Weight Caroline, Parnham Daniel, Waites Richard
Department of Biology, University of York, PO Box 373, York YO10 5YW, UK.
Plant J. 2008 Feb;53(3):578-86. doi: 10.1111/j.1365-313X.2007.03330.x. Epub 2007 Nov 19.
A comprehensive understanding of leaf shape is important in many investigations in plant biology. Techniques to assess variation in leaf shape are often time-consuming, labour-intensive and prohibited by complex calculation of large data sets. We have developed LeafAnalyser, software that uses image-processing techniques to greatly simplify the measurement of leaf shape variation. LeafAnalyser places a large number of evenly distributed landmarks along leaf margins and records the position of each automatically. We used LeafAnalyser to analyse the variation in 3000 leaves from 400 plants of Antirrhinum majus. We were able to summarise the major trends in leaf shape variation using a principal components (PC) analysis and assess the changes in size, width and tip-to-base asymmetry within our leaf library. We demonstrate how this information can be used to develop a model that describes the range and variation of leaf shape within standard wild-type lines, and illustrate the shape transformations that occur between leaf nodes. We also show that information from LeafAnalyser can be used to identify novel trends in shape variation, as low-variance PCs that only affect a subset of position landmarks. These results provide a high-throughput method to calculate leaf shape variation that allows a large number of leaves to be visualised in higher-dimensional phenotypic space. To illustrate the applicability of LeafAnalyser we also calculated the leaf shape variation in 300 leaves from Arabidopsis thaliana.
全面了解叶片形状在植物生物学的许多研究中都很重要。评估叶片形状变化的技术通常耗时、费力，且因大量数据集的复杂计算而受到限制。我们开发了LeafAnalyser软件，该软件使用图像处理技术极大地简化了叶片形状变化的测量。LeafAnalyser沿着叶片边缘放置大量均匀分布的地标，并自动记录每个地标的位置。我们使用LeafAnalyser分析了400株金鱼草的3000片叶子的形状变化。我们能够通过主成分（PC）分析总结叶片形状变化的主要趋势，并评估我们叶片库中大小、宽度和叶尖到基部不对称性的变化。我们展示了如何利用这些信息开发一个模型，该模型描述标准野生型品系内叶片形状的范围和变化，并说明叶节点之间发生的形状转变。我们还表明，来自LeafAnalyser的信息可用于识别形状变化的新趋势，即仅影响一部分位置地标的低方差主成分。这些结果提供了一种高通量方法来计算叶片形状变化，从而使大量叶片能够在高维表型空间中可视化。为了说明LeafAnalyser的适用性，我们还计算了拟南芥300片叶子的形状变化。
