Generative artificial intelligence, integrative bioinformatics, and single-cell analysis reveal Alzheimer's genetic and immune landscape.

Abstract

The research aims to understand Alzheimer's genetic and immune landscapes using the amalgamation of three technologies: artificial intelligence (GenAI), integrative bioinformatics, and single-cell analysis. First, the study aims to identify and characterize the significant genes associated with Alzheimer's disease (AD) using three GenAI models (GPT‑4o, Gemini model, and DeepSeek). After the genes were accumulated from GenAI models, 27 genes associated with AD were recoded. Furthermore, they were analyzed using integrative bioinformatics methods. Similarly, the immune landscape of AD using single-cell analysis was also explored, which reveals a high percentage of effector CD8⁺ T cells (33.42%) and naive T cells (45.95%). The single-cell study found that effector memory T cells have two subsets. It also found that the macrophage population has started to spread and dendritic cells have decreased in Alzheimer's patients. The single-cell gene expression study reveals the top ten highly expressed genes (NDUFV2, CAT, MRPS34, PBX3, THOC2, CCDC57, PBXIP1, SDHAF3, PPP4C, and MAP3K8). The clonal frequency indicates that CD8⁺ T and naive T cell populations show the highest clonal frequency in healthy and AD individuals and are further noted them in the clonotype cell proportion study. Following our GenAI and single-cell profiling strategy, future studies will help in quickly understanding the genetic and immune basis of many diseases.