Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture

Abstract

Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture Abdelilah Benamar,1 Antoine Pierart,1 Volker Baecker,2 Marie-Hélène Avelange-Macherel,3 Aurélia Rolland,1 Sabine Gaudichon,4 Lodovico di Gioia,4 David Macherel11Université d’Angers, Lunam Université, Angers, 2MRI-Montpellier RIO Imaging, Montpellier, 3Agrocampus Ouest, Angers, 4Danone Research, Palaiseau Cedex, FranceBackground: Phenotyping for plant stress tolerance is an essential component of many research projects. Because screening of high numbers of plants and multiple conditions remains technically challenging and costly, there is a need for simple methods to carry out large-scale phenotyping in the laboratory.Methods: We developed a method for phenotyping the germination and seedling growth of Arabidopsis (Arabidopsis thaliana) Col-0 in liquid culture. Culture was performed under rotary shaking in multiwell plates, using Evian natural mineral water as a medium. Nondestructive and accurate quantification of green pixels by digital image analysis allowed monitoring of growth.Results: The composition of the water prevented excessive root elongation growth that would otherwise lead to clumping of seedlings observed when classic nutrient-rich medium or deionized water is used. There was no need to maintain the cultures under aseptic conditions, and seedlings, which are photosynthetic, remained healthy for several weeks. Several proof-of-concept experiments demonstrated the usefulness of the approach for environmental stress phenotyping.Conclusion: The system described here is easy to set up, cost-effective, and enables a single researcher to screen large numbers of lines under various conditions. The simplicity of the method clearly makes it amenable to high-throughput phenotyping using robotics.Keywords: phenotyping, phenomics, stress, seed, seedling, image analysis