Abstract
BackgroundMicroscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. High-resolution 3D confocal images allow extracting quantitative characteristics describing the cell structure of leaf epidermis. For some issues in the study of cereal leaves development, it is required to apply the staining techniques with fluorescent dyes and to scan rather large fragments consisting of several frames. We aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining.ResultsWe elaborated an ImageJ-plugin LSM-W2 that allows extracting data on Leaf Surface Morphology from Laser Scanning Microscopy images. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells. It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. In the article, we also represent some case studies showing the plugin application for solving biological tasks. Namely the plugin is applied in the following cases: defining parameters of jigsaw-puzzle pattern for maize leaf epidermal cells, analysis of the pavement cells morphological parameters for the mature wheat leaf grown under control and water deficit conditions, initiation of cell longitudinal rows, and detection of guard mother cells emergence at the initial stages of the stomatal morphogenesis in the growth zone of a wheat leaf.ConclusionThe proposed plugin is efficient for high-throughput analysis of cellular architecture for cereal leaf epidermis. The workflow implies using inexpensive and rapid sample preparation and does not require the applying of transgenesis and reporter genetic structures expanding the range of species and varieties to study. Obtained characteristics of the cell structure and patterns further could act as a basis for the development and verification for spatial models of plant tissues formation mechanisms accounting for structural features of cereal leaves.AvailabilityThe implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/). The plugin is freely available at https://imagej.net/LSM_Worker, https://github.com/JmanJ/LSM_Workerand http://pixie.bionet.nsc.ru/LSM_WORKER/.
Highlights
Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms
Availability: The implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/)
Microscopic images are widely used as an essential source of information on cells morphometric characteristics and cellular tissue architecture due to modern computer vision algorithms of segmentation [1]
Summary
Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. Microscopic images are widely used as an essential source of information on cells morphometric characteristics and cellular tissue architecture due to modern computer vision algorithms of segmentation [1] Such issues are mentioned in scientific papers where images are used as a source of data for studying patterning of multiple cell types in the plant epidermis [2], investigating the topology of pavement cells for developing Arabidopsis leaves [3] and mechanisms for puzzle-like cells emerging [4], detecting exit from proliferation during Arabidopsis leaf development [5]. Involving large data-sets in the analysis requires high-performance computer image processing methods
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