Cunha Alexandre, Tarr Paul T, Roeder Adrienne H K, Altinok Alphan, Mjolsness Eric, Meyerowitz Elliot M
Center for Advanced Computing Research, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, USA.
Methods Cell Biol. 2012;110:285-323. doi: 10.1016/B978-0-12-388403-9.00012-6.
Quantitative studies in plant developmental biology require monitoring and measuring the changes in cells and tissues as growth gives rise to intricate patterns. The success of these studies has been amplified by the combined strengths of two complementary techniques, namely live imaging and computational image analysis. Live imaging records time-lapse images showing the spatial-temporal progress of tissue growth with cells dividing and changing shape under controlled laboratory experiments. Image processing and analysis make sense of these data by providing computational ways to extract and interpret quantitative developmental information present in the acquired images. Manual labeling and qualitative interpretation of images are limited as they don't scale well to large data sets and cannot provide field measurements to feed into mathematical and computational models of growth and patterning. Computational analysis, when it can be made sufficiently accurate, is more efficient, complete, repeatable, and less biased. In this chapter, we present some guidelines for the acquisition and processing of images of sepals and the shoot apical meristem of Arabidopsis thaliana to serve as a basis for modeling. We discuss fluorescent markers and imaging using confocal laser scanning microscopy as well as present protocols for doing time-lapse live imaging and static imaging of living tissue. Image segmentation and tracking are discussed. Algorithms are presented and demonstrated together with low-level image processing methods that have proven to be essential in the detection of cell contours. We illustrate the application of these procedures in investigations aiming to unravel the mechanical and biochemical signaling mechanisms responsible for the coordinated growth and patterning in plants.
植物发育生物学的定量研究需要监测和测量细胞和组织的变化，因为生长会产生复杂的模式。两种互补技术，即实时成像和计算图像分析的综合优势，增强了这些研究的成功率。实时成像记录延时图像，展示在可控实验室实验中细胞分裂和形状变化时组织生长的时空进程。图像处理和分析通过提供计算方法来提取和解释所采集图像中存在的定量发育信息，从而使这些数据变得有意义。图像的手动标记和定性解释存在局限性，因为它们不太适用于大数据集，也无法提供用于输入生长和模式数学与计算模型的实地测量数据。当计算分析足够准确时，它更高效、完整、可重复且偏差较小。在本章中，我们提出了一些关于拟南芥萼片和茎尖分生组织图像采集与处理的指南，作为建模的基础。我们讨论了荧光标记和使用共聚焦激光扫描显微镜成像，以及进行活体组织延时实时成像和静态成像的方案。还讨论了图像分割和跟踪。介绍并演示了算法以及已被证明在检测细胞轮廓中至关重要的低级图像处理方法。我们说明了这些程序在旨在揭示负责植物协调生长和模式的机械和生化信号传导机制的研究中的应用。
