We obtained significant results in enhancer decoding using a combination of computational and experimental techniques, on a variety of biological model systems. As the first model we used human melanoma, where we studied the regulatory code underlying the two main transcriptional states, namely the melanocytic (MEL) and the mesenchymal-like (MES) state, and the dynamic switch from MEL to MES. We profiled changes in chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) at single-cell resolution and used computational techniques including topic model (cisTopic) and GRN inference (SCENIC and SCENIC+) to identify key transcription factors, genomic enhancers, their constellation of TF binding sites, and the target genes they regulate. We furthermore discovered an intermediate melanoma state and described its regulatory network and collaborated with the JC Marine lab to study phenotype switching in patient-derived xenograft models. As second model we used the mouse liver, for which we generated a single-cell multi-ome atlas and inferred the GRNs underlying each cell type, including the differences between pericentral and periportal hepatocytes (i.e. zonation). Our regulatory models, combined with in vivo validation experiments including high-throughput enhancer reporter assays, revealed the core TFs for hepatocytes and new TFs that control zonation via repression. A third model system we used is the fruit fly Drosophila melanogaster, which allowed us to study enhancer logic in vivo, again during phenotype switching in a tumour model and in a wounding model (switching to a senescent state).
We developed several new technologies, including a custom droplet microfluidics technique called HyDrop, to perform scATAC-seq and scRNA-seq at a cost that is 100x cheaper compared to commercial methods. We then benchmarked HyDrop scATAC-seq in a large international consortium. We also optimized massively parallel enhancer reporter assays and used them to measure the activity of melanoma enhancers in different states and of hepatocyte enhancers in vivo in the mouse liver.
Importantly, we developed multiple computational methods including a Python version of SCENIC, called pySCENIC; NextFlow pipelines to facilitate the use of SCENIC; a topic modeling approach, called cisTopic, to analyze scATAC-seq data; a single-cell viewer called SCope; a method to integrate scATAC-seq and scRNA-seq data on a virtual spatial template, called scoMAP; and finally SCENIC+ to infer enhancer-GRNs from sc-multiome data.
Exploitation and dissemination: our single-cell and spatial data sets are all publicly available as resources, both as raw and processed data (via GEO and SRA), and as online atlases in SCope. Our HyDrop protocols are all publicly available at protocols.io and are used by dozens of labs around the world. All our methods are available via GitHub where we also solved many dozens of issues to help users use our tools, and to improve them.
