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Decoding animal genomes into cell types

Project description

Towards a regulatory understanding of cell identity

The expression pattern of specific cell-type specific genes is responsible for cell identity. However, our knowledge on the regulatory programmes that establish and maintain cell type is limited mainly due to the lack of analytical methods. Funded by the European Research Council, the Genome2Cells project aims to address this unmet need and develop deep learning models that can predict gene regulatory networks from genomic sequences. The project will help understand how the genome translates into cell types starting from Drosophila (fruit flies) and continuing to other species. An intra-species comparison will also provide important insight into the evolution of regulatory elements.

Objective

The genome of an animal encodes a large set of regulatory programs that give rise to the thousands of cell types that make up its tissues and organs. Despite recent progress in single-cell omics, our knowledge about the regulatory programs that control the establishment and maintenance of cell type identity remains limited, and methods are lacking to infer regulatory programs directly from the genome sequence. In this project, which lies at the interface between the genome and single-cell atlases, we ask how the genome sequence translates into cell types. We start with Drosophila as model organism. Its compactness allows sampling of all its cell types and developmental trajectories from egg to adult, using whole-organism single-cell multi-omics, thus capturing the spectrum of activation states that emerge from the regulatory genome. Deep learning models will be trained on regulatory sequences to predict and explain gene regulatory networks (GRN) and GRN transitions between cell states, encoded by enhancers, promoters, transcription factors (TF), effector genes, and feedback loops. Based on a better mechanistic understanding, we will translate this framework to other animals, including octopus, birds, and mammals, and ask how regulatory programs evolve, with a focus on neuronal diversity in the brain. Using new algorithms for cross-species deep learning and combinatorial optimization, we will study how combinations of expressed TFs co-evolve with genomic enhancer logic. We are unique in our approach because we will develop and use new technological assays, deep learning, and massively parallel reporter assays, and combine these with perturbation experiments and synthetic biology to test our hypotheses. After iteratively improving our regulatory models, we ultimately aim to predict which regulatory programs, and thus which cell types, are encoded in an animals genome, and how changes in these programs underlie changes in cell types during evolution.

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Topic(s)

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-ADG

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Host institution

VIB VZW
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 500 000,00
Address
SUZANNE TASSIERSTRAAT 1
9052 ZWIJNAARDE - GENT
Belgium

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Region
Vlaams Gewest Prov. Oost-Vlaanderen Arr. Gent
Activity type
Research Organisations
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 500 000,00

Beneficiaries (1)

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