A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture
Abstract
The cerebral cortex contains multiple areas with distinctive cytoarchitectonical patterns, but the cellular mechanisms underlying the emergence of this diversity remain unclear. Here, we have investigated the neuronal output of individual progenitor cells in the developing mouse neocortex using a combination of methods that together circumvent the biases and limitations of individual approaches. Our experimental results indicate that progenitor cells generate pyramidal cell lineages with a wide range of sizes and laminar configurations. Mathematical modelling indicates that these outcomes are compatible with a stochastic model of cortical neurogenesis in which progenitor cells undergo a series of probabilistic decisions that lead to the specification of very heterogeneous progenies. Our findings support a mechanism for cortical neurogenesis whose flexibility would make it capable to generate the diverse cytoarchitectures that characterize distinct neocortical areas.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
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Funding
H2020 European Research Council (ERC-2017-AdG 787355)
- Oscar Marin
H2020 European Research Council (ERC-2016-CoG 725780)
- Simon Hippenmeyer
European Molecular Biology Organization
- Fong Kuan Wong
H2020 Marie Skłodowska-Curie Actions
- Fong Kuan Wong
Austrian Science Fund (Lise-Meitner program M 2416)
- Robert Beattie
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures were approved by King's College London and IST Austria, and were performed under UK Home Office project licenses, and in accordance with Austrian Federal Ministry of Science and Research license, and European regulations (EU directive 86/609, EU decree 2001- 486).
Copyright
© 2019, Llorca et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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