Integrated Analysis of Single-Cell RNA Sequencing and Transcriptome Data Identifies a Pyroptosis-Associated Diagnostic Model for Parkinson’s Disease

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Despite the emphasis on motor symptom-based diagnosis, there remains an unmet clinical need for effective diagnostic approaches during the prodromal phase of PD. Recent advancements in single-cell RNA sequencing (scRNA-seq) and transcriptomic analyses of PD patients open avenues for identifying potential diagnostic biomarkers. Methods : A comprehensive cell trajectory analysis was conducted using scRNA-seq datasets to pinpoint gene expressions associated with cellular transition from healthy to PD-affiliated state. Integrating the scRNA-seq datasets with Weighted Gene Co-expression Network Analysis (WGCNA) allowed the extraction of pyroptosis-associated differentially expressed genes (PDEGs). Leveraging LASSO logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest methodologies, we devised a diagnostic model centered on PDEGs. Additionally, immunoinfiltration, inflammatory signaling pathways, and intercellular communication were discerned through scRNA-seq analyses. Results : In PD patients, the number of cells including metencephalic-like cells, excitatory neurons, inhibitory neurons, and MHB-like cells were significantly reduced, whereas the proportion of astrocytes and microglia, the immunoinfiltration and inflammatory signaling pathways were upregulated as compared with healthy individuals. Using scRNA-seq and WGCNA analyses, two pyroptosis-related diagnostic genes POLR2K and TIMM8B were identified, and a diagnostic model based on them was constructed, which showed promising performance upon validation. Conclusion : This study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq combined with transcriptome data, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for this neurodegenerative ailment. Biological sciences/Computational biology and bioinformatics/High throughput screening Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Neuroscience/Diseases of the nervous system Health sciences/Neurology/Neurological disorders Parkinson’s disease single cell RNA sequencing pyroptosis diagnostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Parkinson’s disease (PD) stands as the foremost prevalent neurodegenerative motoric disorder, with over six million individuals affected worldwide [ 1 ]. Despite the nebulous nature of its etiology and pathogenesis, the emphasis on early detection and prompt intervention holds paramount significance for efficacious symptom amelioration and retardation of PD progression. Conventional diagnostic methodologies are primarily reliant on clinical manifestations, encompassing static tremor, myotonia, bradykinesia, and postural equilibrium disturbances, which often remain latent in early PD patients [ 2 ]. Detecting the early stages of PD prior to overt clinical symptomatology remains challenging due to a lack of precise molecular diagnostic signatures for PD. Despite the complexity of PD’s pathogenesis, neuroinflammation and dopaminergic neuronal death are widely acknowledged as key pathophysiological features of PD [ 3 ]. During the progression of PD, various cell types, such as microglia, astrocytes, and various immune cells, are involved in the neuroinflammation which leads to the neuronal death. Microglia activation results in the secretion of proinflammatory factors like IL-1β, IL-6, ROS, TGFβ and TNF-α. Infiltration of CD4 + and CD8 + T cells in the substantia nigra pars compacta in PD patients correlates with heightened levels of TNF and IFNγ [ 4 ]. In response to inflammation, the astrocytes can release bioactive molecules including chemokines, neurotrophins, and growth factors [ 5 ]. The elevated proinflammatory factors can induce neuronal cell death both directly and indirectly. Nevertheless, the mechanisms underlying neuronal degradation and death due to inflammation remain a subject of debate. Increased evidence suggests programmed cell deaths (PCDs) might constitute a primary mechanism for neuronal loss [ 6 ]. Pyroptosis is an inflammatory programmed cell death characterized by NLRP3 inflammasome formation, cell swelling, plasma membrane rupture, and the release of cellular contents [ 7 ]. It can be mediated via both caspase-1-dependent and -independent pathways. In the caspase-1-dependent pathway, pyroptosis is initiated by the activation of pattern recognition receptors (PRRs), such as NLRP1b, NLRP3, NLRC4, AIM2, or Pyrin, which respond to pathogen-associated molecular patterns (PAMPs) or host-derived danger signal molecules (DAMPs). Activated inflammasomes NLRP3 and NLRC4 then recruit inflammasome junction protein ASC and protease caspase-1 to form macromolecular complexes. Caspase-1 facilitates both the direct lysis of gasdermin D, triggering plasma membrane rupture, and the conversion of pro-IL-1β and pro-IL-18 into their mature and functional forms, IL-1β and IL-18 [ 8 ]. In the caspase-1-independent pathway, caspase-11/4/5 directly recognize and bind to lipopolysaccharide (LPS) of pathogens, cleave GSDMD to generate active N-terminal fragments, induce the formation of cell membrane pores, and consequently lead to pyroptosis [ 9 ]. Numerous studies have revealed the pivotal role of inflammasome-activated pyroptosis in the degeneration of dopaminergic neurons in PD [ 10 – 12 ]. However, the diagnostic potential of pyroptosis-related genes in PD needs further exploration. While over 70 loci have been significantly linked to PD risk [ 13 ], the genetic underpinnings and diagnostic approaches for PD remain largely uncovered. With the rapid development of high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) combined with transcriptome studies shed light on the searching for diagnostic genes of PD. Currently, scRNA-seq data is primarily utilized to identify specific cell types and their molecular characteristics in PD. For example, one study employing single-cell sequencing technology revealed an increased number of microglia and astrocytes and a decreased number of oligodendrocytes in the midbrain of patients with idiopathic PD [ 14 ]. Furtherly, cell clustering and molecular typing of dopaminergic neurons in the substantia nigra pars compacta was achieved through scRNA-seq analysis [ 15 ]. This analysis delineated ten distinct clusters of dopaminergic neurons, each with unique transcriptomes, and identified a subset of dopaminergic neurons marked by ATGR1 expression that are particularly vulnerable in PD [ 15 ]. But until now, few accurate diagnosis model for PD has been developed or applied in clinic. This study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq data combined with transcriptome data, showing innovativeness and clinical translational value. The differences in cell cluster composition and gene expression between PD and healthy individuals were analyzed using scRNA-seq and transcriptome data. The proportion of microglia and astrocytes, immunoinfiltration, inflammatory signaling pathways, and intercellular interaction in PD patients were significantly increased than that in healthy individuals. The pyroptosis-related differentially expressed genes (PDEGs) were screened using scRNA-seq and WGCNA analyses to determine hub genes, and the diagnostic model and nomogram were then constructed based on the genes POLR2K and TIMM8B. This diagnostic model showed promising diagnostic efficacy upon verification. Materials and Methods Acquisition and screening of expression profile data Single-cell datasets GSE140231, GSE157783, and GSE204796 were downloaded from the NCBI GEO (GeneExpressionOmnibus, GEO, http://www.ncbi.nlm.nih.gov/geo/ ) database. There were 12 healthy samples in GSE140231, 6 healthy and 5 disease samples in GSE157783, and 9 healthy samples in GSE204796. Therefore, a total of 5 disease and 27 healthy samples were included in this study as the subsequent model validation dataset, and all samples were tested for quality control by Seurat. Bulk datasets GSE7621, GSE20164, GSE20163, and GSE8397 were downloaded from NCBI GEO (GeneExpressionOmnibus, GEO, http://www.ncbi.nlm.nih.gov/geo/ ) database. 41 healthy and 59 disease samples were included in the subsequent model construction analysis as the model validation dataset. The processed and standardized probe expression matrix and the corresponding platform annotation files were downloaded, and the probes were converted into gene symbols. The harmony algorithm provided by Seurat software was used to integrate the raw counts and standardize the matrix. The average values were taken as gene expression values in subsequent analyses. Single cell cluster marker gene analysis Cell population classification naming files for GSE157783, GSE140231 and GSE204796 dataset were obtained. scRNA-seq data was analyzed using Seurat (version 4.2.0). Firstly, the raw UMI counts were converted into Seurat objects, and the "FindVariableFeatures" function was used to detect the first 2000 highly variable genes. Principal component analysis (PCA) was then applied to reduce the dimensionality of the 2000-gene-based scRNA-seq data. Differential gene expression analysis of PD vs. healthy was performed to obtain the corresponding information such as P.value and logFC, and to find the gene expression difference of the same cell under disease and healthy state. Cell cluster analysis was performed using the "FindClusters" (resolution = 0.5) and the "FindAllMarkers" function. All gene expression markers of the cell cluster categories were detected to find the marker of each cluster. The min.pct was set to 0.25 and the logFC threshold was set to 0.25. Analysis of intercellular communication in PD and healthy tissue R packages CellChat (version 1.5.0 ) and CellPhoneDB (version 2.0) were used in intercellular interaction analysis. There were four groups based on ligand-receptor pairs in CellChat, including cytokines/chemokines, immune checkpoints, growth factors, etc. The "createCellChat" function was applied to create the CellChat object. Functions "computeCommunProb", "computeCommunProbPathway" and "aggregateNet" were used to infer the communication network. Functions "netVisual_aggregate", "netVisual_signalingRole" and "netVisual_bubble" were used in visualizing intercellular crosstalk,and "CellPhoneDB" was then applied along with the recommended parameters. Identification of differentially expressed genes Based on gene expression profiles of the bulk data, differentially expressed genes between PD and healthy were screened. Linear regression and empirical Bayes methods provided by limma package (Version 3.52.4) were used to analyze the differential expression of all genes. In addition, Benjamini & Hochberg method was used in multiple test adjustment, and the adjusted p value (adj.p.value) was obtained. The difference expression threshold was set as follows: adj.p.value 0.263. Calculation of pyroptosis activity The GSVA (Version 1.46.0) algorithm was used to obtain the most relevant pyropotosis genes based on scRNA-seq data to determine the pyroptosis activity of each PD patient. Using the above genes as a geneset, the GSVA function and ssgsea method were applied to determine the AUC score of each sample in the integrated bulk dataset. WGCNA analysis WGCNA algorithm was utilized to identify genes related to pyroptosis activity. The first 5000 genes in the gene expression matrix were analyzed by WGCNA, the scale-free network fitting index and average connectivity were selected, and β was set to 8 as the soft threshold. The expression levels of key genes in PD and healthy groups were detected, and Pearson test was performed to calculate the correlation between module genes and pyroptosis phenotype. Pyroptosis activity scores of PD-associated genes were screened, and the screening thresholds were abs(datKME[,c]) > 0.8 and |GS| > 0.1). The intersections of single cell differentially expressed active genes and WGCNA module genes were identified as genes related to pyroptosis activity. PPI network and hub gene analyses Human protein-protein interaction (PPI) in STRING (Version: 11.0) database was utilized to obtain the pyroptosis related PPI network. Cytoscape (vesrion: 3.6.1) was used to map the interaction network