Reconstructed cell fate–regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis

Abstract

Cell lineages, which shape the body architecture and specify cell functions, derive from the integration of a plethora of cell intrinsic and extrinsic signals. These signals trigger a multiplicity of decisions at several levels to modulate the activity of dynamic gene regulatory networks (GRNs), which ensure both general and cell-specific functions within a given lineage, thereby establishing cell fates. Significant knowledge about these events and the involved key drivers comes from homogeneous cell differentiation models. Even a single chemical trigger, such as the morphogen all-trans retinoic acid (RA), can induce the complex network of gene-regulatory decisions that matures a stem/precursor cell to a particular step within a given lineage. Here we have dissected the GRNs involved in the RA-induced neuronal or endodermal cell fate specification by integrating dynamic RXRA binding, chromatin accessibility, epigenetic promoter epigenetic status, and the transcriptional activity inferred from RNA polymerase II mapping and transcription profiling. Our data reveal how RA induces a network of transcription factors (TFs), which direct the temporal organization of cognate GRNs, thereby driving neuronal/endodermal cell fate specification. Modeling signal transduction propagation using the reconstructed GRNs indicated critical TFs for neuronal cell fate specification, which were confirmed by CRISPR/Cas9-mediated genome editing. Overall, this study demonstrates that a systems view of cell fate specification combined with computational signal transduction models provides the necessary insight in cellular plasticity for cell fate engineering. The present integrated approach can be used to monitor the in vitro capacity of (engineered) cells/tissues to establish cell lineages for regenerative medicine.

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

“…Some of these regulators were predicted to be important in both cell types, but exhibited differential targeting patterns between the PCeE and the GCeE networks, indicating rewiring of regulators between the cell types. This highlights apparent redundancy and/or cooperation of regulators which control similar cell type specific functions and shows the potential importance of regulatory rewiring in the evolution of cell type specific pathways and functions, something which has been shown previously to occur (83,84). Functional analysis of the targets of the most rewired regulators (Etv4, let-7e-5p, miR-151-3p, Myb and Rora) highlights an overrepresentation of metabolism associated targets in the PCeE network and cell cycle associated targets in the GCeE network.…”

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

“…This highlights apparent redundancy and/or cooperation of regulators which control similar cell type specific functions and shows the potential importance of regulatory rewiring in the evolution of cell type specific pathways and functions, something which has been shown previously to occurr. 83,84 Functional analysis of the targets of the most rewired regulators (Etv4, let-7e-5p, miR-151-3p, Myb and Rora) highlights an overrepresentation of metabolism associated targets in the PCeE network and cell cycle associated targets in the GCeE network. A similar result was observed when functional analysis was carried out on genes with significantly different expression levels between the cell type enriched enteroids and the conventionally differentiated enteroids (Fig.…”

Systems Biology Perspectives for Studying Neurodevelopmental Events

“…Furthermore, TETRAMER simulates transcription regulation propagation over the reconstructed connectivity to identify master TFs, which could then be prioritized for experimental assays. This strategy has been initially used for identifying novel master TFs implicated on neurogenesis by reconstructing gene regulatory networks from temporal transcriptomes [70]; then, it has been extrapolated to a collection of more than 3000 transcriptomes covering 300 cell/tissue types and representing 14 different anatomical systems in the human body. Among them, 58 cell/tissue types composing the human nervous system were analyzed, for which their relevant master TFs as well as their related gene regulatory networks were inferred.…”