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Decoding and controlling cell-state switching: A bottom-up approach based on enhancer logic

Periodic Reporting for period 2 - cis-CONTROL (Decoding and controlling cell-state switching: A bottom-up approach based on enhancer logic)

Reporting period: 2018-12-01 to 2020-05-31

Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. However, there is a significant gap in our understanding of how the DNA sequence of an enhancer encodes the specificity for transcription factor binding, enhancer function, chromatin accessibility, and ultimately target gene regulation. Achieving a better understanding of how enhancers work is important for society because it will improve the interpretation of non-coding genome variation, both in normal and in cancer genomes, and it will empower the generation of cell type specific drivers to manipulate cell types, with applications for regenerative medicine and gene therapy.

In this project we are addressing the enhancer decoding challenge from a single-cell perspective, broadly termed as single-cell regulatory genomics. Particularly, we are using a combination of in vivo massively parallel enhancer-reporter assays, single-cell genomics on microfluidic devices, computational modelling, and synthetic enhancer design. We first apply this strategy to decipher the enhancer code in melanoma, a relevant case study due to the presence of distinct melanoma cell states, and the finding that cells can switch between these states. We also broaden our experimental model systems by looking at epithelial-to-mesenchymal transition, as well as wound response models. Several technological components of this project include the establishment of droplet-based single-cell techniques (e.g. single-cell ATAC-seq and multi-omics); and the development of novel algorithmic and AI approaches to analyze single-cell epigenomics data, and to decipher enhancer logic. Ultimately, our aim is to build enhancer models that can be used to (1) prioritize enhancer mutations in cancer and normal genomes, and (2) to design and optimize enhancers that drive cell type specific reporter activity, so that they can be used in gene therapy applications, for example to activate a suicide gene in a specific cancer cell state.
Since the beginning of the project we have performed work to study the relationship between enhancer accessibility and enhancer activity. Firstly, to determine enhancer accessibility at single-cell level we employed single-cell ATAC-seq during melanoma phenotype switching, throughout a time series, and identified thousands of enhancers that change accessibility. We were able to cluster these, using topic modeling, into classes, and we identified the key transcription factor motifs, such as SOX10 and MITF, that operate these enhancers. This work was published in Bravo & Minnoye et al., Nature Methods 2019. In parallel, we determined the changes in transcription using single-cell RNA-seq, during phenotype switching, for a cohort of melanoma patient cultures (Wouters et al., BioRxiv 2019. Furthermore, we studied enhancer accessibility in epithelial cells, such as the MCF7 breast cancer cells, and discovered that this is mainly driven by the pioneer factor Grainyhead (Jacobs et al., Nature Genetics 2018). Next, we compared enhancer accessibility in melanoma across species, found that the melanoma cell states are conserved between human and dog, built a deep learning model DeepMEL, and used it to interpret enhancer architectures, and to identify functional substitutions underlying enhancer turnover (Minnoye & Taskiran, BioRxiv 2019). Finally, we employed DeepMEL to prioritize enhancer mutations in phased melanoma genomes (Kalender Atak & Taskiran, BioRxiv 2019). Meanwhile, we developed new computational tools, most notably cisTopic, to analyze single-cell ATAC-seq data (Bravo & Minnoye, Nature Methods 2019), and provided several updates to the SCENIC method to infer gene regulatory networks from single-cell RNA-seq data. We also developed a single-cell atlas viewer, called SCope, which use to visualize scRNA-seq, scATAC-seq, and SCENIC results (Davie et al., Cell 2018).
Currently we are progressing beyond the state of the art throughout several of our aims. Firstly, we are optimizing our assays for massively parallel enhancer reporter assays, and we are using them to test large collections of synthetic enhancers, both in melanoma and in the liver injury model in the mouse. To track changes in enhancer activity in different melanoma cell states, we are exploring the use of melanoma biopsy organoids, to gradually replace xenotransplantation in mouse. Secondly, due to the finding of neural crest-related melanoma cell states, we are comparing melanoma cell states to different cell types from the human neural crest, using in vitro differentiation from induced pluripotent stem cells. Thirdly, we are expanding our deep learning framework to gain insight into various classes of enhancers. Fourthly, we are developing new computational strategies to integrate scATAC-seq and scRNAseq data, building further on our complementary work in Drosophila (Bravo et al., BioRxiv 2019). Fifthly, our single-cell analysis during liver injury is progressing steadily, as we have already analyzed scATAC-seq and scRNA-seq during injury, identifying key changes in chromatin and expression. Finally, we are taking our first steps to generate a hybrid wet-lab/dry-lab pipeline to build cell type specific enhancers for gene therapy.