associated with pyroptosis during PD development. Four topological analysis algorithms MCC, MNC, Degree and EPC in Cytoscape's cytoHubba plug-in were conducted to predict the top 30 important hub genes in PPI network, and the intersection genes obtained by the four algorithms were candidate hub genes. Screening of diagnostic genes Based on the expression values of hub genes significantly associated with pyropotosis in each sample, LASSO multiple regression model was used to further screen the diagnostic genes associated with pyroptosis by 10-fold cross-validation analysis. Using the function RFE in caret package, the expression values of characteristic genes were taken as predictive variables, and the PD and healthy groups were taken as diagnostic variables for gene screening. The CV method was utilized for characteristic selection with the cross-validation, and the RandomForest (RF) method was applied to identify the diagnostic genes. The best value of the random forest tree was determined by using the RF in R package. In split training and test sets, key categorical variables were identified according to Boruta features. Then the significance map of variables was produced to show the screened diagnostic genes, and finally the selected model diagnostic genes were extracted. The importance threshold was screened based on the average declining Gini coefficient. Construction and validation of the diagnostic model Multiple logistic regression analysis was performed using stats package, with the expression values of diagnostic genes regarded as independent variables. The diagnostic model was constructed to calculate the difference of diagnostic scores between PD and healthy samples. p.value was calculated through intergroup T test and the box diagram was drawn. ROC curves were performed to evaluate the performance of this diagnostic model. In order to further verify the validity of diagnostic genes, the expression data of diagnostic genes was used to construct a nomogram, and multiple logistic regression analyses were performed to screen significant diagnostic genes. Calibration and clinical prediction curves were produced to validate the model accuracy. Correlation analysis between diagnostic genes and immunity Based on ssGSEA method, the gene expression data of 28 types of immune and stromal cells was used for cell type enrichment analysis, and the relative abundance of immune and stromal cells in each sample was estimated by input of all mRNA expression matrices. The immune microenvironment status and immune cell composition in transcriptome samples were evaluated by using ssGSEA. Expression analysis of diagnostic model genes in cell subsets To elucidate changes in disease signaling pathways, the information flow of signaling pathways, defined as the total communication probability between cell pairs in the communication network, was calculated in both healthy and PD single cell samples. The cell subsets that highly-express these genes, their differentiation stage, the cell species with which they mainly communicate, and the main communication pathways and ligands were figured out. Global intercellular communication networks under healthy and PD conditions were quantified, visualized and compared. The information flow for each signaling pathway was defined as the probability of all communication between all cell pairs in the network and was calculated and compared between healthy and PD samples. The Euclidean distance between shared signaling pathways was calculated. Results Single cell cluster analysis for PD vs Healthy The integrated analysis using harmony algorithm was performed on scRNA-seq data of 5 PD and 27 healthy samples, and a total of 11 clusters of cell populations were identified (Fig. 1 A). The Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) map for PD and healthy samples was shown in Fig. 1 B-C. Further, the genes for each cell cluster were identified (Fig. 1 D). The oligodendrocyte clusters were characterized by dense expression of MOBP and MOG, and the two ODC subtypes were annotated by LGALS1 and PPM1G, respectively. The ODC subtypes were rich in genes related to myelin and myelination, neuronal axon sheath, axonogenesis, etc., which well supported their identity. The astrocyte (AST) cluster was characterized by GFAP and GINS3 expression (Fig. 1 D). Then the differences in cell cluster composition and gene expression between PD and healthy group were analyzed. The number of several cell types such as excitatory neurons, inhibitory neurons, metencephalic-like cells, and MHB-like cells were significantly reduced in PD samples, as compared with that in healthy group (Fig. 1 E). The proportions of 11 kinds of cell clusters in healthy and PD groups were analyzed and shown in Fig. 1 F. The proportion of metencephalic-like cells in PD group was significantly higher than that in healthy group (Fig. 1 F). However, astrocytes and microglia had higher proportions in the PD group than them in the healthy group (Fig. 1 F). The 11 cell clusters differed in marker gene expression profiles (Fig. 1 G). On the other hand, the PD and healthy group also showed differentiated gene expression profiles. As shown in the Fig. 1 H, VIM and RPS2 genes had higher expression levels in healthy state, and NEAT1 gene was more expressed in PD patients. Identification of pyroptosis-related differentially expressed genes (PDEGs) Firstly, PDEGs were found based on scRNA-seq data. The AUCell_calcAUC function was used to assign a pyroptosis score to each cell cluster. As shown in Fig. 2 A-B, the AUC scores of microglia, astrocytes, and metencephalic-like cells were higher, suggesting more active pyroptosis activities of them. According to the median AUC value, the cells were divided into high and low pyroptosis-AUC groups. A total of 3088 pyroptosis-related DEGs (PDEGs) were identified by using Deseq2 algorithm with screening thresholds setted as p value 0.1. The volcano plot of these PDEGs was shown in Fig. 2 C. Furtherly, based on bulk data, a total of 1261 PDEGs significantly differentially expressed in PD and healthy groups were selected using limma package, including 707 up- and 554 down-regulated genes. These genes were made into volcano plot and shown in Fig. 2 D. Then WGCNA network co-expression analysis was applied on the gene expression data from public bulk dataset. The POWER plots were produced to determine the optimal soft threshold (Fig. 2 E-F), and then a coexpression network was constructed based on the optimal soft threshold. After the genes were divided into different modules, the cluster dendrogram and module-trait-relationship plot were drawn (Fig. 2 G-H). According to the correlation between the characteristic value of the sample module ME identified by WGCNA and the AUC pyroptosis score, 8 modules were found to be correlated with pyroptosis, among which turquoise and yellow modules were significantly correlated with pyroptosis score (turquoise: r = 0.62, p < 0.001; yellow: r = 0.38, p < 0.001, Fig. 2 G-H). MM-GS-corralation scatter diagram in Fig. 2 I-J revealed that the correlation coefficient for the turquoise and yellow module was 0.74 and 0.21, respectively. The turquoise and yellow module genes used as WGCNA model genes, were intersected with PDEGs and marker genes, and the number of intersecting PDEGs was 87 (Fig. 2 K). Functional enrichment analyses GO and KEGG analyses were performed on the intersecting genes. As shown in Fig. 3 A, the top 5 biological processes were cytoskeleton-dependent intracellular transport, vesicle cytoskeletal trafficking, regulation of synapse structure or activity, and dendrite morphogenesis. KEGG analysis showed the most enriched pathways mainly involved in neurodegenerative diseases (such as Huntington’s disease, Parkinson’s disease, Alzheimer's disease, etc.) and long-term addictive diseases (Fig. 3 B). PPI network analysis and hub gene identification The protein-protein interaction (PPI) network was constructed according to intersecting PDEGs. The PPI networks composed of the top 30 genes calculated by topological analysis algorithms MCC, MNC, Degree and EPC of Cytoscape were shown in Figs. 3 C-F, respectively. Then, the genes obtained by these four algorithms were intersected and 24 genes were identified as hub genes (Fig. 3 G). The hub genes were listed in table S1 . Construction of pyroptosis-associated diagnostic model Based on the 24 differentially expressed hub genes associated with pyroptosis, 7 optimal characteristic gene combinations and prognostic regression coefficients (coef) were selected by LASSO logistic algorithm. Regression coefficient path diagram (Fig. 4 A) showed 24 curves of different colors represented the change trajectories of 24 independent variable coefficients, and the absolute values of the coefficients increased with the decrease of λ values. Cross-verification curve showed the number of coefficients included after being screened by LASSO regression analysis (Fig. 4 B). SVM algorithm was used to obtain 24 characteristic genes. SVM-RFE plots in Fig. 4 C verified high accuracy rate and low error rate of the SVM analysis. Then random forest (RF) algorithm was applied to identify the genes that were considered valuable with an average reduction of Gini coefficient IncNodePurity > 0.6, and finally 17 diagnostic genes were determined (Fig. 4 D-E). A total of 6 diagnostic genes were preliminarily obtained from the intersection of above three algorithms, namely 'MDH2', 'TUBB2A', 'SRSF1', 'TUBB4B', 'POLR2K' and 'TIMM8B' (Fig. 4 F). Next, a pyroptosis-associated diagnostic model was constructed based on above six genes. According to the expression matrixes of the six genes and the PD vs healthy grouping of samples, glm function was used to perform multiple logistic regression on them. Finally, two genes POLR2K and TIMM8B of the 6 genes were found to be effective variables with p value < 0.05 (Fig. 4 G). The regression coefficients of the diagnostic genes were obtained, and diagnostic scores were calculated through expression matrices of the two diagnostic genes. Diagnostic score = -0.029 * POLR2K − 0.018 * TIMM8B. Performance evaluation of the diagnostic model ROC curves were used to evaluate the performance of diagnostic scores in training dataset, showing high AUC scores of the two diagnostic genes and good performance on diagnosis. The diagnostic score of PD group was significantly higher than that of healthy group (p < 0.0001, Fig. 5 A). ROC curve showed high AUC score of genes POLR2K and TIMM8B, and the diagnostic score (POLR2K-AUC = 0.742, TIMM8B-AUC = 0.777, diagnostic_score-AUC = 0.823, Fig. 5 B). The expression levels of POLR2K and TIMM8B in PD group were significantly lower than them in healthy group (p < 0.001; p < 0.0001, Fig. 5 C-D). Besides, GSE49036 was utilized as the validating dataset to assess performance of the diagnostic model. PD group had higher diagnostic score than healthy group (p < 0.05, Fig. 5 E). The AUC of diagnostic_score was 0.706 (Fig. 5 F), suggesting a good diagnosis performance. In addition, PD group possessed less expression of POLR2K and TIMM8B than healthy group (p < 0.05; p < 0.05, Fig. 5 G-H). Above results indicated an excellent diagnostic performance of this pyroptosis-associated diagnostic model. Construction of diagnostic nomogram Multiple regression analysis was performed for the genes POLR2K and TIMM8B using rms package. A nomogram was established to clearly show the results of multiple regression analysis for POLR2K and TIMM8B genes (Fig. 6 A). Calibration curve showed a good agreement between the prediction probability of the nomogram model and the actual observation (mean absolute error = 0.033), suggesting the model was well distinguished between PD and healthy group (Fig. 6 B). Decision curve analysis (DCA) plot in Fig. 6 C revealed the nomogram had great net benefit on clinical diagnosis when the risk threshold larger than 0.125. Clinical impact curve (CIC) showed that under different probability thresholds, the number of people judged by the diagnostic model as high risk, and the number of people judged by the model as high risk and the outcome event actually occurred (Fig. 6 D). Immune correlation analysis and GSEA analysis The relative infiltration abundance of immune cells and stromal