Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimer's Disease.

Alzheimer's Disease (AD) is marked by the accumulation of pathology, neuronal loss, and gliosis and frequently accompanied by decline in cognition. Understanding brain cell interactions is key to identifying new therapeutic targets to slow its progression. Here, we used systems biology methods to analyze single-nucleus RNA sequencing (snRNASeq) data generated from dorsolateral prefrontal cortex (DLPFC) tissues of 424 participants in the Religious Orders Study or the Rush Memory and Aging Project (ROSMAP). We identified modules of co-regulated genes in seven major cell types and assigned them to coherent cellular processes. We showed that coexpression structure was conserved in the majority of modules across cell types, but we also found distinct communities with altered connectivity, especially when compared to bulk RNASeq, suggesting cell-specific gene co-regulation. These coexpression modules can also capture signatures of cell subpopulations and be influenced by cell proportions. Finally, we performed associations of modules with AD traits such as amyloid-β deposition, tangle density, and cognitive decline, and showed replications in an independent single-nucleus dataset. Using a Bayesian network framework, we modeled the direction of relationships between the modules and AD progression. We highlight one key module, the astrocytic module 19 (ast_M19), associated with cognitive decline through a subpopulation of stress-response cells. Our work provides cell-specific molecular networks modeling the molecular events leading to AD.