Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation

Background This study aims to develop and validate a programmed cell death signature (PCDS) for predicting and classifying Alzheimer’s disease (AD) using an integrated machine learning framework. We further explore the role of S100A4 in AD pathogenesis, particularly in microglia. Methods A total of one single-cell RNA sequencing (scRNA-seq) and four bulk RNA-seq datasets from multiple GEO datasets were analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to identify PCD-related genes. An integrated machine learning framework, combining 12 algorithms was used to construct a PCDS model. The performance of PCDS was validated using multiple independent cohorts. In vitro experiments using BV2 microglia were conducted to validate the role of S100A4 in AD, including siRNA transfection, Western blot, qRT-PCR, cell viability and cytotoxicity assay, flow cytometry, and immunofluorescence. Results ScRNA-seq analysis revealed higher PCD levels in microglia from AD patients. Seventy-seven PCD-related genes were identified, with 70 genes used to construct the PCDS model. The optimal model, combining Stepglm and Random Forest, achieved an average AUC of 0.832 across five cohorts. High PCDS correlated with upregulated pathways related to inflammation and immune response, while low PCDS associated with protective pathways. In vitro , S100A4 knockdown in AbetaO-treated BV2 microglia improved cell viability, reduced LDH release, and partially alleviated apoptosis. S100A4 inhibition attenuated pro-inflammatory responses, as evidenced by the reduced expression of pro-inflammatory mediators (IL-6, iNOS, TNF-α) and promoted an anti-inflammatory state, indicated by increased expression of markers such as IL-10, ARG1, and YM1/2. Furthermore, S100A4 knockdown mitigated oxidative stress, restoring mitochondrial function and decreasing ROS levels. Conclusion This study developed a robust PCDS model for AD prediction and identified S100A4 as a potential therapeutic target. The findings highlight the importance of PCD pathways in AD pathogenesis and provide new insights for early diagnosis and intervention.