AI-guided Engineering of Astrocyte-to-Neuron Reprogramming for Generating Clinically Relevant Neuronal Subtypes

Neuron loss in injury and neurodegenerative disease creates an urgent need for new regenerative strategies. Lineage reprogramming of glial cells is a promising approach, but current methods struggle to generate clinically relevant neuronal subtypes. This project combines biomolecular engineering with AI-driven regulatory modelling to design improved reprogramming strategies. We will generate single-cell multi-omic datasets of astrocyte-to-neuron conversion and integrate them into virtual cell models to identify transcription factors and synthetic enhancer elements that more precisely direct subtype specification, such as SST and Pvalb interneurons. These engineered designs will be tested in vitro and in vivo using standardised molecular and functional assays.
The project will reveal how induced neuronal transcriptomes differ from endogenous neurons and provide a framework for fine-tuning reprogramming cocktails. The student will receive unique interdisciplinary training across AI-guided regulatory engineering, single-cell genomics and neuronal reprogramming within an inclusive, international research environment.