Chasing the cell type‐specific molecular networks involved in Alzheimer’s Disease

Background Alzheimer’s disease (AD) is the primary cause of dementia, characterized by accumulation of neuritic plaques and neurofibrillary tangles, accompanied by neuronal loss and gliosis. Single‐cell profiling shows that AD involves a complex interplay of every major brain cell type. However, the molecular mechanisms responsible for cell‐type‐specific transcriptional changes remain to be defined. Method We used systems biology methods to analyze bulk RNASeq from DLPFC tissues of 1,210 participants and snRNASeq of 424 individuals from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We identified modules of co‐regulated genes, assigned them to coherent cellular processes, and assessed which modules were associated with AD traits such as cognitive decline, tangle density, and β‐amyloid deposition. Result From the bulk RNASeq, we found 34 coexpressed modules with at least 30 genes. These modules were enriched for GO terms such as Lipid metabolism (M09), Apoptosis (M32), and Mitochondria (M24). Because variation in cell type proportions can drive coexpression patterns between genes, we verified that changes in cell proportion did not account for the module trait associations. Integrating the bulk modules with cell counts, we observed that 38% (13) of all modules had their variance significantly explained by specific cell‐type counts, including 41% (7) of the modules enriched for cell‐specific gene makers. Eight modules were associated with AD traits, where module M03 showed the strongest association with cognitive decline (P‐value = 2.56×10−19). This module was not enriched for a particular cell type, suggesting preservation across brain cells. Next, we constructed networks by each cell type and performed Bayesian inference to predict distinct directional relationships between AD traits and modules in each cell‐type network. The strongest associations with pathology were found in mic_M46 (tangles) enriched with genes related to negative regulation of T cell activation, and mic_M45 (β‐amyloid) enriched for transcription factors. By contrast, Oli_M14 (e.g. genes UMAD1, TPCN1, BCL2L1), enriched with bulk M03 genes, was the best module directly associated with cognitive decline after conditioning on all other correlated modules. Conclusion Our findings show that single‐cell co‐expression networks of the aging brain can highlight distinct molecular programs involved in cognition and pathology accumulation.