Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

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.

Image-based phenotyping of plant disease symptoms

Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.

Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

A review of imaging techniques for plant phenotyping

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

High-resolution time-resolved imaging of in vitro Arabidopsis rosette growth

Although quantitative characterization of growth phenotypes is of key importance for the understanding of essential networks driving plant growth, the majority of growth-related genes are still being identified based on qualitative visual observations and/or single-endpoint quantitative measurements. We developed an in vitro growth imaging system (IGIS) to perform time-resolved analysis of rosette growth. In this system, Arabidopsis plants are grown in Petri dishes mounted on a rotating disk, and images of each plate are taken on an hourly basis. Automated image analysis was developed in order to obtain several growth-related parameters, such as projected rosette area, rosette relative growth rate, compactness and stockiness, over time. To illustrate the use of the platform and the resulting data, we present the results for the growth response of Col-0 plants subjected to three mild stress conditions. Although the reduction in rosette area was relatively similar at 19 days after stratification, the time-lapse analysis demonstrated that plants react differently to salt, osmotic and oxidative stress. The rosette area was altered at various time points during development, and leaf movement and shape parameters were also affected differently. We also used the IGIS to analyze in detail the growth behavior of mutants with enhanced leaf size. Analysis of several growth-related parameters over time in these mutants revealed several specificities in growth behavior, underlining the high complexity of leaf growth coordination. These results demonstrate that time-resolved imaging of in vitro rosette growth generates a better understanding of growth phenotypes than endpoint measurements.

Interact to survive: Phyllobacterium brassicacearum improves Arabidopsis tolerance to severe water deficit and growth recovery

Mutualistic bacteria can alter plant phenotypes and confer new abilities to plants. Some plant growth-promoting rhizobacteria (PGPR) are known to improve both plant growth and tolerance to multiple stresses, including drought, but reports on their effects on plant survival under severe water deficits are scarce. We investigated the effect of Phyllobacterium brassicacearum STM196 strain, a PGPR isolated from the rhizosphere of oilseed rape, on survival, growth and physiological responses of Arabidopsis thaliana to severe water deficits combining destructive and non-destructive high-throughput phenotyping. Soil inoculation with STM196 greatly increased the survival rate of A. thaliana under several scenarios of severe water deficit. Photosystem II efficiency, assessed at the whole-plant level by high-throughput fluorescence imaging (Fv/Fm), was related to the probability of survival and revealed that STM196 delayed plant mortality. Inoculated surviving plants tolerated more damages to the photosynthetic tissues through a delayed dehydration and a better tolerance to low water status. Importantly, STM196 allowed a better recovery of plant growth after rewatering and stressed plants reached a similar biomass at flowering than non-stressed plants. Our results highlight the importance of plant-bacteria interactions in plant responses to severe drought and provide a new avenue of investigations to improve drought tolerance in agriculture.

Daily changes in temperature, not the circadian clock, regulate growth rate in Brachypodium distachyon

Plant growth is commonly regulated by external cues such as light, temperature, water availability, and internal cues generated by the circadian clock. Changes in the rate of growth within the course of a day have been observed in the leaves, stems, and roots of numerous species. However, the relative impact of the circadian clock on the growth of grasses has not been thoroughly characterized. We examined the influence of diurnal temperature and light changes, and that of the circadian clock on leaf length growth patterns in Brachypodium distachyon using high-resolution time-lapse imaging. Pronounced changes in growth rate were observed under combined photocyles and thermocycles or with thermocycles alone. A considerably more rapid growth rate was observed at 28°C than 12°C, irrespective of the presence or absence of light. In spite of clear circadian clock regulated gene expression, plants exhibited no change in growth rate under conditions of constant light and temperature, and little or no effect under photocycles alone. Therefore, temperature appears to be the primary cue influencing observed oscillations in growth rate and not the circadian clock or photoreceptor activity. Furthermore, the size of the leaf meristem and final cell length did not change in response to changes in temperature. Therefore, the nearly five-fold difference in growth rate observed across thermocycles can be attributed to proportionate changes in the rate of cell division and expansion. A better understanding of the growth cues in B. distachyon will further our ability to model metabolism and biomass accumulation in grasses.

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays 'Fernandez') plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.

High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines

Drought is one of the most severe stresses, endangering crop yields worldwide. In order to select drought tolerant genotypes, access to exotic germplasm and efficient phenotyping protocols are needed. In this study the high-throughput phenotyping platform "The Plant Accelerator", Adelaide, Australia, was used to screen a set of 47 juvenile (six week old) wild barley introgression lines (S42ILs) for drought stress responses. The kinetics of growth development was evaluated under early drought stress and well watered treatments. High correlation (r=0.98) between image based biomass estimates and actual biomass was demonstrated, and the suitability of the system to accurately and non-destructively estimate biomass was validated. Subsequently, quantitative trait loci (QTL) were located, which contributed to the genetic control of growth under drought stress. In total, 44 QTL for eleven out of 14 investigated traits were mapped, which for example controlled growth rate and water use efficiency. The correspondence of those QTL with QTL previously identified in field trials is shown. For instance, six out of eight QTL controlling plant height were also found in previous field and glasshouse studies with the same introgression lines. This indicates that phenotyping juvenile plants may assist in predicting adult plant performance. In addition, favorable wild barley alleles for growth and biomass parameters were detected, for instance, a QTL that increased biomass by approximately 36%. In particular, introgression line S42IL-121 revealed improved growth under drought stress compared to the control Scarlett. The introgression line showed a similar behavior in previous field experiments, indicating that S42IL-121 may be an attractive donor for breeding of drought tolerant barley cultivars.

Objective definition of rosette shape variation using a combined computer vision and data mining approach

Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.

Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture

BACKGROUND

Light curtain arrays (LC), a recently introduced phenotyping method, yield a binary data matrix from which a shoot silhouette is reconstructed. We addressed the accuracy and applicability of LC in assessing leaf area and maximum height (base to the highest leaf tip) in a phenotyping platform. LC were integrated to an automated routine for positioning, allowing in situ measurements. Two dicotyledonous (rapeseed, tomato) and two monocotyledonous (maize, barley) species with contrasting shoot architecture were investigated. To evaluate if averaging multiple view angles helps in resolving self-overlaps, we acquired a data set by rotating plants every 10° for 170°. To test how rapid these measurements can be without loss of information, we evaluated nine scanning speeds. Leaf area of overlapping plants was also estimated to assess the possibility to scale this method for plant stands.

RESULTS

The relation between measured and calculated maximum height was linear and nearly the same for all species. Linear relations were also found between plant leaf area and calculated pixel area. However, the regression slope was different between monocotyledonous and dicotyledonous species. Increasing the scanning speed stepwise from 0.9 to 23.4 m s-1 did not affect the estimation of maximum height. Instead, the calculated pixel area was inversely proportional to scanning speed. The estimation of plant leaf area by means of calculated pixel area became more accurate by averaging consecutive silhouettes and/or increasing the angle between them. Simulations showed that decreasing plant distance gradually from 20 to 0 cm, led to underestimation of plant leaf area owing to overlaps. This underestimation was more important for large plants of dicotyledonous species and for small plants of monocotyledonous ones.

CONCLUSIONS

LC offer an accurate estimation of plant leaf area and maximum height, while the number of consecutive silhouettes that needs to be averaged is species-dependent. A constant scanning speed is important for leaf area estimations by using LC. Simulations of the effect of varying plant spacing gave promising results for method application in sets of partly overlapping plants, which applies also to field conditions during and after canopy closure for crops sown in rows.

Phenotyping and beyond: modelling the relationships between traits

Plant phenotyping technology has become more advanced with the capacity to measure many morphological and physiological traits on a given individual. With increasing automation, getting access to various traits on a high number of genotypes over time raises the need to develop systems for data storage and analyses, all congregating into plant phenotyping pipelines. In this review, we highlight several studies that illustrate the latest advances in plant multi-trait phenotyping and discuss future needs to ensure the best use of all these quantitative data. We assert that the next challenge is to disentangle how plant traits are embedded in networks of dependencies (and independencies) by modelling the relationships between them and how these are affected by genetics and environment.

Potential use of ground-based sensor technologies for weed detection

Site-specific weed management is the part of precision agriculture (PA) that tries to effectively control weed infestations with the least economical and environmental burdens. This can be achieved with the aid of ground-based or near-range sensors in combination with decision rules and precise application technologies. Near-range sensor technologies, developed for mounting on a vehicle, have been emerging for PA applications during the last three decades. These technologies focus on identifying plants and measuring their physiological status with the aid of their spectral and morphological characteristics. Cameras, spectrometers, fluorometers and distance sensors are the most prominent sensors for PA applications. The objective of this article is to describe-ground based sensors that have the potential to be used for weed detection and measurement of weed infestation level. An overview of current sensor systems is presented, describing their concepts, results that have been achieved, already utilized commercial systems and problems that persist. A perspective for the development of these sensors is given.

Functional approach to high-throughput plant growth analysis

METHOD

Taking advantage of the current rapid development in imaging systems and computer vision algorithms, we present HPGA, a high-throughput phenotyping platform for plant growth modeling and functional analysis, which produces better understanding of energy distribution in regards of the balance between growth and defense. HPGA has two components, PAE (Plant Area Estimation) and GMA (Growth Modeling and Analysis). In PAE, by taking the complex leaf overlap problem into consideration, the area of every plant is measured from top-view images in four steps. Given the abundant measurements obtained with PAE, in the second module GMA, a nonlinear growth model is applied to generate growth curves, followed by functional data analysis.

RESULTS

Experimental results on model plant Arabidopsis thaliana show that, compared to an existing approach, HPGA reduces the error rate of measuring plant area by half. The application of HPGA on the cfq mutant plants under fluctuating light reveals the correlation between low photosynthetic rates and small plant area (compared to wild type), which raises a hypothesis that knocking out cfq changes the sensitivity of the energy distribution under fluctuating light conditions to repress leaf growth.

AVAILABILITY

HPGA is available at http://www.msu.edu/~jinchen/HPGA.

The REIL1 and REIL2 proteins of Arabidopsis thaliana are required for leaf growth in the cold

The evolutionarily conserved proteins REI1-LIKE (REIL1) and REIL2 have four conserved zinc finger domains and are Arabidopsis thaliana homologs of the cytosolic 60S ribosomal maturation factor Rei1p (for Required for isotropic bud growth1 protein) from yeast (Saccharomyces cerevisiae) and its paralog Reh1p (for REI1 homologue1 protein). The yeast and A. thaliana paralogs result from independent gene duplications. The A. thaliana REIL paralogs are required specifically in the cold (10°C) but not for growth at optimal temperature (20°C). A reil1-1 reil2-1 double mutant is arrested at 10°C prior to the emergence of the first rosette leaf. Two allelic reil2 mutants, reil2-1 and reil2-2, form small spoon-shaped leaves at 10°C. This phenomenon reverts after emergence of the inflorescence in the cold or upon shift to 20°C. Except for a slightly delayed germination, a reil1-1 mutant shows no further growth phenotype under the currently investigated conditions. A comparative analysis demonstrates conserved coexpression of orthologous genes from yeast and A. thaliana that are coregulated with yeast rei1 or with A. thaliana REIL2, respectively. The conserved correlations point to a role of A. thaliana REIL proteins in the maturation of the eukaryotic ribosomal 60S subunit. We support this conclusion by heterologous complementation of the cold-induced growth defect of the yeast Δrei1 deletion.

