ImageJ SurfCut: a user-friendly pipeline for high-throughput extraction of cell contours from 3D image stacks

Abstract

Background Many methods have been developed to quantify cell shape in 2D in tissues. 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. However, proper extraction of 2D cell contours from 3D confocal stacks for such analysis can be problematic. Results 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. As a reference point, we compared our output to that obtained with MorphoGraphX (MGX). 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). SurfCut was however not appropriate for cell or tissue samples with high curvature, as evidenced by a significant bias in shape and area quantification. Conclusion We provide a new ImageJ pipeline, SurfCut, that allows the extraction of cell contours from 3D confocal stacks. 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. 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. Electronic supplementary material The online version of this article (10.1186/s12915-019-0657-1) contains supplementary material, which is available to authorized users.

LocalZProjector and DeProj: A toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

“…Third, the tissue surface and the XY plane may have in some regions a large angle. Beyond 30º, the morphological features of cells measured on the 2D projection will be significantly altered, as noted in (12).…”

“…To address this limitation, several projection tools have been developed that aim at including in the projection only the signal coming from the tissue layer. Among them there is StackFocuser (9), PreMosa (10), Extended Depth of Field ( EDF ) (11), SurfCut (12), MinCostZSurface (13–15), the Smooth Manifold Extraction ( SME ) tool (8) and a new implementation of the latter: FastSME (16). In (17), authors also proposed an approach based on Deep-Learning for the projection along the Z-axis, but it requires a set of images along with their already computed projections for its training (see Supplementary Information for descriptions).…”

External mechanical cues reveal core molecular pathway behind tissue bending in plants

“…CMTs in these 93 mutants react slowly to both endogenous and exogenous mechanical cues (Hervieux, et 94 al., 2017; Sampathkumar, et al, 2014; Uyttewaal, et al, 2012). However, attaining 95 anisotropic CMT orientation even in the absence of katanin activity is theoretically 96 possible through growth, cell shape and geometry-induced self-organization of CMTs 97 (Chakrabortty, et al, 2018) and from directional stabilization of CMT arrays by prolonged 98 exogenous mechanical cues (Hamant, et al, 2019). It seems therefore that exogenous 99 and endogenous mechanical stress can act in synergy to control CMT organization.…”

“…Yet, the associated mechanotransduction pathways have remained 392 unknown so far. Among the candidate mechanisms to exclude, CMTs seem to align with 393 local mechanical stress reorientation in the shoot apical meristem even when calcium 394 signals are blocked (Li, et al, 2019) or when auxin gradients are largely impaired (Heisler, 395 et al, 2010) Therefore it has been proposed that CMTs may be their own 396 mechanosensors (Hamant, et al, 2019) echoing what has been proposed for actin in 397 animals (Yu, et al, 2017; Risca, et al, 2012) and building on evidence from stretched 398 microtubules in vitro (Franck, et al, 2007). Yet, the integration of such behaviors in 399 development requires a coordinator, a role that could be played by auxin and mediated 400 by a TMK dependent, non-canonical auxin response pathway.…”

“…Interestingly, CMTs have been shown to align with directions of maximal tensile stress 85 that tissue shape and/ or growth imposes (Hamant, et al, 2019; Sampathkumar, et al, 86 2014; Uyttewaal, et al, 2012). However, exogenously applied force can override the 87 endogenous stress pattern and reorient CMTs (Robinson and Kuhlemeier, 2018; 88 Uyttewaal, et al, 2012).…”