cells in each sample was estimated based on ssgsea algorithm. The relative abundance of each type of cells was shown in Fig. 7 A. Further, the wilcoxon test was used to compare and calculate the significance of individual immune cells between healthy and PD groups, and the results showed that a total of 10 microenvironment cells were significantly different between the two groups. Among these 10 kinds of cells, some cell types including central memory CD8 T cells, effector memory CD8 T cells, immature B cells, mast cells, macrophages, myeloid derived suppressor cells and plasmacytoid dendritic cells showed more abundant infiltration in PD group than in healthy group (Fig. 7 A). A heatmap revealed correlation between diagnostic genes and immunoinfiltrating cells (Fig. 7 B). Among these infiltrating cells, mast cells, natural killer T cells, neutrophils, immature B cells, and myeloid derived suppressor cells negatively correlated with 2 diagnostic genes (POLR2K and TIMM8B, Fig. 7 B). Whereas effector memory CD4 positive T cells positively correlated with 2 diagnostic genes (Fig. 7 B). Spearman algorithm of stats package was used to calculate the correlation between 2 diagnostic genes and all genes, and GSEA analysis was conducted to identify genes positively correlated with diagnostic genes. According to the gene correlation results, the signaling pathways related to diagnostic genes were analyzed by GSEA algorithm and shown in Fig. 7 C-D. For POLR2K and TIMM8B genes, the related pathways mainly included neurodegenerative diseases such as Parkinson’s disease (PD) and Altheimer’s disease (AD), cytokine-receptor interaction, Toll-like-receptor signaling pathway, and JAK-STAT pathway, etc (Fig. 7 C-D). Expression analysis of diagnostic genes in cell subpopulations According to the results in Fig. 8 A-B, POLR2K gene was significantly expressed in MHB-like cell population, while TIMM8B gene was significantly expressed in metencephalic like cell population. In order to analyze the global changes of the diagnostic genes under healthy and PD states, cellchat package was used to calculate the differential information flow of each signaling pathway, which was defined as the total communication probability between all cell pairs in the communication network. The number of cell communication was inferred and shown in Fig. 8 C. It seemed that the PD group had more intercellular interaction than the healthy group (Number of inferred interaction, PD group = 477, healthy group = 366; Fig. 8 C). Then the communication of different cell populations was shown in lines, in which the red and blue lines represented the increase and decrease of cell communication in PD state, respectively (Fig. 8 D). We found the communication of several cell populations under PD state was strengthened, including pericytes, inhibitory neurons, and oligodendrocyte precursor cells (Fig. 8 D). Further, the information flow of multiple signaling pathways in healthy and PD states was calculated. As shown in Fig. 8 E-F, some signaling pathways such as MK and CHEMERIN were shut down in PD group, whereas some other pathways including IL-16, WNT, GALECTIN, GAS, and GRN were turned on in the PD group. Next, we found increased kinds of ligand-receptor binding pairs in PD group than that in healthy group (Fig. 8 G). Several ligand-receptor binding pairs including GRN-SORT1, WNT5A-FZD3/4, and SPP1-(ITGA5 + ITGB1) were specifically expressed in different interacting cell pairs in the PD group (Fig. 8 G). Finally, the differential expression patterns of multiple signaling pathways in 11 types of cell populations were analyzed. There were significant differences in enriched pathways of multiple cell types between the PD and healthy groups (Fig. 8 H). For example, as for microglia, enriched pathways in PD group were more abundant than that in healthy group, including GAS, GALECTIN, ncWNT, IL-16, and GRN (Fig. 8 H). Besides, some pathways such as GRN, GAS, GALECTIN, WNT, and IL-16 were absent in the healthy group, whereas they were enriched in some cell types in the PD group. On the contrary, MK and CHEMERIN were enriched in the healthy group but absent in the PD group (Fig. 8 H). It suggested that there may be more inflammatory pathways in the PD patients than in the healthy individuals. Discussion PD is a neurodegenerative disorder characterized by an insidious onset and gradual progression. The early diagnosis of PD, particularly during its latent period, poses significant challenges but is crucial for effective therapeutic interventions. Traditional diagnostic methods rely heavily on clinical motor symptoms and physical examinations, yet these motor manifestations often lag behind the molecular and pathological changes of PD [ 16 ]. It is challenging and critical to realize an early diagnosis of PD before clinical symptoms, given the substantial impact of early intervention on slowing PD progression and improving patient management. Accumulated evidence has revealed great progress in the molecular diagnosis of PD. A series of biomarkers for PD have been proposed in recent years, including alpha-synuclein (aS), amyloid-beta (Aβ), neurofilament light chain (NfL), lysosomal biomarkers, and metabolomics [ 17 – 19 ]. Despite these developments, a universally recognized, efficient, and accurate diagnostic model for PD is still under exploration. Dopaminergic neuron death is recognized as the most prominent hallmark for PD progression. Pyroptosis, a form of programmed cell death associated with inflammatory responses, has been implicated in the death of dopaminergic neurons in PD. Pharmacological inhibition of pyroptosis-associated molecules such as NLRP3, caspase-1, and IL-18 alleviated symptoms of PD mice [ 20 , 21 ]. More specifically, pyroptosis is associated with inflammatory factor release and glial cell activation in PD [ 22 ], which contributes to the inflammatory death of dopaminergic neurons. Numerous pyroptosis-related inflammatory factors, such as TNF-α, IL-10, IL-1β, IL-6, IL-2, and NLRP3, have been identified as potential biomarkers for PD, suggesting their potential as diagnostic hallmarks [ 23 , 24 ]. However, diagnoses based on these factors lack accuracy due to deficiencies in sensitivity, specificity, and variability. Thus, the identification of pyroptosis-related genes suitable for PD diagnosis remains an open question. Cells are the basic units of life activities. Cell heterogeneity, even among cells of the same genotype or clone, plays a pivotal role in both physiological and pathological processes [ 25 – 27 ]. A large number of studies have revealed that cell heterogeneity plays a pivotal role in PD microenvironment [ 28 – 30 ]. Single-cell sequencing technology makes it possible to reveal gene expression at the level of cell populations. In the previous researches, single-cell RNA sequencing (scRNA seq) was utilized to depict cell population composition and intercellular communication in PD microenvironment [ 31 ]. There are multiple cell types identified in midbrain specimens from PD patients, including astrocytes, dopaminergic neurons, endothelial cells, excitatory cells, inhibitory cells, microglial cells, oligodendrocyte precursor cells, oligodendrocytes, and pericytes [ 32 ]. Notably, PD patients exhibit distinct cell population composition compared to healthy individuals. For example, idiopathic PD patients have been found to possess an increased number of microglia and astrocytes but fewer oligodendrocytes in the midbrain [ 14 ]. In this study, 11 cell types were identified including oligodendrocytes, microglia, astrocytes, metencephalic like cells, oligodendrocyte precursor cells, inhibitory neurons, excitatory neurons, endocelial, pericytes, MHB like cells and ependymal. Among these subpopulations, the metencephalic like cells, inhibitory neurons, excitatory neurons, and MHB like cells were substantially reduced in PD group, as compared with the healthy group. These findings align with the pathological characteristics of PD, namely the loss of dopaminergic neurons in substantia nigra pars compacta. Additionally, the numbers of astrocytes and microglia were almost comparable between the PD and healthy groups, but their proportions were significantly improved in PD than in healthy individuals. It suggested astrocytes and microglia probably play a key role in the pathogenesis of PD. Furtherly, scRNA seq data derived from PD patients and healthy individuals was applied for diagnostic gene identification and immune correlation analysis. POLR2K and TIMM8K were screened and identified as the pyroptosis-associated diagnostic genes for PD. The diagnostic model based on POLR2K and TIMM8K genes showed superior diagnostic performance, although their diagnostic sensitivity and specificity require clinical validation in the future. On the other hand, there were significantly increased immunoinfiltration levels and inflammatory pathway number in PD patients than in healthy individuals. It conformed to the knowledge that inflammation plays an important role in the pathogenesis of neurodegeneration in PD. Alleviating neuroinflammation can reduce symptoms of early PD [ 33 ]. The inflammatory neurodegeneration in PD involves activation of microglia, upregulation of pro-inflammatory factors, and gut microbiota, etc. It accorded with the results that the proportions of microglia and astrocytes were significantly increased and inflammatory pathways including GRN, GAS, GALECTIN, WNT, and IL-16 were upregulated in PD group than in healthy group. Additionally, the diagnostic genes POLR2K and TIMM8K were found related to inflammatory signaling pathways including cytokine-receptor interaction, toll-like-receptor, and JAK-STAT pathway, etc. Although immunotherapies have been developed for PD treatment in recent years, few immune-related targets are verified to be clinically beneficial. In this study, the diagnostic genes POLR2K and TIMM8K corralated with many types of infiltrating immune cells, and immune-related signaling pathways. However, whether these two diagnostic genes can be considered as immune-related targets in PD needs further exploration. This study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq data combined with transcriptome data, showing innovativeness and clinical translational value. There were several limitations of our study. First, the scRNA-seq data downloaded from the database, instead of experimental data, was applied to identify diagnostic genes, and analyze immunoinfiltration and intercellular communication. Therefore, further experiments will be needed to verify the results in the future. Second, in this study, the molecular phenotypes of activated microglia were not further analyzed. In the following studies, we will identify the gene expression related to microglia activation, so as to reveal molecules or pathways related to PD pathogenesis in the activation process of microglia. In summary, based on integrated analysis of scRNA-seq and transcriptome data, this study demonstrated the differences in cell cluster composition and gene expression between PD and healthy group. The proportion of microglia and astrocytes, immunoinfiltration, inflammatory signaling pathways and intercellular interaction in PD patients was significantly increased than that in healthy individuals. The pyroptosis-related differentially expressed genes (PDEGs) were screened to determine hub genes using scRNA-seq and WGCNA analyses, and the diagnostic model and nomogram was constructed based on the genes POLR2K and TIMM8B. This diagnostic model showed promising diagnostic performance in verification. We constructed a novel pyroptosis-linked diagnostic model for PD, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for PD. Declarations Conflict of Interest We declare that we have no conflicts of interest to this work. Funding This study was supported by Science and Technology Department Project of Jilin Province [], National Natural Science Foundation of China [82203647], and Special Project of Health Research Talents of Jilin Province [2022SC234]. Author Contribution Lin Wang wrote the main manuscript text and prepared figures 1-4; Yidan Qin prepared figures 5-8; Jia Song, Jing Xu, Wei Quan, and Hang Su processed data; Huibin Zeng and Jian Zhang collected data. Jia Li and Jiajun Chen supervised and instructed the study. All authors reviewed the manuscript. Data availability Data can be obtained from http://www.ncbi.nlm.nih.gov/geo/ . References Grover, S., et al., Genome-wide Association and Meta-analysis of Age at Onset in Parkinson Disease: Evidence From the COURAGE-PD Consortium . Neurology, 2022. 