⁶. 6 The desired concentration was achieved at / mL. Approximately 13,000 cells were added to each channel to generate nanoscale gel beads (GEMs) containing barcode information, which were then reverse transcribed in a C1000 Touch Thermal Cycler (Bio-Rad). The amplified cDNA fragments were then digested into 200–300 bp segments using a Biorupters sonic analyzer, and conventional next-generation sequencing methods, such as linker P5 sequencing and primer R1 sequencing, were employed. Finally, the DNA library was amplified by PCR.

[0080] 4. Single-cell RNA sequencing

[0081] Quantitative analysis of libraries was performed using qubits, and qualified libraries were bridge-amplified using cBot. All libraries prepared in this study were sequenced on an Illumina Nova 6000PE150 platform. Each library was sequenced as a single gene segment with 150 bp paired ends. Four fluorescently labeled A, T, G, and C dNTPs were added each cycle. Based on AT and GC pairing, DNA polymerase bound the corresponding dNTPs to the template DNA strand, and other unbound dNTPs were washed away. When the binding site was removed, a fluorescent signal was released, which could be captured and converted by a computer; thus, basic information about the binding site was obtained. This application utilizes the Cell Ranger software workflow (version 2.2.0) provided by 10×Genomics to perform demultiplex analysis on cell barcodes. It maps the read data to the genome and transcriptome using the Splicing Transcripts Alignment to a Reference (STAR) method, and downscales sample read data as needed to generate standardized summary data for each sample, thus producing a matrix of gene and cell counts. This application uses the R package Seurat. 20 (Version 2.3.4) Handles the unique molecular identifier (UMI) counting matrix.

[0082] 5. Quality Control

[0083] To remove low-quality cells and potential multiple captures (a primary concern in droplet-based experiments), this application filtered cells whose UMI / gene counts deviated from the mean by more than 4 standard deviations, assuming that the UMI / gene counts of cells followed a Gaussian distribution. After visually assessing cell distribution based on the proportion of expressed mitochondrial genes, this application further discarded low-quality cells, where >10% of the count belonged to mitochondrial genes. According to quality control (QC) standards, 48,523 single cells (2,475 cells filtered out from a total of 50,998 cells) were still included in downstream analysis. The filtered matrix was normalized to library size in Seurat to obtain normalized counts.

[0084] 6. Data Processing

[0085] Raw image data files obtained from single-cell RNA sequencing were converted into raw sequences using base call analysis. The resulting data is called raw data or raw reads. These results are stored in FASTQ (.fq) file format, containing sequence information (reads) and their corresponding sequencing quality information. The most variable genes in single cells were identified using the method described by Macosko et al. Simply put, the mean expression level and dispersion of each gene were calculated, and then the genes were divided into 12 bins based on expression level. Principal component analysis (PCA) was used to reduce the dimensionality of the log-transformed gene barcode matrix of the top-ranked genes. Cells were clustered using a gene-based clustering method, and UMAP visualization was performed using "RunUMAP". Cell type characteristic genes are listed in Table 1. T cell and NK cell subsets were analyzed using the same method. Likelihood ratio tests were used to simultaneously detect changes in mean expression and percentage of expressing cells to identify differentially expressed genes (DEGs) between different clusters. The R package SingleR is a novel computational method for unbiased cell type identification in single-cell RNA-seq. It independently infers the cell origin and identifies the cell type for each single cell by referencing the transcriptome datasets "Blueprint Epigenomics" and "Encode". Pearson correlations between samples are calculated using the Find Markers function (test.use = MAST) with R(cor(data,method="Pearson"). Differential expression thresholds are set at p < 0.05 and |log2(fold change)| > 0.26. Heatmaps, volcano plots, and violin plots are generated using R. Enriched pathways associated with DEGs are analyzed using the Kyoto Encyclopedia of Genetics and Genomes (KEGG) database (https: / / www.kegg.jp / ). Venn diagrams and circular heatmaps are generated using TBtools (https: / / doi.org / 10.1016 / j.molp.2020.06.009).

