Showing papers in "Plant Methods in 2015"

Plant phenotyping: from bean weighing to image analysis

Abstract: Plant phenotyping refers to a quantitative description of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Today, rapid developments are taking place in the field of non-destructive, image-analysis -based phenotyping that allow for a characterization of plant traits in high-throughput. During the last decade, ‘the field of image-based phenotyping has broadened its focus from the initial characterization of single-plant traits in controlled conditions towards ‘real-life’ applications of robust field techniques in plant plots and canopies. An important component of successful phenotyping approaches is the holistic characterization of plant performance that can be achieved with several methodologies, ranging from multispectral image analyses via thermographical analyses to growth measurements, also taking root phenotypes into account.

Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize

Abstract: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.

HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging.

Abstract: Background Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

High-throughput phenotyping of seminal root traits in wheat

Abstract: Water availability is a major limiting factor for wheat (Triticum aestivum L.) production in rain-fed agricultural systems worldwide. Root system architecture has important functional implications for the timing and extent of soil water extraction, yet selection for root architectural traits in breeding programs has been limited by a lack of suitable phenotyping methods. The aim of this research was to develop low-cost high-throughput phenotyping methods to facilitate selection for desirable root architectural traits. Here, we report two methods, one using clear pots and the other using growth pouches, to assess the angle and the number of seminal roots in wheat seedlings– two proxy traits associated with the root architecture of mature wheat plants. Both methods revealed genetic variation for seminal root angle and number in the panel of 24 wheat cultivars. The clear pot method provided higher heritability and higher genetic correlations across experiments compared to the growth pouch method. In addition, the clear pot method was more efficient – requiring less time, space, and labour compared to the growth pouch method. Therefore the clear pot method was considered the most suitable for large-scale and high-throughput screening of seedling root characteristics in crop improvement programs. The clear-pot method could be easily integrated in breeding programs targeting drought tolerance to rapidly enrich breeding populations with desirable alleles. For instance, selection for narrow root angle and high number of seminal roots could lead to deeper root systems with higher branching at depth. Such root characteristics are highly desirable in wheat to cope with anticipated future climate conditions, particularly where crops rely heavily on stored soil moisture at depth, including some Australian, Indian, South American, and African cropping regions.

Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification

Abstract: Roots are vital to plants for soil exploration and uptake of water and nutrients. Root performance is critical for growth and yield of plants, in particular when resources are limited. Since roots develop in strong interaction with the soil matrix, tools are required that can visualize and quantify root growth in opaque soil at best in 3D. Two modalities that are suited for such investigations are X-ray Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Due to the different physical principles they are based on, these modalities have their specific potentials and challenges for root phenotyping. We compared the two methods by imaging the same root systems grown in 3 different pot sizes with inner diameters of 34 mm, 56 mm or 81 mm. Both methods successfully visualized roots of two weeks old bean plants in all three pot sizes. Similar root images and almost the same root length were obtained for roots grown in the small pot, while more root details showed up in the CT images compared to MRI. For the medium sized pot, MRI showed more roots and higher root lengths whereas at some spots thin roots were only found by CT and the high water content apparently affected CT more than MRI. For the large pot, MRI detected much more roots including some laterals than CT. Both techniques performed equally well for pots with small diameters which are best suited to monitor root development of seedlings. To investigate specific root details or finely graduated root diameters of thin roots, CT was advantageous as it provided the higher spatial resolution. For larger pot diameters, MRI delivered higher fractions of the root systems than CT, most likely because of the strong root-to-soil contrast achievable by MRI. Since complementary information can be gathered with CT and MRI, a combination of the two modalities could open a whole range of additional possibilities like analysis of root system traits in different soil structures or under varying soil moisture.

Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review.