A segmentation procedure using colour features applied to images of Arabidopsis thaliana

In studies of environmental effects on plant growth, the images of plants are often used for non-destructive measurements in phenotyping. In this work, a computational procedure has been developed to segment images of plants allowing an improved separation of plants and other types of objects in the frame such as moss or soil. The proposed procedure is based on colour analysis and image morphology. The red-green-blue (RGB) values are transformed into a colour space as ratios of R, G and B vs the sum of R, G, and B channels. We introduce an approach to render the training set of pixels on a Microsoft Excel two-dimensional graph and a technique to determine the discriminant regions of pixel classes. Two approaches for the classification based on colour analysis are shown: an automatic method using support vector machines and a procedure based on visual inspection. The segmentation procedure is designed to classify more than two object types utilising flexibly curved boundaries of discriminant regions that can also be non-convex. We propose a machine-vision algorithm to detect plant features - leaf anthocyanin accumulation and trichomes. The procedures of segmentation and feature detection are applied to images of Arabidopsis thaliana (L.) Heynh. that grow under either normal or drought stress conditions.

Regulation of growth by the trehalose pathway: relationship to temperature and sucrose

Carbon signaling can override carbon supply in the regulation of growth. At least some of this regulation is imparted by the sugar signal trehalose 6-phosphate (T6P) through the protein kinase, SnRK1. This signaling pathway regulates biosynthetic processes involved in growth under optimal growing conditions. Recently, using a seedling system we showed that under sub-optimal conditions, such as cold, carbon signaling by T6P/ SnRK1 enables recovery of growth following relief of the stress. The T6P/ SnRK1 mechanism thus could be selected as a means of improving low temperature tolerance. High-throughput automated Fv/Fm measurements provide a potential means to screen for T6P/ SnRK1, and here we confirm through measurements of Fv/Fm in rosettes that T6P promotes low temperature tolerance and recovery during cold to warm transfer. Further, to better understand the coordination between sugars, trehalose pathway, and temperature-dependent growth, we examine the interrelationship between sugars, trehalose phosphate synthase (TPS), and trehalose phosphate phosphatase (TPP) gene expression and T6P content in seedlings. Sucrose, particularly when fed exogenously, correlated well with TPS1 and TPPB gene expression, suggesting that these enzymes are involved in maintaining carbon flux through the pathway in relation to sucrose supply. However, when sucrose accumulated to higher levels under low temperature and low N, TPS1 and TPPB expression were less directly related to sucrose; other factors may also contribute to regulation of TPS1 and TPPB expression under these conditions. TPPA expression was not related to sucrose content and all genes were not well correlated with endogenous glucose. Our work has implications for understanding acclimation to sink-limited growth conditions such as low temperature and for screening cold-tolerant genotypes with altered T6P/ SnRK1 signaling.

Cell to whole-plant phenotyping: the best is yet to come

Imaging and image processing have revolutionized plant phenotyping and are now a major tool for phenotypic trait measurement. Here we review plant phenotyping systems by examining three important characteristics: throughput, dimensionality, and resolution. First, whole-plant phenotyping systems are highlighted together with advances in automation that enable significant throughput increases. Organ and cellular level phenotyping and its tools, often operating at a lower throughput, are then discussed as a means to obtain high-dimensional phenotypic data at elevated spatial and temporal resolution. The significance of recent developments in sensor technologies that give access to plant morphology and physiology-related traits is shown. Overall, attention is focused on spatial and temporal resolution because these are crucial aspects of imaging procedures in plant phenotyping systems.

Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack Leaf)

BACKGROUND

We present a novel method for quantitative analysis of dicot leaf expansion at high temporal resolution. Image sequences of growing leaves were assessed using a marker tracking algorithm. An important feature of the method is the attachment of dark beads that serve as artificial landmarks to the leaf margin. The beads are mechanically constricted to the focal plane of a camera. Leaf expansion is approximated by the increase in area of the polygon defined by the centers of mass of the beads surrounding the leaf. Fluctuating illumination conditions often pose serious problems for tracking natural structures of a leaf; this problem is circumvented here by the use of the beads.

RESULTS

The new method has been used to assess leaf growth in environmental situations with different illumination conditions that are typical in agricultural and biological experiments: Constant illumination via fluorescent light tubes in a climate chamber, a mix of natural and artificial illumination in a greenhouse and natural illumination of the situation on typical summer days in the field. Typical features of diel (24h) soybean leaf growth patterns were revealed in all three conditions, thereby demonstrating the general applicability of the method. Algorithms are provided to the entire community interested in using such approaches.

CONCLUSIONS

The implementation Martrack Leaf presented here is a robust method to investigate diel leaf growth rhythms both under natural and artificial illumination conditions. It will be beneficial for the further elucidation of genotype x environment x management interactions affecting leaf growth processes.

Phenotyping the kinematics of leaf development in flowering plants: recommendations and pitfalls

Leaves of flowering plants are produced from the shoot apical meristem at regular intervals and they grow according to a developmental program that is determined by both genetic and environmental factors. Detailed frameworks for multiscale dynamic analyses of leaf growth have been developed in order to identify and interpret phenotypic differences caused by either genetic or environmental variations. They revealed that leaf growth dynamics are non-linearly and nonhomogeneously distributed over the lamina, in the leaf tissues and cells. The analysis of the variability in leaf growth, and its underlying processes, has recently gained momentum with the development of automated phenotyping platforms that use various technologies to record growth at different scales and at high throughput. These modern tools are likely to accelerate the characterization of gene function and the processes that underlie the control of shoot development. Combined with powerful statistical analyses, trends have emerged that may have been overlooked in low throughput analyses. However, in many examples, the increase in throughput allowed by automated platforms has led to a decrease in the spatial and/or temporal resolution of growth analyses. Concrete examples presented here indicate that simplification of the dynamic leaf system, without consideration of its spatial and temporal context, can lead to important misinterpretations of the growth phenotype.

