[Show abstract][Hide abstract] ABSTRACT: 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.
Proceedings of the National Academy of Sciences 04/2013; · 9.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Characterizing 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.
We 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.
We 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.
[Show abstract][Hide abstract] ABSTRACT: In order to enjoy a digital version of the Jordan Curve Theorem, it is common to use the closed topology for the foreground and the open topology for the background of a 2-dimensional binary image. In this paper, we introduce a single topology that enjoys this theorem for all thresholds decomposing a real-valued image into foreground and background. This topology is easy to construct and it generalizes to n-dimensional images.
Voronoi Diagrams in Science and Engineering (ISVD), 2012 Ninth International Symposium on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Interest in the structure and function of physical biological networks has spurred the development of a number of theoretical models that predict optimal network structures across a broad array of taxonomic groups, from mammals to plants. In many cases, direct tests of predicted network structure are impossible given the lack of suitable empirical methods to quantify physical network geometry with sufficient scope and resolution. There is a long history of empirical methods to quantify the network structure of plants, from roots, to xylem networks in shoots and within leaves. However, with few exceptions, current methods emphasize the analysis of portions of, rather than entire networks. Here, we introduce the Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI), a user-assisted software tool that facilitates improved empirical understanding of leaf network structure. LEAF GUI takes images of leaves where veins have been enhanced relative to the background, and following a series of interactive thresholding and cleaning steps, returns a suite of statistics and information on the structure of leaf venation networks and areoles. Metrics include the dimensions, position, and connectivity of all network veins, and the dimensions, shape, and position of the areoles they surround. Available for free download, the LEAF GUI software promises to facilitate improved understanding of the adaptive and ecological significance of leaf vein network structure.
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Plants allocate a significant fraction of their biomass to sub-surface root systems. The resulting root systems are complex networks whose structural properties influence plant functions such as anchorage and resource uptake. However, the quantitative link between plant structure and plant function remains unclear because of the paucity of data on the spatial and temporal distribution of root systems for the majority of plant species. One of the main barriers to analysis is the lack of non-invasive experimental methods to monitor and characterize the growth of plant root systems dynamically in time. Here, we utilize a recently developed gel-based growth and imaging system to characterize root system architecture (RSA) properties of 10 different varieties of rice (Iyer-Pascuzzi et al., in prep). Using this system we observe the growth of complex root systems and automatically measure properties of the network as a whole after germination. These ontogenetic series reveal different profiles of root growth and allow us to characterize invariant features of root system growth (when they exist) as well as heritable phenotypic differences between varieties.
The dynamics of a growing root network are assessed by automatically computing a set of 12 structural properties related to how a root network grows in space. Several of the properties we estimate, such as specific root length and average root width, are already known to be important factors contributing to plant fitness. In particular, we find that some properties, such as network area and total length, grow linearly with time for all varieties. Quantification of multiple individuals for each variety demonstrates that growth profiles are heritable and that intra-variety differences of linear growth rates are less than between-variety differences. We also present preliminary evidence for allocation tradeoffs in rice root system growth between extension (as measured by the depth of a root system) and exploration (as measured by the number of roots). Varieties which allocate more biomass to extension immediately after germination show less exploration capacity, whereas the converse also holds over the first two weeks of growth. Finally, we describe ongoing attempts to characterize those aspects of root system development that are most conserved and those that are most divergent between rice varieties as part of ongoing efforts for improving crop yields and crop resilience.
[Show abstract][Hide abstract] ABSTRACT: Plant health and survival is dependent on the root system architecture (RSA), the spatial configuration of different types and ages of roots on a single plant. Root systems are highly plastic, allowing for soil exploration in diverse conditions. Modification of RSA could contribute to improvements of desirable agronomic traits such as drought tolerance and resistance to nutrient deficiencies. Although roots are central to plant fitness, knowledge regarding the genes underlying RSA is limited, in part due to the inaccessibility of root systems. We have developed a non-destructive gel-based imaging and analysis system for automated phenotyping of root system architecture in two and three dimensions. Here, we use this system for QTL analysis of rice root architecture. We imaged and automatically phenotyped 16 traits in the root systems of 180 recombinant inbred lines of rice under nutrient replete conditions across three days. We find multiple QTL on each day, several of which correspond to those previously identified using sand or soil-based systems. In addition, we explore the effects of different abiotic stresses on the root system. This work forms the foundation for fine-mapping and cloning the genes responsible for root system architecture in a variety of environmental conditions.
International Plant and Animal Genome Conference XX 2012;