Abstract
Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual web-tool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.
Highlights
Cell differentiation, the building block of development, is a strong representation of regulatory precision
On the other hand, examining the Gene Ontology (GO) enrichment based on the Differential gene expression (DEG) analysis publicly available for the same dataset, differentially expressed genes in day 0 vs. day 1 and day 0 vs. day 11 showed no significant enrichment of specific terms associated with neural development but rather more general terms
We developed a workflow to analyze and represent regulatory cascades and a web tool based on the corresponding model
Summary
The building block of development, is a strong representation of regulatory precision. The discovery of induced pluripotent stem cells (iPSCs) [6,7,8], opened the door to a rising number of cell differentiation experiments. Owing to the decreasing prices of RNA-seq, these experiments generated a big and growing number of time series datasets that aim to track a certain process of differentiation by taking snapshots of the gene expression at different time points. These datasets could be further analyzed to obtain a better extensive explanatory model of the regulatory processes and to identify new important regulators that can be manipulated to enhance the process. Deriving as much information as possible from such experiments is a crucial goal in the fields of medical and biological research [9,10,11,12,13], yet there is still a need for computational methods that analyze such unique models in a way tailored to their special properties
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