[0086] Table 1: Characteristic genes of 6 cell subpopulations

[0087]

[0088]

[0089] 7. Expression Distance Analysis

[0090] Expression differences between matched subpopulations were determined using the "estimateExpressionShiftMagnitudes()" function in Cacoa. In short, the expression distance between samples was determined as a normalized weighted sum of the correlation distances of all cell subpopulations, where the weights are equal to the proportion of the cell subpopulation with the smallest proportion in the two samples being compared. The expression distance between samples was projected onto a two-dimensional space using a multidimensional scale.

[0091] 8. Gene set scoring analysis

[0092] Gene sets related to "PD" were obtained from the DisGeNET database (https: / / www.disgenet.org / home / ). The transcriptome of each input cell was analyzed using the Seurat function "AddModuleScore" to obtain a gene set score. Variations in scores between different cell groups were analyzed using the Wilcoxon test and the ggpubr software package.

[0093] 9. Fluorescence-activated cell sorting (FACS)

[0094] PBMCs from 42 PD patients and 14 healthy controls were isolated by flow cytometry to further validate the results of scRNA-seq. In short, all PBMCs were washed, counted, and resuspended in ice-cold PBS, followed by staining for cell surface antigens. Immunofluorescence staining was performed using monoclonal antibodies against CD3, CD45, CD56, CD19 (CD45 / CD56 / CD19 / CD3 detection kit) and CD16 (CD16-PE). Stained cells (T cells (%):CD3) were analyzed and sorted using BD (BD, USA). + Excluding dead cells, B cells (%): CD19 + CD3 - Excluding dead cells, NK cells (%): CD16 + CD56 + CD3 - (Excluding all dead cells), and finally analyzed the data using FlowJo (version 10.0). The detailed gating strategy is as follows: 1) Initial gating to exclude dead cells: We use the forward scattering height (FSC-A) and side scattering height (SSC-A) parameters to gate dead cells, obtaining region 1 (R1); 2) CD45 + Cell identification: In R1, we selected CD45 (FITC) + ) Positive cells produce region 2 (R2); 3) CD45 + CD3+ Isolation of cells (T cells): From R2, we gate CD45 + CD3 + T cells (PC5) + ), yielding region 3 (R3), representing T cells; 4) CD19 + Cell (B cell) isolation: In R2, we gated CD19-positive cells (ECD) and CD3-negative cells (PC5). - ), yielding region 4 (R4), representing B cells; 5) CD56 + CD16 + Cell (NK cell) isolation: In R2, we selected CD56 + CCD16 + Positive cells (RD1, PE) and CD3-negative cells (PC5) - This generates region 5 (R5), which represents NK cells.

[0095] 10. Immunofluorescence staining

[0096] Human PBMC smears and brain tissue sections were prepared for immunofluorescence staining. Human brain sections were retrieval of antigens with sodium citrate and washed three times with 0.01M PBS for 5 min each time. The cell smears and brain sections were then incubated with 5% goat serum and 0.3% Triton X-100 at room temperature for 2 hours. Then, primary antibodies diluted with 2% goat serum (CD16, CD56, rabbit, Bioss, 1:50; TH, rabbit, Abcam, 1:250) were added, and the sections were incubated at 4°C for 18 hours. Negative controls were treated with 2% goat serum. After washing three times with PBS, the sections were incubated with secondary antibody (DyLight 488, goat anti-rabbit IgG, Abbkine, 1:400) at room temperature for 3 hours, washed three times with PBS, and stained with DAPI. Finally, the sections were mounted with antifluorescent mounting medium (Antifade Mounting Medium, P0126, Beyotime). Fluorescence changes in brain tissue were observed under a light microscope. ImageJ software was used to quantify five randomly selected fields of view (1.33 mm) in the green fluorescence channel (positive cells) and the blue fluorescence channel (DAPI). 2 The number of cells was determined. The positive rate was then calculated as: (number of positive cells / number of DAPI-stained nuclei) × 100. TH intensity was measured using ImageJ software, and the TH-positive area was normalized to the DAPI-positive area (3 sections per group, 1.33 mm²). 2 )).

