Phenomics Platform Research Articles

Legume Crops and Biotrophic Pathogen Interactions: A Continuous Cross-Talk of a Multilayered Array of Defense Mechanisms.

Legume species are recognized for their nutritional benefits and contribution to the sustainability of agricultural systems. However, their production is threatened by biotic constraints with devastating impacts on crop yield. A deep understanding of the molecular and genetic architecture of resistance sources culminating in immunity is critical to assist new biotechnological approaches for plant protection. In this review, the current knowledge regarding the major plant immune system components of grain and forage legumes challenged with obligate airborne biotrophic fungi will be comprehensively evaluated and discussed while identifying future directions of research. To achieve this, we will address the multi-layered defense strategies deployed by legume crops at the biochemical, molecular, and physiological levels, leading to rapid pathogen recognition and carrying the necessary information to sub-cellular components, on-setting a dynamic and organized defense. Emphasis will be given to recent approaches such as the identification of critical components of host decentralized immune response negatively regulated by pathogens while targeting the loss-of-function of susceptibility genes. We conclude that advances in gene expression analysis in both host and pathogen, protocols for effectoromics pipelines, and high-throughput disease phenomics platforms are rapidly leading to a deeper understanding of the intricate host-pathogen interaction, crucial for efficient disease resistance breeding initiatives.

PhenoChip: A single-cell phenomic platform for high-throughput photophysiological analyses of microalgae.

Photosynthetic microorganisms are key players in aquatic ecosystems with strong potential for bioenergy production, yet their systematic selection at the single-cell level for improved productivity or stress resilience ("phenotyping") has remained largely inaccessible. To facilitate the phenotyping of microalgae and cyanobacteria, we developed "PhenoChip," a platform for the multiparametric photophysiological characterization and selection of unicellular phenotypes under user-controlled physicochemical conditions. We used PhenoChip to expose single cells of the coral symbiont Symbiodinium to thermal and chemical treatments and monitor single-cell photophysiology via chlorophyll fluorometry. This revealed strain-specific thermal sensitivity thresholds and distinct pH optima for photosynthetic performance, and permitted the identification of single cells with elevated resilience toward rising temperature. Optical expulsion technology was used to collect single cells from PhenoChip, and their propagation revealed indications of transgenerational preservation of photosynthetic phenotypes. PhenoChip represents a versatile platform for the phenotyping of photosynthetic unicells relevant to biotechnology, ecotoxicology, and assisted evolution.

Rapid Screening of Nitrogen Use Efficiency in Perennial Ryegrass (Lolium perenne L.) Using Automated Image-Based Phenotyping.

Perennial ryegrass (Lolium perenne L.) is a dominant species in temperate Australian pastures. Currently, nitrogenous fertilizers are used to support herbage production for pasture and fodder. Increasing the nitrogen use efficiency (NUE) of pasture grasses could decrease the amount of fertilizer application and reduce nitrogen (N) leaching into the environment. NUE, defined as units of dry matter production per unit of supplied nitrogen, is a complex trait in which genomic selection may provide a promising strategy in breeding. Our objective was to develop a rapid, high-throughput screening method to enable genomic selection for y -60NUE in perennial ryegrass. NUE of 76 genotypes of perennial ryegrass from a breeding population were screened in a greenhouse using an automated image-based phenomics platform under low (0.5 mM) and moderate (5 mM) N levels over 3 consecutive harvests. Significant (p < 0.05) genotype, treatment, and genotype by treatment interactions for dry matter yield and NUE were observed. NUE under low and moderate N treatments was significantly correlated. Of the seven plant architecture features directly extracted from image analysis and four secondarily derived measures, mean projected plant area (MPPA) from the two side view images had the highest correlation with dry matter yield (r = 0.94). Automated digital image-based phenotyping enables temporal plant growth responses to N to be measured efficiently and non-destructively. The method developed in this study would be suitable for screening large populations of perennial ryegrass growth in response to N for genomic selection purposes.

