Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.

ABSTRACT: Neurological disorders result from the complex and poorly understood contributions of many cell types, essential for uncovering mechanisms behind these disorders and identifying specific therapeutic targets. Single-nucleus technologies have advanced brain disease research, but remain limited by their low nuclear transcriptional coverage, high cost, and technical complexity. To address this, we applied a transformer-based deep learning model that restores cell type-specific investigation transcriptional programs from bulk RNA-seq, significantly outperforming previous methods. This enables large-scale and cost-effective investigation of cell type-specific transcriptomes in complex and heterogeneous phenotypes such as cognitive resilience or brain resistance to Alzheimer's disease. Our analys

SUBMITTER: Berson E 

PROVIDER: S-EPMC12404794 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature