Department of Biology, University of Ottawa, Ontario, Canada K1N 6N5.
Plant Physiol. 2012 May;159(1):27-39. doi: 10.1104/pp.112.194662. Epub 2012 Mar 8.
Growth patterns vary in space and time as an organ develops, leading to shape and size changes. Quantifying spatiotemporal variations in organ growth throughout development is therefore crucial to understand how organ shape is controlled. We present a novel method and computational tools to quantify spatial patterns of growth from three-dimensional data at the adaxial surface of leaves. Growth patterns are first calculated by semiautomatically tracking microscopic fluorescent particles applied to the leaf surface. Results from multiple leaf samples are then combined to generate mean maps of various growth descriptors, including relative growth, directionality, and anisotropy. The method was applied to the first rosette leaf of Arabidopsis (Arabidopsis thaliana) and revealed clear spatiotemporal patterns, which can be interpreted in terms of gradients in concentrations of growth-regulating substances. As surface growth is tracked in three dimensions, the method is applicable to young leaves as they first emerge and to nonflat leaves. The semiautomated software tools developed allow for a high throughput of data, and the algorithms for generating mean maps of growth open the way for standardized comparative analyses of growth patterns.
器官在发育过程中，其生长模式在空间和时间上会发生变化，导致形状和大小的改变。因此，量化器官生长在整个发育过程中的时空变化对于理解器官形状的控制方式至关重要。我们提出了一种新的方法和计算工具，用于从叶片的腹面三维数据中量化生长的空间模式。首先，通过半自动跟踪应用于叶片表面的微观荧光粒子来计算生长模式。然后，将多个叶片样本的结果结合起来，生成各种生长描述符的平均值图，包括相对生长、方向性和各向异性。该方法应用于拟南芥（Arabidopsis thaliana）的第一片莲座叶，揭示了明显的时空模式，可以根据生长调节物质浓度的梯度来解释。由于在三维空间中跟踪表面生长，因此该方法适用于刚出现的年轻叶片和非平面叶片。开发的半自动软件工具允许进行高通量的数据处理，并且生成生长平均值图的算法为生长模式的标准化比较分析开辟了道路。
