Informatics approaches to analysing the interplay between microglia, astrocytes and neurons in Aß-induced associated synapse loss

Supervisors:

Prof Giles Hardingham, Director, UK Dementia Research Institute at the University of Edinburgh

Dr Owen Dando, Head Bioinformatician, UK Dementia Research Institute at the University of Edinburgh

Duration: 3 years at UKRI Stipend rate (rate for 2023/24: £18,622). Start date flexible.

A PhD project within the UK Dementia Research Institute at the University of Edinburgh is available for talented scientists with experience and interest in bioinformatics and image analysis to build predicative models on determinants of key aspects of Alzheimer’s Disease pathology and their capacity for manipulation

Aß pathology causes synapse loss but the intra and inter-cellular signaling pathways leading to this are incompletely understood. Genetic evidence (for Alzheimer’s disease (AD) risk) points to a role for both microglia and astrocytes (a type of macroglia). There is a general consensus that oligomeric Aß is a key toxic moiety and in models of ß-amyloidopathy leads to synapse loss where levels are particularly high (e.g. near plaques) or throughout vulnerable regions such as the hippocampus. Aß-dependent synapse loss is associated with changes to microglia and astrocytes, but the primary effector(s) of Aß and sequence of events relevant to AD progression remain controversial. This project will aim to determine how astrocytes change in response to Aß pathology, and the role that microglia play in mediating these changes, and in synapse loss. The student will use data from established in vivo models of ß-amyloidopathy, including genetic models as well as the administration of human brain AD homogenate, plus mice lacking microglia (with and without microglial replacement therapy). Different types of data will be integrated, including spatial ‘omics, molecular pathology, single cell ‘omics and electron microscopy. The focus of the approach is to build models that integrate different data modalities using a variety of informatics approaches to describe the relationship between the aforementioned key aspects of AD pathology. The work will have a strong informatics component and the student will be both part of the Hardingham lab and UKDRI Informatics Team. The candidate will have experience in coding (Python or R) and an interest in image analysis, machine learning and predictive modelling.

To apply please send a CV and 1 page statement of your research interests to [Email Address Removed] and [Email Address Removed]

Nothing else is needed at this stage.

For informal enquiries contact [Email Address Removed] or [Email Address Removed]