Mapping gene regulatory networks in the era of single-cell multi-omics

Cellular differentiation, from pluripotent stem cells to the diverse cell types in our body, is guided by complex gene regulatory networks (GRN). Unravelling these GRNs is key to predicting the cell’s response to a stimulus and to manipulate its fate, for example, to treat disease. Recent technological advances have allowed us to profile the combined transcriptome and epigenome of individual cells. Since the transcriptome provides the output of the network (e.g. the genes that are being expressed), and chromatin accessibility indicates the regions that regulate the process, we propose to combine both types of measures to reverse engineer GRNs in single cells. Building on the computational approaches developed in the van Oudenaarden lab (ProximID, RaceID) and the Aerts lab (SCENIC, cisTopic, ScoMap), the student will develop a machine-learning framework to predict - and explain - high-confidence GRNs. From this foundational layer, the student will explore in the following aims how this framework can be adapted to study GRN dynamics based on trajectory inference and single-cell nascent RNA-seq; and to study GRN robustness through modeling stochasticity of gene expression and of chromatin accessibility. Between both our labs, a variety of biological model systems are available, including C. elegans, Drosophila, octopus, zebrafish, mouse, and human. Depending on the interest and expertise of the student, this computational project can be complemented with a technology angle, creating new single-cell multi-ome and spatial sequencing data sets, and a biology angle, validating GRN predictions in model organisms and tissue culture using perturbation assays. Combining advanced computational analyses with experimental validation, this project aims to decipher dynamic gene regulatory networks of cellular differentiation at single-cell resolution.

Keywords:single-cell multi-omics, gene regulatory networks, gene expression noise, genomic enhancers, computational biology, bioinformatics

Disciplines:Bioinformatics data integration and network biology, Single-cell data analysis, Data mining, Computational transcriptomics and epigenomics, Genome structure and regulation, Epigenomics