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Association of candidate genes with drought tolerance traits in diverse perennial ryegrass accessions

Drought is a major environmental stress limiting growth of perennial grasses in temperate regions. Plant drought tolerance is a complex trait that is controlled by multiple genes. Candidate gene association mapping provides a powerful tool for dissection of complex traits. Candidate gene association mapping of drought tolerance traits was conducted in 192 diverse perennial ryegrass (Lolium perenne L.) accessions from 43 countries. The panel showed significant variations in leaf wilting, leaf water content, canopy and air temperature difference, and chlorophyll fluorescence under well-watered and drought conditions across six environments. Analysis of 109 simple sequence repeat markers revealed five population structures in the mapping panel. A total of 2520 expression-based sequence readings were obtained for a set of candidate genes involved in antioxidant metabolism, dehydration, water movement across membranes, and signal transduction, from which 346 single nucleotide polymorphisms were identified. Significant associations were identified between a putative LpLEA3 encoding late embryogenesis abundant group 3 protein and a putative LpFeSOD encoding iron superoxide dismutase and leaf water content, as well as between a putative LpCyt Cu-ZnSOD encoding cytosolic copper-zinc superoxide dismutase and chlorophyll fluorescence under drought conditions. Four of these identified significantly associated single nucleotide polymorphisms from these three genes were also translated to amino acid substitutions in different genotypes. These results indicate that allelic variation in these genes may affect whole-plant response to drought stress in perennial ryegrass.

Effects of altered α- and β-branch carotenoid biosynthesis on photoprotection and whole-plant acclimation of Arabidopsis to photo-oxidative stress

Functions of α- and β-branch carotenoids in whole-plant acclimation to photo-oxidative stress were studied in Arabidopsis thaliana wild-type (wt) and carotenoid mutants, lutein deficient (lut2, lut5), non-photochemical quenching1 (npq1) and suppressor of zeaxanthin-less1 (szl1) npq1 double mutant. Photo-oxidative stress was applied by exposing plants to sunflecks. The sunflecks caused reduction of chlorophyll content in all plants, but more severely in those having high α- to β-branch carotenoid composition (α/β-ratio) (lut5, szl1npq1). While this did not alter carotenoid composition in wt or lut2, which accumulates only β-branch carotenoids, increased xanthophyll levels were found in the mutants with high α/β-ratios (lut5, szl1npq1) or without xanthophyll-cycle operation (npq1, szl1npq1). The PsbS protein content increased in all sunfleck plants but lut2. These changes were accompanied by no change (npq1, szl1npq1) or enhanced capacity (wt, lut5) of NPQ. Leaf mass per area increased in lut2, but decreased in wt and lut5 that showed increased NPQ. The sunflecks decelerated primary root growth in wt and npq1 having normal α/β-ratios, but suppressed lateral root formation in lut5 and szl1npq1 having high α/β-ratios. The results highlight the importance of proper regulation of the α- and β-branch carotenoid pathways for whole-plant acclimation, not only leaf photoprotection, under photo-oxidative stress.

Future scenarios for plant phenotyping

With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.

Applications of high-throughput plant phenotyping to study nutrient use efficiency

Remote sensing and spectral reflectance measurements of plants has long been used to assess the growth and nutrient status of plants in a noninvasive manner. With improved imaging and computer technologies, these approaches can now be used at high-throughput for more extensive physiological and genetic studies. Here, we present an example of how high-throughput imaging can be used to study the growth of plants exposed to different nutrient levels. In addition, the color of the leaves can be used to estimate leaf chlorophyll and nitrogen status of the plant.

Remote chlorophyll fluorescence measurements with the laser-induced fluorescence transient approach

The interaction of plants with their environment is very dynamic. Studying the underlying processes is important for understanding and modeling plant response to changing environmental conditions. Photosynthesis varies largely between different plants and at different locations within a canopy of a single plant. Thus, continuous and spatially distributed monitoring is necessary to assess the dynamic response of photosynthesis to the environment. Limited scale of observation with portable instrumentation makes it difficult to examine large numbers of plants under different environmental conditions. We report here on the application of a recently developed technique, laser-induced fluorescence transient (LIFT), for continuous remote measurement of photosynthetic efficiency of selected leaves at a distance of up to 50 m. The ability to make continuous, automatic, and remote measurements of photosynthetic efficiency of leaves with the LIFT provides a new approach for studying the interaction of plants with the environment and may become an important tool in phenotyping photosynthetic properties in field applications.

What lies beneath: underlying assumptions in bioimage analysis

The need for plant image analysis tools is established and has led to a steadily expanding literature and set of software tools. This is encouraging, but raises a question: how does a plant scientist with no detailed knowledge or experience of image analysis methods choose the right tool(s) for the task at hand, or satisfy themselves that a suggested approach is appropriate? We believe that too great an emphasis is currently being placed on low-level mechanisms and software environments. In this opinion article we propose that a renewed focus on the core theories and algorithms used, and in particular the assumptions upon which they rely, will better equip plant scientists to evaluate the available resources.

Advanced phenotyping offers opportunities for improved breeding of forage and turf species

BACKGROUND AND AIMS

Advanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources.

SCOPE

This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed.

CONCLUSIONS

Progress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystems.

