1. Plant Biology

Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform

  1. Therese LaRue
  2. Heike Lindner
  3. Ankit Srinivas
  4. Moises Exposito-Alonso
  5. Guillaume Lobet
  6. José R Dinneny  Is a corresponding author
  1. Stanford University, United States
  2. Carnegie Institution for Science, United States
  3. Forschungszentrum Jülich, Germany
https://doi.org/10.7554/eLife.76968
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  1. Therese LaRue
  2. Heike Lindner
  3. Ankit Srinivas
  4. Moises Exposito-Alonso
  5. Guillaume Lobet
  6. José R Dinneny
(2022)
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
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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

The following previously published data sets were used
    1. Fick SE
    2. Hijmans RJ
    (2017) WORLCLIM2
    https://doi.org/10.1002/joc.5086.
    https://worldclim.org

Article and author information

Author details

  1. Therese LaRue

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Heike Lindner

    Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Ankit Srinivas

    Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Moises Exposito-Alonso

    Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5711-0700

  5. Guillaume Lobet

    Agrosphere Institute, Forschungszentrum Jülich, Jülich, Germany
    Competing interests
    The authors declare that no competing interests exist.

  6. José R Dinneny

    Department of Biology, Stanford University, Stanford, United States
    For correspondence
    dinneny@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3998-724X

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.

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  1. Therese LaRue
  2. Heike Lindner
  3. Ankit Srinivas
  4. Moises Exposito-Alonso
  5. Guillaume Lobet
  6. José R Dinneny
(2022)
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

Share this article

https://doi.org/10.7554/eLife.76968

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