Similarly, when cell-cell adhesion is less prominent naturally, cells can also round up or exhibit irregular shapes, as in the leaf mesophyll and spongy parenchyma for instance.
When cell-cell adhesion is artificially affected, cells can round up. This explains why most plant cells in fully adhesive tissues have a brick shape (e.g., hypocotyl cells). In tissues, cell shape is also constrained by the presence of adjacent cells, through packing and adhesion at the middle lamella. Typically, when they are still growing, larger cells are more susceptible to wall failure than smaller cells. Beyond the wall properties, the mechanical balance operating in plant cells also depends on cell shape. In fact, when cellulose deposition is impaired, cells also tend to become spherical, as in protoplasts. Cellulose microfibrils are classically thought to play a load-bearing role here, and their alignment supports the mechanical anisotropy of the wall.
Typically, wall-less protoplasts are spherical. Because turgor pressure is in essence isotropic, any deviation from a spherical shape is determined by the mechanical anisotropy of the cell wall. An isolated plant cell is shaped by the balance between turgor pressure and cell wall resistance to turgor. Plant cell shapes depend on internal and external factors. Whether in kinematic analyses (e.g., ), in functional genetics (e.g., ), in cell biology (e.g., ), and in computational modeling (e.g., ), quantifying cell contours during growth is thus crucial to understand plant development as a whole. From a geometric perspective, this means that plant morphogenesis mainly depends on the cell growth rate and growth anisotropy. Because plant cells do not migrate, and usually do not go through apoptosis in young tissues, plant morphogenesis primarily relies on cell elongation and cell division. The combination of these two methods thus provides an ideal suite of tools for cell contour extraction in most biological samples, whether 3D precision or high-throughput analysis is the main priority.Ĭell shape is a primary variable in morphogenesis in all kingdoms, either as a building block for multicellular shape or because cell shape in turn biases the behavior of structural elements (e.g., cytoskeleton) or morphogens. SurfCut and MGX have complementary advantages: MGX is well suited for curvy samples and more complex analyses, up to computational cell-based modeling on real templates SurfCut is well suited for rather flat samples, is simple to use, and has the advantage to be easily automated for batch analysis of images in ImageJ. We provide a new ImageJ pipeline, SurfCut, that allows the extraction of cell contours from 3D confocal stacks. SurfCut was however not appropriate for cell or tissue samples with high curvature, as evidenced by a significant bias in shape and area quantification. While both methods differ in the approach used to extract the layer of signal, they output comparable results for tissues with shallow curvature, such as pavement cell shape in cotyledon epidermis (as quantified with PaCeQuant). As a reference point, we compared our output to that obtained with MorphoGraphX (MGX). We developed a macro in ImageJ, SurfCut, with the goal to provide a user-friendly pipeline specifically designed to extract epidermal cell contour signals, segment cells in 2D and analyze cell shape. However, proper extraction of 2D cell contours from 3D confocal stacks for such analysis can be problematic. For instance, the analysis of epithelial cells in Drosophila embryogenesis or jigsaw puzzle-shaped pavement cells in plant epidermis has led to the development of numerous quantification methods that are applied to 2D images. Many methods have been developed to quantify cell shape in 2D in tissues.