Rosette tracker: an open source image analysis tool for automatic quantification of genotype effects

Image analysis of Arabidopsis (Arabidopsis thaliana) rosettes is an important nondestructive method for studying plant growth. Some work on automatic rosette measurement using image analysis has been proposed in the past but is generally restricted to be used only in combination with specific high-throughput monitoring systems. We introduce Rosette Tracker, a new open source image analysis tool for evaluation of plant-shoot phenotypes. This tool is not constrained by one specific monitoring system, can be adapted to different low-budget imaging setups, and requires minimal user input. In contrast with previously described monitoring tools, Rosette Tracker allows us to simultaneously quantify plant growth, photosynthesis, and leaf temperature-related parameters through the analysis of visual, chlorophyll fluorescence, and/or thermal infrared time-lapse sequences. Freely available, Rosette Tracker facilitates the rapid understanding of Arabidopsis genotype effects.

Phenotyping plants: genes, phenes and machines

No matter how fascinating the discoveries in the field of molecular biology are, in the end it is the phenotype that matters. In this paper we pay attention to various aspects of plant phenotyping. The challenges to unravel the relationship between genotype and phenotype are discussed, as well as the case where 'plants do not have a phenotype'. More emphasis has to be placed on automation to match the increased output in the molecular sciences with analysis of relevant traits under laboratory, greenhouse and field conditions. Currently, non-destructive measurements with cameras are becoming widely used to assess plant structural properties, but a wider range of non-invasive approaches and evaluation tools has to be developed to combine physiologically meaningful data with structural information of plants. Another field requiring major progress is the handling and processing of data. A better e-infrastructure will enable easier establishment of links between phenotypic traits and genetic data. In the final part of this paper we briefly introduce the range of contributions that form the core of a special issue of this journal on plant phenotyping.

The art of growing plants for experimental purposes: a practical guide for the plant biologist

Every year thousands of experiments are conducted using plants grown under more-or-less controlled environmental conditions. The aim of many such experiments is to compare the phenotype of different species or genotypes in a specific environment, or to study plant performance under a range of suboptimal conditions. Our paper aims to bring together the minimum knowledge necessary for a plant biologist to set up such experiments and apply the environmental conditions that are appropriate to answer the questions of interest. We first focus on the basic choices that have to be made with regard to the experimental setup (e.g. where are the plants grown; what rooting medium; what pot size). Second, we present practical considerations concerning the number of plants that have to be analysed considering the variability in plant material and the required precision. Third, we discuss eight of the most important environmental factors for plant growth (light quantity, light quality, CO2, nutrients, air humidity, water, temperature and salinity); what critical issues should be taken into account to ensure proper growth conditions in controlled environments and which specific aspects need attention if plants are challenged with a certain a-biotic stress factor. Finally, we propose a simple checklist that could be used for tracking and reporting experimental conditions.

GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons

Root systems play an essential role in ensuring plant productivity. Experiments conducted in controlled environments and simulation models suggest that root geometry and responses of root architecture to environmental factors should be studied as a priority. However, compared with aboveground plant organs, roots are not easily accessible by non-invasive analyses and field research is still based almost completely on manual, destructive methods. Contributing to reducing the gap between laboratory and field experiments, we present a novel phenotyping system (GROWSCREEN-Rhizo), which is capable of automatically imaging roots and shoots of plants grown in soil-filled rhizotrons (up to a volume of ~18L) with a throughput of 60 rhizotrons per hour. Analysis of plants grown in this setup is restricted to a certain plant size (up to a shoot height of 80cm and root-system depth of 90cm). We performed validation experiments using six different species and for barley and maize, we studied the effect of moderate soil compaction, which is a relevant factor in the field. First, we found that the portion of root systems that is visible through the rhizotrons' transparent plate is representative of the total root system. The percentage of visible roots decreases with increasing average root diameter of the plant species studied and depends, to some extent, on environmental conditions. Second, we could measure relatively minor changes in root-system architecture induced by a moderate increase in soil compaction. Taken together, these findings demonstrate the good potential of this methodology to characterise root geometry and temporal growth responses with relatively high spatial accuracy and resolution for both monocotyledonous and dicotyledonous species. Our prototype will allow the design of high-throughput screening methodologies simulating environmental scenarios that are relevant in the field and will support breeding efforts towards improved resource use efficiency and stability of crop yields.

The leaf reticulate mutant dov1 is impaired in the first step of purine metabolism

A series of reticulated Arabidopsis thaliana mutants were previously described. All mutants show a reticulate leaf pattern, namely green veins on a pale leaf lamina. They have an aberrant mesophyll structure but an intact layer of bundle sheath cells around the veins. Here, we unravel the function of the previously described reticulated EMS-mutant dov1 (differential development of vascular associated cells 1). By positional cloning, we identified the mutated gene, which encodes glutamine phosphoribosyl pyrophosphate aminotransferase 2 (ATase2), an enzyme catalyzing the first step of purine nucleotide biosynthesis. dov1 is allelic to the previously characterized cia1-2 mutant that was isolated in a screen for mutants with impaired chloroplast protein import. We show that purine-derived total cytokinins are lowered in dov1 and crosses with phytohormone reporter lines revealed differential reporter activity patterns in dov1. Metabolite profiling unraveled that amino acids that are involved in purine biosynthesis are increased in dov1. This study identified the molecular basis of an established mutant line, which has the potential for further investigation of the interaction between metabolism and leaf development.

