Indiana University-Purdue University Indianapolis (IUPUI) / Background: Alzheimer’s disease (AD) is the third leading cause of death for the older demographic in the United States, just after heart disease and cancer. However, unlike heart disease and cancer, the death rates for AD are increasing. Despite extensive research, the cause or origin of AD remains unclear and there is no existing cure. However, with the improvement of single-cell RNA-sequencing (scRNA-seq) technologies and drug repurposing tools, we can further our knowledge of AD and its pathogenesis. Method: Our primary aim was to identify repurposable drug and compound candidates for AD treatment and identify significant cell types and signaling pathways using two scRNA-seq datasets from cortex samples of AD patients and controls. To achieve this aim, we generated differential gene expression profiles, calculated log fold-changes, and estimated standard errors to make pairwise comparisons between the diseased and healthy samples. We used the 21,304 drugs/compounds with response gene expression profiles in 98 cell lines from the LINCS L1000 project to detect consistent differentially expressed genes (DEGs), that were either i) up-regulated in cells of diseased samples and down-regulated in cells with treatment, or ii) down-regulated in cells from diseased samples but up-regulated in cells with treatment. To evaluate these identified drugs, we compared the p-value, false discovery rate (FDR) and A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) drug score for each cell type. We further annotated and assessed doublet cell types within the Grubman et al. dataset using cell type proportions. Result: The analysis provided several potential therapeutic treatments for AD and its target genes and pathways as well as important cell type interactions. Notably, we identified an interaction between endothelial cells and microglia, and further identified drug candidates to target this interaction. Conclusion: We identified repurposable drugs/compounds candidates in each dataset which were also identified in literature. We further identified doublet cell type interactions of interest and drugs that target this interaction.