Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by random circuit...

Area of Science:

• Computational biology
• Developmental biology
• Systems biology

Background:

• Stem cells exhibit robust cellular differentiation and lineage commitment, known as stemness.
• The precise mechanisms by which gene regulatory networks (GRNs) specify cell fates in stemness are not fully understood.

Purpose of the Study:

• To investigate the gene regulatory network (GRN) underlying stemness and identify robust gene states.
• To understand how network topology influences cell fate specification and differentiation.

Main Methods:

• Application of a computational method, random circuit perturbation (RACIPE), to a nine-component stemness GRN.
• Analysis of gene states and their robustness to network perturbations.

Main Results:

• Identification of five robust gene states, with four matching early mouse embryonic cell gene expression patterns.
• Discovery of a hierarchical GRN structure with Oct4/Cdx2 as the primary decision module, followed by Gata6/Nanog.
• Demonstration that network topology, not kinetic parameters, primarily determines stemness GRN function.

Conclusions:

• Stem cell populations are heterogeneous mixtures of cells in various states, not a single state.
• External signals perturbing stem cells lead to differentiation by limiting access to certain states.
• RACIPE-identified gene states and transition parameters can guide experiments in differentiation and reprogramming.