Abstract: Current methods of in-house plant phenotyping are providing a powerful new tool for plant biology studies. The self-constructed and commercial platforms established in the last few years, employ non-destructive methods and measurements on a large and high-throughput scale. The platforms offer to certain extent, automated measurements, using either simple single sensor analysis, or advanced integrative simultaneous analysis by multiple sensors. However, due to the complexity of the approaches used, it is not always clear what such forms of plant phenotyping can offer the potential end-user, i.e. plant biologist. This review focuses on imaging methods used in the phenotyping of plant shoots including a brief survey of the sensors used. To open up this topic to a broader audience, we provide here a simple introduction to the principles of automated non-destructive analysis, namely RGB, chlorophyll fluorescence, thermal and hyperspectral imaging. We further on present an overview on how and to which extent, the automated integrative in-house phenotyping platforms have been used recently to study the responses of plants to various changing environments.

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Abstract: Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput. Here, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size. DIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.

Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach

Abstract: Field-based high throughput phenotyping is a bottleneck for crop breeding research. We present a novel method for repeated remote phenotyping of maize genotypes using the Zeppelin NT aircraft as an experimental sensor platform. The system has the advantage of a low altitude and cruising speed compared to many drones or airplanes, thus enhancing image resolution while reducing blurring effects. Additionally there was no restriction in sensor weight. Using the platform, red, green and blue colour space (RGB), normalized difference vegetation index (NDVI) and thermal images were acquired throughout the growing season and compared with traits measured on the ground. Ground control points were used to co-register the images and to overlay them with a plot map. NDVI images were better suited than RGB images to segment plants from soil background leading to two separate traits: the canopy cover (CC) and its NDVI value (NDVIPlant). Remotely sensed CC correlated well with plant density, early vigour, leaf size, and radiation interception. NDVIPlant was less well related to ground truth data. However, it related well to the vigour rating, leaf area index (LAI) and leaf biomass around flowering and to very late senescence rating. Unexpectedly, NDVIPlant correlated negatively with chlorophyll meter measurements. This could be explained, at least partially, by methodical differences between the used devices and effects imposed by the population structure. Thermal images revealed information about the combination of radiation interception, early vigour, biomass, plant height and LAI. Based on repeatability values, we consider two row plots as best choice to balance between precision and available field space. However, for thermography, more than two rows improve the precision. We made important steps towards automated processing of remotely sensed data, and demonstrated the value of several procedural steps, facilitating the application in plant genetics and breeding. Important developments are: the ability to monitor throughout the season, robust image segmentation and the identification of individual plots in images from different sensor types at different dates. Remaining bottlenecks are: sufficient ground resolution, particularly for thermal imaging, as well as a deeper understanding of the relatedness of remotely sensed data and basic crop characteristics.

Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions

Abstract: The detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods. A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 – 700 nm) and near infrared (700 – 1000 nm) range 3 to 14 days after inoculation. Data analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes. With this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

A rapid and sensitive method for determination of carotenoids in plant tissues by high performance liquid chromatography

Abstract: The dietary carotenoids serve as precursor for vitamin A and prevent several chronic-degenerative diseases. The carotenoid profiling is necessary to understand their importance on human health. However, the available high-performance liquid chromatography (HPLC) methods to resolve the major carotenoids require longer analysis times and do not adequately resolve the violaxanthin and neoxanthin. A fast and sensitive HPLC method was developed using a C30 column at 20°C with a gradient consisting of methanol, methyl-tert-butyl ether and water. A total of 15 major carotenoids, including 14 all-trans forms and one cis form were resolved within 20 min. The method also distinctly resolved violaxanthin and neoxanthin present in green tissues. Additionally this method also resolved geometrical isomers of the carotenoids. The HPLC coupled with C30 column efficiently resolved fifteen carotenoids and their isomers in shorter runtime of 20 min. Application of this method to diverse matrices such as tomato fruits and leaves, Arabidopsis leaves and green pepper fruits showed the versatility and robustness of the method. The method would be useful for high throughput analysis of large number of samples.

