A customizable method to characterize Arabidopsis thaliana transpiration under drought conditions

Background Characterization of the dynamic response of plant transpiration to decreasing soil water content in a reproducible way is required for the correct phenotyping of traits related to water saving strategies. Nowadays, an increasing number of automated high throughput platforms are available, but their development requires a great economic investment and it is not always desirable/feasible to outsource these analyses. We propose a medium-throughput protocol to characterize transpiration responses to decreasing soil moisture in a quantitative and highly reproducible way with a minimum investment of resources. Results The quantitative characterization of plant responses to a decreasing soil water content using our phenotyping platform has showed high reproducibility between different experiments. The proposed irrigation strategy allowed us to harvest plants ranging from a well-watered condition to the loss-of-turgor point in a predictable and controlled way. Coupling this protocol with hormone profiling allows investigation of hormonal responses (metabolite accumulation as well as plant sensitivity) to water stress. As a proof-of-concept, we have characterized the dynamic responses of leaf transpiration to decreasing soil water contents in an abscisic acid (ABA) deficient genotype (aba1-1) as well as in genotypes with altered sensitivity to ABA (abi1-1 and hab1-1abi1-1), which are insensitive and hypersensitive to ABA, respectively. Conclusions This protocol allows for assessment of quantitative differences in rosette transpiration responses to water depletion in both ABA biosynthesis mutants and genotypes with altered sensitivity to the hormone. Data indicate a correlation between ABA levels and/or hormone perception and growth rate and/or water content. The protocol guarantees the correct application of water stress to adult plants, which is essential to understand responses of mutants and/or natural accessions.