Clearance of toxic aggregates in Huntington's disease: exploring the roles of reactive glial cells with multi-omics approaches


Huntington’s disease (HD) is a rare neurodegenerative disease leading to motor, cognitive and psychiatric symptoms. The mechanisms causing neuronal death in this disease are still unclear despite identification of the causing mutated gene (Htt). Recent studies suggest that glial cells (astrocytes and microglial cells), which are key partners to neurons, may play a role in HD progression as well. In particular, during HD, these cells change and become reactive. Glial reactivity is usually considered as detrimental. On the contrary, we found that reactive astrocytes promote mutant Htt (mHtt) clearance in HD mouse models and have beneficial effects on vulnerable neurons (Ben Haim et al, J. Neuro., 2015; Abjean et al., in preparation).

In this project, we will implement state-of-the-art, multi-level omics approaches to identify how reactive glial cells can help neurons clear toxic mHtt. Through transcriptomic and proteomic analysis of acutely sorted astrocytes and microglial cells from the brain of HD mice, after modulation of their reactive state, we will identify molecular changes that could contribute to glial clearance of mHtt. Pathway and network analysis, as well as comparison with available datasets from other models or patients will be performed to identify key candidate glial genes or proteins that could mediate protective effects on neurons. We will finally demonstrate their therapeutic impact on HD clinical outcomes, through gain and loss of function approaches with viral gene transfer in HD mouse models. All biological samples and some omics data required for this project have already been generated.

This project will provide novel insight into the molecular changes occurring in glial cells during HD and identify endogenous protective glial responses, amenable to therapeutic improvement.


Training in bioinformatics, including on experimental methods and analysis software and algorithms for RNA-seq and functional genomics. Knowledge in statistics, data mining and reporting. A complementary training in Neuroscience (molecular or cellular neuroscience, neuropathology or methods in neuroscience) would be an asset.

Start date

October 01, 2019

Start date

To be determined

How to Apply

Send CV and motivation letter


Carole Escartin