Zhang Xi, Hu Zijian, Guo Yayu, Shan Xiaoyi, Li Xiaojuan, Lin Jinxing
College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 10083 China.
Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 10083 China.
Plant Methods. 2020 Jul 28;16:100. doi: 10.1186/s13007-020-00642-0. eCollection 2020.
The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis. However, complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Therefore, it is essential to develop an efficient method for identifying plant complex multicellularity in raw micrographs in plants.
Here, we developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler. This procedure allows for the rapid, accurate, automatic quantification of cell patterns and organization at different scales, from large tissues down to the cellular level. We validated our method using different images captured from roots and seeds and stems, including fluorescently labeled images, Micro-CT scans, and dyed sections. Finally, we determined the area, centroid coordinate, perimeter, and Feret's diameter of the cells and harvested the cell distribution patterns from Voronoï diagrams by setting the threshold at localization density, mean distance, or area.
This procedure can be used to determine the character and organization of multicellular plant tissues at high efficiency, including precise parameter identification and polygon-based segmentation of plant cells.
用于细胞大规模、多维度成像的新方法不断增加，为分析植物形态发生创造了前所未有的机会。然而，复杂的图像处理，包括识别特定细胞和量化参数，以及一些图像分析软件的高运行成本仍然具有挑战性。因此，开发一种在植物原始显微图像中识别植物复杂多细胞性的有效方法至关重要。
在这里，我们开发了一种高效的程序，使用开源软件包ImageJ和SR-Tesseler对各种原始图像中的植物多细胞性进行表征、分割和量化。该程序允许从大组织到细胞水平，在不同尺度上快速、准确、自动地量化细胞模式和组织。我们使用从根、种子和茎中捕获的不同图像验证了我们的方法，包括荧光标记图像、显微CT扫描和染色切片。最后，我们确定了细胞的面积、质心坐标、周长和费雷特直径，并通过在定位密度、平均距离或面积处设置阈值，从沃罗诺伊图中获取细胞分布模式。
该程序可用于高效确定多细胞植物组织的特征和组织，包括精确的参数识别和基于多边形的植物细胞分割。
