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通过X射线荧光图像处理对天蓝遏蓝菜中空间金属积累模式进行定量分析以用于遗传学研究。

Quantification of spatial metal accumulation patterns in Noccaea caerulescens by X-ray fluorescence image processing for genetic studies.

作者信息

van der Zee Lucas, Corzo Remigio Amelia, Casey Lachlan W, Purwadi Imam, Yamjabok Jitpanu, van der Ent Antony, Kootstra Gert, Aarts Mark G M

机构信息

Farm Technology, Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands.

Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane, Australia.

出版信息

Plant Methods. 2021 Aug 3;17(1):86. doi: 10.1186/s13007-021-00784-9.

Abstract

BACKGROUND

Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation.

RESULTS

Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach.

CONCLUSIONS

A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.

摘要

背景

微量元素的超积累是植物中一种罕见的特性,目前正在对其进行研究,以加深我们对金属积累调控的理解以及在植物技术中的应用。天蓝遏蓝菜(十字花科)是一种经过深入研究的超积累模式植物,能够在组织中积累极高浓度的锌和镍,且在种群水平上存在显著的遗传变异。微X射线荧光光谱(µXRF)成像技术是一种灵敏的高分辨率技术,可用于获取水合样品中植物金属组的空间分布信息。我们利用基于实验室的µXRF技术对来自欧洲各地的86份遗传多样性不同的天蓝遏蓝菜种质进行了表征。我们开发了一种图像处理方法,用于分割µXRF图像中的不同植物亚结构。我们引入了浓度商(CQ)来量化金属积累的空间模式,并将其与遗传变异联系起来。

结果

图像处理实现了将µXRF植物图像自动分割为叶柄、叶缘、叶脉间和叶脉亚结构。召回率和精确率的调和均值(F1分数)分别为0.79、0.80、0.67和0.68。对于所有亚结构,通过CQ确定的空间金属积累在天蓝遏蓝菜中具有高度遗传性,广义遗传力(H)范围为76%至92%,并且与其他可遗传性状的相关性较弱。在图像分割算法中插入噪声几乎不会降低分割亚结构的CQ遗传力得分,这说明了该性状和量化方法的稳健性。如果比较随机生成的亚结构,则发现CQ的遗传力非常低,从而验证了该方法的有效性。

结论

提出了一种分割天蓝遏蓝菜µXRF图像的策略,并开发了浓度商以提供金属积累模式的定量测量,可用于确定该模式的遗传变异。该指标对分割误差具有稳健性,并提供可靠的H估计值。该策略为量化XRF数据以分析植物中金属分布模式的遗传学以及随后发现调节植物金属稳态和螯合的新基因提供了一条途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d59/8336263/711267fbc458/13007_2021_784_Fig1_HTML.jpg

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