99(7): p. e698-e710. Tolosa, E., et al., Challenges in the diagnosis of Parkinson's disease . Lancet Neurol, 2021. 20(5): p. 385–397. Marogianni, C., et al., Neurodegeneration and Inflammation-An Interesting Interplay in Parkinson's Disease . Int J Mol Sci, 2020. 21(22). Brochard, V., et al., Infiltration of CD4 + lymphocytes into the brain contributes to neurodegeneration in a mouse model of Parkinson disease . J Clin Invest, 2009. 119(1): p. 182–92. Linnerbauer, M., M.A. Wheeler, and F.J. Quintana, Astrocyte Crosstalk in CNS Inflammation . Neuron, 2020. 108(4): p. 608–622. Moujalled, D., A. Strasser, and J.R. Liddell, Molecular mechanisms of cell death in neurological diseases . Cell Death Differ, 2021. 28(7): p. 2029–2044. Kesavardhana, S., R.K.S. Malireddi, and T.D. Kanneganti, Caspases in Cell Death, Inflammation, and Pyroptosis . Annu Rev Immunol, 2020. 38: p. 567–595. Man, S.M., R. Karki, and T.D. Kanneganti, Molecular mechanisms and functions of pyroptosis, inflammatory caspases and inflammasomes in infectious diseases . Immunol Rev, 2017. 277(1): p. 61–75. Shi, J., W. Gao, and F. Shao, Pyroptosis: Gasdermin-Mediated Programmed Necrotic Cell Death . Trends Biochem Sci, 2017. 42(4): p. 245–254. Wang, S., et al., The mechanisms of NLRP3 inflammasome/pyroptosis activation and their role in Parkinson's disease . Int Immunopharmacol, 2019. 67: p. 458–464. Wu, K.J., et al., Pyroptosis in neurodegenerative diseases: from bench to bedside . Cell Biol Toxicol, 2023. 39(6): p. 2467–2499. Han, Y.H., et al., Role of NLRP3 inflammasome-mediated neuronal pyroptosis and neuroinflammation in neurodegenerative diseases . Inflamm Res, 2023. 72(9): p. 1839–1859. Foo, J.N., et al., Identification of Risk Loci for Parkinson Disease in Asians and Comparison of Risk Between Asians and Europeans: A Genome-Wide Association Study . JAMA Neurol, 2020. 77(6): p. 746–754. Smajic, S., et al., Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state . Brain, 2022. 145(3): p. 964–978. Kamath, T., et al., Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson's disease . Nat Neurosci, 2022. 25(5): p. 588–595. Sarkar, A., et al., Unequivocal Biomarker for Parkinson's Disease: A Hunt that Remains a Pester . Neurotox Res, 2019. 36(3): p. 627–644. Parnetti, L., et al., Cerebrospinal fluid biomarkers in Parkinson disease . Nat Rev Neurol, 2013. 9(3): p. 131–40. Parnetti, L., et al., CSF and blood biomarkers for Parkinson's disease . Lancet Neurol, 2019. 18(6): p. 573–586. Raghunathan, R., K. Turajane, and L.C. Wong, Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson's Disease . Int J Mol Sci, 2022. 23(16). Zhang, X., et al., Salidroside ameliorates Parkinson's disease by inhibiting NLRP3-dependent pyroptosis . Aging (Albany NY), 2020. 12(10): p. 9405–9426. Rui, W., et al., Baicalein Attenuates Neuroinflammation by Inhibiting NLRP3/caspase-1/GSDMD Pathway in MPTP Induced Mice Model of Parkinson's Disease . Int J Neuropsychopharmacol, 2020. 23(11): p. 762–73. Hu, Y., et al., Pyroptosis, and its Role in Central Nervous System Disease . J Mol Biol, 2022. 434(4): p. 167379. Qin, X.Y., et al., Aberrations in Peripheral Inflammatory Cytokine Levels in Parkinson Disease: A Systematic Review and Meta-analysis . JAMA Neurol, 2016. 73(11): p. 1316–1324. Reale, M., et al., Peripheral cytokines profile in Parkinson's disease . Brain Behav Immun, 2009. 23(1): p. 55–63. Lichtenberger, B.M. and M. Kasper, Cellular heterogeneity and microenvironmental control of skin cancer . J Intern Med, 2021. 289(5): p. 614–628. Dadwal, S. and M.T. Heneka, Microglia heterogeneity in health and disease . FEBS Open Bio, 2024. 14(2): p. 217–229. Dwivedi, N.V., et al., GPCRs and fibroblast heterogeneity in fibroblast-associated diseases . FASEB J, 2023. 37(8): p. e23101. Wullner, U., et al., The heterogeneity of Parkinson's disease . J Neural Transm (Vienna), 2023. 130(6): p. 827–838. Gaertner, Z., et al., Molecular heterogeneity in the substantia nigra: A roadmap for understanding PD motor pathophysiology . Neurobiol Dis, 2022. 175: p. 105925. Ryczko, D., The Mesencephalic Locomotor Region: Multiple Cell Types, Multiple Behavioral Roles, and Multiple Implications for Disease. Neuroscientist, 2022: p. 10738584221139136. He, Z., et al., Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases . Eur J Neurosci, 2024. 59(3): p. 333–357. Badanjak, K., et al., iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease . Front Cell Dev Biol, 2021. 9: p. 740758. Krashia, P., et al., Blunting neuroinflammation with resolvin D1 prevents early pathology in a rat model of Parkinson's disease . Nat Commun, 2019. 10(1): p. 3945. Additional Declarations No competing interests reported. Supplementary Files tableS1.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Apr, 2024 Reviews received at journal 02 Apr, 2024 Reviewers agreed at journal 01 Apr, 2024 Reviews received at journal 28 Mar, 2024 Reviews received at journal 20 Mar, 2024 Reviewers agreed at journal 20 Mar, 2024 Reviewers agreed at journal 20 Mar, 2024 Reviewers agreed at journal 20 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Editor invited by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 08 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4045950","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":282103582,"identity":"34cdea99-b053-4ce3-a143-4f1c03944a84","order_by":0,"name":"Lin Wang","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Wang","suffix":""},{"id":282103583,"identity":"39724587-b632-4cb5-894c-7c03d752153f","order_by":1,"name":"Yidan Qin","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Qin","suffix":""},{"id":282103584,"identity":"e439b656-4002-4b3c-8d0b-fa40c3f79f02","order_by":2,"name":"Jia Song","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Song","suffix":""},{"id":282103585,"identity":"5717dafd-0e7f-4bbe-84b3-e08bb465fa1d","order_by":3,"name":"Jing Xu","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""},{"id":282103586,"identity":"25c1e1be-51e0-47cb-9d6d-291e1a372f45","order_by":4,"name":"Wei Quan","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Quan","suffix":""},{"id":282103587,"identity":"73793e20-b2fd-41c1-9893-9e3fa7629544","order_by":5,"name":"Hang Su","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Su","suffix":""},{"id":282103588,"identity":"85f5066f-a99e-40d0-8e0d-8e858b76ae2f","order_by":6,"name":"Huibin Zeng","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Huibin","middleName":"","lastName":"Zeng","suffix":""},{"id":282103589,"identity":"b22691b8-0bbc-4adb-879e-ac63aefade8a","order_by":7,"name":"Jian Zhang","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhang","suffix":""},{"id":282103590,"identity":"9100b5ce-f0de-4fd2-9a20-df8b324e0d49","order_by":8,"name":"Jia Li","email":"","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":282103591,"identity":"7f444bf7-0e4a-40f7-80e3-e99e8cde557c","order_by":9,"name":"Jiajun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYFCCA4kPJApAjATitSQbSBiQpoWBTYKBJC38jQeeVVgYHGbgZ88xYPi5gwgtEgcOpN2QAGqR7HljwNh7hggtBgxQLQY3cgyYGduI1FIA0mJPkhYGsC0SxGoB+iVZQsIgnUfizLOCg73EaOGfcSbxs0SFtRx/e/LGBz+J0cIgcSaBWYKBgQfEPkCMBqA17QcYPxCndBSMglEwCkYqAABfcDQWWuO7JwAAAABJRU5ErkJggg==","orcid":"","institution":"China-Japan Union Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-08 15:05:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4045950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4045950/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80185-9","type":"published","date":"2024-11-18T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53254673,"identity":"b4a0d9b7-c8fc-4fb5-b660-fa70e3f3ccaf","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2541964,"visible":true,"origin":"","legend":"\u003cp\u003eSingle cell cluster analysis for PD vs Healthy. A. UMAP plot showing 11 cell clusters identified in PD and healthy groups. B. UMAP plot of 3 datasets. C. the intergrated UMAP of PD and healthy groups. D. heatmap showing marker genes of 11 cell clusters. E. UMAP plot of PD and healthy groups, respectively. F. the proportion of 11 cell types in PD and healthy groups. G. the expression of marker genes of the 11 cell types. H. the expression of VIM, RPS2, and NEAT1 genes in PD and healthy groups.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/c5e251f44ef772fbf34a8caf.png"},{"id":53256237,"identity":"9054b5bf-ad46-4d7a-bbff-b588e87eeaaf","added_by":"auto","created_at":"2024-03-22 13:31:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3683648,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of PDEGs. A. UMAP plot showing the AUC scores of 11 cell clusters. B. UMAP plot of high- and low-AUC groups. C-D. volcano plots showing PDEGs identified by using scRNA-seq data (C) and bulk data (D). E-F. POWER plots determining the optimal soft threshold. G-H. the cluster dendrogram (G) and module-trait-relationship plot (H). I-J. the correlation coefficient for the turquoise (I) and yellow (J) module. K. the intersection of turquoise-yellow-module genes, single marker genes, and PDEGs.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/425e8f6c5ca0a75817d59170.png"},{"id":53254671,"identity":"a93edc07-d106-4a30-9ea3-6d185d4244af","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2418783,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analyses and hub gene identification. A-B. GO (A) and KEGG (B) analyses. C-F. PPI networks using MCC (C), MNC (D), Degree (E), and EPC (F) algorithms, respectively. G. the intersecting genes obtained from above four algorithms.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/3e740f931e34b96691362a9c.png"},{"id":53254678,"identity":"fad2bf40-f810-46fa-8440-b79e1f2a2018","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1160704,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of pyroptosis-associated diagnostic model. A. regression coefficient path diagram. B. cross-verification curve. C. SVM-RFE plots. D-E. variable screening (D) and variable importance ranking (E) using random forest algorithm. F. the intersecting genes obtained from LASSO, SVM-REF, and RF algorithms. G. multiple logistic regression analysis screening diagnostic genes.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/4c6b3fe1e924b1b7beb0d7cf.png"},{"id":53254674,"identity":"259976ea-cce7-4de1-9895-f07fb0314154","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":566857,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of the diagnostic model. A. the diagnostic scores of PD and healthy groups. B. ROC curve showing AUC score of genes POLR2K and TIMM8B, and the diagnostic score. C-D. the expression levels of diagnostic genes between PD and healthy groups. E. the diagnostic scores in validating dataset. F. ROC curve showing AUC score in validating dataset. G-H. the expression levels of diagnostic genes in validating dataset.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/cce74add37893a8819b463fd.png"},{"id":53254682,"identity":"f0ee36c8-9897-491e-8ef0-a43fa5018791","added_by":"auto","created_at":"2024-03-22 13:23:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":736489,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic nomogram construction. A. the nomogram based on multiple regression analysis. B. the calibration curve. C. the decision curve analysis (DCA) plot. D. the clinical impact curve.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/c15e76a4302af4f77fdae95c.png"},{"id":53254676,"identity":"b5e2d304-9343-4066-8616-7eb107678daf","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2011868,"visible":true,"origin":"","legend":"\u003cp\u003eImmune correlation analysis and GSEA analysis. A. relative abundance of each cell type. B. a heatmap showing correlation between diagnostic genes and immunoinfiltrating cells. C-D. GSEA analyses for diagnostic genes POLR2K (C) and TIMM8B (D).