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Related Experiment Video

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

Mi-Mi Li1, Ying-Xia Yang1, Ya-Li Huang1

  • 1Department of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.

Frontiers in Immunology
|August 11, 2025
PubMed
Summary

This study developed a programmed cell death signature (PCDS) to predict Alzheimer's disease (AD) and identified S100A4 as a potential therapeutic target. The PCDS model shows promise for early AD diagnosis and intervention strategies.

Keywords:
Alzheimer’s diseasemachine learningmicrogliaprogrammed cell deathsingle-cell

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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.