Aberrant temporal growth pattern and morphology of root and shoot caused by a defective circadian clock in Arabidopsis thaliana

Circadian clocks synchronized with the environment allow plants to anticipate recurring daily changes and give a fitness advantage. Here, we mapped the dynamic growth phenotype of leaves and roots in two lines of Arabidopsis thaliana with a disrupted circadian clock: the CCA1 over-expressing line (CCA1ox) and the prr9 prr7 prr5 (prr975) mutant. We demonstrate leaf growth defects due to a disrupted circadian clock over a 24 h time scale. Both lines showed enhanced leaf growth compared with the wild-type during the diurnal period, suggesting increased partitioning of photosynthates for leaf growth. Nocturnal leaf growth was reduced and growth inhibition occurred by dawn, which may be explained by ineffective starch degradation in the leaves of the mutants. However, this growth inhibition was not caused by starch exhaustion. Overall, these results are consistent with the notion that the defective clock affects carbon and energy allocation, thereby reducing growth capacity during the night. Furthermore, rosette morphology and size as well as root architecture were strikingly altered by the defective clock control. Separate analysis of the primary root and lateral roots revealed strong suppression of lateral root formation in both CCA1ox and prr975, accompanied by unusual changes in lateral root growth direction under light-dark cycles and increased lateral extension of the root system. We conclude that growth of the whole plant is severely affected by improper clock regulation in A. thaliana, resulting not only in altered timing and capacity for growth but also aberrant development of shoot and root architecture.

Acclimatory responses of Arabidopsis to fluctuating light environment: comparison of different sunfleck regimes and accessions

Acclimation to fluctuating light environment with short (lasting 20 s, at 650 or 1,250 μmol photons m(-2) s(-1), every 6 or 12 min) or long (for 40 min at 650 μmol photons m(-2) s(-1), once a day at midday) sunflecks was studied in Arabidopsis thaliana. The sunfleck treatments were applied in the background daytime light intensity of 50 μmol photons m(-2) s(-1). In order to distinguish the effects of sunflecks from those of increased daily irradiance, constant light treatments at 85 and 120 μmol photons m(-2) s(-1), which gave the same photosynthetically active radiation (PAR) per day as the different sunfleck treatments, were also included in the experiments. The increased daily total PAR in the two higher constant light treatments enhanced photosystem II electron transport and starch accumulation in mature leaves and promoted expansion of young leaves in Columbia-0 plants during the 7-day treatments. Compared to the plants remaining under 50 μmol photons m(-2) s(-1), application of long sunflecks caused upregulation of electron transport without affecting carbon gain in the form of starch accumulation and leaf growth or the capacity of non-photochemical quenching (NPQ). Mature leaves showed marked enhancement of the NPQ capacity under the conditions with short sunflecks, which preceded recovery and upregulation of electron transport, demonstrating the initial priority of photoprotection. The distinct acclimatory responses to constant PAR, long sunflecks, and different combinations of short sunflecks are consistent with acclimatory adjustment of the processes in photoprotection and carbon gain, depending on the duration, frequency, and intensity of light fluctuations. While the responses of leaf expansion to short sunflecks differed among the seven Arabidopsis accessions examined, all plants showed NPQ upregulation, suggesting limited ability of this species to utilize short sunflecks. The increase in the NPQ capacity was accompanied by reduced chlorophyll contents, higher levels of the xanthophyll-cycle pigments, faster light-induced de-epoxidation of violaxanthin to zeaxanthin and antheraxanthin, increased amounts of PsbS protein, as well as enhanced activity of superoxide dismutase. These acclimatory mechanisms, involving reorganization of pigment-protein complexes and upregulation of other photoprotective reactions, are probably essential for Arabidopsis plants to cope with photo-oxidative stress induced by short sunflecks without suffering from severe photoinhibition and lipid peroxidation.

A novel mesh processing based technique for 3D plant analysis

BACKGROUND

In recent years, imaging based, automated, non-invasive, and non-destructive high-throughput plant phenotyping platforms have become popular tools for plant biology, underpinning the field of plant phenomics. Such platforms acquire and record large amounts of raw data that must be accurately and robustly calibrated, reconstructed, and analysed, requiring the development of sophisticated image understanding and quantification algorithms. The raw data can be processed in different ways, and the past few years have seen the emergence of two main approaches: 2D image processing and 3D mesh processing algorithms. Direct image quantification methods (usually 2D) dominate the current literature due to comparative simplicity. However, 3D mesh analysis provides the tremendous potential to accurately estimate specific morphological features cross-sectionally and monitor them over-time.

RESULT

In this paper, we present a novel 3D mesh based technique developed for temporal high-throughput plant phenomics and perform initial tests for the analysis of Gossypium hirsutum vegetative growth. Based on plant meshes previously reconstructed from multi-view images, the methodology involves several stages, including morphological mesh segmentation, phenotypic parameters estimation, and plant organs tracking over time. The initial study focuses on presenting and validating the accuracy of the methodology on dicotyledons such as cotton but we believe the approach will be more broadly applicable. This study involved applying our technique to a set of six Gossypium hirsutum (cotton) plants studied over four time-points. Manual measurements, performed for each plant at every time-point, were used to assess the accuracy of our pipeline and quantify the error on the morphological parameters estimated.

CONCLUSION

By directly comparing our automated mesh based quantitative data with manual measurements of individual stem height, leaf width and leaf length, we obtained the mean absolute errors of 9.34%, 5.75%, 8.78%, and correlation coefficients 0.88, 0.96, and 0.95 respectively. The temporal matching of leaves was accurate in 95% of the cases and the average execution time required to analyse a plant over four time-points was 4.9 minutes. The mesh processing based methodology is thus considered suitable for quantitative 4D monitoring of plant phenotypic features.

