Programmed cell death signatures-driven microglial transformation in Alzheimer's disease: single-cell transcriptomics...

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Alzheimer's disease (AD) pathogenesis involves complex cellular processes, including programmed cell death (PCD).
• Microglia play a critical role in neuroinflammation and AD progression.
• Identifying reliable biomarkers for AD is crucial for early diagnosis and treatment.

Purpose of the Study:

• To develop and validate a novel programmed cell death signature (PCDS) for predicting and classifying Alzheimer's disease (AD).
• To explore the specific role of the S100A4 protein in AD pathogenesis, particularly within microglia.
• To establish a machine learning framework for robust AD biomarker discovery.

Main Methods:

• Integrated analysis of single-cell and bulk RNA sequencing data from multiple cohorts.
• Weighted Gene Co-expression Network Analysis (WGCNA) to identify PCD-related genes.
• Development and validation of a PCDS model using an ensemble of 12 machine learning algorithms.
• In vitro validation of S100A4 function in BV2 microglia using siRNA, Western blot, qRT-PCR, and functional assays.

Main Results:

• A PCDS model, combining Stepglm and Random Forest algorithms, achieved an average AUC of 0.832 across five independent cohorts.
• High PCDS scores correlated with upregulated inflammatory and immune response pathways in AD microglia.
• S100A4 knockdown in microglia reduced apoptosis, inflammation, and oxidative stress, suggesting a protective role.

Conclusions:

• A robust PCDS model was successfully developed for AD prediction, offering potential for early diagnosis.
• S100A4 emerged as a significant factor in AD pathogenesis and a potential therapeutic target.
• These findings underscore the importance of PCD pathways in AD and provide novel insights for therapeutic interventions.