Project Summary Alzheimer’s disease (AD) is a multifactorial disorder with complex etiologies and the impact of sex on AD varies over the course of clinical and neuropathological development. Basic and clinical research studies support sex- specific contributions to AD pathogenesis and progression. Apolipoprotein E4 (APOE4) allele has been identified as a primary genetic risk factor. The interplay between sex and APOE4 allele in AD risks, clinical manifestation, pathological processes as well as treatment responsiveness in various clinical trials have been explored. However, the molecular mechanisms underlying sex dimorphism in AD and how APOE4 stratifies sex divergence in AD remain elusive. Multi-omics data in tandem with systems biology approaches offer a new avenue to not only dissect sex- and APOE-stratified molecular mechanisms of AD but also develop sex-specific diagnostic and therapeutic interventions for AD. Single-cell transcriptomic datasets as well as cell deconvolution of bulk tissue transcriptomes provide in-depth insights into brain region-specific and cell-type specific impact on sex dimorphism in AD. In this application, we propose to develop sex- and APOE-specific network models of AD by performing integrative multiscale network analysis of large-scale bulk and single multi-omics data. In particular, we will develop the first cohort of single nucleus multi-omics data in AD (simultaneous RNA-sequencing and ATAC-sequencing of each single cell) that can meaningfully be stratified by sex and APOE genotype. We will also curate existing single nucleus RNA-seq datasets in AD and combine with our own single cell multi-omics dataset to identify sex-specific genetic variants and molecular signatures of AD (Aim 1). We will perform integrative network analysis to investigate the interplay between sex and APOE genotypes in AD at brain-region and single-cell levels and identify from the network models sex- and APOE-specific, network drivers for AD (Aim 2). We will then identify potential therapeutics of selected key drivers with drug candidate prediction through virtual clinical trials of large electronic medical record (EMR) databases (Aim 3). Finally, we will functionally validate the top predicted sex- and APOE-specific molecular network drivers by genetic manipulations (up- or down-regulation of gene expression), as well as pharmacological perturbation with drug candidates identified from virtual drug screening using relevant human iPSC culture systems and AD mouse models (Aim 4). The findings from this project will guide future development of efficacious sex- and APOE-stratified interventions for AD.