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Coordination Of Patterning And Growth In The Spinal Cord

Periodic Reporting for period 4 - GROWTHPATTERN (Coordination Of Patterning And Growth In The Spinal Cord)

Reporting period: 2021-01-01 to 2021-06-30

Individuals of the same species can differ widely in size, but the structure and function of their organs is highly reproducible. To understand how this reproducibility is achieved, we are interested in the basic mechanisms that control the growth and gene expression pattern in the organ, which later defines its structure. During embryonic development, molecules, called morphogens, control both growth and pattern. Morphogens are secreted by cells in specialized locations and form gradients of concentration across the organ.

Our goal is to determine the relationship between morphogen signaling gradients, the rate of tissue growth and the gene expression pattern. To approach this, we develop experiments that allow precise manipulation and measurements of morphogen activity and cell divisions. In addition, we study how growth itself may affect morphogen activity and pattern. We use the mouse and chick spinal cord as a model system, but the principles are likely to apply to many organs and in vitro engineered tissues. The basic understanding of how morphogens work to control growth and pattern will help understand disease states such as cancer and embryo malformations.
We obtained the following key results:
1) We uncovered a novel mechanism by which cells integrate information from the opposing BMP and Shh gradients in the developing spinal cord. This mechanism allows for the distinct neural progenitors types, which give rise to different neurons later in development, to be organized in a precise spatial pattern. The strategy that cells use to interpret the morphogen gradients relies on a morphogen-driven transcriptional network. This mechanism explains how pattern is accurately established early in development and maintained at late developmental stages, when the morphogen signaling levels decrease. These results lead to a publication in the journal Science in 2017.
2) We used a novel lineage tracing approach to accurately measure the growth rate and growth anisotropy within the neuroepithelium at different developmental stages. We also developed an in silico (vertex) model of the neuroepithelium which allowed us to uncover a relationship between the growth rate and growth anisotropy of the tissue. These results were published in Development in 2019. The model will allow us to systematically investigate the relationships between tissue growth, morphogen signaling and mechanical forces and how these factors lead to the robustness and reproducibility of pattern formation during development.
3) We have established novel approaches with which to accurately measure and perturb the growth of the notochord and floor plate, which are the source of production of the morphogen Shh. The notochord is a separate rod-like organ located underneath the neural tube. Our measurements will allow us to understand how exactly the notochord influences the formation of the Shh gradient and the neural progenitor pattern in the neural tube. These results will provide new insights into how the development of adjacent tissues is coordinated.
Our research work has resulted in the generation of quantitative high resolution data that allows us to study tissue growth and pattern formation in the context of rigorous theoretical frameworks. This has so far been difficult to achieve, in particular in the context of growing vertebrate organs.
Our quantitative approach combining in vivo genetics, ex vivo quantitative assays using chick explants and mathematical modeling have provided us with an unique opportunity to study the interpretation of opposing morphogen gradients with unprecedented spatio-temporal resolution. This represents a major advance compared to the state of knowledge at the beginning of the reporting period.
We also employed a state-of the art lineage tracing approach that has not been previously used in this tissue to study the epithelial dynamics and growth parameters of the developing neural tube. Based on this data, we established a data-driven computational cell based model of the neural tube epithelium, which will be instrumental in understanding the feedbacks between tissue growth and morphogen signaling.