Plant Phenomics: Fundamental Bases, Software and Hardware Platforms, and Machine Learning

In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e., physical and biochemical characteristics, considered as a set of phenotypes of a plant organism, has emerged. Phenomics is a postgenomic discipline that actively uses the achievements of the genomic era and bioinformatics. It supplements them with standardized and statistically significant factual material on phenotypes with a high degree of detail. The technique of obtaining and analyzing information about phenotypes in phenomics is called phenotyping. High-performance phenotyping, providing digital automated analysis of large data samples, has become widespread. Recent progress in high-performance phenotyping has been associated with the development of image registration systems in various spectral regions, approaches to cultivating plant objects under standardized conditions, sensory technologies, robotics, and methods for data processing and analysis, such as computer vision and machine learning (artificial neural network). Phenomics technologies have a high information content analysis, surpassing human capabilities, performing measurements in the hyperspectral range using X-ray tomography and ultra-precise “thermal” images, and have a number of other low-invasive and precision approaches. Arrays of data obtained using phenomics technologies are recorded and processed automatically and are free from the problems of subjective assessment and inadequate statistical processing. It is assumed that phenotyping will allow for the creation of digital models of the vital activity processes and the “formation” of plant productivity at the organism level in connection with the dynamics of transcriptomes, proteomes, and metabolomes. Phenomics helps researchers transform a large amount of information received from modern sensors into new knowledge using computer data processing and modeling, reducing the distance from basic science to the practical application of results in crop production and breeding. Phenotyping is actively developing both in laboratory and in greenhouse conditions as well as on open agricultural sites, forests, and in real natural phytocenoses. The review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.

Characterization of the response to abiotic stresses of high ascorbate Arabidopsis lines using phenomic approaches

L-Ascorbic acid (AsA, vitamin C) is a key antioxidant and enzyme cofactor in plants. Ascorbate controls cell division, affects cell expansion, and plays an important role in modulating plant senescence. It protects plants against reactive oxygen species that are produced in response to abiotic and biotic stresses. Manual phenotyping indicated that Arabidopsis lines over-expressing enzymes in the myo-inositol pathway have elevated AsA, accumulate more biomass of both aerial and root tissues and are tolerant to abiotic stresses including salt, cold, heat, and environmental pollutants. However, manual phenotyping is time consuming, low throughput, subjective, and limited to the resolution of the human eye. In contrast, high throughput phenotyping technologies are accurate, non-destructive, and more sensitive, allowing the detection of subtle phenotypes. Therefore, we used a phenomics platform to phenotype our high AsA Arabidopsis lines with visible, fluorescence, and near infrared cameras. Based on this approach, high AsA lines grew faster, accumulated more biomass, and displayed healthier chlorophyll fluorescence and water content profiles than controls. By studying abiotic stress in a high throughout fashion using optimized protocols, we have also shown that these high AsA lines are tolerant to salt and water limitation stresses. In addition, we developed open source algorithms to analyze images and by comparing results obtained with a widely used commercial software against our algorithms, here we show that our method achieved good accuracy for all phenotypic parameters of interest including projected leaf area, rosette diameter (caliper length), compactness, and color classification.

Acquired Traits Contribute More to Drought Tolerance in Wheat Than in Rice.

Drought tolerance is governed by constitutive and acquired traits. Combining them has relevance for sustaining crop productivity under drought. Mild levels of stress induce specific mechanisms that protect metabolism when stress becomes severe. Here, we report a comparative assessment of “acquired drought tolerance (ADT)” traits in two rice cultivars, IR64 (drought susceptible) and Apo (tolerant), and a drought-tolerant wheat cultivar, Weebill. Young seedlings were exposed to progressive concentrations of methyl viologen (MV), a stress inducer, before transferring to a severe concentration. “Induced” seedlings showed higher tolerance and recovery growth than seedlings exposed directly to severe stress. A novel phenomic platform with an automated irrigation system was used for precisely imposing soil moisture stress to capture ADT traits during the vegetative stage. Gradual progression of drought was achieved through a software-controlled automated irrigation facility. This facility allowed the maintenance of the same level of soil moisture irrespective of differences in transpiration, and hence, this platform provided the most appropriate method to assess ADT traits. Total biomass decreased more in IR64 than in Apo. The wheat cultivar showed lower levels of damage and higher recovery growth even compared to Apo. Expression of ROS-scavenging enzymes and drought-responsive genes was significantly higher in Apo than in IR64, but differences were only marginal between Apo and Weebill. The wheat cultivar showed significantly higher stomatal conductance, carbon gain, and biomass than the rice cultivars, under drought. These differences in ADT traits between cultivars as well as between species can be utilised for improving drought tolerance in crop plants.

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER.

ObjectiveElectronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research.Materials and MethodsWe implemented a rule-based phenotyping framework, with up to 6 approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DM+D prescription codes.ResultsUsing the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications.ConclusionsWe describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research.

EIFiso4G Augments the Synthesis of Specific Plant Proteins Involved in Normal Chloroplast Function.

