Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications.

HighlightAutomated imaging-based plant phenotyping combined with GC-MS-based metabolite profiling of four lentil accessions differing in their drought and salt tolerance shows common and specific responses and yields characteristic stress markers

Crop plants are regularly challenged by a range of environmental stresses which typically retard their growth and ultimately compromise economic yield. The stress response involves the reprogramming of approximately 4% of the transcriptome. Here, the behavior of AtRD22 and AtUSPL1, both members of the Arabidopsis thaliana BURP (BNM2, USP, RD22 and polygalacturonase isozyme) domain-containing gene family, has been characterized. Both genes are up-regulated as part of the abscisic acid (ABA) mediated moisture stress response. While AtRD22 transcript was largely restricted to the leaf, that of AtUSPL1 was more prevalent in the root. As the loss of function of either gene increased the plant's moisture stress tolerance, the implication was that their products act to suppress the drought stress response. In addition to the known involvement of AtUSPL1 in seed development, a further role in stress tolerance was demonstrated. Based on transcriptomic data and phenotype we concluded that the enhanced moisture stress tolerance of the two loss-of-function mutants is a consequence of an enhanced basal defense response.

Main conclusionThe plasticity of plant growth response to differing nitrate availability renders the identification of biomarkers difficult, but allows access to genetic factors as tools to modulate root systems to a wide range of soil conditions. Nitrogen availability is a major determinant of crop yield. While the application of fertiliser substantially increases the yield on poor soils, it also causes nitrate pollution of water resources and high costs for farmers. Increasing nitrogen use efficiency in crop plants is a necessary step to implement low-input agricultural systems. We exploited the genetic diversity present in the worldwide Arabidopsis thaliana population to study adaptive growth patterns and changes in gene expression associated with chronic low nitrate stress, to identify biomarkers associated with good plant performance under low nitrate availability. Arabidopsis accessions were grown on agar plates with limited and sufficient supply of nitrate to measure root system architecture as well as shoot and root fresh weight. Differential gene expression was determined using Affymetrix ATH1 arrays. We show that the response to differing nitrate availability is highly variable in Arabidopsis accessions. Analyses of vegetative shoot growth and root system architecture identified accession-specific reaction modes to cope with limited nitrate availability. Transcription and epigenetic factors were identified as important players in the adaption to limited nitrogen in a global gene expression analysis. Five nitrate-responsive genes emerged as possible biomarkers for NUE in Arabidopsis. The plasticity of plant growth in response to differing nitrate availability in the substrate renders the identification of morphological and molecular features as biomarkers difficult, but at the same time allows access to a multitude of genetic factors which can be used as tools to modulate and adjust root systems to a wide range of soil conditions. Abbreviations NUENitrogen use efficiency (m)RIL (Mixed) recombinant inbred line

With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of plant phenotyping experiments.