Protocol: an improved and universal procedure for whole-mount immunolocalization in plants

Abstract: Rapid advances in microscopy have boosted research on cell biology. However sample preparation enabling excellent reproducible tissue preservation and cell labeling for in depth microscopic analysis of inner cell layers, tissues and organs still represents a major challenge for immunolocalization studies. Here we describe a protocol for whole-mount immunolocalization of proteins which is applicable to a wide range of plant species. The protocol is improved and robust for optimal sample fixation, tissue clearing and multi-protein staining procedures and can be used in combination with simultaneous detection of specific sequences of nucleic acids. In addition, cell wall and nucleus labelling can be implemented in the protocol, thereby allowing a detailed analysis of morphology and gene expression patterns with single-cell resolution. Besides enabling accurate, high resolution and reproducible protein detection in expression and localization studies, the procedure takes a single working day to complete without the need for robotic equipment.

Monitoring leaf water content with THz and sub-THz waves.

Abstract: Terahertz technology is still an evolving research field that attracts scientists with very different backgrounds working on a wide range of subjects. In the past two decades, it has been demonstrated that terahertz technology can provide a non-invasive tool for measuring and monitoring the water content of leaves and plants. In this paper we intend to review the different possibilities to perform in-vivo water status measurements on plants with the help of THz and sub-THz waves. The common basis of the different methods is the strong absorption of THz and sub-THz waves by liquid water. In contrast to simpler, yet destructive, methods THz and sub-THz waves allow for the continuous monitoring of plant water status over several days on the same sample. The technologies, which we take into focus, are THz time domain spectroscopy, THz continuous wave setups, THz quasi time domain spectroscopy and sub-THz continuous wave setups. These methods differ with respect to the generation and detection schemes, the covered frequency range, the processing and evaluation of the experimental data, and the mechanical handling of the measurements. Consequently, we explain which method fits best in which situation. Finally, we discuss recent and future technological developments towards more compact and budget-priced measurement systems for use in the field.

Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images

Abstract: Flowering (spikelet anthesis) is one of the most important phenotypic characteristics of paddy rice, and researchers expend efforts to observe flowering timing. Observing flowering is very time-consuming and labor-intensive, because it is still visually performed by humans. An image-based method that automatically detects the flowering of paddy rice is highly desirable. However, varying illumination, diversity of appearance of the flowering parts of the panicles, shape deformation, partial occlusion, and complex background make the development of such a method challenging. We developed a method for detecting flowering panicles of rice in RGB images using scale-invariant feature transform descriptors, bag of visual words, and a machine learning method, support vector machine. Applying the method to time-series images, we estimated the number of flowering panicles and the diurnal peak of flowering on each day. The method accurately detected the flowering parts of panicles during the flowering period and quantified the daily and diurnal flowering pattern. A powerful method for automatically detecting flowering panicles of paddy rice in time-series RGB images taken under natural field conditions is described. The method can automatically count flowering panicles. In application to time-series images, the proposed method can well quantify the daily amount and the diurnal changes of flowering during the flowering period and identify daily peaks of flowering.

The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Abstract: Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.

Quantifying spatial heterogeneity of chlorophyll fluorescence during plant growth and in response to water stress

Abstract: Effects of abiotic and biotic stresses on plant photosynthetic performance lead to fitness and yield decrease. The maximum quantum efficiency of photosystem II (F v/F m) is a parameter of chlorophyll fluorescence (ChlF) classically used to track changes in photosynthetic performance. Despite recent technical and methodological advances in ChlF imaging, the spatio-temporal heterogeneity of F v/F m still awaits for standardized and accurate quantification. We developed a method to quantify the dynamics of spatial heterogeneity of photosynthetic efficiency through the distribution-based analysis of F v/F m values. The method was applied to Arabidopsis thaliana grown under well-watered and severe water deficit (survival rate of 40%). First, whole-plant F v/F m shifted from unimodal to bimodal distributions during plant development despite a constant mean F v/F m under well-watered conditions. The establishment of a bimodal distribution of F v/F m reflects the occurrence of two types of leaf regions with contrasted photosynthetic efficiency. The distance between the two modes (called S) quantified the whole-plant photosynthetic heterogeneity. The weighted contribution of the most efficient/healthiest leaf regions to whole-plant performance (called W max) quantified the spatial efficiency of a photosynthetically heterogeneous plant. Plant survival to water deficit was associated to high S values, as well as with strong and fast recovery of W max following soil rewatering. Hence, during stress surviving plants had higher, but more efficient photosynthetic heterogeneity compared to perishing plants. Importantly, S allowed the discrimination between surviving and perishing plants four days earlier than the mean F v/F m. A sensitivity analysis from simulated dynamics of F v/F m showed that parameters indicative of plant tolerance and/or stress intensity caused identifiable changes in S and W max. Finally, an independent comparison of six Arabidopsis accessions grown under well-watered conditions indicated that S and W max are related to the genetic variability of growth. The distribution-based analysis of ChlF provides an efficient tool for quantifying photosynthetic heterogeneity and performance. S and W max are good indicators to estimate plant survival under water stress. Our results suggest that the dynamics of photosynthetic heterogeneity are key components of plant growth and tolerance to stress.

Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea (Pisum sativum L.)

Abstract: Recently emerging approaches to high-throughput plant phenotyping have discovered their importance as tools in unravelling the complex questions of plant growth, development and response to the environment, both in basic and applied science. High-throughput methods have been also used to study plant responses to various types of biotic and abiotic stresses (drought, heat, salinity, nutrient-starving, UV light) but only rarely to cold tolerance. We present here an experimental procedure of integrative high-throughput in-house phenotyping of plant shoots employing automated simultaneous analyses of shoot biomass and photosystem II efficiency to study the cold tolerance of pea (Pisum sativum L.). For this purpose, we developed new software for automatic RGB image analysis, evaluated various parameters of chlorophyll fluorescence obtained from kinetic chlorophyll fluorescence imaging, and performed an experiment in which the growth and photosynthetic activity of two different pea cultivars were followed during cold acclimation. The data obtained from the automated RGB imaging were validated through correlation of pixel based shoot area with measurement of the shoot fresh weight. Further, data obtained from automated chlorophyll fluorescence imaging analysis were compared with chlorophyll fluorescence parameters measured by a non-imaging chlorophyll fluorometer. In both cases, high correlation was obtained, confirming the reliability of the procedure described. This study of the response of two pea cultivars to cold stress confirmed that our procedure may have important application, not only for selection of cold-sensitive/tolerant varieties of pea, but also for studies of plant cold-response strategies in general. The approach, provides a very broad tool for the morphological and physiological selection of parameters which correspond to shoot growth and the efficiency of photosystem II, and is thus applicable in studies of various plant species and crops.

Rapid phenotyping of crop root systems in undisturbed field soils using X-ray computed tomography.

Abstract: Background X-ray computed tomography (CT) has become a powerful tool for root phenotyping. Compared to rather classical, destructive methods, CT encompasses various advantages. In pot experiments the growth and development of the same individual root can be followed over time and in addition the unaltered configuration of the 3D root system architecture (RSA) interacting with a real field soil matrix can be studied. Yet, the throughput, which is essential for a more widespread application of CT for basic research or breeding programs, suffers from the bottleneck of rapid and standardized segmentation methods to extract root structures. Using available methods, root segmentation is done to a large extent manually, as it requires a lot of interactive parameter optimization and interpretation and therefore needs a lot of time.

WallProtDB, a database resource for plant cell wall proteomics

Abstract: During the last fifteen years, cell wall proteomics has become a major research field with the publication of more than 50 articles describing plant cell wall proteomes. The WallProtDB database has been designed as a tool to facilitate the inventory, the interpretation of cell wall proteomics data and the comparisons between cell wall proteomes. WallProtDB ( http://www.polebio.lrsv.ups-tlse.fr/WallProtDB/ ) presently contains 2170 proteins and ESTs identified experimentally in 36 cell wall proteomics studies performed on 11 different plant species. Two criteria have to be met for entering WallProtDB. First one is related to the identification of proteins. Only proteins identified in plant with available genomic or ESTs data are considered to ensure unambiguous identification. Second criterion is related to the difficulty to obtain clean cell wall fractions. Indeed, since cell walls constitute an open compartment difficult to isolate, numerous proteins predicted to be intracellular and/or having functions inside the cell have been identified in cell wall extracts. Then, except proteins predicted to be plasma membrane proteins, only proteins having a predicted signal peptide and no known intracellular retention signal are included in the database. In addition, WallProtDB contains information about the strategies used to obtain cell wall protein extracts and to identify proteins by mass spectrometry and bioinformatics. Mass spectrometry data are included when available. All the proteins of WallProtDB are linked to ProtAnnDB, another database, which contains structural and functional bioinformatics annotations of proteins as well as links to other databases (Aramemnon, CAZy, Planet, Phytozome). A list of references in the cell wall proteomics field is also provided. WallProtDB aims at becoming a cell wall proteome reference database. It can be updated at any time on request and provide a support for sharing cell wall proteomics data and literature references with researchers interested in plant cell wall biology.

