Cell Rearrangement Generates Pattern Emergence as a Function of Temporal Morphogen Exposure

Timothy Fulton, Kay Spiess, Lewis Thomson, Yuxuan Wang, Bethan Clark, Seongwon Hwang, Brooks Paige, Berta Verd, Benjamin Steventon

Preprint posted on 7 April 2022

What does it take to study patterning in a tissue as it grows? “Live modeling” combines cell tracking, quantitative imaging and mathematical modeling to give insight into how cell movements tune morphogen exposure.

Selected by Andrew Montequin

Categories: developmental biology


As a single fertilized egg cell develops into an adult organism, chemical morphogen signals and mechanical rearrangements of cells must be precisely coordinated to ensure a properly formed body plan. The tailbud of a zebrafish embryo provides an excellent system to study how these two components are coordinated to pattern a developing organism. In zebrafish, as in all vertebrates, the embryo elongates during the process of somitogenesis when the body plan is segmented along the Anterior-Posterior axis (Henrique et al., 2015). To support this growth and concurrent fate specification, progenitor cells in the mesoderm must balance self-renewal with differentiation.

Previous research into this balance of self-renewal and differentiation in the zebrafish tailbud has revealed that graded signals of Wnt and FGF relate to a switch in T-box gene expression, from tbx16 in the progenitor state found in the posterior to tbx6 in the differentiated presomitic mesoderm (PSM) state found in the anterior (Nikaido et al., 2002; Warga et al., 2013). Further, progenitor cells in the posterior are known to undergo extensive cell rearrangements and mixing, while cells in the posterior exist in a more “solid” like state with fewer rearrangements. In this preprint, Fulton et. al investigate if these rearrangements play a role in the differentiation of these progenitor cells. By combining live cell tracking with genetic regulatory network inference and mathematical modeling, their findings support the hypothesis that cell rearrangements tune morphogenetic signals in the developing tailbud to coordinate progenitor differentiation.

Main Findings

The intrinsic ability of PSM progenitor cells to differentiate is temporally regulated in vivo

The authors first isolated individual PSM progenitor cells from the posterior tailbud and examined the expression of transcription factors that mark the progenitor (tbx16) and differentiated (tbx6) states. The proportion of cells expressing tbx6 increased between three to six hours following dissociation of the cells at the expense of tbx16 positive cells, indicating that a subset of progenitor cells differentiate in the absence of external signals. When the authors followed up with live imaging of a Tbx6::GFP reporter in dissociated cells, they found that expression of the differentiation marker is remarkably synchronized across cells.

The authors did not observe the same synchronized differentiation when examining the process in the context of the developing tailbud. By labeling a clone of cells in the posterior progenitor region, the authors observed cells exiting this region and entering new somites over a range of several hours. Despite the movements of individual cells, a stable pattern of T-box gene expression was observed across the PSM, with tbx16 expressed in the posterior and tbx6 expressed in the anterior. Based on these observations, the authors proposed a model where cells mix in the tailbud and switch from tbx16 to tbx6 expression as they exit the posterior progenitor domain.

Cell rearrangements of PSM progenitor cells in vivo provide a possible mechanism for temporally regulating differentiation signals

After observing that the differentiation dynamics of progenitor cells is linked to their movement away from the posterior progenitor domain, the authors asked how these movements may relate to changes in external signals that serve as inputs to the T-box gene regulatory network (GRN). The authors inferred a minimal GRN using Approximate Gene Expression Trajectories (AGETs) and developed a mathematical model to predict the gene expression dynamics of the inferred GRN. By combining cell tracking data obtained through live imaging of the PSM with relative levels of Wnt and FGF signals across the PSM, the authors were able to model the dynamics of tbx6 and tbx16 expression within individual cells as a function of their position within the PSM.

This “live modeling” approach to predicting gene expression dynamics in the PSM gave insights into multiple experimental observations, including heterogeneity of tbx6 expression in the posterior progenitor domain and the tuning of tbx6 expression to correspond with exiting the tailbud. Cells that remain in the progenitor region experience high Wnt and low FGF signals, while cells that exit the progenitor region will downregulate Wnt and upregulate FGF, leading to increased expression of tbx6.

Because of the temporal delays in signal responses that are inherent to GRNs, some cells that exit the progenitor region and begin expressing tbx6 can be displaced posteriorly into the tbx16 domain but still show tbx6 expression, providing insight into the observed heterogeneity. Taken together, these results suggest a mechanism where cell movements modulate the level and duration of external Wnt and FGF signals experienced by individual cells to regulate their differentiation.

Why I chose this preprint

Embryonic development has long been understood as a combination of chemical and mechanical components, yet the interplay between the two has proven enormously difficult to study. Instead of choosing a system where the two components can be easily separated, the authors of this study leveraged multiple state-of-the-art techniques to understand how mechanical movements of cells can be used as a feature to tune the chemical signals those cells receive. I believe this preprint is a fantastic example of one type of question that can be asked when mathematicians, biologists, computer scientists and others collaborate.


  • How stable are the morphogen patterns over the time period that you look at? If you were to use a reporter for Wnt and FGF in live embryos, would you expect to see qualitative changes over time in the curves plotted in Figure 3c?
  • Does the mathematical model for the T-box gene regulatory network qualitatively recapitulate experimental results from perturbations to the Wnt and/or FGF signals?


Henrique, D., Abranches, E., Verrier, L., & Storey, K. G. (2015). Neuromesodermal progenitors and the making of the spinal cord. Development, 142(17), 2864–2875.

Nikaido, M., Kawakami, A., Sawada, A., Furutani-Seiki, M., Takeda, H., & Araki, K. (2002). Tbx24, encoding a T-box protein, is mutated in the zebrafish somite-segmentation mutant fused somites. Nature Genetics, 31(2), 195–199.

Warga, R. M., Mueller, R. L., Ho, R. K., & Kane, D. A. (2013). Zebrafish Tbx16 regulates intermediate mesoderm cell fate by attenuating Fgf activity. Developmental Biology, 383(1), 75–89.

Tags: hcr, live-imaging, modeling, pattern formation, zebrafish

Posted on: 9 May 2022


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Author's response

Timothy Fulton shared

  1. The expression of the morphogens appear very stable over the period which we have looked at (14 somite stage to 28 somite stage). We have taken measurements of the activity of FGF and Wnt signalling and plotted these values along a normalised PSM length (as in preprint Fig3C) and when we do this, we see that the curves overlap very closely with one another. This overlap is only present when we plot the activity along a normalised PSM length however, as the entire tissue is actually reducing in anterior-posterior length (Morelli et al., 2014; Simsek., et al 2017). When we consider the profile of signalling in real terms, the pattern is scaling to the total A-P length of the tissue.
  2. This is an interesting question and something we’re hoping we can examine in much more detail later on. Currently we have only examined the wild type condition in order to develop our modelling approach. We know from the literature that both FGF and Wnt signalling are involved in both the regulation of cell fate decisions and also the regulation of morphogenesis and cell movements, so in order to simulate how experimental perturbations to signalling will impact the cell fate decisions, we also need to also examine how cell movements change following signal modification. We can now get this information using in toto imaging of an embryo during an experiment to inhibit a signalling pathway (as we have in our preprint, using a WT embryo) and then use this cell tracking data to simulate our GRN on. We would also need to quantitatively describe the impact of signal inhibition on the other signalling inputs to our GRN, so we can input them into our model. We describe our ideas on how to approach this sort of experiment further in a recently published review and also consider how we might use our approach to consider the differences in morphogenesis between species may regulate pattern formation:



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