[0097] 11. Real-time quantitative PCR (RT-qPCR)

[0098] This application further validated the differential gene expression of nine key upregulated DEGs in NK cells using RT-qPCR. In short, total RNA was extracted from flow-cytosorized NK cells using RNAiso plus (TaKaRa, Japan) according to the manufacturer's protocol, and then analyzed by Bestar. TM The qPCR RT Kit (DBI, Germany) was used for reverse transcription to complementary DNA (cDNA). A CFX96 was used. TM RT-qPCR was performed using a real-time system (Bio-Rad, USA). The expression levels of the target gene were normalized to the GAPDH level in each sample. Primer sequences are listed in Table 2.

[0099] Table 2 Primer information for gene detection

[0100]

[0101] 11. NK cell subtype classification in PBMCs

[0102] We categorized cell clusters expressing strong signals GNLY, GZMB, and NKG7 as NK cells. We isolated NK cells for subpopulation identification. The original UMI counts were normalized using the "NormalizeData" function in Seurat with a scaling factor of 10,000. Then, the top 3,000 highly variable genes were obtained using the default variance stabilization process via the "FindVariableFeatures" function in Seurat. We further integrated the kernels based on summarized anchor features using the "IntegrateData" function and visualized the resulting kernels after embedding them into the PCA dimensions using UMAP. We selected an appropriate dimensionality reduction parameter (dims = 20) based on ElbowPlot and JackStraw. After multiple "FindClusters" analyses, we selected the most suitable resolution (resolution = 0.2). Based on the expression of two classic functional markers, FCGR3A / CD16 and NCAM1 / CD56, NK clusters were first classified as the major population. Cluster-specific marker genes were identified using the "FindMarkers()" function in Seurat with options "logfc.thresh old=0.25,min.pct=0.25". The P-value was corrected using the Bonferroni method, with 0.05 set as the significance threshold. Based on differences in marker gene expression, they were classified into different subtypes.

[0103] 12. Statistical Analysis

[0104] Sequencing data were analyzed using R software (Version 2.3.4) and Cell Ranger. All data were presented in raw form. Statistical analysis was performed using SPSS Statistics 25.0 (IBM, Chicago, IL, USA). Independent samples t-tests were used for comparisons between groups, and one-way ANOVA was used for comparisons among multiple groups. If the variances were homogeneous, the least significant difference (LSD) result of multiple comparisons was selected; otherwise, Dunnett's T3 test was used. Kruskal-Wallis tests were used for comparisons of multiple clusters that did not meet traditional homogeneity and homogeneity of variance. Clinical efficacy evaluation data of JUN and XCL2 were analyzed using Kruskal-Wallis tests. RT-qPCR results were used for receiver operating characteristic (ROC) curve analysis. Spearman correlation analysis was used to analyze the correlation between JUN and XCL2 expression levels and HY scores, as they did not conform to a normal distribution. The correlations between T, B, and NK cell percentages and PD severity, as well as the correlation between NK cell count and TH fluorescence intensity, were analyzed using Pearson correlation analysis because they conformed to a normal distribution. Independent samples t-tests were used to exclude gender interference. Venn diagrams were generated using TBtools (a toolbox for biologists integrating various biological data processing tools) (https: / / doi.org / 10.1016 / j.molp.2020.06.009). KEGG and GO analyses were performed using the clusterProfile package in R software, and heatmap analysis was performed using the heatmap package. Co-expression of key genes was analyzed using online STRING analysis (https: / / string-db.org / ) and Cytoscape software (https: / / cytoscape.org / ).