Imaging plants dynamics in heterogenic environments

Noninvasive imaging sensors and computer vision approaches are key technologies to quantify plant structure, physiological status, and performance. Today, imaging sensors exploit a wide range of the electromagnetic spectrum, and they can be deployed to measure a growing number of traits, also in heterogenic environments. Recent advances include the possibility to acquire high-resolution spectra by imaging spectroscopy and classify signatures that might be informative of plant development, nutrition, health, and disease. Three-dimensional (3D) reconstruction of surfaces and volume is of particular interest, enabling functional and mechanistic analyses. While taking pictures is relatively easy, quantitative interpretation often remains challenging and requires integrating knowledge of sensor physics, image analysis, and complex traits characterizing plant phenotypes.

Trait dissection of salinity tolerance with plant phenomics

The phenotypic response of plants to high external levels of NaCl is very dynamic so the methods used for phenotyping should be able to capture those dynamics. Non-destructive high-throughput plant imaging enables researchers to measure salt stress-induced changes in plant growth over time. In addition, the color information of the digital images allows the quantification of leaf senescence induced through long-term exposure to NaCl. In combination with destructive leaf sampling to measure leaf [Na(+)], this approach allows dissection of salinity tolerance into its individual components.

High-throughput phenotyping of plant shoots

Advances in automated plant handling and image acquisition now make it possible to use digital imaging for the high-throughput phenotyping of plants. Various traits can be extracted from individual images. However, the potential of this technology lies in the acquisition of time series. Since whole shoot imaging is nondestructive, plants can now be monitored throughout their lifecycle, and dynamic traits such as plant growth and development can be captured and quantified. The technique is applicable to a wide range of plants and research areas and makes high-throughput screens possible, reducing the time and labor needed for the phenotypic characterization of plants.

Engineered drought tolerance in tomato plants is reflected in chlorophyll fluorescence emission

Drought stress is one of the most important factors that limit crop productivity worldwide. In order to obtain tomato plants with enhanced drought tolerance, we inserted the transcription factor gene ATHB-7 into the tomato genome. This gene was demonstrated earlier to be up-regulated during drought stress in Arabidopsis thaliana thus acting as a negative regulator of growth. We compared the performance of wild type and transgenic tomato line DTL-20, carrying ATHB-7 gene, under well-irrigated and water limited conditions. We found that transgenic plants had reduced stomatal density and stomatal pore size and exhibited an enhanced resistance to soil water deficit. We used the transgenic plants to investigate the potential of chlorophyll fluorescence to report drought tolerance in a simulated high-throughput screening procedure. Wild type and transgenic tomato plants were exposed to drought stress lasting 18 days. The stress was then terminated by rehydration after which recovery was studied for another 2 days. Plant growth, leaf water potential, and chlorophyll fluorescence were measured during the entire experimental period. We found that water potential in wild type and drought tolerant transgenic plants diverged around day 11 of induced drought stress. The chlorophyll fluorescence parameters: the non-photochemical quenching, effective quantum efficiency of PSII, and the maximum quantum yield of PSII photochemistry yielded a good contrast between wild type and transgenic plants from day 7, day 12, and day 14 of induced stress, respectively. We propose that chlorophyll fluorescence emission reports well on the level of water stress and, thus, can be used to identify elevated drought tolerance in high-throughput screens for selection of resistant genotypes.

Phenomics--technologies to relieve the phenotyping bottleneck

Global agriculture is facing major challenges to ensure global food security, such as the need to breed high-yielding crops adapted to future climates and the identification of dedicated feedstock crops for biofuel production (biofuel feedstocks). Plant phenomics offers a suite of new technologies to accelerate progress in understanding gene function and environmental responses. This will enable breeders to develop new agricultural germplasm to support future agricultural production. In this review we present plant physiology in an 'omics' perspective, review some of the new high-throughput and high-resolution phenotyping tools and discuss their application to plant biology, functional genomics and crop breeding.

Non-invasive approaches for phenotyping of enhanced performance traits in bean

Plant phenotyping is an emerging discipline in plant biology. Quantitative measurements of functional and structural traits help to better understand gene-environment interactions and support breeding for improved resource use efficiency of important crops such as bean (Phaseolus vulgaris L.). Here we provide an overview of state-of-the-art phenotyping approaches addressing three aspects of resource use efficiency in plants: belowground roots, aboveground shoots and transport/allocation processes. We demonstrate the capacity of high-precision methods to measure plant function or structural traits non-invasively, stating examples wherever possible. Ideally, high-precision methods are complemented by fast and high-throughput technologies. High-throughput phenotyping can be applied in the laboratory using automated data acquisition, as well as in the field, where imaging spectroscopy opens a new path to understand plant function non-invasively. For example, we demonstrate how magnetic resonance imaging (MRI) can resolve root structure and separate root systems under resource competition, how automated fluorescence imaging (PAM fluorometry) in combination with automated shape detection allows for high-throughput screening of photosynthetic traits and how imaging spectrometers can be used to quantify pigment concentration, sun-induced fluorescence and potentially photosynthetic quantum yield. We propose that these phenotyping techniques, combined with mechanistic knowledge on plant structure-function relationships, will open new research directions in whole-plant ecophysiology and may assist breeding for varieties with enhanced resource use efficiency varieties.

A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects

• To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes. • The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times. • Using the system, a single researcher can phenotype up to 7000 plants d⁻¹. Technical variance is very low (typically < 2%). We show quantitative results for the growth-impaired starch-excess mutant sex4-3 and the growth-enhanced mutant grf9. • We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.

