Single-cell digital twins identify drug targets and repurposable medicine in Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is a complex and poorly understood neurodegenerative disorder without sufficiently effective treatments. Novel approaches to identify FDA-approved drugs that may hold potential for mitigating symptoms of AD hold promise for addressing this problem. One such strategy is the use of digital twins (DTs), which are virtual representations of physical entities that facilitate therapeutic target identification by accurately characterizing disease heterogeneities in real time through continuous feedback and dynamic model updates. In this study, we developed a single-cell digital twin (scDT) framework using single-nuclei RNA-seq (1,197,032 nuclei) and ATAC-seq (740,875 nuclei) data from the middle temporal gyrus of 84 donors across 4 degrees of AD neuropathological change (ADNC). We observed differential gene expression for six major cell types intensified at severe ADNC. We also constructed cell type-specific transcription factor (TF)-target gene networks by leveraging peak-to-gene linkages and motif enrichment analyses. By integrating genome-wide association study (GWAS) loci with cell type-specific cis-regulatory DNA elements (CREs), we identified 141 ADNC-associated genes. Using gene set enrichment analysis (GSEA) and network proximity analysis, we identified 13 candidate repurposable drugs that were associated with these ADNC-associated genes. In summary, we constructed a single-cell digital twin (scDT) framework cell type-specific target identification and drug repurposing in AD and other complex diseases if broadly applied.