The plant-specific translation initiation complex eIFiso4F is encoded by three genes in Arabidopsis (Arabidopsis thaliana)-genes encoding the cap binding protein eIFiso4E (eifiso4e) and two isoforms of the large subunit scaffolding protein eIFiso4G (i4g1 and i4g2). To quantitate phenotypic changes, a phenomics platform was used to grow wild-type and mutant plants (i4g1, i4g2, i4e, i4g1 x i4g2, and i4g1 x i4g2 x i4e [i4f]) under various light conditions. Mutants lacking both eIFiso4G isoforms showed the most obvious phenotypic differences from the wild type. Two-dimensional differential gel electrophoresis and mass spectrometry were used to identify changes in protein levels in plants lacking eIFiso4G. Four of the proteins identified as measurably decreased and validated by immunoblot analysis were two light harvesting complex binding proteins 1 and 3, Rubisco activase, and carbonic anhydrase. The observed decreased levels for these proteins were not the direct result of decreased transcription or protein instability. Chlorophyll fluorescence induction experiments indicated altered quinone reduction kinetics for the double and triple mutant plants with significant differences observed for absorbance, trapping, and electron transport. Transmission electron microscopy analysis of the chloroplasts in mutant plants showed impaired grana stacking and increased accumulation of starch granules consistent with some chloroplast proteins being decreased. Rescue of the i4g1 x i4g2 plant growth phenotype and increased expression of the validated proteins to wild-type levels was obtained by overexpression of eIFiso4G1. These data suggest a direct and specialized role for eIFiso4G in the synthesis of a subset of plant proteins.

Open-Source High-Throughput Phenomics of Bacterial Promoter-Reporter Strains.

Open-source electronics are becoming more prevalent in biological sciences, enabling novel and unique means of data acquisition. Here, we present 3D-printed, open-source tools to acquire fluorescence phenotypes with high temporal resolution. Printed fluorescence imaging boxes (PFIboxes) cost approximately 200US dollars to assemble, can be placed in incubators or hypoxic chambers, and accurately read high-density colony arrays of microorganisms. We demonstrate the utility of PFIboxes using a timecourse gene expression approach, examining globalEscherichia coli promoter activity using a fluorescent reporter library across a diverse panel of 15 antibiotics, each at several concentrations. Many secondary and indirect effects were observed when E.coli was challenged with various drugs, including increased gene expression in carbon metabolism processes. Further, kinetic data acquisition enabled non-destructive time course gene expression, clustering of which revealed patterns of co-expression. In all, PFIboxes provide an open solution to gene expression, for about 2US dollars per treatment condition, including technical replicates.

Non-invasive quantification of tomato (Solanum lycopersicum L.) plant biomass through digital imaging using phenomics platform

Phenotyping approaches, using high throughput imaging techniques, are being adopted over the traditional methodologies which are manpower intensive, time consuming and low throughput. However, the effectiveness of high throughput plant phenotyping through imaging in plant phenomics facility essentially requires establishing relationship between plant areas quantified through imaging and the actual biomass. The present study was conducted with an aim to standardise the methodology for digital quantification of tomato biomass using plant phenomics facility. A strong linear relationship was observed between the actual tomato plant fresh mass, digital biomass and projected shoot area. The correlations between plant fresh mass, plant digital biomass and projected shoot area were highly significant at 30, 45 and 60 days after transplanting, but at 75 days no correlation was observed. Hence, the present study clearly demonstrated that the growth of tomato plants could be monitored through digital imaging using either projected shoot area or digital biomass till 60 days after transplanting across genotypes for high throughput phenotyping.

Massively Parallelized Pollen Tube Guidance and Mechanical Measurements on a Lab-on-a-Chip Platform.

Pollen tubes are used as a model in the study of plant morphogenesis, cellular differentiation, cell wall biochemistry, biomechanics, and intra- and intercellular signaling. For a “systems-understanding” of the bio-chemo-mechanics of tip-polarized growth in pollen tubes, the need for a versatile, experimental assay platform for quantitative data collection and analysis is critical. We introduce a Lab-on-a-Chip (LoC) concept for high-throughput pollen germination and pollen tube guidance for parallelized optical and mechanical measurements. The LoC localizes a large number of growing pollen tubes on a single plane of focus with unidirectional tip-growth, enabling high-resolution quantitative microscopy. This species-independent LoC platform can be integrated with micro-/nano-indentation systems, such as the cellular force microscope (CFM) or the atomic force microscope (AFM), allowing for rapid measurements of cell wall stiffness of growing tubes. As a demonstrative example, we show the growth and directional guidance of hundreds of lily (Lilium longiflorum) and Arabidopsis (Arabidopsis thaliana) pollen tubes on a single LoC microscopy slide. Combining the LoC with the CFM, we characterized the cell wall stiffness of lily pollen tubes. Using the stiffness statistics and finite-element-method (FEM)-based approaches, we computed an effective range of the linear elastic moduli of the cell wall spanning the variability space of physiological parameters including internal turgor, cell wall thickness, and tube diameter. We propose the LoC device as a versatile and high-throughput phenomics platform for plant reproductive and development biology using the pollen tube as a model.

