Drug Development.

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Alzheimer's disease (AD) is a complex neurodegenerative disorder lacking effective treatments.
• Digital twins (DTs), integrating AI and multi-omics, offer a novel approach for characterizing disease heterogeneity and identifying therapeutic targets.
• DTs enable real-time disease modeling and dynamic updates for personalized treatment strategies.

Purpose of the Study:

• To develop a single-cell digital twin (scDT) framework for modeling AD progression.
• To identify AD-associated genes and potential drug candidates using integrated multi-omics data.
• To prioritize therapeutic targets and facilitate drug repurposing for AD.

Main Methods:

• Integrated single-nuclei RNA-seq (snRNA-seq) and ATAC-seq (snATAC-seq) data from 84 donors across four AD neuropathological change (ADNC) stages.
• Constructed cell type-specific transcription factor (TF)-target gene regulatory networks.
• Applied gene set enrichment analysis (GSEA) and network proximity analysis for gene and drug prioritization.

Main Results:

• Identified distinct gene expression dynamics across six major cell types during AD progression.
• Discovered 141 ADNC-associated genes by integrating GWAS loci with cell type-specific cis-regulatory DNA elements (cCREs).
• Highlighted 13 potential repurposable drugs, including galantamine and resveratrol, linked to ADNC-associated genes.

Conclusions:

• Demonstrated a single-cell digital twin framework for cell type-specific target identification in AD.
• The scDT framework shows promise for drug repurposing in AD and related dementias.
• This approach facilitates the discovery of novel therapeutic strategies for neurodegenerative diseases.