"Rhizoponics": a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants.

Abstract: Well-developed and functional roots are critical to support plant life and reach high crop yields. Their study however, is hampered by their underground growth and characterizing complex root system architecture (RSA) therefore remains a challenge. In the last few years, several phenotyping methods, including rhizotrons and x-ray computed tomography, have been developed for relatively thick roots. But in the model plant Arabidopsis thaliana, in vitro culture remains the easiest and preferred method to study root development, which technically limits the analyses to young seedlings. We present here an innovative design of hydroponic rhizotrons (rhizoponics) adapted to Arabidopsis thaliana. The setup allows to simultaneously characterize the RSA and shoot development from seedling to adult stages, i.e. from seed to seed. This system offers the advantages of hydroponics such as control of root environment and easy access to the roots for measurements or sampling. Being completely movable and low cost, it can be used in controlled cabinets. We chose the case of cadmium treatment to illustrate potential applications, from cell to organ levels. Rhizoponics makes possible, on the same plants of Arabidopsis, RSA measurements, root sampling and characterization of aerial development up to adult size. It therefore provides a valuable tool for addressing fundamental questions in whole plant physiology.

Guidelines for measuring and reporting environmental parameters for experiments in greenhouses

Abstract: The importance of appropriate, accurate measurement and reporting of environmental parameters in plant sciences is a significant aspect of quality assurance for all researchers and their research. There is a clear need for ensuring research across the world can be compared, understood and where necessary replicated by fellow researchers. A common set of guidelines to educate, assist and encourage comparativeness is of great importance. On the other hand, the level of effort and attention to detail by an individual researcher should be commensurate with the particular research being conducted. For example, a researcher focusing on interactions of light and temperature should measure all relevant parameters and report a measurement summary that includes sufficient detail allowing for replication. Such detail may be less relevant when the impact of environmental parameters on plant growth and development is not the main research focus. However, it should be noted that the environmental experience of a plant during production can have significant impact when subsequent experiments investigate plants at a molecular, biochemical or genetic level or where species interactions are considered. Thus, researchers are encouraged to make a critical assessment of what parameters are of primary importance in their research and these parameters should be measured and reported. This paper brings together a collection of parameters that the authors, as members of International Committee on Controlled Environment Guidelines (ICCEG) in consultation with members of our three parent organizations, believe constitute those which should be recorded and reported when publishing scientific data from experiments in greenhouses. It provides recommendations to end users on when, how and where these parameters should be measured along with the appropriate internationally standardized units that should be used.

Fast virtual histology using X-ray in-line phase tomography: application to the 3D anatomy of maize developing seeds

Abstract: Background Despite increasing demand, imaging the internal structure of plant organs or tissues without the use of transgenic lines expressing fluorescent proteins remains a challenge. Techniques such as magnetic resonance imaging, optical projection tomography or X-ray absorption tomography have been used with various success, depending on the size and physical properties of the biological material.

An ontology approach to comparative phenomics in plants.

Abstract: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.

A high-throughput pipeline for detecting locus-specific polymorphism in hexaploid wheat (Triticum aestivum L.)

Abstract: Bread wheat (Triticum aestivum L., 2n = 6x = 42) is an allohexaploid with a huge genome. Due to the presence of extensive homoeologs and paralogs, generating locus-specific sequences can be challenging, especially when a large number of sequences are required. Traditional methods of generating locus-specific sequences are rather strenuous and time-consuming if large numbers of sequences are to be handled. To improve the efficiency of isolating sequences for targeted loci, a time-saving and high-throughput pipeline integrating orthologous sequence alignment, genomic sequence retrieving, and multiple sequence alignment was developed. This pipeline was successfully employed in retrieving and aligning homoeologous sequences and 83% of the primers designed based on the pipeline successfully amplified fragments from the targeted subgenomes. The high-throughput pipeline developed in this study makes it feasible to efficiently identify locus-specific sequences for large numbers of sequences. It could find applications in all research projects where locus-specific sequences are required. In addition to generating locus-specific markers, the pipeline was also used in our laboratory to identify differentially expressed genes among the three subgenomes of bread wheat. Importantly, the pipeline is not only valuable for research in wheat but should also be applicable to other allopolyploid species.

Within leaf variation is the largest source of variation in agroinfiltration of Nicotiana benthamiana.

Abstract: Transient gene expression utilizing syringe agroinfiltration offers a simple and efficient technique for different transgenic applications. Leaves of Nicotiana benthamiana show reliable and high transformation efficiency, but in quantitative assays also a certain degree of variation. We used a nested design in our agroinfiltration experiments to dissect the sources of this variation. An intron containing firefly luciferase gene was used as a reporter for agroinfiltration. A number of 6 week old tobacco plants were infiltrated for their top leaves, several samples were punched from the leaves after 2 days of transient expression, and protein extracts from the samples were repeatedly measured for luciferase activity. Interestingly, most of the variation was due to differences between the sampling spots in the leaves, the next important source being the different leaves on each plant. Variation between similar experiments, between plants and between repetitive measurements of the extracts could be easily minimized. Efforts and expenditure of agroinfiltration experiments can be optimized when sources of variation are known. In summary, infiltrate more plants but less leaves, sample more positions on the leaf but run only few technical replicates.

Multiscale imaging of plants: current approaches and challenges.

Abstract: We review a set of recent multiscale imaging techniques, producing high-resolution images of interest for plant sciences. These techniques are promising because they match the multiscale structure of plants. However, the use of such high-resolution images is challenging in the perspective of their application to high-throughput phenotyping on large populations of plants, because of the memory cost for their data storage and the computational cost for their processing to extract information. We discuss how this renews the interest for multiscale image processing tools such as wavelets, fractals and recent variants to analyse such high-resolution images.

High-throughput phenotyping of plant resistance to aphids by automated video tracking

Abstract: Piercing-sucking insects are major vectors of plant viruses causing significant yield losses in crops. Functional genomics of plant resistance to these insects would greatly benefit from the availability of high-throughput, quantitative phenotyping methods. We have developed an automated video tracking platform that quantifies aphid feeding behaviour on leaf discs to assess the level of plant resistance. Through the analysis of aphid movement, the start and duration of plant penetrations by aphids were estimated. As a case study, video tracking confirmed the near-complete resistance of lettuce cultivar ‘Corbana’ against Nasonovia ribisnigri (Mosely), biotype Nr:0, and revealed quantitative resistance in Arabidopsis accession Co-2 against Myzus persicae (Sulzer). The video tracking platform was benchmarked against Electrical Penetration Graph (EPG) recordings and aphid population development assays. The use of leaf discs instead of intact plants reduced the intensity of the resistance effect in video tracking, but sufficiently replicated experiments resulted in similar conclusions as EPG recordings and aphid population assays. One video tracking platform could screen 100 samples in parallel. Automated video tracking can be used to screen large plant populations for resistance to aphids and other piercing-sucking insects.

