A single-cell resolved cell-cell communication model explains lineage commitment in hematopoiesis

Abstract

Cells do not make fate decisions independently. Arguably, every cell-fate decision occurs in response to environmental signals. In many cases, cell-cell communication alters the dynamics of the internal gene regulatory network of a cell to initiate cell-fate transitions, yet models rarely take this into account. Here, we have developed a multiscale perspective to study the granulocyte-monocyte versus megakaryocyte-erythrocyte fate decisions. This transition is dictated by the GATA1-PU.1 network: a classical example of a bistable cell-fate system. We show that, for a wide range of cell communication topologies, even subtle changes in signaling can have pronounced effects on cell-fate decisions. We go on to show how cell-cell coupling through signaling can spontaneously break the symmetry of a homogenous cell population. Noise, both intrinsic and extrinsic, shapes the decision landscape profoundly, and affects the transcriptional dynamics underlying this important hematopoietic cell-fate decision-making system. This article has an associated ‘The people behind the papers’ interview.

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era

“…To account for the heterogeneity in direct cell reprogramming populations as revealed by single cell data, Rommelfanger and colleagues [ 26 ] generalize the ODE framework to create a model at the single cell level. They achieve this by incorporating additional terms into the ODEs which represent how cells send a ‘be like me’ signal to other cells which helps to coordinate cell fate decisions during differentiation ( Figure 3 ).…”

Profiling intermediate cell states in high resolution

“…Cell states are defined and maintained by a tug-and-pull of internal and external cell signals. The effects of external signaling on cell fate determination are oft-neglected, yet can exert key control over the processes of fate determination ( Rommelfanger and MacLean, 2021 ). Currently, Capybara does not incorporate the effects of cell-cell interactions into its methods.…”

The art of modeling gene regulatory circuits

“… 3 ). These studies have helped us to understand the fundamentals of life (e.g., cell cycle regulation 4 , 5 ; cell differentiation 6 ; cellular interactions with the environment and other cells 7 , 8 ) to develop important applications in biotechnology (e.g., enhancing a plant’s response to parasites 9 , 10 ; improving the yield of microbial factories 11 ; or even engineering bacterial structures capable of sensing pressure 12 ), and to facilitate advances in medicine (e.g., unveiling mechanisms relevant for disease progression 13 ).…”