Deciphering gene regulatory networks and enhancer logic from single-cell multi-omics data

Single-cell transcriptomics and single-cell epigenomics allow building cell atlases of any tissue and species, providing new opportunities to predict gene regulatory networks that control the identity of cell types and cell states. I will present new computational strategies that take advantage of the joint analysis of scRNA-seq and scATAC-seq data, and that derive “enhancer-GRNs” (eGRN) with key transcription factors, genomic enhancers, and predicted target genes per cell type. On the scATAC-seq side, our strategy exploits topic modelling and motif discovery in co-accessible regions to predict TFs. On the scRNA-seq side, we use random forest regression (GENIE3) to link both accessible regions, as well as upstream TFs, to candidate target genes. In parallel, we use deep learning on the scATAC-seq topics to prioritize enhancers based on TF motif combinations. I will discuss the results of several case studies where we applied this strategy to, including the Drosophila brain, the mammalian brain, and the mouse liver. Finally, I will discuss how enhancer models based on deep learning can be exploited to design synthetic enhancers for Drosophila and human cell types.

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