Reverse engineering cellular identity at single cell resolution

Cellular identity is maintained by Gene Regulatory Networks (GRNs), which in turn underlie cell morphology and function. GRNs are defined by a particular combination of Transcription Factors (TFs), which orchestrate the specific spatiotemporal patterns of gene expression that drive cellular fate by recognising and binding to cis-regulatory regions. These genomic enhancers are characterised by the presence of recognition sequences to which TFs bind, and by a structural context. However, the architectural rules of enhancers and how they are linked to target genes in regulatory circuits is poorly understood. Today, new technologies such as single cell epigenomics and single-cell transcriptomics allow the characterisation of cell type specific regulatory activity and gene expression patterns from complex heterogeneous tissues. In this project, we will combine wet and dry lab approaches to: (1) develop a framework for the assessment of cofunctional enhancers and their features overcoming the intrinsic noise and sparsity of single cell epigenomics data; (2) derive unified models for the genome-wide detection of enhancers and  regulatory circuits (i.e. enhancer-to-target relationships); and (3) apply our framework to decipher the regulatory circuits underlying tissue regeneration in the liver, using Massively Parallel enhancer-Reporter Assays to validate our predictions.