Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by Random Circuit Perturbation

TL;DR

This study uses Random Circuit Perturbation to analyze a gene regulatory network governing stemness, revealing robust gene states, hierarchical decision modules, and the importance of network topology in cell fate determination.

Contribution

The paper introduces the application of RACIPE to a stemness gene network, identifying key gene states and hierarchical modules that explain cell fate decisions.

Findings

Identified fifteen robust gene states in stemness network.

Discovered hierarchical structure with Oct4/Cdx2 as initial decision module.

Network topology, not kinetic parameters, primarily determines stemness functions.

Abstract

Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, Random Circuit Perturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified fifteen robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.