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Feedback control of neurogenesis by tissue packing

Tom W. Hiscock, Joel B. Miesfeld, Kishore R. Mosaliganti, Brian A. Link, Sean G. Megason

Preprint posted on January 23, 2018 https://www.biorxiv.org/content/early/2018/01/23/252445

Article now published in Development at http://dx.doi.org/10.1242/dev.157040

Using the force: mechanical regulation of tissue growth and architecture during neural tube development.

Selected by Sarah Morson

Background:

The journey from a single fertilised cell to a complete organism is an incredibly complex and tightly regulated process. It critically requires a precise balance between cellular proliferation, which produces appropriate size, and differentiation, which generates the required complexity. Understanding the many mechanisms controlling growth and differentiation and how they interact remains a fundamental question in developmental biology. While extensive research has shed light on molecular mechanisms regulating growth, researchers are now considering what role physical forces may play in governing proliferation and differentiation. Previous research has revealed a role for mechanical force in controlling the proliferative balance, but this work tends to be restricted to 2-D monolayers that are not representative of a growing organism.

This preprint from Tom Hiscock and colleagues utilises advances in live-imaging and the genetically malleable zebrafish embryo to investigate the effects of physical forces on the proliferation/differentiation balance during neural tube growth.

 

Key Findings:

In this study the authors demonstrate a role for cell geometry and tissue packing in controlling tissue growth, propose a negative feedback model describing this control, and postulate how molecular interactions allow cells to determine force and shape.

Using in toto timelapse imaging of single cells in the developing zebrafish neural tube, they present a model in which a high density of apically-dividing progenitor cells displaces surrounding cells, forcing them away from the apical surface and inducing their differentiation.

From this they describe a feedback mechanism whereby the physical constraints of the organism allow tissue packing to increase the differentiation rate, thus inhibiting overall cell number. Mathematical models reveal a possible purpose of this feedback: to reduce variability in tissue growth, ensuring similar growth within different tissue regions and between embryos.

 

Why I chose this preprint:

As a developmental biologist I found this study fascinating: to me the idea how, despite so many environmental variables, organisms of the same species (and all the organs within them) end up the appropriate size and complexity is a compelling puzzle. We know that there is rarely one factor at work in controlling such complex processes, and most of the previous research has focused on the signalling and genetic factors. This paper adds a whole new layer of complexity to be investigated, and much work will be required to uncover how this fits into the puzzle of development.

 

Open Questions:

  1. How do the mechanical forces interact with signalling? While the authors briefly considered the idea of Notch signalling playing a role, this opens a new avenue of research about how mechanical forces and intercellular signalling interact together to control growth.
  2. How are these mechanical forces disrupted in developmental disorders that impact tissue growth? Particularly with neurodevelopmental disorders e.g. Zika Virus induced microcephaly, there is disruption to tissue size. Does disruption to the feedback control contribute to this?

 

Tags: development, growth, live-imaging, mechanics, model, neural tube, neurogenesis, zebrafish

Posted on: 5th March 2018 , updated on: 8th March 2018

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Author's response

Sean Megason shared

Thanks for the highlight! Yes there is lot to be worked in terms of how cell shape and/or mechanical changes are molecularly connected to changes in differentiation rate. But in general, I think it can be useful to try to identify cell/tissue level mechanisms and design principles and then try to connect this with what is known at the molecular level although the full integration can be challenging.

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