Integrated Single-Cell and Spatial Transcriptomic Analysis Identifies ISR-Related Genes Driving Immune Regulation in Parkinson’s Disease - Jiang H; Zhang X; Feng S; Feng W (Journal of Inflammation Research)
Hua Jiang,1 Xiaotian Zhang,2,3 Shengyu Feng,2,3 Wei Feng2,3 1Clinical Laboratory, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China; 3Laboratory of Anesthesia and Brain Function, The Affiliated Hospital of Qingdao University Qingdao, Shandong, People’s Republic of ChinaCorrespondence: Wei Feng, Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email fengweisdqd@qdu.edu.cn Shengyu Feng, Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email fengshengyu0618@163.comBackground: Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor dysfunction and dopaminergic neuron loss. Although its genetic and molecular underpinnings have been increasingly studied, the pathways driving PD progression remain unclear. The integrated stress response (ISR), a conserved mechanism activated by cellular stress, has been linked to several neurological diseases, but its role in PD and the key ISR-related genes involved are still poorly understood.Materials and Methods: We used publicly available transcriptomic data from GEO, including single-cell RNA sequencing and spatial transcriptomics, to identify ISR-related genes involved in PD progression. ISR scores were compared across brain cell types, and differentially expressed genes in microglia were further screened using Lasso regression and random forest algorithms. Enrichment analyses (GSEA and GSVA) revealed their involvement in immune-related pathways. CIBERSORT was applied to assess immune cell infiltration, while spatial transcriptomics mapped the regional expression of key genes. Finally, DDIT4 expression was validated in PD cell and mouse models.Results: We identified four key ISR-related genes (DDIT4, GNA13, HSPA1B, and SLC7A5) that were differentially expressed in PD microglia. Functional enrichment analysis revealed that these genes were predominantly involved in immune-related signaling pathways, including JAK-STAT, NF-κB, and Notch, suggesting their potential role in regulating neuroinflammation. Spatial transcriptomics revealed distinct regional expression patterns of these ISR-related genes across brain tissues. In vitro and in vivo experiments confirmed the upregulation of DDIT4 in PD models, and its silencing alleviated neurotoxicity and reduced α-synuclein aggregation, highlighting its potential role in PD pathogenesis.Conclusion: This study provides new insights into the molecular mechanisms of PD and highlights DDIT4 as a promising therapeutic target. Its regulatory role in immune signaling and cellular stress pathways may offer novel avenues for clinical intervention and personalized treatment strategies in PD.Plain Language Summary: Parkinson’s disease (PD) is a brain disorder that causes movement problems and nerve cell damage. While scientists understand some of its causes, the key pathways driving PD progression are still unclear. The Integrated Stress Response (ISR) helps cells cope with stress and is linked to neurodegenerative diseases, but its role in PD is not well known. In this study, we used advanced transcriptomic techniques, including single-cell RNA sequencing and spatial transcriptomics, to analyze gene expression patterns in PD. These methods allowed us to identify four key ISR-related genes (DDIT4, GNA13, HSPA1B, and SLC7A5) that may affect PD progression, mainly through immune-related pathways. Further experiments in cell and mouse models confirmed that DDIT4 is highly expressed in PD, and reducing its levels helped protect nerve cells. These findings improve our understanding of PD and may help in developing new treatments.Keywords: Parkinson’s disease, integrated stress response, single-cell transcriptomics, spatial transcriptomics, bioinformatics, DDIT4