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Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

François Vasseur, George Wang, Justine Bresson, Rebecca Schwab, Detlef Weigel
doi: https://doi.org/10.1101/208512
François Vasseur
1Max Planck Institute for Developmental Biology, D-72076 Tübingen, Germany
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George Wang
1Max Planck Institute for Developmental Biology, D-72076 Tübingen, Germany
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Justine Bresson
2Center for Plant Molecular Biology (ZMBP), General Genetics, University of Tübingen, D-72076 Tübingen, Germany.
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Rebecca Schwab
1Max Planck Institute for Developmental Biology, D-72076 Tübingen, Germany
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Detlef Weigel
1Max Planck Institute for Developmental Biology, D-72076 Tübingen, Germany
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Abstract

Background The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.

Results We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modelling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.

Conclusions The method we propose here is based on the automated computerization of plant images with ImageJ, and subsequent statistical modelling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large plant populations.

  • List of abbreviations

    t0
    first day of growth after vernalization
    tinf
    inflection point (days) of the logistic growth curve
    A
    upper asymptote of the logistic growth curve (mg)
    B
    inverse of the exponential constant of the logistic growth curve
    DAG
    days after t0
    M
    rosette dry mass (mg)
    GR
    absolute growth rate (mg d-1)
    RGR
    relative growth rate (mg d-1 g-1)
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted February 21, 2018.
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    Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana
    François Vasseur, George Wang, Justine Bresson, Rebecca Schwab, Detlef Weigel
    bioRxiv 208512; doi: https://doi.org/10.1101/208512
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    Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana
    François Vasseur, George Wang, Justine Bresson, Rebecca Schwab, Detlef Weigel
    bioRxiv 208512; doi: https://doi.org/10.1101/208512

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