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Deciphering enhancer logic of cell fate decisions during human neural tube and neural crest development, using single-cell multi-omics and machine learning.

Precise, robust and evolutionary conserved mechanisms underlie the ontogeny of cellular identities. Enhancers play a major role in this, by regulating the expression of key factors involved in cell fate decisions in a precise temporal (i.e. at a specific point of time) and spatial (i.e. in specific cell types) manner. The properties of enhancers have been vastly documented, still the fundamental molecular mechanisms (i.e. enhancer logic) underlying its functional properties remain unknown to a great extent. The aim of this project is to decipher enhancer logic underlying cell fate decisions and pattern formation in human development. To this end we will make use of two in vitro model systems for human development; the differentiating neural crest and the developing neural tube organoid. In these systems we will profile single-cell chromatin accessibility and gene expression at several time points. Artificial intelligence will be used to identify enhancers and their architecture from this data. The latter will be validated using in vitro massively parallel enhancer reporter assays and perturbation studies. This project will provide important resources and a framework to understand human mutations; particularly single-cell multi-omics data of the developing human neural crest and neural tube and a genome-wide map of the underlying enhancer logic. It will also provide fundamental insight into the molecular functioning and architecture of human enhancers.

Date:8 Jul 2020  →  Today
Keywords:Single-cell muti-omics, Enhancer logic, Neural crest, Neural tube, Organoid, Machine learning, Deep learning
Disciplines:Developmental genetics , Epigenetics, Genome structure and regulation, Analysis of next-generation sequence data, Bioinformatics data integration and network biology, Computational biomodelling and machine learning, Computational transcriptomics and epigenomics, Single-cell data analysis
Project type:PhD project