UCSF

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition characterized by heterogeneous molecular alterations across various brain cell types, posing significant challenges for the development of effective treatments. To address this complexity, we undertook two complementary projects aimed at advancing precision medicine approaches for AD, each building on the other to create a robust foundation for targeted therapies.The first project serves as a proof of concept for an innovative drug discovery strategy, utilizing a network correction approach grounded in direct human evidence and real-world data. By integrating diverse datasets, including single-cell human transcriptomics, drug perturbations, and electronic clinical records, we identified the combination of letrozole and irinotecan as potential therapeutics designed to correct gene expression alterations across multiple cell types implicated in AD. Rigorous validation in AD mouse models demonstrated that this combination therapy, targeting both neurons and glial cells, significantly ameliorated memory deficits and other AD-related pathologies, outperforming the single-drug treatments targeting either neurons or glial cells alone. The success of this project underscores the potential of cell-type-directed network-correcting therapy, demonstrating that targeting the transcriptomic landscape at a cell-type-specific level may offer a more efficacious approach to treating AD. Building on this foundation, the second project was designed to expand the knowledge foundation for precision medicine by comprehensively characterizing the molecular influences of major AD risk factors, such as age, apolipoprotein E4 (APOE4), and sex using AD mouse models. By analyzing single-nucleus RNA-sequencing (snRNA-seq) data from the hippocampus of human APOE4 and APOE3 knock-in (KI) female and male mice across different ages, we identified significant variations in cell type abundance and gene expression patterns, particularly driven by sex differences and the interplay between age and APOE genotype. This detailed molecular profiling not only enriches our understanding of the disease but also provides a valuable dataset for future applications of the network correction method. Specifically, it enables the mapping of distinct risk profiles and the identification of tailored therapeutic interventions for individuals based on their unique transcriptomic signatures. Together, these two projects are highly complementary: the first project validates the efficacy of network correction therapy, while the second project supplies the essential molecular data needed to understand disease heterogeneity, enabling the application of this therapeutic approach in a precision medicine framework. By integrating these insights, we can more accurately tailor treatments to individuals with distinct risk profiles, thereby advancing the development of personalized therapies for AD.