Transgenerational cell fate profiling

The illicit generation of tetraploid cells constitutes a prominent driver of oncogenesis, as it often precedes the development of aneuploidy and genomic instability. In addition, tetraploid (pre-)malignant cells display an elevated resistance against radio- and chemotherapy. Here, we report a strategy to preferentially kill tetraploid tumor cells based on the broad-spectrum kinase inhibitor SP600125. Live videomicroscopy revealed that SP600125 affects the execution of mitosis, impedes proper cell division and/or activates apoptosis in near-to-tetraploid, though less so in parental, cancer cells. We propose a novel graphical model to quantify the differential response of diploid and tetraploid cells to mitotic perturbators, including SP600125, which we baptized “transgenerational cell fate profiling.” We speculate that this representation constitutes a valid alternative to classical “single-cell fate” and “genealogical” profiling and, hence, may facilitate the analysis of cell fate within a heterogeneous population as well as the visual examination of cell cycle alterations.     

A model of sequential branching in hierarchical cell fate determination

Multipotent stem or progenitor cells undergo a sequential series of binary fate decisions, which ultimately generate the diversity of differentiated cells. Efforts to understand cell fate control have focused on simple gene regulatory circuits that predict the presence of multiple stable states, bifurcations and switch-like transitions. However, existing gene network models do not explain more complex properties of cell fate dynamics such as the hierarchical branching of developmental paths. Here, we construct a generic minimal model of the genetic regulatory network controlling cell fate determination, which exhibits five elementary characteristics of cell differentiation: stability, directionality, branching, exclusivity, and promiscuous expression. We argue that a modular architecture comprising repeated network elements reproduces these features of differentiation by sequentially repressing selected modules and hence restricting the dynamics to lower dimensional subspaces of the high-dimensional state space. We implement our model both with ordinary differential equations (ODEs), to explore the role of bifurcations in producing the one-way character of differentiation, and with stochastic differential equations (SDEs), to demonstrate the effect of noise on the system. We further argue that binary cell fate decisions are prevalent in cell differentiation due to general features of the underlying dynamical system. This minimal model makes testable predictions about the structural basis for directional, discrete and diversifying cell phenotype development and thus can guide the evaluation of real gene regulatory networks that govern differentiation.