\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/2f081ef5a0a94cf8e795f904.png"},{"id":53254675,"identity":"2a73dab5-2091-4578-a113-4e25eaecc6c2","added_by":"auto","created_at":"2024-03-22 13:23:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2733772,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of diagnostic genes in cell subpopulations. A-B. expression of diagnostic genes POLR2K and TIMM8B in scRNA-seq data. C. the number of inferred cell communication in PD and healthy groups. D. the network plot showing the communication among different cell populations. E-F. relative information flow (E) and information flow (F) of multiple signaling pathways in PD and healthy groups. G. ligand-receptor binding pairs in different cell populations of PD and healthy groups. H. differential expression patterns of multiple signaling pathways in 11 types of cell populations.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/56b47c00c7f84780314a9986.png"},{"id":53254680,"identity":"1900325d-6f1d-49a3-87af-628b84d925c4","added_by":"auto","created_at":"2024-03-22 13:23:55","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":10055,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4045950/v1/9290fd341574070041f6dbf8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Analysis of Single-Cell RNA Sequencing and Transcriptome Data Identifies a Pyroptosis-Associated Diagnostic Model for Parkinson’s Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) stands as the foremost prevalent neurodegenerative motoric disorder, with over six million individuals affected worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the nebulous nature of its etiology and pathogenesis, the emphasis on early detection and prompt intervention holds paramount significance for efficacious symptom amelioration and retardation of PD progression. Conventional diagnostic methodologies are primarily reliant on clinical manifestations, encompassing static tremor, myotonia, bradykinesia, and postural equilibrium disturbances, which often remain latent in early PD patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Detecting the early stages of PD prior to overt clinical symptomatology remains challenging due to a lack of precise molecular diagnostic signatures for PD.\u003c/p\u003e \u003cp\u003eDespite the complexity of PD\u0026rsquo;s pathogenesis, neuroinflammation and dopaminergic neuronal death are widely acknowledged as key pathophysiological features of PD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During the progression of PD, various cell types, such as microglia, astrocytes, and various immune cells, are involved in the neuroinflammation which leads to the neuronal death. Microglia activation results in the secretion of proinflammatory factors like IL-1β, IL-6, ROS, TGFβ and TNF-α. Infiltration of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the substantia nigra pars compacta in PD patients correlates with heightened levels of TNF and IFNγ [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In response to inflammation, the astrocytes can release bioactive molecules including chemokines, neurotrophins, and growth factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The elevated proinflammatory factors can induce neuronal cell death both directly and indirectly. Nevertheless, the mechanisms underlying neuronal degradation and death due to inflammation remain a subject of debate. Increased evidence suggests programmed cell deaths (PCDs) might constitute a primary mechanism for neuronal loss [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePyroptosis is an inflammatory programmed cell death characterized by NLRP3 inflammasome formation, cell swelling, plasma membrane rupture, and the release of cellular contents [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It can be mediated via both caspase-1-dependent and -independent pathways. In the caspase-1-dependent pathway, pyroptosis is initiated by the activation of pattern recognition receptors (PRRs), such as NLRP1b, NLRP3, NLRC4, AIM2, or Pyrin, which respond to pathogen-associated molecular patterns (PAMPs) or host-derived danger signal molecules (DAMPs). Activated inflammasomes NLRP3 and NLRC4 then recruit inflammasome junction protein ASC and protease caspase-1 to form macromolecular complexes. Caspase-1 facilitates both the direct lysis of gasdermin D, triggering plasma membrane rupture, and the conversion of pro-IL-1β and pro-IL-18 into their mature and functional forms, IL-1β and IL-18 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the caspase-1-independent pathway, caspase-11/4/5 directly recognize and bind to lipopolysaccharide (LPS) of pathogens, cleave GSDMD to generate active N-terminal fragments, induce the formation of cell membrane pores, and consequently lead to pyroptosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Numerous studies have revealed the pivotal role of inflammasome-activated pyroptosis in the degeneration of dopaminergic neurons in PD [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the diagnostic potential of pyroptosis-related genes in PD needs further exploration.\u003c/p\u003e \u003cp\u003eWhile over 70 loci have been significantly linked to PD risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the genetic underpinnings and diagnostic approaches for PD remain largely uncovered. With the rapid development of high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) combined with transcriptome studies shed light on the searching for diagnostic genes of PD. Currently, scRNA-seq data is primarily utilized to identify specific cell types and their molecular characteristics in PD. For example, one study employing single-cell sequencing technology revealed an increased number of microglia and astrocytes and a decreased number of oligodendrocytes in the midbrain of patients with idiopathic PD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furtherly, cell clustering and molecular typing of dopaminergic neurons in the substantia nigra pars compacta was achieved through scRNA-seq analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This analysis delineated ten distinct clusters of dopaminergic neurons, each with unique transcriptomes, and identified a subset of dopaminergic neurons marked by ATGR1 expression that are particularly vulnerable in PD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. But until now, few accurate diagnosis model for PD has been developed or applied in clinic.\u003c/p\u003e \u003cp\u003eThis study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq data combined with transcriptome data, showing innovativeness and clinical translational value. The differences in cell cluster composition and gene expression between PD and healthy individuals were analyzed using scRNA-seq and transcriptome data. The proportion of microglia and astrocytes, immunoinfiltration, inflammatory signaling pathways, and intercellular interaction in PD patients were significantly increased than that in healthy individuals. The pyroptosis-related differentially expressed genes (PDEGs) were screened using scRNA-seq and WGCNA analyses to determine hub genes, and the diagnostic model and nomogram were then constructed based on the genes POLR2K and TIMM8B. This diagnostic model showed promising diagnostic efficacy upon verification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition and screening of expression profile data\u003c/h2\u003e \u003cp\u003eSingle-cell datasets GSE140231, GSE157783, and GSE204796 were downloaded from the NCBI GEO (GeneExpressionOmnibus, GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. There were 12 healthy samples in GSE140231, 6 healthy and 5 disease samples in GSE157783, and 9 healthy samples in GSE204796. Therefore, a total of 5 disease and 27 healthy samples were included in this study as the subsequent model validation dataset, and all samples were tested for quality control by Seurat. Bulk datasets GSE7621, GSE20164, GSE20163, and GSE8397 were downloaded from NCBI GEO (GeneExpressionOmnibus, GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. 41 healthy and 59 disease samples were included in the subsequent model construction analysis as the model validation dataset. The processed and standardized probe expression matrix and the corresponding platform annotation files were downloaded, and the probes were converted into gene symbols. The harmony algorithm provided by Seurat software was used to integrate the raw counts and standardize the matrix. The average values were taken as gene expression values in subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell cluster marker gene analysis\u003c/h2\u003e \u003cp\u003eCell population classification naming files for GSE157783, GSE140231 and GSE204796 dataset were obtained. scRNA-seq data was analyzed using Seurat (version 4.2.0). Firstly, the raw UMI counts were converted into Seurat objects, and the \"FindVariableFeatures\" function was used to detect the first 2000 highly variable genes. Principal component analysis (PCA) was then applied to reduce the dimensionality of the 2000-gene-based scRNA-seq data. Differential gene expression analysis of PD vs. healthy was performed to obtain the corresponding information such as P.value and logFC, and to find the gene expression difference of the same cell under disease and healthy state. Cell cluster analysis was performed using the \"FindClusters\" (resolution\u0026thinsp;=\u0026thinsp;0.5) and the \"FindAllMarkers\" function. All gene expression markers of the cell cluster categories were detected to find the marker of each cluster. The min.pct was set to 0.25 and the logFC threshold was set to 0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of intercellular communication in PD and healthy tissue\u003c/h2\u003e \u003cp\u003eR packages CellChat (version 1.5.0 ) and CellPhoneDB (version 2.0) were used in intercellular interaction analysis. There were four groups based on ligand-receptor pairs in CellChat, including cytokines/chemokines, immune checkpoints, growth factors, etc. The \"createCellChat\" function was applied to create the CellChat object. Functions \"computeCommunProb\", \"computeCommunProbPathway\" and \"aggregateNet\" were used to infer the communication network. Functions \"netVisual_aggregate\", \"netVisual_signalingRole\" and \"netVisual_bubble\" were used in visualizing intercellular crosstalk,and \"CellPhoneDB\" was then applied along with the recommended parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed genes\u003c/h2\u003e \u003cp\u003eBased on gene expression profiles of the bulk data, differentially expressed genes between PD and healthy were screened. Linear regression and empirical Bayes methods provided by limma package (Version 3.52.4) were used to analyze the differential expression of all genes. In addition, Benjamini \u0026amp; Hochberg method was used in multiple test adjustment, and the adjusted p value (adj.p.value) was obtained. The difference expression threshold was set as follows: adj.p.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; |logFC| \u0026gt; 0.263.