Understanding size-robust self-organization of morphogen gradients

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Our textbook understanding of how morphogen gradients form has been challenged by recent research showing that organoids self-organize morphogen gradients despite the absence of external signalling centres thought to be essential for patterning. A striking example is the ability of aggregated embryonic cells to initiate gastrulation in vitro, with the primary body axes forming without maternal cues. In this proposal we combine complementary expertise in mathematical modelling and quantitative experimental approaches to develop a predictive model of this fundamental phenomenon.

We will take two complementary approaches. First, we will study one system in detail, investigating how a Nodal gradient self-organises in aggregates of embryonic zebrafish cells (pescoids), occurring reliably across a range of pescoid sizes. We will model the spatiotemporal dynamics of Nodal ligands, inhibitors and receptors, together with known feedback interactions, iteratively refining the model with quantitative measurements of gene expression and cell movement. We will characterize the robustness of patterning to pescoid size and will test our central hypothesis that cell movement is required for size-robust symmetry breaking.

In parallel, we will build mathematical models of organoids known to break symmetry with distinct signalling pathways and in different morphogenetic contexts. By comparing these models to the size-robust patterning in pescoids, we will investigate why patterning in other organoid systems is often highly sensitive to tissue size and will identify the key parameters that confer size-robustness. A quantitative understanding will enable us to rationally design bioengineering strategies that promote symmetry breaking across a range of organoid sizes. This will inform efforts to improve the reproducibility of organoid culture systems, which often suffer from high error rates compromising their utility as a model for human disease and organ replacement therapies.