[0105] Example 1 – ScRNA-seq analysis of heterogeneity and correlation in PBMC population

[0106] The detailed procedure flow of this study is as follows: Figure 1 As shown in Figure A, peripheral blood samples were extracted from healthy controls, early-stage Parkinson's disease (PD-Early), and late-stage Parkinson's disease (PD-Late) patients for single-cell sequencing (scRNA-seq). Cell subpopulation and differentially expressed gene information were obtained using the 10×Genomics platform. Figure 1A). The number of cells captured in each group were as follows: Sample 16: 8269 cells; Sample 17: 7292 cells; Sample 19: 13675 cells; Sample 20: 9005 cells; Sample 25: 4110 cells; and Sample 26: 8647 cells. After quality control, a total of 48523 cells were finally obtained. Figure 1 B), and the analysis showed no significant differences in mean UMI and mean gene count among and within each group. This application performed cluster analysis to study the cellular heterogeneity of cell subpopulations in peripheral blood. Using UMAP visualization, 12 cell clusters of different cell types were labeled and displayed. Figure 1 C). Based on differentially expressed marker genes, these 12 clusters were divided into 6 major cell populations: CD4+ T cells, CD8+ T cells, NK cells, B cells, monocytes, and granulocyte-monocyte progenitor cells (GMP cells). Figure 1 D, E).

[0107] To elucidate the relationship between peripheral immune cells and PD risk, key genes expressed in different cell types were first identified (B cells: CD79A, T cells: CD3D, CD8A, and CD4, monocytes: LYZ, NK cells: GNLY, GMP cells: SOX4). Figure 2 A). The heatmap then shows the expression of differentially expressed genes (DEGs) in these six cell types (red indicates upregulation, green indicates downregulation). Figure 2 B). These DEGs, which are upregulated or downregulated during PD progression (healthy-early-late), along with PD pathway-related genes selected from the GSEA website, were imported into the Venn diagram for intersection analysis. The results showed a large number of overlapping DEGs in each cell type, indicating a potential association between these DEGs and PD risk. Figure 2 C), these findings highlight the close relationship between peripheral immune cells and PD (C). Figure 2 D). Next, the changes in six immune cell types during disease progression were analyzed. Figure 3 A and B show a comparison of UMAP plots of changes in the six cell populations in each sample. Overall, the data showed high cell densities of CD8+ T cells and CD4+ T cells (in clusters 0, 1, 4, 6, 8, and 10) and NK cells (in cluster 2) in each sample. Figure 3 C). In peripheral blood mononuclear cells (PBMCs) of individuals with early and late PD, the proportion of NK cells showed a decreasing trend compared to healthy controls, while the proportion of T cells showed a trend of first increasing and then decreasing compared to healthy controls. Figure 3 C, D).

[0108] Example 2 – Changes in NK cells and T cells in peripheral blood and brain during PD progression

[0109] Based on scRNA-seq results, this application further collected PBMCs from 14 healthy controls, 28 patients with early-stage PD, and 14 patients with advanced PD to expand the sample inclusion. T cells, B cells, and NK cells in the PBMCs were first sorted by flow cytometry. CD45 cells were selected. + (FITC + Cells were selected from the R2 region, from which CD45 was selected. + CD3 + The cell is located in the R3 region (T cell). CD19 is recognized from the R2 region. + (ECD) and CD3 - Cells (PC5) - CD16 was selected from the R2 region as the R4 region (B cells). + Cells (PE), CD56 + Cells (RD1) and CD3-cells (PC5) - As the R5 region (NK cells) Figure 4 A). The results showed that the proportion of NK cells in PBMCs of PD patients was reduced, especially in patients with advanced PD, and the decrease in the proportion of NK cells was significantly negatively correlated with the severity of PD. Figure 4 AC). There was no significant change in B cell count across the three groups. Although T cell counts were significantly reduced in patients with advanced PD, the T cell proportion did not show a significant correlation with PD severity in our results (AC). Figure 4 The significant reduction in CD16 and CD56 (NK cell marker genes) expression in PBMCs further demonstrates the reduction in NK cells. Figure 4 (D, E). These findings highlight the potential association between NK cells and PD progression, prompting us to consider whether NK cells can infiltrate the brain and influence the pathological process of PD. Therefore, we used human brain tissue sections for further validation. First, the results showed significant degenerative changes and markedly decreased fluorescence intensity of tyrosine hydroxylase (TH) in the amygdala and motor cortex of patients with advanced PD. Figure 4 F, G). Notably, we found NK cells in the amygdala and motor cortex of patients with advanced PD, and the number of NK cells in the motor cortex showed a significant upward trend (F, G). Figure 4 H, I). Furthermore, a decrease in TH intensity was significantly negatively correlated with an increase in the number of NK cells in the amygdala and motor cortex (H, I). Figure 4 These results suggest that NK cells may infiltrate the brain and play an important role in the pathological process of PD.

