RootScape: A Landmark-Based System for Rapid Screening of Root Architecture in Arabidopsis
RootScape: A Landmark-Based System for Rapid Screening of Root Architecture in Arabidopsis
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DOI：
10.1104/pp.112.210872
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发表时间：
2013-03-01
期刊：
影响因子：
7.4
通讯作者：
Coruzzi, Gloria M.
中科院分区：
文献类型：
--
作者：
Ristova, Daniela;Rosas, Ulises;Coruzzi, Gloria M.
植物根系的构型影响着包括养分和水分吸收、土壤固定和共生相互作用在内的基本功能。根构型包括许多由主根和侧根的生长所产生的特征。这些根部特征由遗传背景决定，但也对环境有很高的反应能力。因此，根系构型(RSA)代表了一个重要而复杂的性状，它高度可变，受基因3环境互作的影响，并与生存/表现相关。用平板组织培养法定量测定拟南芥中的RSA是一种非常常见且相对快速的方法，但当涉及到用于突变或基因筛选的中高通量方法时，定量RSA代表着一个实验瓶颈。在这里，我们介绍了RootScape，一种基于里程碑的异速生长方法，用于RSA的快速表型鉴定，以拟南芥为例。使用AAMToolbox软件，我们创建了一个20点的里程碑式的模型，该模型将RSA作为一个综合性状，并使用该模型来量化在不同激素处理下生长的拟南芥(Columbia)野生型植物的RSA的变化。主成分分析被用来比较RootScapp和传统的测量根构型的方法。这一分析表明，RootScape有效地捕获了通过测量单个根部性状检测到的几乎所有根构型变异，并且比传统评分快5到10倍。我们通过量化RSA在几个影响激素信号转导的突变系中的可塑性来验证RootScape。RootScape分析概括了先前描述突变体中复杂表型的结果，并确定了新的基因3环境相互作用。
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 3 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 3 environment interactions.