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of pyroptosis activity\u003c/h2\u003e \u003cp\u003eThe GSVA (Version 1.46.0) algorithm was used to obtain the most relevant pyropotosis genes based on scRNA-seq data to determine the pyroptosis activity of each PD patient. Using the above genes as a geneset, the GSVA function and ssgsea method were applied to determine the AUC score of each sample in the integrated bulk dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA analysis\u003c/h2\u003e \u003cp\u003eWGCNA algorithm was utilized to identify genes related to pyroptosis activity. The first 5000 genes in the gene expression matrix were analyzed by WGCNA, the scale-free network fitting index and average connectivity were selected, and β was set to 8 as the soft threshold. The expression levels of key genes in PD and healthy groups were detected, and Pearson test was performed to calculate the correlation between module genes and pyroptosis phenotype. Pyroptosis activity scores of PD-associated genes were screened, and the screening thresholds were abs(datKME[,c])\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and |GS| \u0026gt; 0.1). The intersections of single cell differentially expressed active genes and WGCNA module genes were identified as genes related to pyroptosis activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and hub gene analyses\u003c/h2\u003e \u003cp\u003eHuman protein-protein interaction (PPI) in STRING (Version: 11.0) database was utilized to obtain the pyroptosis related PPI network. Cytoscape (vesrion: 3.6.1) was used to map the interaction network associated with pyroptosis during PD development. Four topological analysis algorithms MCC, MNC, Degree and EPC in Cytoscape's cytoHubba plug-in were conducted to predict the top 30 important hub genes in PPI network, and the intersection genes obtained by the four algorithms were candidate hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eScreening of diagnostic genes\u003c/h2\u003e \u003cp\u003eBased on the expression values of hub genes significantly associated with pyropotosis in each sample, LASSO multiple regression model was used to further screen the diagnostic genes associated with pyroptosis by 10-fold cross-validation analysis. Using the function RFE in caret package, the expression values of characteristic genes were taken as predictive variables, and the PD and healthy groups were taken as diagnostic variables for gene screening. The CV method was utilized for characteristic selection with the cross-validation, and the RandomForest (RF) method was applied to identify the diagnostic genes. The best value of the random forest tree was determined by using the RF in R package. In split training and test sets, key categorical variables were identified according to Boruta features. Then the significance map of variables was produced to show the screened diagnostic genes, and finally the selected model diagnostic genes were extracted. The importance threshold was screened based on the average declining Gini coefficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the diagnostic model\u003c/h2\u003e \u003cp\u003eMultiple logistic regression analysis was performed using stats package, with the expression values of diagnostic genes regarded as independent variables. The diagnostic model was constructed to calculate the difference of diagnostic scores between PD and healthy samples. p.value was calculated through intergroup T test and the box diagram was drawn. ROC curves were performed to evaluate the performance of this diagnostic model.\u003c/p\u003e \u003cp\u003eIn order to further verify the validity of diagnostic genes, the expression data of diagnostic genes was used to construct a nomogram, and multiple logistic regression analyses were performed to screen significant diagnostic genes. Calibration and clinical prediction curves were produced to validate the model accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between diagnostic genes and immunity\u003c/h2\u003e \u003cp\u003eBased on ssGSEA method, the gene expression data of 28 types of immune and stromal cells was used for cell type enrichment analysis, and the relative abundance of immune and stromal cells in each sample was estimated by input of all mRNA expression matrices. The immune microenvironment status and immune cell composition in transcriptome samples were evaluated by using ssGSEA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis of diagnostic model genes in cell subsets\u003c/h2\u003e \u003cp\u003eTo elucidate changes in disease signaling pathways, the information flow of signaling pathways, defined as the total communication probability between cell pairs in the communication network, was calculated in both healthy and PD single cell samples. The cell subsets that highly-express these genes, their differentiation stage, the cell species with which they mainly communicate, and the main communication pathways and ligands were figured out. Global intercellular communication networks under healthy and PD conditions were quantified, visualized and compared. The information flow for each signaling pathway was defined as the probability of all communication between all cell pairs in the network and was calculated and compared between healthy and PD samples. The Euclidean distance between shared signaling pathways was calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell cluster analysis for PD vs Healthy\u003c/h2\u003e \u003cp\u003eThe integrated analysis using harmony algorithm was performed on scRNA-seq data of 5 PD and 27 healthy samples, and a total of 11 clusters of cell populations were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) map for PD and healthy samples was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C. Further, the genes for each cell cluster were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The oligodendrocyte clusters were characterized by dense expression of MOBP and MOG, and the two ODC subtypes were annotated by LGALS1 and PPM1G, respectively. The ODC subtypes were rich in genes related to myelin and myelination, neuronal axon sheath, axonogenesis, etc., which well supported their identity. The astrocyte (AST) cluster was characterized by GFAP and GINS3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThen the differences in cell cluster composition and gene expression between PD and healthy group were analyzed. The number of several cell types such as excitatory neurons, inhibitory neurons, metencephalic-like cells, and MHB-like cells were significantly reduced in PD samples, as compared with that in healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The proportions of 11 kinds of cell clusters in healthy and PD groups were analyzed and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF. The proportion of metencephalic-like cells in PD group was significantly higher than that in healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). However, astrocytes and microglia had higher proportions in the PD group than them in the healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The 11 cell clusters differed in marker gene expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eOn the other hand, the PD and healthy group also showed differentiated gene expression profiles. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, VIM and RPS2 genes had higher expression levels in healthy state, and NEAT1 gene was more expressed in PD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of pyroptosis-related differentially expressed genes (PDEGs)\u003c/h2\u003e \u003cp\u003eFirstly, PDEGs were found based on scRNA-seq data. The AUCell_calcAUC function was used to assign a pyroptosis score to each cell cluster. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B, the AUC scores of microglia, astrocytes, and metencephalic-like cells were higher, suggesting more active pyroptosis activities of them. According to the median AUC value, the cells were divided into high and low pyroptosis-AUC groups. A total of 3088 pyroptosis-related DEGs (PDEGs) were identified by using Deseq2 algorithm with screening thresholds setted as p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and abs (logFC)\u0026thinsp;\u0026gt;\u0026thinsp;0.1. The volcano plot of these PDEGs was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. Furtherly, based on bulk data, a total of 1261 PDEGs significantly differentially expressed in PD and healthy groups were selected using limma package, including 707 up- and 554 down-regulated genes. These genes were made into volcano plot and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. Then WGCNA network co-expression analysis was applied on the gene expression data from public bulk dataset. The POWER plots were produced to determine the optimal soft threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F), and then a coexpression network was constructed based on the optimal soft threshold. After the genes were divided into different modules, the cluster dendrogram and module-trait-relationship plot were drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H). According to the correlation between the characteristic value of the sample module ME identified by WGCNA and the AUC pyroptosis score, 8 modules were found to be correlated with pyroptosis, among which turquoise and yellow modules were significantly correlated with pyroptosis score (turquoise: r\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; yellow: r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-H). MM-GS-corralation scatter diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-J revealed that the correlation coefficient for the turquoise and yellow module was 0.74 and 0.21, respectively. The turquoise and yellow module genes used as WGCNA model genes, were intersected with PDEGs and marker genes, and the number of intersecting PDEGs was 87 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analyses\u003c/h2\u003e \u003cp\u003eGO and KEGG analyses were performed on the intersecting genes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the top 5 biological processes were cytoskeleton-dependent intracellular transport, vesicle cytoskeletal\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003etrafficking, regulation of synapse structure or activity, and dendrite morphogenesis. KEGG analysis showed the most enriched pathways mainly involved in neurodegenerative diseases (such as Huntington\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, Alzheimer's disease, etc.) and long-term addictive diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePPI network analysis and hub gene identification\u003c/h2\u003e \u003cp\u003eThe protein-protein interaction (PPI) network was constructed according to intersecting PDEGs. The PPI networks composed of the top 30 genes calculated by topological analysis algorithms MCC, MNC, Degree and EPC of Cytoscape were shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-F, respectively. Then, the genes obtained by these four algorithms were intersected and 24 genes were identified as hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The hub genes were listed in table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of pyroptosis-associated diagnostic model\u003c/h2\u003e \u003cp\u003eBased on the 24 differentially expressed hub genes associated with pyroptosis, 7 optimal characteristic gene combinations and prognostic regression coefficients (coef) were selected by LASSO logistic algorithm. Regression coefficient path diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) showed 24 curves of different colors represented the change trajectories of 24 independent variable coefficients, and the absolute values of the coefficients increased with the decrease of λ values.\u003c/p\u003e \u003cp\u003eCross-verification curve showed the number of coefficients included after being screened by LASSO regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). SVM algorithm was used to obtain 24 characteristic genes. SVM-RFE plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC verified high accuracy rate and low error rate of the SVM analysis. Then random forest (RF) algorithm was applied to identify the genes that were considered valuable with an average reduction of Gini coefficient IncNodePurity\u0026thinsp;\u0026gt;\u0026thinsp;0.6, and finally 17 diagnostic genes were determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). A total of 6 diagnostic genes were\u003c/p\u003e \u003cp\u003epreliminarily obtained from the intersection of above three algorithms, namely 'MDH2', 'TUBB2A', 'SRSF1', 'TUBB4B', 'POLR2K' and 'TIMM8B' (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Next, a pyroptosis-associated diagnostic model was constructed based on above six genes. According to the expression matrixes of the six genes and the PD vs healthy grouping of samples, glm function was used to perform multiple logistic regression on them. Finally, two genes POLR2K and TIMM8B of the 6 genes were found to be effective variables with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). The regression coefficients of the diagnostic genes were obtained, and diagnostic scores were calculated through expression matrices of the two diagnostic genes. Diagnostic score = -0.029 * POLR2K \u0026minus;\u0026thinsp;0.018 * TIMM8B.