Evolution of the genomic regulatory code underlying neuronal diversity

The nervous system, particularly the brain, shows the greatest diversity of cell types of all tissues. This variety in neuronal cell types is created during development, due to a precise spatiotemporal control of gene expression. Currently, the exact combinations of transcription factors, the genomic target enhancers where they bind, and the target genes they regulate, are poorly characterized. To investigate how this complex and dynamic regulatory logic is encoded in the genome sequence, and how it is conserved throughout evolution, we will exploit single-cell multiome datasets obtained from developing brains of different species. We will detect dynamic gene regulatory networks and infer cellular trajectories of differentiating neurons. To elucidate the regulatory logic of neurogenesis, we will employ deep learning techniques and develop a method for the analysis of enhancer dynamics. Furthermore, comparative analyses between different species, including human and mouse, will lead
to identification of conserved regulatory steps and enhancer grammar involved in directing neuronal differentiation. Finally, we will characterise enhancer sequences and validate identified enhancers using synthetic enhancers and in vivo massively parallel enhancer reporter assays. This project will enhance our understanding of the molecular basis of neuronal diversity and contribute to our understanding of gene regulation in dynamic processes.