Literature DB >> 34179267

Measurement of Arabidopsis thaliana Plant Traits Using the PHENOPSIS Phenotyping Platform.

Wojciech Rymaszewski1, Myriam Dauzat2, Alexis Bédiée2, Gaëlle Rolland2, Nathalie Luchaire2, Christine Granier2,3, Jacek Hennig1, Denis Vile2.   

Abstract

High-throughput phenotyping of plant traits is a powerful tool to further our understanding of plant growth and its underlying physiological, molecular, and genetic determinisms. This protocol describes the methodology of a standard phenotyping experiment in PHENOPSIS automated platform, which was engineered in INRA-LEPSE (https://www6.montpellier.inra.fr/lepse) and custom-made by Optimalog company. The seminal method was published by Granier et al. (2006). The platform is used to explore and test various ecophysiological hypotheses (Tisné et al., 2010; Baerenfaller et al., 2012; Vile et al., 2012; Bac-Molenaar et al., 2015; Rymaszewski et al., 2017). Here, the focus concerns the preparation and management of experiments, as well as measurements of growth-related traits (e.g., projected rosette area, total leaf area and growth rate), water status-related traits (e.g., leaf dry matter content and relative water content), and plant architecture-related traits (e.g., stomatal density and index and lamina/petiole ratio). Briefly, a completely randomized (block) design is set up in the growth chamber. Next, the substrate is prepared, its initial water content is measured and pots are filled. Seeds are sown onto the soil surface and germinated prior to the experiment. After germination, soil watering and image (visible, infra-red, fluorescence) acquisition are planned by the user and performed by the automaton. Destructive measurements may be performed during the experiment. Data extraction from images and estimation of growth-related trait values involves semi-automated procedures and statistical processing.
Copyright © 2018 The Authors; exclusive licensee Bio-protocol LLC.

Entities:  

Keywords:  Arabidopsis thaliana; Growth; PHENOPSIS; Phenotyping; Water deficit

Year:  2018        PMID: 34179267      PMCID: PMC8203920          DOI: 10.21769/BioProtoc.2739

Source DB:  PubMed          Journal:  Bio Protoc        ISSN: 2331-8325


  11 in total

1.  Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants.

Authors:  D C Boyes; A M Zayed; R Ascenzi; A J McCaskill; N E Hoffman; K R Davis; J Görlach
Journal:  Plant Cell       Date:  2001-07       Impact factor: 11.277

2.  Keep on growing under drought: genetic and developmental bases of the response of rosette area using a recombinant inbred line population.

Authors:  Sébastien Tisné; Inga Schmalenbach; Matthieu Reymond; Myriam Dauzat; Marjorie Pervent; Denis Vile; Christine Granier
Journal:  Plant Cell Environ       Date:  2010-11       Impact factor: 7.228

3.  Genome-wide association mapping of time-dependent growth responses to moderate drought stress in Arabidopsis.

Authors:  Johanna A Bac-Molenaar; Christine Granier; Joost J B Keurentjes; Dick Vreugdenhil
Journal:  Plant Cell Environ       Date:  2015-11-09       Impact factor: 7.228

Review 4.  Future scenarios for plant phenotyping.

Authors:  Fabio Fiorani; Ulrich Schurr
Journal:  Annu Rev Plant Biol       Date:  2013-02-28       Impact factor: 26.379

5.  Arabidopsis growth under prolonged high temperature and water deficit: independent or interactive effects?

Authors:  Denis Vile; Marjorie Pervent; Michaël Belluau; François Vasseur; Justine Bresson; Bertrand Muller; Christine Granier; Thierry Simonneau
Journal:  Plant Cell Environ       Date:  2011-11-09       Impact factor: 7.228

Review 6.  Phenotyping and beyond: modelling the relationships between traits.

Authors:  Christine Granier; Denis Vile
Journal:  Curr Opin Plant Biol       Date:  2014-03-15       Impact factor: 7.834

7.  Stress-Related Gene Expression Reflects Morphophysiological Responses to Water Deficit.

Authors:  Wojciech Rymaszewski; Denis Vile; Alexis Bediee; Myriam Dauzat; Nathalie Luchaire; Dominika Kamrowska; Christine Granier; Jacek Hennig
Journal:  Plant Physiol       Date:  2017-05-18       Impact factor: 8.340

8.  NIH Image to ImageJ: 25 years of image analysis.

Authors:  Caroline A Schneider; Wayne S Rasband; Kevin W Eliceiri
Journal:  Nat Methods       Date:  2012-07       Impact factor: 28.547

9.  Genome-wide association mapping of growth dynamics detects time-specific and general quantitative trait loci.

Authors:  Johanna A Bac-Molenaar; Dick Vreugdenhil; Christine Granier; Joost J B Keurentjes
Journal:  J Exp Bot       Date:  2015-04-28       Impact factor: 6.992

10.  Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit.

Authors:  Katja Baerenfaller; Catherine Massonnet; Sean Walsh; Sacha Baginsky; Peter Bühlmann; Lars Hennig; Matthias Hirsch-Hoffmann; Katharine A Howell; Sabine Kahlau; Amandine Radziejwoski; Doris Russenberger; Dorothea Rutishauser; Ian Small; Daniel Stekhoven; Ronan Sulpice; Julia Svozil; Nathalie Wuyts; Mark Stitt; Pierre Hilson; Christine Granier; Wilhelm Gruissem
Journal:  Mol Syst Biol       Date:  2012       Impact factor: 11.429

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