Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r 2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.Oryza sativa | QTL | three-dimensional | live root imaging | multivariate analysis R oot systems are high-value targets for crop improvement because of their potential to boost or stabilize yields in saline, dry, and acid soils, improve disease resistance, and reduce the need for unsustainable fertilizers (1-7). Root system architecture (RSA) describes the spatial organization of root systems, which is critical for root function in challenging environments (1-10). Modern genomics could allow us to leverage both natural and engineered variation to breed more efficient crops, but the lack of parallel advances in plant phenomics is widely considered to be a primary hindrance to developing "next-generation" agriculture (3,11,12). Root imaging and analysis have been particularly intractable: Decades of phenotyping efforts have failed to identify genes controlling quantitative RSA traits in crop species. Several factors confound RSA gene identification, including polygenic inheritance of root traits, soil opacity, and a complex 3D morphology that can be influenced heavily by the environment. Most phenotyping efforts have relied on small numbers of basic measurements to extrapolate system-wide traits. For example, given the length and mass of a few sample roots and the excavated root system mass, one can estimate the total root length, volume, and average root width of the entire root system (13,14). Other common measurements involve measuring the root surface exposed on a soil core or pressed against a transparent surface to estimate root coverage at a certain soil horizon. In these cases, the choices of sample roots and phenotyping standards, the size and shape of the container, and the limitations of 2D descriptions of 3D struct...

The ability to nondestructively image and automatically phenotype complex root systems, like those of rice (Oryza sativa), is fundamental to identifying genes underlying root system architecture (RSA). Although root systems are central to plant fitness, identifying genes responsible for RSA remains an underexplored opportunity for crop improvement. Here we describe a nondestructive imaging and analysis system for automated phenotyping and trait ranking of RSA. Using this system, we image rice roots from 12 genotypes. We automatically estimate RSA traits previously identified as important to plant function. In addition, we expand the suite of features examined for RSA to include traits that more comprehensively describe monocot RSA but that are difficult to measure with traditional methods. Using 16 automatically acquired phenotypic traits for 2,297 images from 118 individuals, we observe (1) wide variation in phenotypes among the genotypes surveyed; and (2) greater intergenotype variance of RSA features than variance within a genotype. RSA trait values are integrated into a computational pipeline that utilizes supervised learning methods to determine which traits best separate two genotypes, and then ranks the traits according to their contribution to each pairwise comparison. This trait-ranking step identifies candidate traits for subsequent quantitative trait loci analysis and demonstrates that depth and average radius are key contributors to differences in rice RSA within our set of genotypes. Our results suggest a strong genetic component underlying rice RSA. This work enables the automatic phenotyping of RSA of individuals within mapping populations, providing an integrative framework for quantitative trait loci analysis of RSA.

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

SUMMARY Stress responses in plants are tightly coordinated with developmental processes, but the interaction of these pathways is poorly understood. We used genome-wide assays at high spatio-temporal resolution to understand the processes that link development and stress in the Arabidopsis root. Our meta-analysis finds little evidence for a universal stress response. However, common stress responses appear to exist with many showing cell-type specificity. Common stress responses may be mediated by cell identity regulators, as mutations in these genes resulted in altered responses to stress. Evidence for a direct role for cell identity regulators came from genome-wide binding profiling of the key regulator SCARECROW, which showed binding to regulatory regions of stress-responsive genes. Co-expression in response to stress was used to identify genes involved in specific developmental processes. These results reveal surprising linkages between stress and development at cellular resolution, and show the power of multiple genome-wide datasets to elucidate biological processes.

Root system growth and development is highly plastic and is influenced by the surrounding environment. Roots frequently grow in heterogeneous environments that include interactions from neighboring plants and physical impediments in the rhizosphere. To investigate how planting density and physical objects affect root system growth, we grew rice in a transparent gel system in close proximity with another plant or a physical object. Root systems were imaged and reconstructed in three dimensions. Root-root interaction strength was calculated using quantitative metrics that characterize the extent to which the reconstructed root systems overlap each other. Surprisingly, we found the overlap of root systems of the same genotype was significantly higher than that of root systems of different genotypes. Root systems of the same genotype tended to grow toward each other but those of different genotypes appeared to avoid each other. Shoot separation experiments excluded the possibility of aerial interactions, suggesting root communication. Staggered plantings indicated that interactions likely occur at root tips in close proximity. Recognition of obstacles also occurred through root tips, but through physical contact in a size-dependent manner. These results indicate that root systems use two different forms of communication to recognize objects and alter root architecture: root-root recognition, possibly mediated through root exudates, and root-object recognition mediated by physical contact at the root tips. This finding suggests that root tips act as local sensors that integrate rhizosphere information into global root architectural changes.3D reconstruction | imaging | kin recognition P lants interact with the environment in a number of ways (1, 2).Aboveground tissues may identify volatile cues that provide information about their neighbors (3, 4) and detect irradiance, directional light, and light quality (5), whereas belowground tissues, such as roots, can detect changes in soil moisture, nutrient availability, and physical obstacles (6-8). Plants not only detect but also respond to changes in their environment, exhibiting adaptation in their morphology and physiology in response to environmental stimuli (9-14), such as alteration in total root length, root system volume, and root depth (15,16). Phenotypic plasticity of plants in response to environmental heterogeneity may have consequences for plant fitness.Communication among plants is mediated by interactions that take place aboveground (17, 18) and belowground (2,(19)(20)(21). Aboveground interactions have been studied in greater detail, in part because of the accessibility of aerial tissue. However, there is growing interest in root-system architecture and its effect on plant function and fitness (12, 15). Studies of root-system architecture suggest that root systems develop differently in the presence of other root systems. For example, when exposed to the roots of a neighboring plant, common bean plants altered the vertical and horizontal distribution of roo...

Ralstonia solanacearum is the causal agent of bacterial wilt and infects over 200 plant species in 50 families. The soilborne bacterium is lethal to many solanaceous species, including tomato. Although resistant plants can carry high pathogen loads (between 10 and 10 CFU/g fresh weight), the disease is best controlled by the use of resistant cultivars, particularly resistant rootstocks. How these plants have latent infections yet maintain resistance is not clear. R. solanacearum first infects the plant through the root system and, thus, early root colonization events may be key to understanding resistance. We hypothesized that the distribution and timing of bacterial invasion differed in roots of resistant and susceptible tomato cultivars. Here, we use a combination of scanning electron microscopy and light microscopy to investigate R. solanacearum colonization in roots of soil-grown resistant and susceptible tomato cultivars at multiple time points after inoculation. Our results show that colonization of the root vascular cylinder is delayed in resistant 'Hawaii7996' and that, once bacteria enter the root vascular tissues, colonization in the vasculature is spatially restricted. Our data suggest that resistance is due, in part, to the ability of the resistant cultivar to restrict bacterial root colonization in space and time.

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