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

Abstract

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. 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 modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H 2 < 0.93), as well as estimated fruit number ( H 2 = 0.68). In addition, we validated the method for estimating fruit number with rev5 , a mutant with increased flower abortion. The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling 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 populations of a model species.

Plant phenotyping relevance

画像から植物乾物重・果実数・成長動態を推定する手法を開発し、交差検証と変異体で検証しており、表現型取得・推定法が研究の中心である。

Code and data availability

The paper's availability statement explicitly links authors' code on GitHub and the phenotypic dataset on Dryad; supplementary files also contain the ImageJ macros and R code used for the phenotyping analysis.