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.