Abstract
BackgroundThe 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.ResultsHere, 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 Arabidopsis thaliana roots and seeds and Populus tremula 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.ConclusionsThis 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.
Highlights
The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis
We used ImageJ to identify and quantify an image of a propidium iodide-labeled Populus tremula embryo captured by light sheet fluorescence microscopy (LSFM), which uncovered thousands of cellular structures (Fig. 1a)
Overview of the procedure for quantifying and segmenting plant cells Here, we describe how to recognize and quantify multicellular parameters from raw images using ImageJ and how to perform segmentation and organization analysis of plant cells based on their centroids with SR-Tesseler
Summary
The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis. Complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Plants derive multiple adaptive advantages from their complex multicellular structural organization [1], which includes intricate molecule interactions, vesicle transport, and cellular interactions [2,3,4,5]. ImageJ has been successfully used for several types of cell biology analysis, including co-localization analysis, fluorescence intensity quantitation, and 3D image reconstruction. Various ImageJ plugins allow it to be used for high-throughput image analysis for accurate, rapid export of massive amounts of data [14]. The ability to perform rapid, accurate analysis of multicellular properties is desirable in various cell biology fields, providing rich data for developmental and system organization studies. We previously performed particle analysis to calculate the properties of endocytic dots [18,19,20,21]
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