[0110] Example 3 – Bioinformatics Analysis Reveals the Important Role of NK Cells in the Progression of PD

[0111] Bioinformatics analysis was used to further explore the biological functions of NK cells. These differentially expressed genes (DEGs) that were continuously upregulated in these NK cells are presented in the heatmap. Figure 5 A). Intersection analysis of DEGs upregulated at different stages identified a total of 200 key genes. Figure 5 B). GO analysis showed that the top ten enriched biological process (BP), molecular function (MF), and cellular component (CC) terms involved in these overlapping genes were mostly related to immunity. Figure 5 C). Among the upregulated DEGs, more critical genes were further screened using volcano plots and heatmaps. Figure 5 D, E). GO analysis showed that these key upregulated DEGs were associated with immune regulation and cytokine responses (D, E). Figure 5 F) indicates that NK cells may play an important role in the immune regulation of PD. Furthermore, this application also performed a similar analysis on downregulated DEGs in NK cells (F). Figure 5 G), 202 genes that were continuously downregulated during the disease process were screened out. Figure 5 H). GO analysis of these DEGs showed that they were primarily associated with DNA transcription and signal transduction (H). Figure 5 I). KEGG analysis identified that these downregulated DEGs were significantly involved in the MAPK signaling pathway (I). Figure 5 The key genes identified through PPI analysis (C5AR1, GNG7, PPBP, CXCR3, PF4, UBR1, FBXO11, TRAF7, KCTD6) can provide a basis for further research on related functions and screening pathways. Therefore, our subsequent research will mainly focus on NK cells to explore their biomarkers in PD progression.

[0112] Example 4 – Identification and Validation of PD-Related Key Genes Specifically Expressed by NK Cells

[0113] Given the abnormal immune response of NK cells in Parkinson's disease, this application screened differentially expressed genes related to immune pathways and cytokine responses from the aforementioned upregulated DEGs, aiming to identify key genes in NK cells that may play an important role in the progression of Parkinson's disease. After excluding genes in the blood related to hemoglobin (HBB), cytoskeleton (ACTB), and lymphotoxins (PSME2, ​​LTB), this application validated the remaining nine genes related to signal transduction and immune regulation using RT-qPCR. Figure 6 A). Of the nine DEGs, only JUN and XCL2 were significantly upregulated in PD ( Figure 6B). The areas under the ROC curves (95% confidence intervals) for JUN predicting PD 2.0, PD 3.5, and PD were 0.7222 (p = 0.0551), 0.6857 (p = 0.2046), and 0.6080 (p = 0.3242), respectively. In contrast, the AUCs for XCL-2 predicting PD were 0.7556 (p = 0.0274), 0.9286 (p = 0.0034), and 0.8040 (p = 0.0055), respectively. This indicates that XCL2's predictive performance is superior to JUN (…). Figure 6 C). Furthermore, this application compared the relative expression levels of nine DEGs in men and women, finding that, except for MT2A, the expression of other genes did not differ significantly between men and women. Additionally, the predictive performance of JUN (AUC, 0.5667) and XCL2 (AUC, 0.5635) did not differ significantly between male and female patients. Therefore, gender differences were not considered in the study design. Analysis of JUN and XCL2 expression in different cell populations revealed that JUN was present in B cells, NK cells, and monocytes, while XCL2 was expressed in NK cells and T cells, particularly increasing in NK cells with disease progression. Figure 6 D, E). These results suggest that the specific differential expression of XCL2 in NK cells may be a key biomarker for PD patients. Furthermore, correlation analysis of JUN and XCL2 in NK cells showed that XCL2 was significantly positively correlated with the HY score in PD patients (D, E). Figure 6 F, G).

[0114] Example 5 – XCL2, a specific biomarker of NK cell subsets in PD progression

[0115] After re-analysis of NK cell subsets, this application ultimately identified eight NK subsets (C0-C7). The average cell count of these eight subsets was 995 (163-2684). Figure 7 A). By analyzing the specific marker genes of these 8 subgroups, we found that the C2 and C5 subgroups specifically highly express XCL2 ( Figure 7 B, C). Post-standardized cell count analysis revealed that, compared to healthy individuals, the C2 subset significantly increased in the early stages of PD and maintained a high proportion throughout disease progression; the C5 subset showed a slight increase in the early stages of PD and a significant increase in the later stages, indicating that XCL2-positive NK cell subsets (C2 and C5) are associated with the occurrence and progression of PD. Figure 7 D). Cell density visualization showed that XCL2 was highly enriched in the C2 and C5 subsets. Furthermore, differential gene analysis showed that XCL2 expression was higher in the C2 and C5 subsets of PD patients compared to healthy individuals. Figure 7E, F). Further GO enrichment and enrichment network analyses were performed on the specific marker genes of the C2 and C5 subgroups. We found that genes highly expressed specifically in C2 are mainly associated with biological functions such as positive regulation of T cell activation, interferon-beta production, interferon-gamma production, and chemokine-mediated signaling pathway. Genes highly expressed specifically in C5 are mainly associated with biological functions such as gamma-delta T cell activation and interleukin-12 production. Figure 7 G). XCL2 is involved in all of these biological functions, further demonstrating the important role of XCL2 in the development and progression of PD and disease-related NK subgroups.

[0116] Example 6 - Using the K-nearest neighbor classifier to classify and predict Parkinson's disease patients

[0117] Differential gene expression in venous blood mononuclear cells of healthy controls and PD patients at different stages was detected using single-cell sequencing (scRNA-seq) to screen for differentially expressed genes. XCL2, a gene showing a consistently downregulated trend, was selected by comparing sequencing and RT-qPCR results. One gene with the highest predictive accuracy for PD was chosen as a candidate PD characteristic gene, and a predictive model for PD versus healthy controls was established. Statistical analysis was performed using SPSS 22.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 9 (GraphPad Software, San Diego, California, USA). Data from each dataset were analyzed independently. Since the data were quantitative and did not conform to a normal distribution, rank-sum tests were used for analysis. Predictive values ​​were calculated using binary logistic regression models with RT-qPCR results from datasets 1, 2, 3, and 4 as covariates, and then used for ROC curve analysis. All tests were two-tailed, and the significance level was set at P < 0.05.

[0118] The relative expression levels of the XCL2 gene marker in the peripheral blood of 15 confirmed Parkinson's disease (PD) patients and 9 healthy controls were detected using quantitative real-time RT-PCR. A PD prediction model was used to classify and predict the expression levels of these samples. The prediction results were compared with the clinical diagnostic results of PD to determine the sensitivity, specificity, and accuracy of the XCL2 gene marker for PD prediction. The Hoehn-Yahr (HY) staging scale can be used to classify PD patients according to their symptoms and severity. Early stage Parkinson's disease is defined as H-Y1–2, intermediate stage as H-Y3–4, and late stage as H-Y5. The higher the stage, the more severe the disease. In dataset 1, PD2.0 represents early-stage PD, with 11 patients; in dataset 2, PD3.5 represents mid-stage PD, with 4 patients; dataset 3 contains all 15 PD patients, and these three datasets share 9 healthy controls; dataset 4 contains 17 male PD patients and 15 female PD patients. These results show that XCL2 is highly expressed in PD patients (including early and mid-stage PD) and has a good diagnostic effect on PD. However, there is no difference in XCL2 expression between male and female PD patients, and the area under the AUC curve is small, indicating that XCL2 diagnosis of PD is not affected by gender. Specific detection results are shown in Table 3 and the ROC curve. Figure 8 , Figure 9 , Figure 10 and Figure 11 .

[0119] Table 3

[0120]

[0121] Results Analysis

[0122] This application preliminarily mapped the peripheral immune atlas of PD patients using scRNA-seq technology and defined specific diagnostic biomarkers for PD, including six major immune cell subsets: T cells (CD4+, CD8+), B cells, NK cells, monocytes, and GMPs. It demonstrated the changing patterns of these cell subsets and the alteration of different gene expression modes during PD progression. Further expanding the sample size, this application clinically validated the findings, revealing that NK cells are involved in many immune-related biological processes within these cell subsets, and their levels significantly decrease in blood samples as PD progresses. With disease progression, NK cells infiltrate the cerebral motor cortex, demonstrating a close communication between the peripheral immune response and the central nervous system. NK cell-specific XCL2 is specifically expressed in NK cell subsets C2 and C5, potentially serving as a specific diagnostic biomarker for PD.

[0123] In this application, we constructed a peripheral blood immune atlas of PD patients, revealing changes in cellular subsets in the peripheral blood of PD patients. The results showed that NK cell numbers decrease with PD progression and that NK cells are involved in immune-related biological processes. Disease-related risk scores showed a correlation between NK cells and PD, and PD risk genes present in NK cells were also related to immune regulation. These results suggest that abnormal changes in NK cells lead to peripheral blood immune dysregulation in PD patients, thereby exacerbating the severity of the disease. The findings of this application highlight the important role of NK cells in the progression of Parkinson's disease. The results of NK cell research in PD patients also showed enrichment in mitochondrial-related pathways. ATP produced by mitochondria is an important energy source for maintaining neuronal and immune cell function. According to previous reports, dopaminergic neurons in PD patients often exhibit mitochondrial dysfunction, leading to insufficient energy metabolism and increased oxidative stress, exacerbating neuronal damage. In inflammatory states, the activation and proliferation of immune cells require a large amount of energy, which mitochondria meet through metabolic adaptation. Simultaneously, changes in energy supply may affect neuronal function and survival, thereby influencing neuroimmune inflammatory responses. Our results consistently highlight the crucial role of NK cells in the progression of PD.

[0124] There is a close link between the peripheral immune response and the central nervous system (CNS). NK cells infiltrate the brain in neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) and regulate the expression of peripheral NK cell-related immune genes, promoting NK cell infiltration and neuroinflammatory changes in the brain in AD. Meanwhile, studies on animal models of PD primarily driven by α-synuclein (α-syn) have shown that infiltrating NK cells participate in inflammatory processes and substantia nigra neurodegeneration, as the removal of these cellular components affects neuronal survival. This application demonstrates that peripheral blood NK cells can enter the motor cortex of the brain and participate in the pathogenesis of Parkinson's disease. This application hypothesizes that during the progression of PD, the integrity of the blood-brain barrier is impaired, leading to the recruitment of peripheral innate immune cells by activated innate immune cells in the brain (including microglia and astrocytes). These results suggest that NK cells participate in the deterioration of the central nervous system's immune environment within the peripheral immune system. Furthermore, it is well known that Parkinson's disease (PD) patients exhibit significant characteristic motor symptoms, such as bradykinesia and rigidity, which are closely related to the motor cortex of the brain. This application, using brain samples from patients with advanced PD, demonstrates that peripheral NK cells may infiltrate the motor cortex and amygdala, potentially participating in the pathogenesis of Parkinson's disease. Recent studies have reported significant thinning of the motor cortex and amygdala volume reduction in severe Parkinson's disease; these morphological changes are associated with increased disease duration and severity of motor symptoms, which may influence future PD progression. Further validation and in-depth exploration of the mechanisms of NK cells in a larger clinical population are necessary steps to advance PD research. Current research on Parkinson's disease largely focuses on dopamine neurons and whether the motor cortex is also involved, which could contribute to further research on the progression of Parkinson's disease.