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePerformance evaluation of the diagnostic model\u003c/h2\u003e \u003cp\u003eROC curves were used to evaluate the performance of diagnostic scores in training dataset, showing high AUC scores of the two diagnostic genes and good performance on diagnosis. The diagnostic score of PD group was significantly higher than that of healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). ROC curve showed high AUC score of genes POLR2K and TIMM8B, and the diagnostic score (POLR2K-AUC\u0026thinsp;=\u0026thinsp;0.742, TIMM8B-AUC\u0026thinsp;=\u0026thinsp;0.777, diagnostic_score-AUC\u0026thinsp;=\u0026thinsp;0.823, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The expression levels of POLR2K and TIMM8B in PD group were significantly lower than them in healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Besides, GSE49036 was utilized as the validating dataset to assess performance of the diagnostic model. PD group had higher diagnostic score than healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The AUC of diagnostic_score was 0.706 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), suggesting a good diagnosis performance. In addition, PD group possessed less expression of POLR2K and TIMM8B than healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-H). Above results indicated an excellent diagnostic performance of this pyroptosis-associated diagnostic model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of diagnostic nomogram\u003c/h2\u003e \u003cp\u003eMultiple regression analysis was performed for the genes POLR2K and TIMM8B using rms package. A nomogram was established to clearly show the results of multiple regression analysis for POLR2K and TIMM8B genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Calibration curve showed a good agreement between the prediction probability of the nomogram model and the actual observation (mean absolute error\u0026thinsp;=\u0026thinsp;0.033), suggesting the model was well distinguished between PD and healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Decision curve analysis (DCA) plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC revealed the nomogram had great net benefit on clinical diagnosis when the risk threshold larger than 0.125. Clinical impact curve (CIC) showed that under different probability thresholds, the number of people judged by the diagnostic model as high risk, and the number of people judged by the model as high risk and the outcome event actually occurred (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImmune correlation analysis and GSEA analysis\u003c/h2\u003e \u003cp\u003eThe relative infiltration abundance of immune cells and stromal cells in each sample was estimated based on ssgsea algorithm. The relative abundance of each type of cells was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA. Further, the wilcoxon test was used to compare and calculate the significance of individual immune cells between healthy and PD groups, and the results showed that a total of 10 microenvironment cells were significantly different between the two groups. Among these 10 kinds of cells, some cell types including central memory CD8 T cells, effector memory CD8 T cells, immature B cells, mast cells, macrophages, myeloid derived suppressor cells and plasmacytoid dendritic cells showed more abundant infiltration in PD group than in healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). A heatmap revealed correlation between diagnostic genes and immunoinfiltrating cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among these infiltrating cells, mast cells, natural killer T cells, neutrophils, immature B cells, and myeloid derived suppressor cells negatively correlated with 2 diagnostic genes (POLR2K and TIMM8B, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Whereas effector memory CD4 positive T cells positively correlated with 2 diagnostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Spearman algorithm of stats package was used to calculate the correlation between 2 diagnostic genes and all genes, and GSEA analysis was conducted to identify genes positively correlated with diagnostic genes. According to the gene correlation results, the signaling pathways related to diagnostic genes were analyzed by GSEA algorithm and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D. For POLR2K and TIMM8B genes, the related pathways mainly included neurodegenerative diseases such as Parkinson\u0026rsquo;s disease (PD) and Altheimer\u0026rsquo;s disease (AD), cytokine-receptor interaction, Toll-like-receptor signaling pathway, and JAK-STAT pathway, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eExpression analysis of diagnostic genes in cell subpopulations\u003c/h2\u003e \u003cp\u003eAccording to the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B, POLR2K gene was significantly expressed in MHB-like cell population, while TIMM8B gene was significantly expressed in metencephalic like cell population. In order to analyze the global changes of the diagnostic genes under healthy and PD states, cellchat package was used to calculate the differential information flow of each signaling pathway, which was defined as the total communication probability between all cell pairs in the communication network. The number of cell communication was inferred and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC. It seemed that the PD group had more intercellular interaction than the healthy group (Number of inferred interaction, PD group\u0026thinsp;=\u0026thinsp;477, healthy group\u0026thinsp;=\u0026thinsp;366; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen the communication of different cell populations was shown in lines, in which the red and blue lines represented the increase and decrease of cell communication in PD state, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). We found the communication of several cell populations under PD state was strengthened, including pericytes, inhibitory neurons, and oligodendrocyte precursor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Further, the information flow of multiple signaling pathways in healthy and PD states was calculated. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE-F, some signaling pathways such as MK and CHEMERIN were shut down in PD group, whereas some other pathways including IL-16, WNT, GALECTIN, GAS, and GRN were turned on in the PD group. Next, we found increased kinds of ligand-receptor binding pairs in PD group than that in healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Several ligand-receptor binding pairs including GRN-SORT1, WNT5A-FZD3/4, and SPP1-(ITGA5\u0026thinsp;+\u0026thinsp;ITGB1) were specifically expressed in different interacting cell pairs in the PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Finally, the differential expression patterns of multiple signaling pathways in 11 types of cell populations were analyzed. There were significant differences in enriched pathways of multiple cell types between the PD and healthy groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). For example, as for microglia, enriched pathways in PD group were more abundant than that in healthy group, including GAS, GALECTIN, ncWNT, IL-16, and GRN (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). Besides, some pathways such as GRN, GAS, GALECTIN, WNT, and IL-16 were absent in the healthy group, whereas they were enriched in some cell types in the PD group. On the contrary, MK and CHEMERIN were enriched in the healthy group but absent in the PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). It suggested that there may be more inflammatory pathways in the PD patients than in the healthy individuals.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePD is a neurodegenerative disorder characterized by an insidious onset and gradual progression. The early diagnosis of PD, particularly during its latent period, poses significant challenges but is crucial for effective therapeutic interventions. Traditional diagnostic methods rely heavily on clinical motor symptoms and physical examinations, yet these motor manifestations often lag behind the molecular and pathological changes of PD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is challenging and critical to realize an early diagnosis of PD before clinical symptoms, given the substantial impact of early intervention on slowing PD progression and improving patient management. Accumulated evidence has revealed great progress in the molecular diagnosis of PD. A series of biomarkers for PD have been proposed in recent years, including alpha-synuclein (aS), amyloid-beta (Aβ), neurofilament light chain (NfL), lysosomal biomarkers, and metabolomics [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite these developments, a universally recognized, efficient, and accurate diagnostic model for PD is still under exploration.\u003c/p\u003e \u003cp\u003eDopaminergic neuron death is recognized as the most prominent hallmark for PD progression.\u003c/p\u003e \u003cp\u003ePyroptosis, a form of programmed cell death associated with inflammatory responses, has been implicated in the death of dopaminergic neurons in PD. Pharmacological inhibition of pyroptosis-associated molecules such as NLRP3, caspase-1, and IL-18 alleviated symptoms of PD mice [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. More specifically, pyroptosis is associated with inflammatory factor release and glial cell activation in PD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which contributes to the inflammatory death of dopaminergic neurons. Numerous pyroptosis-related inflammatory factors, such as TNF-α, IL-10, IL-1β, IL-6, IL-2, and NLRP3, have been identified as potential biomarkers for PD, suggesting their potential as diagnostic hallmarks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, diagnoses based on these factors lack accuracy due to deficiencies in sensitivity, specificity, and variability. Thus, the identification of pyroptosis-related genes suitable for PD diagnosis remains an open question.\u003c/p\u003e \u003cp\u003eCells are the basic units of life activities. Cell heterogeneity, even among cells of the same genotype or clone, plays a pivotal role in both physiological and pathological processes [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A large number of studies have revealed that cell heterogeneity plays a pivotal role in PD microenvironment [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Single-cell sequencing technology makes it possible to reveal gene expression at the level of cell populations. In the previous researches, single-cell RNA sequencing (scRNA seq) was utilized to depict cell population composition and intercellular communication in PD microenvironment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. There are multiple cell types identified in midbrain specimens from PD patients, including astrocytes, dopaminergic neurons, endothelial cells, excitatory cells, inhibitory cells, microglial cells, oligodendrocyte precursor cells, oligodendrocytes, and pericytes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Notably, PD patients exhibit distinct cell population composition compared to healthy individuals. For example, idiopathic PD patients have been found to possess an increased number of microglia and astrocytes but fewer oligodendrocytes in the midbrain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, 11 cell types were identified including oligodendrocytes, microglia, astrocytes, metencephalic like cells, oligodendrocyte precursor cells, inhibitory neurons, excitatory neurons, endocelial, pericytes, MHB like cells and ependymal. Among these subpopulations, the metencephalic like cells, inhibitory neurons, excitatory neurons, and MHB like cells were substantially reduced in PD group, as compared with the healthy group. These findings align with the pathological characteristics of PD, namely the loss of dopaminergic neurons in substantia nigra pars compacta. Additionally, the numbers of astrocytes and microglia were almost comparable between the PD and healthy groups, but their proportions were significantly improved in PD than in healthy individuals. It suggested astrocytes and microglia probably play a key role in the pathogenesis of PD. Furtherly, scRNA seq data derived from PD patients and healthy individuals was applied for diagnostic gene identification and immune correlation analysis. POLR2K and TIMM8K were screened and identified as the pyroptosis-associated diagnostic genes for PD. The diagnostic model based on POLR2K and TIMM8K genes showed superior diagnostic performance, although their diagnostic sensitivity and specificity require clinical validation in the future. On the other hand, there were significantly increased immunoinfiltration levels and inflammatory pathway number in PD patients than in healthy individuals. It conformed to the knowledge that inflammation plays an important role in the pathogenesis of neurodegeneration in PD. Alleviating neuroinflammation can reduce symptoms of early PD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The inflammatory neurodegeneration in PD involves activation of microglia, upregulation of pro-inflammatory factors, and gut microbiota, etc. It accorded with the results that the proportions of microglia and astrocytes were significantly increased and inflammatory pathways including GRN, GAS, GALECTIN, WNT, and IL-16 were upregulated in PD group than in healthy group. Additionally, the diagnostic genes POLR2K and TIMM8K were found related to inflammatory signaling pathways including cytokine-receptor interaction, toll-like-receptor, and JAK-STAT pathway, etc. Although immunotherapies have been developed for PD treatment in recent years, few immune-related targets are verified to be clinically beneficial. In this study, the diagnostic genes POLR2K and TIMM8K corralated with many types of infiltrating immune cells, and immune-related signaling pathways. However, whether these two diagnostic genes can be considered as immune-related targets in PD needs further exploration.