RootScape: A Landmark-Based System for Rapid Screening of Root Architecture in Arabidopsis

The architecture of plant roots affects essential functions including nutrient and water uptake, soil anchorage, and symbiotic interactions. Root architecture comprises many features that arise from the growth of the primary and lateral roots. These root features are dictated by the genetic background but are also highly responsive to the environment. Thus, root system architecture (RSA) represents an important and complex trait that is highly variable, affected by genotype × environment interactions, and relevant to survival/performance. Quantification of RSA in Arabidopsis (Arabidopsis thaliana) using plate-based tissue culture is a very common and relatively rapid assay, but quantifying RSA represents an experimental bottleneck when it comes to medium- or high-throughput approaches used in mutant or genotype screens. Here, we present RootScape, a landmark-based allometric method for rapid phenotyping of RSA using Arabidopsis as a case study. Using the software AAMToolbox, we created a 20-point landmark model that captures RSA as one integrated trait and used this model to quantify changes in the RSA of Arabidopsis (Columbia) wild-type plants grown under different hormone treatments. Principal component analysis was used to compare RootScape with conventional methods designed to measure root architecture. This analysis showed that RootScape efficiently captured nearly all the variation in root architecture detected by measuring individual root traits and is 5 to 10 times faster than conventional scoring. We validated RootScape by quantifying the plasticity of RSA in several mutant lines affected in hormone signaling. The RootScape analysis recapitulated previous results that described complex phenotypes in the mutants and identified novel gene × environment interactions.Roots have a crucial impact on plant survival because of their major functions: anchorage of the plant in the soil, water and nutrient acquisition, and symbiotic interaction with other organisms (Den Herder et al., 2010). One important characteristic of root systems is the manner in which the primary and lateral roots comprise the superstructure or root architecture. Root architecture is an ideal system for studying developmental plasticity, as it continually integrates intrinsic and environmental responses (Malamy, 2005), which represents a vital and dynamic component of agricultural productivity (Lynch, 1995).Root system architecture (RSA) is defined as the spatial configuration of the roots in their environment (Lynch, 1995). The complexity of RSA was initially appreciated several decades ago, and terms like morphology, topology, distribution, and architecture were often used to describe the nature of RSA (Fitter, 1987; Fitter and Stickland, 1991; Lynch, 1995). These early reports argued that simple traits like root mass are insufficient to describe roots, because they do not capture the spatial configuration of roots in the soil, which is critical to plant performance (Fitter and Stickland, 1991). Root systems are integrated organs that adopt specific architectures to maximal foraging of the heterogeneous soil environment in different ways (Fitter, 1987; Fitter and Stickland, 1991; Lynch, 1995). More recently, new approaches have incorporated the measurement of many individual developmental traits that together comprise RSA (De Smet et al., 2012; Dubrovsky and Forde, 2012). For example, one recent report identified three fundamental components of RSA in generating complex topologies, including the contribution of lateral axes to branching, the rate and path of growth of the axis, and the increase in root surface area (Topp and Benfey, 2012). Thus, RSA is an important and complex trait that requires convenient measurement methods for rapid screening of diverse plant mutants and genotypes.With increasing research in RSA in the genetically tractable model plant Arabidopsis (Arabidopsis thaliana), the need for high-throughput methods of root phenotyping has dramatically increased over the years. Consequently, different methods and approaches have been developed in order to address this demand. Currently, three major approaches for phenotyping RSA are used (for review, see Zhu et al., 2011; De Smet et al., 2012). The first group of methods uses classical measures of RSA, which involve measurements of individual root traits. These methods often use software to manually draw the RSA onto digital two-dimensional images to quantify root length and number (Abramoff et al., 2004; http://www.machinevision.nl). These traditional methods provide the most accurate measurements of the root system but have a major disadvantage in being extremely time consuming.The second group of methods utilizes advanced semiautomated software for RSA measurements like EZ-Rhizo (Armengaud et al., 2009). EZ-Rhizo also uses digital two-dimensional images of plants grown on vertical plates (similar to the classical methods above) but is faster and produces different traits and basic statistics. The method works best when root features do not physically overlap, but we have found root overlap to be common when working with Arabidopsis plants older than 10 d. Other recent programs also provide semiautomated analysis of RSA, including RootReader2D (http://www.plantmineralnutrition.net/rootreader.htm) and SmartRoot (Lobet et al., 2011). However, while completely automated detection is potentially the highest throughput, we found that the root surface detection step is frequently prone to failure when using both of these programs, even after considerable adjustment by the user, where root features are missed or background noise is incorrectly labeled as roots.Finally, in a third group, recent developments include three-dimensional analysis of RSA of plants grown on transparent gel cylinders or in soil. The three-dimensional gel-based imaging approach is reported to be suitable for high-throughput phenotyping (Iyer-Pascuzzi et al., 2010). However, this approach requires special equipment, and imaging the root system of single plants can take 10 min (Iyer-Pascuzzi et al., 2010). X-ray computed tomography (Perret et al., 2007; Tracy et al., 2010) and magnetic resonance imaging (Van As, 2007) also provide highly detailed three-dimensional RSA analysis, but they require long scanning times and are extremely expensive and inaccessible. Most laboratories still utilize relatively convenient, inexpensive, and rapid two-dimensional phenotypic characterization of RSA, at least for initial screening purposes.The aim of this work is to address the need for a simple method to measure many different aspects of root architecture for high-throughput laboratory screening of mutants and genotypes in Arabidopsis. Here, we describe a landmark-based allometric (size and shape) approach called RootScape, a user-friendly software platform that enables rapid, comprehensive, and integrative phenotyping of the RSA in Arabidopsis. Unlike recent methods that collect information on different root traits to describe the RSA, RootScape places user-defined root landmarks on a two-dimensional grid to measure root architecture as a single integrated root system. The method employs rapid manual placement of root system landmarks. This manual step avoids one of the most problematic steps in automated image analysis (recognition of the root surface), providing a simple tool that does not require image processing. This method uses simple, two-dimensional digital images of the root system and a 20-point landmark model created in AAMToolbox, a freely available MATLAB plugin. While in-depth developmental analysis of root systems will often require knowing the contribution of individual traits, RootScape is a rapid method to access the holistic contribution of many individual root traits to RSA and to capture the overall property of the spatial configuration of roots in the soil (Fitter and Stickland, 1991). To demonstrate its utility, we used RootScape to quantify the root plasticity of Arabidopsis plants (Columbia [Col-0]) grown on four different media and compared the RootScape results with conventional measurements of individual root traits captured using the Optimas6 image-analysis software or Image J (Abramoff et al., 2004). This analysis showed that by measuring integrative root traits using RootScape, we could capture the vast majority of the individual trait variation, as verified by multiple regression analysis. Additionally, we tested the ability of RootScape to quantify the plasticity response in Arabidopsis mutants defective in hormone signaling. For this analysis, wild-type Col-0 and three hormone signaling mutants (auxin-resistant4 [axr4], abscisic acid insensitive4 [abi4], and cytokinin response1 [cre1]) were treated with auxin, cytokinin, or abscisic acid (ABA) versus controls. Statistical analyses (ANOVA/multivariate ANOVA [MANOVA]) allowed us to confirm most of the previously known interactions of genotype with these distinct environments and to potentially identify novel ones. Thus, we demonstrate that RootScape can be used as a rapid and efficient approach for quantifying the plasticity of the RSA in mutant (or ecotype) backgrounds of Arabidopsis and can identify new conditional root phenotypes.