Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform
- Stanford University, United States
- Carnegie Institution for Science, United States
- Forschungszentrum Jülich, Germany
Abstract
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Data availability
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925Image analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708422Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009General code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-serverRhizotron laser cutting files are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.6694558)Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/, https://doi.org/10.1002/joc.5086
Article and author information
Author details
Moises Exposito-Alonso
Funding
U.S. Department of Energy (DE-SC0008769)
- José R Dinneny
U.S. Department of Energy (DE-SC0018277)
- José R Dinneny
National Institutes of Health (T32GM007276)
- Therese LaRue
Deutsche Forschungsgemeinschaft (LI 2776/1-1)
- Heike Lindner
National Institutes of Health (1DP5OD029506-01)
- Moises Exposito-Alonso
U.S. Department of Energy (DE-SC0021286)
- Moises Exposito-Alonso
Deutsche Forschungsgemeinschaft (EXC-2070 - 390732324)
- Guillaume Lobet
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, LaRue 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.
Metrics
-
- 2,668
- views
-
- 504
- downloads
-
- 19
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Citations by DOI
-
- 19
- citations for umbrella DOI https://doi.org/10.7554/eLife.76968
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform
eLife 11:e76968.
https://doi.org/10.7554/eLife.76968
