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Phenoplant:用于探索大型叶绿素荧光图像数据集的网络资源。

Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets.

作者信息

Rousseau Céline, Hunault Gilles, Gaillard Sylvain, Bourbeillon Julie, Montiel Gregory, Simier Philippe, Campion Claire, Jacques Marie-Agnès, Belin Etienne, Boureau Tristan

机构信息

PHENOTIC, SFR 4207 QUASAV, F-49045 Angers, France.

Université d'Angers, Laboratoire d'Hémodynamique, Interaction Fibrose et Invasivité tumorale hépatique, UPRES 3859, IFR 132, F-49045 Angers, France.

出版信息

Plant Methods. 2015 Apr 3;11:24. doi: 10.1186/s13007-015-0068-4. eCollection 2015.

Abstract

BACKGROUND

Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping.

RESULTS

We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler.

CONCLUSIONS

Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org freely accessible to users of the plant phenotyping community.

摘要

背景

图像分析在植物表型分析中的应用日益广泛。在可用于植物表型分析的各种成像技术中,叶绿素荧光成像能够对生物或非生物胁迫对叶片的影响进行成像。可以测量或计算众多叶绿素荧光参数,但在给定条件下只有少数参数能产生差异。因此,需要有助于筛选叶绿素荧光图像数据集的自动化程序,尤其是从高通量植物表型分析的角度来看。

结果

我们开发了一种自动化程序,旨在便于识别受胁迫影响的叶片叶绿素荧光参数。首先,对于每个叶绿素荧光参数,该程序通过自动创建图像联系表和/或直方图来提供数据概述。这种联系表能够快速比较各种处理对叶片的影响,或实验过程中的差异动态。其次,基于每个叶绿素荧光参数的全局强度,该程序自动生成径向图和箱线图,使用户能够识别区分不同处理的叶绿素荧光参数。此外,还会自动生成基本的统计分析。第三,对于每个叶绿素荧光参数,该程序根据直方图自动进行聚类分析。此分析根据植物的健康状况对植物图像进行聚类。我们应用此程序来监测根寄生植物黄独脚金接种对拟南芥生态型Col-0和Ler的影响。

结论

使用此自动化程序,我们识别出了区分两种拟南芥生态型的八个叶绿素荧光参数,以及受黄独脚金感染影响的五个参数。更一般地说,该程序可能有助于识别受各种胁迫影响的叶绿素荧光参数。我们已在http://www.phenoplant.org实施此程序,供植物表型分析社区的用户免费使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7135/4392743/c89fb41ccc73/13007_2015_68_Fig1_HTML.jpg

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