\u003c/p\u003e \u003cp\u003eThis study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq data combined with transcriptome data, showing innovativeness and clinical translational value. There were several limitations of our study. First, the scRNA-seq data downloaded from the database, instead of experimental data, was applied to identify diagnostic genes, and analyze immunoinfiltration and intercellular communication. Therefore, further experiments will be needed to verify the results in the future. Second, in this study, the molecular phenotypes of activated microglia were not further analyzed. In the following studies, we will identify the gene expression related to microglia activation, so as to reveal molecules or pathways related to PD pathogenesis in the activation process of microglia.\u003c/p\u003e \u003cp\u003eIn summary, based on integrated analysis of scRNA-seq and transcriptome data, this study demonstrated the differences in cell cluster composition and gene expression between PD and healthy group. The proportion of microglia and astrocytes, immunoinfiltration, inflammatory signaling pathways and intercellular interaction in PD patients was significantly increased than that in healthy individuals. The pyroptosis-related differentially expressed genes (PDEGs) were screened to determine hub genes using scRNA-seq and WGCNA analyses, and the diagnostic model and nomogram was constructed based on the genes POLR2K and TIMM8B. This diagnostic model showed promising diagnostic performance in verification. We constructed a novel pyroptosis-linked diagnostic model for PD, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for PD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eWe declare that we have no conflicts of interest to this work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Science and Technology Department Project of Jilin Province [], National Natural Science Foundation of China [82203647], and Special Project of Health Research Talents of Jilin Province [2022SC234].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLin Wang wrote the main manuscript text and prepared figures 1-4; Yidan Qin prepared figures 5-8; Jia Song, Jing Xu, Wei Quan, and Hang Su processed data; Huibin Zeng and Jian Zhang collected data. Jia Li and Jiajun Chen supervised and instructed the study. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData can be obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrover, S., et al., \u003cem\u003eGenome-wide Association and Meta-analysis of Age at Onset in Parkinson Disease: Evidence From the COURAGE-PD Consortium\u003c/em\u003e. Neurology, 2022. 99(7): p. e698-e710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolosa, E., et al., \u003cem\u003eChallenges in the diagnosis of Parkinson's disease\u003c/em\u003e. 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Int J Mol Sci, 2022. 23(16).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., et al., \u003cem\u003eSalidroside ameliorates Parkinson's disease by inhibiting NLRP3-dependent pyroptosis\u003c/em\u003e. Aging (Albany NY), 2020. 12(10): p. 9405\u0026ndash;9426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRui, W., et al., \u003cem\u003eBaicalein Attenuates Neuroinflammation by Inhibiting NLRP3/caspase-1/GSDMD Pathway in MPTP Induced Mice Model of Parkinson's Disease\u003c/em\u003e. Int J Neuropsychopharmacol, 2020. 23(11): p. 762\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, Y., et al., \u003cem\u003ePyroptosis, and its Role in Central Nervous System Disease\u003c/em\u003e. J Mol Biol, 2022. 434(4): p. 167379.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin, X.Y., et al., \u003cem\u003eAberrations in Peripheral Inflammatory Cytokine Levels in Parkinson Disease: A Systematic Review and Meta-analysis\u003c/em\u003e. JAMA Neurol, 2016. 73(11): p. 1316\u0026ndash;1324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReale, M., et al., \u003cem\u003ePeripheral cytokines profile in Parkinson's disease\u003c/em\u003e. Brain Behav Immun, 2009. 23(1): p. 55\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLichtenberger, B.M. and M. Kasper, \u003cem\u003eCellular heterogeneity and microenvironmental control of skin cancer\u003c/em\u003e. J Intern Med, 2021. 289(5): p. 614\u0026ndash;628.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadwal, S. and M.T. Heneka, \u003cem\u003eMicroglia heterogeneity in health and disease\u003c/em\u003e. FEBS Open Bio, 2024. 14(2): p. 217\u0026ndash;229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwivedi, N.V., et al., \u003cem\u003eGPCRs and fibroblast heterogeneity in fibroblast-associated diseases\u003c/em\u003e. FASEB J, 2023. 37(8): p. e23101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWullner, U., et al., \u003cem\u003eThe heterogeneity of Parkinson's disease\u003c/em\u003e. J Neural Transm (Vienna), 2023. 130(6): p. 827\u0026ndash;838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaertner, Z., et al., \u003cem\u003eMolecular heterogeneity in the substantia nigra: A roadmap for understanding PD motor pathophysiology\u003c/em\u003e. Neurobiol Dis, 2022. 175: p. 105925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyczko, D., \u003cem\u003eThe Mesencephalic Locomotor Region: Multiple Cell Types, Multiple Behavioral Roles, and Multiple Implications for Disease.\u003c/em\u003e Neuroscientist, 2022: p. 10738584221139136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Z., et al., \u003cem\u003eSingle-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases\u003c/em\u003e. Eur J Neurosci, 2024. 59(3): p. 333\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadanjak, K., et al., \u003cem\u003eiPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease\u003c/em\u003e. Front Cell Dev Biol, 2021. 9: p. 740758.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrashia, P., et al., \u003cem\u003eBlunting neuroinflammation with resolvin D1 prevents early pathology in a rat model of Parkinson's disease\u003c/em\u003e. Nat Commun, 2019. 10(1): p. 3945.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, single cell RNA sequencing, pyroptosis, diagnostic model","lastPublishedDoi":"10.21203/rs.3.rs-4045950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4045950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by insidious onset. Despite the emphasis on motor symptom-based diagnosis, there remains an unmet clinical need for effective diagnostic approaches during the prodromal phase of PD. Recent advancements in single-cell RNA sequencing (scRNA-seq) and transcriptomic analyses of PD patients open avenues for identifying potential diagnostic biomarkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A comprehensive cell trajectory analysis was conducted using scRNA-seq datasets to pinpoint gene expressions associated with cellular transition from healthy to PD-affiliated state. Integrating the scRNA-seq datasets with Weighted Gene Co-expression Network Analysis (WGCNA) allowed the extraction of pyroptosis-associated differentially expressed genes (PDEGs). Leveraging LASSO logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest methodologies, we devised a diagnostic model centered on PDEGs. Additionally, immunoinfiltration, inflammatory signaling pathways, and intercellular communication were discerned through scRNA-seq analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: In PD patients, the number of cells including metencephalic-like cells, excitatory neurons, inhibitory neurons, and MHB-like cells were significantly reduced, whereas the proportion of astrocytes and microglia, the immunoinfiltration and inflammatory signaling pathways were upregulated as compared with healthy individuals. Using scRNA-seq and WGCNA analyses, two pyroptosis-related diagnostic genes POLR2K and TIMM8B were identified, and a diagnostic model based on them was constructed, which showed promising performance upon validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study cleverly established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq combined with transcriptome data, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for this neurodegenerative ailment.\u003c/p\u003e","manuscriptTitle":"Integrated Analysis of Single-Cell RNA Sequencing and Transcriptome Data Identifies a Pyroptosis-Associated Diagnostic Model for Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 13:23:49","doi":"10.21203/rs.3.rs-4045950/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-12T18:35:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-02T22:06:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f30bbadc-e728-4138-82b4-896d0ef1704b","date":"2024-04-01T05:20:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-28T16:26:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-20T17:37:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268753d1-50c7-4141-9061-b76b6a6a6ed8","date":"2024-03-20T17:36:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2a431299-f72d-46ca-a4fb-abafa64e566a","date":"2024-03-20T14:58:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28c8f48a-bdf1-47b0-ab78-dc4d6dd5187a","date":"2024-03-20T14:36:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-20T14:32:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T10:31:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-20T07:31:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T07:21:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-08T14:36:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"366dc223-74a9-4134-b4c2-af0fc7d048a6","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29703460,"name":"Biological sciences/Computational biology and bioinformatics/High throughput screening"},{"id":29703461,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"},{"id":29703462,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"},{"id":29703463,"name":"Health sciences/Neurology/Neurological disorders"}],"tags":[],"updatedAt":"2024-11-25T16:08:30+00:00","versionOfRecord":{"articleIdentity":"rs-4045950","link":"https://doi.org/10.1038/s41598-024-80185-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-18 15:58:09","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-03-22 13:23:49","video":"","vorDoi":"10.1038/s41598-024-80185-9","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80185-9","workflowStages":[]},"version":"v1","identity":"rs-4045950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4045950","identity":"rs-4045950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}