Abstract
With the development of next-generation sequencing technologies, high-throughput phenotyping has become the new bottleneck of quantitative genetics analyses. The model species Arabidopsis thaliana offers extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits, such as growth dynamics and reproductive allocation. However, reproducible and cost-effective methods need to be developed for the measurement of growth and especially fitness related traits in large populations. Here we describe image-based methods that can be adapted to a wide range of laboratory conditions, and enable the reliable estimation of biomass accumulation and fruit production in thousands of A. thaliana individuals. We propose a semi-invasive approach, where part of a population is used to predict plant biomass from image analysis. The other part of the population is daily imaged during three weeks, then harvested at the end of the life cycle where rosette and inflorescence are separately imaged. We developed ImageJ macros and R codes for image segmentation, 2D skeletonization and subsequent statistical analysis. First, ontogenetic growth is modelled from estimated and measured dry mass for all individuals with non-linear regressions, from which the dynamics of absolute growth rate (GR) and relative growth rate (RGR) are calculated. Second, analysis of the 2D inflorescence skeleton allows the estimation of fruit production, an important component of plant fitness. Our method was evaluated across 451 natural accessions of A. thaliana. Cross-validation revealed that our image-based method allows predicting approximately 90% of biomass variation and 70% of fruit production. Furthermore, estimated traits - like measured traits - showed high heritabilities and inter-experiment reproducibility. We propose a flexible toolkit for the measurement of growth and fitness related traits in large plant populations. It is based on simple imaging, making the method reproducible at low cost in different facilities. However, as manual imaging of large plant populations can quickly become a limiting factor, we also describe an automated high-throughput imaging coupled with micro-computers that enables large phenotypic screening for genome-wide association studies and stress experiments.
