Integrating large-scale datasets into a metamodel of multiscale communication networks underlying Alzhimer’s disease progression
Alzheimer’s disease (AD) is a progressive neurodegenerative disease of old age and the most common cause of dementia. Despite great advances made in understanding the pathogenic features leading to AD, we still only partially understand its cause, and currently have no effective treatments and prevention strategies. While AD research has been largely focused on the damage to neuronal cells, accumulating evidence suggests that multiple non-neuronal cell types in the brain are directly involved in the degeneration process. Cellular communication between different types of brain cells are predicted to have a major contribution to AD progression, yet much remains unknown regarding cellular communication networks spanning multiple cell types.
We are interested in expanding the research focus from single cell types to profiling entire cellular environments, aiming to build a model of the cellular cascade leading to AD, i.e. the crosstalk between cell types and the consequent changes in their internal cell states that drives the disease. We are relying on our combined expertise in: applying cutting edge genomics and imaging technologies in the brain to generate large scale datasets with single cell resolution; in machine learning, image analysis, and graph theory approaches to tackle the data analysis challenges; and in integrative modeling of dynamic biological systems across scales using Bayesian modeling approaches.
To create a comprehensive multi-resolution model that describes the cascade of cellular communications during Alzheimer’s disease (AD) progression
What we did