[0125] Furthermore, this application aims to explore cell-specific immune-related biomarkers specifically expressed on NK cells during the progression of Parkinson's disease (PD). Differential expression of XCL2 in NK cells has shown good predictive power for different stages of PD and is significantly positively correlated with the severity of PD. Building on this, this application further analyzed NK cell subsets and surprisingly found that XCL2 expression levels in NK cell subsets C2 and C5 significantly increased with increasing PD severity. This observation suggests that NK cell subtypes C2 and C5 may be the main cell types infiltrating the brain in PD and may be involved in immune responses within the brain. Further research can explore the precise functions and mechanisms of these specific NK cell subtypes in the pathogenesis of PD. As mentioned earlier, the XCL2 transcript is closely related to the genetic labeling of NK cells and is associated with overall survival in cancer patients. Currently, few studies elucidate the characteristics of NK cell-specific XCL2 expression in the development and progression of PD. It has been reported that human NK cells can produce chemokines CCL5, XCL1, and XCL2, but the latter two are rare sources in the blood under homeostatic conditions. This could explain the abnormal upregulation of XCL2 in peripheral blood NK cells as PD progresses. These studies reveal key features of the peripheral immune response in the pathogenesis and progression of PD and identify NK cell populations with specific gene expression programs.

[0126] In summary, this application mapped the peripheral immune system of Parkinson's disease (PD) patients using scRNA-seq, revealing changes in cellular subsets and gene expression during PD progression. Furthermore, the infiltration of NK cells into the motor cortex as the disease progresses demonstrates a close link between the peripheral immune response and the central nervous system (CNS). Notably, NK cell-specific XCL2 expression is positively correlated with PD severity and exhibits good predictive value for PD, providing better clues and therapeutic targets for future PD treatment.

[0127] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0128] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0129] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0130] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. Use of a substance for detecting XCL2 gene expression in NK cells in the manufacture of a product for diagnosing Parkinson's disease, wherein the substance for detecting XCL2 gene expression in NK cells comprises at least one of a primer pair, a probe or an antibody.

2. Use according to claim 1, wherein The sample detected by the product is peripheral blood, and further, the product is used for detecting the expression of the XCL2 gene in NK cells of peripheral blood; the product is selected from a product for detecting the progression of Parkinson's disease or a product for assessing the risk of Parkinson's disease.

3. The use according to claim 1, wherein The product is any one of a kit, a chip, a membrane strip, a protein array or a composition.

4. Use according to claim 3, wherein the compound is ###0002### The product comprises one or more of a DNA polymerase, a mixture of deoxynucleotides (dNTPs), a buffer solution, a positive control or a negative control.

5. The use according to claim 1, wherein the compound is ###0002### The product further comprises a substance for detecting the expression of a CCL3, FOS, IER2, IFITM3, LY6E, MT2A or NFKBIA gene, wherein the substance for detecting the expression of the CCL3, FOS, IER2, IFITM3, LY6E, MT2A or NFKBIA gene comprises at least one of a primer pair, a probe or an antibody.

6. The use according to claim 1, wherein The use method of the product comprises the following steps: (1) extracting genomic DNA of NK cells in the peripheral blood sample to be detected; (2) performing an amplification reaction to obtain the expression amount of the XCL2 gene in the sample to be detected; and (3) determining the progression of Parkinson's disease in the patient based on the expression amount of the gene.

7. Use according to claim 6, wherein (2) further comprising detecting the expression amount of at least one of a CCL3, FOS, IER2, IFITM3, LY6E, MT2A or NFKBIA gene.

8. The use according to claim 6 or 7, wherein the primer pair for detecting FOS has a nucleotide sequence as shown in SEQ ID NO. 5 and SEQ ID NO. 6; and / or, the primer pair for detecting IER2 has a nucleotide sequence as shown in SEQ ID NO. 7 and SEQ ID NO. 8; and / or, the primer pair for detecting IFITM3 has a nucleotide sequence as shown in SEQ ID NO. 9 and SEQ ID NO. 10; and / or, the primer pair for detecting LY6E has a nucleotide sequence as shown in SEQ ID NO. 13 and SEQ ID NO. 14; and / or, the primer pair for detecting MT2A has a nucleotide sequence as shown in SEQ ID NO. 15 and SEQ ID NO. 16; and / or, the primer pair for detecting NFKBIA has a nucleotide sequence as shown in SEQ ID NO. 17 and SEQ ID NO.

18. ​

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