Segmentation and Tracking of Adherens Junctions in 3D for the Analysis of Epithelial Tissue Morphogenesis

Abstract

Epithelial morphogenesis generates the shape of tissues, organs and embryos and is fundamental for their proper function. It is a dynamic process that occurs at multiple spatial scales from macromolecular dynamics, to cell deformations, mitosis and apoptosis, to coordinated cell rearrangements that lead to global changes of tissue shape. Using time lapse imaging, it is possible to observe these events at a system level. However, to investigate morphogenetic events it is necessary to develop computational tools to extract quantitative information from the time lapse data. Toward this goal, we developed an image-based computational pipeline to preprocess, segment and track epithelial cells in 4D confocal microscopy data. The computational pipeline we developed, for the first time, detects the adherens junctions of epithelial cells in 3D, without the need to first detect cell nuclei. We accentuate and detect cell outlines in a series of steps, symbolically describe the cells and their connectivity, and employ this information to track the cells. We validated the performance of the pipeline for its ability to detect vertices and cell-cell contacts, track cells, and identify mitosis and apoptosis in surface epithelia of Drosophila imaginal discs. We demonstrate the utility of the pipeline to extract key quantitative features of cell behavior with which to elucidate the dynamics and biomechanical control of epithelial tissue morphogenesis. We have made our methods and data available as an open-source multiplatform software tool called TTT (http://github.com/morganrcu/TTT)

SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics

“…However, segmentation errors that lead to 1% untracked cells in each frame of a movie are predicted to interrupt more than half of all cell trajectories after 70 time points, making fully automated methods of limited use for long-term tracking. As an alternative strategy, several methods enable the user to inspect and manually correct the segmentation output (McMahon et al, 2008;Fernandez-Gonzalez and Zallen, 2011;Gelbart et al, 2012;Giurumescu et al, 2012;Mashburn et al, 2012;Barbier de Reuille et al, 2015;Cilla et al, 2015;MoralesNavarrete et al, 2015;Rozbicki et al, 2015). These methods have the potential to achieve high accuracy but require substantial effort to manually correct the segmentation at each time point, decreasing the throughput of these approaches.…”

Deciphering tissue morphodynamics using bioimage informatics

“…In other studies where either the cell shape or the local neighbourhood is required (for instance to characterize cell -cell interaction and intercalation), the full cell outline must be extracted, and therefore imposes that the cell membranes be imaged. In contrast to nuclear labelling, fluorescent signal is usually less homogeneous along the cell membrane (notably due to poor resolution and light scattering), therefore segmentation is particularly more challenging even after suitable pre-processing [32,33], and often requires the help of nuclear localization to initialize the extraction process [18,20,31].…”

“…Deformable models are more flexible in comparison to other approaches, at the expense of slightly increased computation times, although efficient implementations are available [35,42]. (2) Direct approaches rely primarily on the membrane signal, and rather consider the cell membranes as a network that is to be extracted from the image [33,43]. These approaches typically start from an intensity-based analysis, followed either by a polygonal fitting procedure [33] or morphological operations followed by surface reconstruction and local refinement [43], not unlike active contours.…”

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

“…The segmentation is used to generate a polygonal approximation to the cell tessellation (figure 1 b,c ). Such approximations are an adequate assumption for many epithelia [11,31–34]. …”