植物肌动蛋白网络的自动提取。

IF 4.3 2区 生物学
PLoS Computational Biology Pub Date : 2023-08-30 eCollection Date: 2023-08-01 DOI:10.1371/journal.pcbi.1011407
Jordan Hembrow, Michael J Deeks, David M Richards
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引用次数: 0

摘要

肌动蛋白细胞骨架在真核生物中至关重要,尤其是在植物界,它在细胞扩张、细胞分裂、环境反应和病原体防御中发挥着关键作用。然而,植物肌动蛋白网络性质的精确结构-功能关系仍有待解开,包括网络结构如何取决于细胞类型、组织类型和发育阶段的细节。部分问题在于难以从显微镜数据中提取高质量、定量的肌动蛋白网络特征。为了解决这个问题,我们开发了DRAGoN,这是一种新的图像分析算法,可以自动提取一系列细胞类型的肌动蛋白网络,提供17种不同的定量测量方法,在局部水平上描述网络。然后,我们使用该算法研究了拟南芥的许多病例,包括几种不同的组织、各种受肌动蛋白影响的突变体和对白粉菌有反应的细胞。在许多情况下,我们发现肌动蛋白网络特性存在统计学上的显著差异。除这些结果外,我们的算法被设计为易于适应其他组织、突变体和植物,因此将成为研究和未来全球重要作物肌动蛋白细胞骨架生物工程的宝贵资产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic extraction of actin networks in plants.

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.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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