A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies
Abstract
Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labelled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, e.g., amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.
Data availability
All data presented in the figure article and supplementary information are available on a Zenodo repository with the following DOI 10.5281/zenodo.2557396
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Lab513/Landscape of Gene Expression DatasetZenodo, 10.5281/zenodo.2557396.
Article and author information
Author details
Funding
European Commission (724813)
- Pascal Hersen
Agence Nationale de la Recherche (ICEBERG-ANR-10-BINF-06-01)
- Pascal Hersen
Agence Nationale de la Recherche (ANR-16-CE12-0025-01)
- Pascal Hersen
Agence Nationale de la Recherche (ANR-11-LABX-0071)
- Pascal Hersen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Marinkovic 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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Further reading
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- Computational and Systems Biology
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- Computational and Systems Biology
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