PROJECT SUMMARY Late onset Alzheimer’s disease (AD), the most common form of dementia among elderly population of age over 65, is an irreversible, progressive brain disorder. Many AD drug candidates aiming to clear the abnormal protein aggregates of amyloid-β (Aβ) and intracellular phosphorylated tau (p-tau) have failed to benefit patients, urging a rapid identification of new therapeutic targets. Genome-wide association studies (GWAS) of AD have identified a growing number of more than 70 risk loci. Yet the real disease-causal genes at these risk loci remain unknown. Given the polygenic etiology of AD, there are various potential pathways implicated in AD. Recent advancements in gene regulatory network studies which integrate multi-Omics data in postmortem brain tissues of AD and control subjects have identified and linked dysfunctional gene network modules (or pathways) to AD, hypothesizing that gene modules are target-rich substance for analyzing Alzheimer's pathology. This opens a new avenue to nominating novel AD targets from the network key regulators (key drivers) as modulating the key drivers could potentially reverse the dysfunctional networks which in turn reverse the AD phenotypes. Network analyses from us and others have predicted numerous AD network drivers in different pathways or cell types, however, experimental validating the phenotypic and gene regulatory roles of these predicted drivers is a rate limiting step. In this proposal, we will conduct CRISPR-based screens to investigate the functional and transcriptomic consequences of perturbing genome-wide genes or a selected panel of AD neuronal network drivers. Our approach includes multiplexed and precise gene editing in human induced pluripotent stem cell (hIPSC)-derived neurons using two sets of CRISPR guide RNA libraries. By comparing the gRNA representations in the cell population before and after AD-related pathological Aβ treatment, we will identify the critical genes that can modify the cellular response to Aβ exposure. Meanwhile, using single-cell transcriptomic sequencing of the multiplex perturbed cells, our approach will provide an unbiased and parallel characterization of the transcriptomic signatures of each of the AD neuronal network driver genes. Taken together with human Omics data of AD, we aim to discover novel regulators of dysfunctional neuronal networks implicated in AD. The analytical and experimental approaches developed herein will enable the scalable and efficient screening, testing, and validation of critical disease network drivers from rapidly expanding multi-Omic datasets in AD. In the long term, the new approaches can be widely applied to reveal the functional consequences of candidate risk genes and identify novel therapeutic targets of AD.