InfraPhenoGrid: A scientific workflow infrastructure for plant phenomics on the Grid

Plant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges, in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the datasets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data.In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage datasets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middleware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users.

Editorial: Branching and Rooting Out with a CT Scanner: The Why, the How, and the Outcomes, Present and Possibly Future.

Editorial: Branching and Rooting Out with a CT Scanner: The Why, the How, and the Outcomes, Present and Possibly Future.

Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture

Our capacity to measure performance- and efficiency-related phenotypes in grazing livestock in a timely manner, ideally in real-time without human interference, has been severely limited. Future demands and constraints on grazing livestock production will require a step change beyond our current approaches to obtaining phenotypic data. Animal phenomics is a relatively new term that describes the next generation of animal trait measurement, including methodologies and equipment used to acquire data on traits, and computational approaches required to turn data into phenotypic information. Phenomics offers a range of emerging opportunities to define new traits specific to grazing livestock, including intake and efficiency at pasture, and to measure many traits simultaneously or at a level of detail previously unachievable in the grazing environment. Application of this approach to phenotyping can improve the precision with which nutritional and other management strategies are applied, enable development of predictive biological traits, and accelerate the rate at which genetic gain is achieved for existing and new traits. In the present paper, we briefly outline the potential for livestock phenomics and describe (1) on-animal sensory-based approaches to develop traits diagnostic of productivity and efficiency, as well as resilience, health and welfare and (2) on-farm methods for data collection that drive management solutions to reduce input costs and accelerate genetic gain. The technological and analytical challenges associated with these objectives are also briefly considered, along with a brief overview of a promising field of work in which phenomics will affect animal agriculture, namely efficiency at pasture.

A Comprehensive Approach to Assess Arabidopsis Survival Phenotype in Water-Limited Condition Using a Non-invasive High-Throughput Phenomics Platform.

With the rapid rise in global population and the challenges caused by climate changes, the maximization of plant productivity and the development of sustainable agriculture strategies are vital for food security. One of the resources more affected in this new environment will be the limitation of water. In this study, we describe the use of non-invasive technologies exploiting sensors for visible, fluorescent, and near-infrared lights to accurately screen survival phenotypes in Arabidopsis thaliana exposed to water-limited conditions. We implemented two drought protocols and a robust analysis methodology that enabled us to clearly assess the wilting or dryness status of the plants at different time points using a phenomics platform. In conclusion, our approach has shown to be very accurate and suitable for experiments where hundred of samples have to be screened making a manual evaluation unthinkable. This approach can be used not only in functional genomics studies but also in agricultural applications.

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

BackgroundPlant 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.DescriptionHere, 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.ConclusionDIRT 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0093-3) contains supplementary material, which is available to authorized users.

Extrahepatic manifestations of ATP-binding cassette transporter B4 (ABCB4) deficiency detected in a systematic phenomics platform

Extrahepatic manifestations of ATP-binding cassette transporter B4 (ABCB4) deficiency detected in a systematic phenomics platform

Physiological responses to Megafol® treatments in tomato plants under drought stress: A phenomic and molecular approach

Drought is one of the most significant abiotic stresses that limits the growth and productivity of crop plants. We investigated the physiological and molecular responses of tomato plants treated with Megafol® (Valagro S.p.A), under specific drought conditions. The goal was to evaluate the impact of Megafol®, a biostimulant composed of a complex of vitamins, aminoacids, proteins and betaines, in attenuating the negative physiological responses of drought. Tomato plants were grown in a greenhouse, and physiological parameters were collected using Scanalyzer 3D (LemnaTec, GmbH), a plant phenomics platform. Using this technology it is possible to dynamically study the effects of biostimulants, such as Megafol®, on plant development in terms of early detection of physiological plant stress responses. The results showed that drought-stressed plants treated with Megafol® were healthier in terms of the biomass produced and chlorophyll fluorescence, thus highlighting the higher tolerance to stress of the treated plants. The effects of Megafol® were also studied at a molecular level by analysing the induction of genes typically involved in drought stress responses. Our results demonstrate the efficacy of Megafol® to reduce drought-stress related damage in tomato plants.