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

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, ra ndom ci rcuit pe rturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified 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 new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. 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.

Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing

“…To explore the range of dynamic behaviors the inferred network can exhibit, we used the recently proposed random circuit perturbation approach (38). This approach identifies the steady-state behaviors of a network by simulating the network dynamics for an ensemble of parameters (39,40), and was initially developed to analyze the dynamical behavior of gene regulatory networks with transcription factors as nodes. Here, we directly apply this method to analyze the behavior of a network with different cellular pathways as nodes.…”

A mechanistic modeling framework reveals the key principles underlying tumor metabolism

“…This approach is in contrast to previous mechanistic modeling approaches that focus on fine tuning model parameters so that the model behavior is in agreement with the observations from a specific experimental setup (see, for example, Roy and Finley [ 16 ]), and allows us to develop a framework that can be utilized to analyze the metabolic behavior of tumor cells across cancer types and in different environments. The present framework is motivated by the success of a similar strategy in modeling the phenotypic heterogeneity and plasticity arising from gene regulatory networks [ 19 , 20 ].…”

Cell Fate Decisions Within the Vascular Cambium–Initiating Wood and Bast Formation

“…After division, when cells exit the central domain, they lose their ability to fluctuate to other states, whereas the cell which remains in the central domain can still fluctuate between all the cell states ( Figure 1B ). State fluctuation represents a way of creating cellular heterogeneity within animal embryonic stem cells ( Okamoto et al, 2018 ; Huang et al, 2020 ) and noise in gene activity is often important for promoting cell fate transitions ( Eldar and Elowitz, 2010 ), making the second scenario worth considering in the CSC context.…”

“…In both the aforementioned scenarios, the cells in the central domain of the cambium are pluripotent stem cells. In animals, gene regulatory networks (GRNs) mediating pluripotency in embryonic stem cells are centered around a handful of transcription factors (TFs) and are well understood ( Nichols et al, 1998 ; Li and Izpisua Belmonte, 2018 ; Huang et al, 2020 ). In CSCs, however, it is still unclear which genes make up the “stemness” GRN.…”