Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity

Authors

  • Sébastien Tisné,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Yann Serrand,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Liên Bach,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Elodie Gilbault,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Rachid Ben Ameur,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Hervé Balasse,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Roger Voisin,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • David Bouchez,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Mylène Durand-Tardif,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Philippe Guerche,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Gaël Chareyron,

    1. ESILV – Ecole Supérieure d'Ingénieurs Léonard de Vinci, Département d'Enseignement et de Recherche Informatique et Intelligence de l'Information, Pôle Universitaire Léonard de Vinci, Paris, France
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  • Jérôme Da Rugna,

    1. ESILV – Ecole Supérieure d'Ingénieurs Léonard de Vinci, Département d'Enseignement et de Recherche Informatique et Intelligence de l'Information, Pôle Universitaire Léonard de Vinci, Paris, France
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  • Christine Camilleri,

    1. INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
    2. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
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  • Olivier Loudet

    Corresponding author
    1. AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
    • INRA – Institut National de la Recherche Agronomique, UMR 1318, Institut Jean-Pierre Bourgin, Versailles, France
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For correspondence (e-mail Olivier.Loudet@versailles.inra.fr).

Summary

Increased phenotyping accuracy and throughput are necessary to improve our understanding of quantitative variation and to be able to deconstruct complex traits such as those involved in growth responses to the environment. Still, only a few facilities are known to handle individual plants of small stature for non-destructive, real-time phenotype acquisition from plants grown in precisely adjusted and variable experimental conditions. Here, we describe Phenoscope, a high-throughput phenotyping platform that has the unique feature of continuously rotating 735 individual pots over a table. It automatically adjusts watering and is equipped with a zenithal imaging system to monitor rosette size and expansion rate during the vegetative stage, with automatic image analysis allowing manual correction. When applied to Arabidopsis thaliana, we show that rotating the pots strongly reduced micro-environmental disparity: heterogeneity in evaporation was cut by a factor of 2.5 and the number of replicates needed to detect a specific mild genotypic effect was reduced by a factor of 3. In addition, by controlling a large proportion of the micro-environmental variance, other tangible sources of variance become noticeable. Overall, Phenoscope makes it possible to perform large-scale experiments that would not be possible or reproducible by hand. When applied to a typical quantitative trait loci (QTL) mapping experiment, we show that mapping power is more limited by genetic complexity than phenotyping accuracy. This will help to draw a more general picture as to how genetic diversity shapes phenotypic variation.

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