Understanding Glial Signaling in Neurodegeneration via Gene Network Analysis

Recent scientific discoveries suggest that multiple cell types might participate in Alzheimer’s disease (AD), and understanding the key players and their effects on dementia would advance our ability to design new drugs and therapies. However, the complexity of the brain’s different cell types presents a unique challenge to scientific inquiry. Here I propose work to bridge the divide by using cutting edge technology to profile the different cells of the dementia brain at unprecedented resolution. The results of this work will be new candidate drug targets for dementia and a new approach for studying complex brain diseases.

Alzheimer’s disease (AD) has traditionally been studied one gene, cell type, or biological process at a time; however, it is increasingly clear that this disease involves multiple interacting cell types and pathways within those cells. We currently lack a refined view of AD from the cellular and multicellular levels. Such an understanding would advance data driven approaches to identify disease mechanisms and inform rational drug design. Transcriptomic network analysis and functional genomics approaches performed at the whole tissue level have uncovered a complex role for neuron-glial interactions.  However, glial contributions to disease are particularly challenging to identify by these methods in whole tissue alone due to complex transcellular interactions and functional heterogeneity among glia, for example, microglia and astrocytes. The objective of this proposal is to complete longitudinal single-cell transcriptional profiling and integrate the results with data from mouse models and human disease, to add the cellular resolution needed to fully leverage transcriptomic networks to understand glial signaling mechanisms in AD.