DeepDynamics resolves cell-subtype and clinicopathological dynamics from bulk RNA-seq to identify mediators of Alzheimer's disease risk

Abstract

Processes such as Alzheimers disease and aging are shaped by dynamic cascades of cellular and molecular changes. Mapping how genetic and environmental risk factors alter these cascades is critical for pinpointing disease susceptibility and guiding therapeutic intervention, yet this task requires dense sampling of cell-subtype and -subpopulation changes throughout the full course of disease. Bulk measurements provide scale but obscure cell-type and subpopulation resolution, whereas single-cell assays offer resolution but lack sufficient sample size and temporal coverage. We present DeepDynamics, a deep-learning framework that combines a smaller reference sc/snRNA-seq atlas with a larger bulk RNA-seq cohorts to infer cell-subpopulation and clinicopathological dynamics along annotated trajectories. Applied to Alzheimers disease (AD), DeepDynamics mapped 1,092 cortical bulk profiles onto aging and disease trajectories, revealing faster accumulation of AD clinicopathologies, disease-associated glia, and specific neuronal subpopulations in APOE4 carriers. These changes, stronger in females, were accompanied by upregulation of cell-type-specific molecular pathways and early downregulation of heat-shock proteins across cell types. By unifying genetic risk, pathology, and cell-subpopulation dynamics, DeepDynamics provides a scalable approach to prioritize putative mediators of risk and resilience amid the myriad changes along the cascade, thus informing targeted therapies.