A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed.
A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed.
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DOI：
10.1186/s13007-022-00948-1
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发表时间：
2022-11-03
期刊：
影响因子：
5.1
通讯作者：
Hong, Dengfeng
中科院分区：
文献类型：
--
作者：
Jiao, Yushun;Liang, Baoling;Yang, Guangsheng;Xin, Qiang;Hong, Dengfeng
对油菜种子大小感兴趣的研究人员需要量化种皮、胚和角果壁中细胞的大小和数量。基于扫描电子显微镜的方法已被证明是可行的，但费力和昂贵。在图像准备之后，通常手动评估细胞参数，这是耗时的并且是大规模分析的主要瓶颈。最近，两个基于机器学习的算法，可训练的Weka分割（TWS）和Cellpose，被发布来克服这个长期存在的问题。此外，斐济的MorphoLibJ和LabelsToROI插件提供了用户友好的工具来处理细胞分割文件。我们试图验证这些先进工具对油菜各种类型细胞的实用性和效率。我们通过跳过固定步骤简化了当前的图像准备程序，并证明了简化程序的可行性。我们开发了三种方法来自动处理油菜各种组织的多细胞图像。TWS-Fiji（TF）方法将TWS的细胞检测和Fiji的细胞测量相结合，能够准确定量种皮细胞。Cellpose-Fiji（CF）方法基于Cellpose的细胞分割和Fiji的量化，实现了良好的性能，但表现出系统误差。通过使用MorphoLibJ去除边界标签并使用LabelsToROI检测感兴趣区域（ROI），Cellpose-MorphoLibJ-LabelsToROI（CML）方法在种皮细胞的明场图像上实现了人类水平的性能。有趣的是，CML方法几乎不需要手动校准，这一特性使其适用于大规模图像处理。通过对种皮细胞的大规模定量评价，我们证明了CML方法在单细胞水平和样品水平上的稳健性和高效性。此外，我们将CML方法的应用扩展到开发种皮，胚和角果壁细胞，并获得了高度精确和可靠的结果，表明该方法在多种情况下使用的通用性。CML方法具有高度准确性，无需人工校正。因此，它可以应用于低成本，高通量的定量油菜籽中的不同细胞类型的高效率。我们设想，这种方法将有助于油菜和其他作物的功能基因组学和微表型组学研究。在线版本包含补充材料，可通过10.1186/s13007-022-00948-1获得。
Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS–Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose–Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose–MorphoLibJ–LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops. The online version contains supplementary material available at 10.1186/s13007-022-00948-1.
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影响因子：
7.7
作者：
Barbier de Reuille P;Routier-Kierzkowska AL;Kierzkowski D;Bassel GW;Schüpbach T;Tauriello G;Bajpai N;Strauss S;Weber A;Kiss A;Burian A;Hofhuis H;Sapala A;Lipowczan M;Heimlicher MB;Robinson S;Bayer EM;Basler K;Koumoutsakos P;Roeder AH;Aegerter-Wilmsen T;Nakayama N;Tsiantis M;Hay A;Kwiatkowska D;Xenarios I;Kuhlemeier C;Smith RS
通讯作者：
Smith RS
影响因子：
5.4
作者：
Zhang, Xiaohui;Huang, Qiyang;Hong, Dengfeng
通讯作者：
Hong, Dengfeng
影响因子：
6.9
作者：
Zhou, Xianming;Zhang, Haiyan;Hong, Dengfeng
通讯作者：
Hong, Dengfeng
影响因子：
10.6
作者：
Kromp, Florian;Fischer, Lukas;Taschner-Mandl, Sabine
通讯作者：
Taschner-Mandl, Sabine
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian