Decoding neuroinflammation in Alzheimer's disease: a multi-omics and AI-driven perspective for precision medicine

Abstract

Alzheimer's disease (AD) is a common neurodegenerative disease, which is characterized by β-amyloid (Aβ) deposition, Tau hyperphosphorylation, synaptic dysfunction and chronic neuroinflammation. Despite significant advances in research in recent years, effective therapeutic options remain limited. The development of single-cell RNA sequencing (scRNA-seq) has made it possible to analyze cellular heterogeneity in AD brain tissues at high resolution, breaking through the limitation of signal averaging in traditional large-scale tissue analysis. This technology has led to the discovery of novel disease-associated cell subsets, such as pro-inflammatory microglia and reactive astrocytes, and the identification of key molecular markers linked to disease progression. Integrating scRNA-seq with AI-driven analytics and multi-omics platforms further enhances our ability to decode the intricate immune-inflammatory networks underlying AD. This strategy is expected to achieve accurate classification and early diagnosis of AD subtypes, and promote the development of individualized treatment strategies based on individual molecular and immune characteristics.

Keywords: Alzheimer’s disease (AD); heterogeneity; neuroinflammation; prognostic biomarkers; single-cell RNA sequencing.

Figure 1

Application of scRNA-seq in AD.