Living Systems Institute and Department of Physics and Astronomy, University of Exeter, Exeter, United Kingdom.
Department of Biosciences, University of Exeter, Exeter, United Kingdom.
PLoS Comput Biol. 2023 Aug 30;19(8):e1011407. doi: 10.1371/journal.pcbi.1011407. eCollection 2023 Aug.
The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.
肌动蛋白细胞骨架在真核生物中至关重要，在植物界更是如此，它在细胞扩张、细胞分裂、环境响应和病原体防御中发挥着关键作用。然而，植物中肌动蛋白网络的精确结构-功能关系及其性质仍有待揭示，包括网络结构如何依赖于细胞类型、组织类型和发育阶段等细节。部分问题在于难以从显微镜数据中提取肌动蛋白网络特征的高质量、定量测量。为了解决这个问题，我们开发了一种新的图像分析算法 DRAGoN，它可以自动提取一系列细胞类型中的肌动蛋白网络，提供十七种不同的定量测量方法来描述局部网络。然后，我们使用该算法研究了拟南芥中的一些案例，包括几种不同的组织、多种肌动蛋白相关突变体以及对白粉病有反应的细胞。在许多情况下，我们发现肌动蛋白网络性质存在统计学上的显著差异。除了这些结果外，我们的算法旨在易于适应其他组织、突变体和植物，因此将成为全球重要作物肌动蛋白细胞骨架研究和未来生物工程的宝贵资产。