Genetic control of plant organ growth

CONTENTS

Summary 319 I. Introduction 320 II. The cell biology and biophysics of growth 320 III. Timing is everything: what determines when proliferation gives way to expansion? 323 IV. Anisotropic growth and the importance of polarity 325 V. How does organ identity and developmental patterning modulate growth behaviour? 326 VI. Coordination of growth at different scales 327 VII. Conclusions 329 Acknowledgements 329 References 330

SUMMARY

The growth of plant organs is under genetic control. Work in model species has identified a considerable number of genes that regulate different aspects of organ growth. This has led to an increasingly detailed knowledge about how the basic cellular processes underlying organ growth are controlled, and which factors determine when proliferation gives way to expansion, with this transition emerging as a critical decision point during primordium growth. Progress has been made in elucidating the genetic basis of allometric growth and the role of tissue polarity in shaping organs. We are also beginning to understand how the mechanisms that determine organ identity influence local growth behaviour to generate organs with characteristic sizes and shapes. Lastly, growth needs to be coordinated at several levels, for example between different cell layers and different regions within one organ, and the genetic basis for such coordination is being elucidated. However, despite these impressive advances, a number of basic questions are still not fully answered, for example, whether and how a growing primordium keeps track of its size. Answering these questions will likely depend on including additional approaches that are gaining in power and popularity, such as combined live imaging and modelling.

Natural genetic variation in plant photosynthesis

Natural genetic variation in plant photosynthesis is a largely unexplored and as a result an underused genetic resource for crop improvement. Numerous studies show genetic variation in photosynthetic traits in both crop and wild species, and there is an increasingly detailed knowledge base concerning the interaction of photosynthetic phenotypes with their environment. The genetic factors that cause this variation remain largely unknown. Investigations into natural genetic variation in photosynthesis will provide insights into the genetic regulation of this complex trait. Such insights can be used to understand evolutionary processes that affect primary production, allow greater understanding of the genetic regulation of photosynthesis and ultimately increase the productivity of our crops.

UV-induced fluorescence spectra and lifetime determination for detection of leaf rust (Puccinia triticina) in susceptible and resistant wheat (Triticum aestivum) cultivars

In modern agriculture, the use of cultivars that are resistant against specific stresses, e.g. pathogen infections, is an integral component. Considering the great demand for a rapid and objective screening method for stress resistance of new cultivars, the question arises, whether time resolved fluorescence spectroscopy is suitable for such purposes. Amongst others, infected plants might accumulate specific compounds such as salicylic acid and phenylpropanoid compounds as key substances in plant disease resistance, whereas synthesis and accumulation may influence fluorescence parameters such as absolute intensity of single peaks, ratios between peaks and lifetime. Experiments were conducted in a controlled-environment cabinet cultivating four leaf rust susceptible and three leaf rust resistant genotypes. Fluorescence measurements were conducted using a compact fibre-optic fluorescence spectrometer with a nanosecond time-resolution. Results of experiments revealed that UV-induced measurements of spectral characteristics as well as determination of fluorescence lifetime are suited to detect leaf rust (Puccinia triticina) in wheat (Triticum aestivum L.) cultivars as early as 2 days after inoculation (dai). For this purpose several parameters such as the fluorescence (F) amplitude ratios F451/F522, F451/F687, F451/F736, F522/F687, F522/F736 as well as fluorescence mean lifetime especially at 470nm, might be used. Discrimination between resistant and susceptible cultivars to the leaf rust pathogen could be accomplished 3dai by using the ratio of fluorescence amplitude between the blue (F451nm) and red (F687nm) peak, and mean lifetime at 440, 500 and 530nm. Our results indicate that the combination of spectrally and time-resolved fluorescence could be an additional tool in plant breeding programs for an automatic and precise high-throughput system for evaluation of the pathogen resistance of new genotypes.

Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana

A major challenge in evolutionary biology and plant breeding is to identify the genetic basis of complex quantitative traits, including those that contribute to adaptive variation. Here we review the development of new methods and resources to fine-map intraspecific genetic variation that underlies natural phenotypic variation in plants. In particular, the analysis of 107 quantitative traits reported in the first genome-wide association mapping study in Arabidopsis thaliana sets the stage for an exciting time in our understanding of plant adaptation. We also argue for the need to place phenotype-genotype association studies in an ecological context if one is to predict the evolutionary trajectories of plant species.

Chlorophyll fluorescence imaging of plant-pathogen interactions

Chlorophyll fluorescence imaging provides a noninvasive, non-destructive method with which to measure heterogenous changes in photosynthetic metabolism in plants infected by pathogens. The availability of commercial imaging fluorimeters has helped make this technique available to the wider scientific community, but considerable care is needed, both in experimental design and in the interpretation of results, to make the most of this powerful analytical tool. The origins of changes in chlorophyll fluorescence yield are discussed and the use of conventional and novel combinatorial imaging approaches explored, together with complementary techniques such as thermal imaging. This review examines the use of chlorophyll fluorescence imaging as a method for the early detection of viral, bacterial and fungal infection, before symptoms are visible by eye, and also as a means with which to probe underlying pathogen-induced changes in host physiology in both compatible and incompatible interactions. The use of chlorophyll fluorescence imaging to study host physiology is greatly enhanced when the atmosphere around the leaf is manipulated and simultaneous measurements of gas exchange made: The cost to the host plant of different resistance mechanisms can be calculated, the fate of the products of photosynthetic electron transport determined and localised alterations in the source-sink status of host tissue visualised.

New phenotyping methods for screening wheat and barley for beneficial responses to water deficit

This review considers stomatal conductance as an indicator of genotypic differences in the growth response to water stress. The benefits of using stomatal conductance are compared with photosynthetic rate and other indicators of genetic variation in water stress tolerance, along with the use of modern phenomics technologies. Various treatments for screening for genetic diversity in response to water deficit in controlled environments are considered. There is no perfect medium: there are pitfalls in using soil in pots, and in using hydroponics with ionic and non-ionic osmotica. Use of mixed salts or NaCl is recommended over non-ionic osmotica. Developments in infrared thermography provide new and feasible screening methods for detecting genetic variation in the stomatal response to water deficit in controlled environments and in the field.