Protocol: a medium-throughput method for determination of cellulose content from single stem pieces of Arabidopsis thaliana

Abstract: Lignocellulosic biomass is an important renewable resource for biofuels and materials. How plants synthesise cellulose is not completely understood. It is known that cellulose synthase complex (CSCs) moving in the plasma membrane synthesise the cellulose. CESA proteins are the core components of CSC. In Arabidopsis, in vitro mutagenesis of proteins followed by complementation analysis of mutants lacking the gene represents an important tool for studying any biological process, including cellulose biosynthesis. Analysis of a large number of plants is crucial for these types of studies. By using aspiration rather than centrifugation to remove liquids during various stages of protocol, we were able to increase the throughput of the method as well as minimise the sample loss. As a test case, we determined cellulose content of wild type and secondary wall cesa mutants across the length of primary shoot which was fond to be rather uniform in 7-week-old plants. Additionally, we found that the cellulose content of single mutants was comparable to the higher order mutants. Here we describe a medium-throughput adaptation of Updegraff’s method that allowed us to determine cellulose content of 200 samples each week.

TRiP: Tracking Rhythms in Plants, an automated leaf movement analysis program for circadian period estimation

Abstract: A well characterized output of the circadian clock in plants is the daily rhythmic movement of leaves. This process has been used extensively in Arabidopsis to estimate circadian period in natural accessions as well as mutants with known defects in circadian clock function. Current methods for estimating circadian period by leaf movement involve manual steps throughout the analysis and are often limited to analyzing one leaf or cotyledon at a time. In this study, we describe the development of TRiP (Tracking Rhythms in Plants), a new method for estimating circadian period using a motion estimation algorithm that can be applied to whole plant images. To validate this new method, we apply TRiP to a Recombinant Inbred Line (RIL) population in Arabidopsis using our high-throughput imaging platform. We begin imaging at the cotyledon stage and image through the emergence of true leaves. TRiP successfully tracks the movement of cotyledons and leaves without the need to select individual leaves to be analyzed. TRiP is a program for analyzing leaf movement by motion estimation that enables high-throughput analysis of large populations of plants. TRiP is also able to analyze plant species with diverse leaf morphologies. We have used TRiP to estimate period for 150 Arabidopsis RILs as well as 5 diverse plant species, highlighting the broad applicability of this new method.

Two-dimensional multifractal detrended fluctuation analysis for plant identification

Abstract: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I 0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 − fold cross validation, while the accuracy reaches 93.96% for all fifteen species. Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

A whole-plant chamber system for parallel gas exchange measurements of Arabidopsis and other herbaceous species

Abstract: Photosynthetic assimilation of carbon is a defining feature of the plant kingdom. The fixation of large amounts of carbon dioxide supports the synthesis of carbohydrates, which make up the bulk of plant biomass. Exact measurements of carbon assimilation rates are therefore crucial due to their impact on the plants metabolism, growth and reproductive success. Commercially available single-leaf cuvettes allow the detailed analysis of many photosynthetic parameters, including gas exchange, of a selected leaf area. However, these cuvettes can be difficult to use with small herbaceous plants such as Arabidopsis thaliana or plants having delicate or textured leaves. Furthermore, data from single leaves can be difficult to scale-up for a plant shoot with a complex architecture and tissues in different physiological states. Therefore, we constructed a versatile system—EGES-1—to simultaneously measure gas exchange in the whole shoots of multiple individual plants. Our system was designed to be able record data continuously over several days. The EGES-1 system yielded comparable measurements for eight plants for up to 6 days in stable, physiologically realistic conditions. The chambers seals have negligible permeability to carbon dioxide and the system is designed so as to detect any bulk-flow air leaks. We show that the system can be used to monitor plant responses to changing environmental conditions, such as changes in illumination or stress treatments, and to compare plants with phenotypically severe mutations. By incorporating interchangeable lids, the system could be used to measure photosynthetic gas exchange in several genera such as Arabidopsis, Nicotiana, Pisum, Lotus and Mesembryanthemum. EGES-1 can be introduced into a variety of growth facilities and measure gas exchange in the shoots diverse plant species grown in different growth media. It is ideal for comparing photosynthetic carbon assimilation of wild-type and mutant plants and/or plants undergoing selected experimental treatments. The system can deliver valuable data for whole-plant growth studies and help understanding mutant phenotypes. Overall, the EGES-1 is complementary to the readily-available single leaf systems that focus more on the photosynthetic process in within the leaf lamina.