The power of cell fate modeling
Model of signal transduction propagation and gene regulatory networks involved in cell differentiation after retinoic acid treatment. The starting node where the initial cue activates the signal transduction is depicted, as well as the downstream node interconnections required for its propagation. The temporal transcriptional state for each gene (node) is defined as 1, 0 or -1 (up-regulated, non-responsive or down-regulated respectively).
Sept. 20, 2016
By using the integration of multiple functional genomic read-outs, Hinrich Gronemeyer’s team has modeled stem cell fate through the reconstruction of gene regulatory networks involved in this process.
In this work published on September 20th in Genome Research, the authors achieved to modify cell fate by activating some key genes identified through computational modeling. Such integrative approach will surely generate valuable predictions for regenerative medecine.
Revealing the fate, defining the destiny…
During development, totipotent stem cells will achieve differentiation through multiple and complex steps. Each step during differentiation is the result of cell fate decisions for which we have only little knowledge. The team of Hinrich Gronemeyer utilized as a model system P19 and F9 stem cells that are known to acquire respectively neuronal and endodermal cell fate upon the activation of a common molecule: retinoid acid.
Temporal analysis of cell fate acquisition
After retinoid acid induction, the scientists compared transcriptomic, epigenomic, and chromatin accessibility datasets at different time points in the two stem cell lines. By listing the several thousands of genes differentially expressed during cell differentiation, and by integrating genomic data, they could deduce temporal and hierarchical relations between regulatory genes. They successfully reconstituted gene regulatory networks involved in retinoid acid dependent cell fate acquisition.
In silico identification of key regulators
The researchers analysed regulatory signaling pathways reconstituted within gene regulatory networks by using computational modeling. They identified key regulatory stem cell specific genes (repressed after cell fate induction), and genes specific to each cell identity (neuronal and endodermal).
New tools for cell fate reprogramming
To validate their in silico model, the scientists induced key identified target genes predicted to provoke P19 neuronal cell differentiation. They successfully obtained neurons without the addition of retinoid acid. They also managed to produce neurons by activating the same target genes in F9 cells that are known to differentiate into endodermal cells upon retinoid acid treatment. They could then modify cell fate through genetic reprogramming, revealing new opportunities for regenerative medecine. Furthermore those results show that computational modeling methods have a strong predictive power to identify key regulators of the studied biological processes.
Not only this work shows promising perspectives in regenerative medecine, but it also reveals the power of this type of integrative studies, and computational modeling, that could be applied to any other biological process such as tumorigenesis and cell aging.
AVIESAN-ITMO Cancer, the Ligue Nationale Contre le Cancer, the Institut National du Cancer (INCA), and the Agence Nationale de la Recherche (ANRT-07-PCVI-0031-01, ANR-10-LABX-0030-INRT, and ANR-10-IDEX-0002-02).