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Segmentation and tracking of adherens junctions in 3D for the analysis of epithelial tissue morphogenesis.

Cilla R, Mechery V, Hernandez de Madrid B, Del Signore S, Dotu I, Hatini V - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental, Molecular & Chemical Biology. Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, United States of America.

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).

No MeSH data available.


Establishing connectivity among AJ vertices to segment epithelial tissues.A) Voronoi regions are expanded from vertex locations to generate the Voronoi Diagram associating each voxel to the nearest AJ vertex. B) Supervertices are expanded inside Voronoi regions to link adjacent vertices. C) The AJs graph is built adding an edge between contiguous vertices through pairs of adjacent supervertices. D) Vertices, Voronoi regions and supervertices are superimposed.
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pcbi.1004124.g005: Establishing connectivity among AJ vertices to segment epithelial tissues.A) Voronoi regions are expanded from vertex locations to generate the Voronoi Diagram associating each voxel to the nearest AJ vertex. B) Supervertices are expanded inside Voronoi regions to link adjacent vertices. C) The AJs graph is built adding an edge between contiguous vertices through pairs of adjacent supervertices. D) Vertices, Voronoi regions and supervertices are superimposed.

Mentions: To identify edges based on segmented vertices, we devised a method based on dividing the input space into supervertices (Fig 5B) and inferring their connectivity relationships. A supervertex is the 3D region around each vertex v ∈ VA such that v is the vertex minimizing a time of travel cost function to be introduced later. We build the formal definition of a supervertex from the notion of Voronoi Region around a vertex. The Voronoi Region [43] around a vertex v ∈ VA is the subset of all the points in the 3D space that are closer to v than to any other vertex in VA:Voronoi(v)=[x∈ℝ3∣argminv′∈VA∥v′-x∥2=v](1)


Segmentation and tracking of adherens junctions in 3D for the analysis of epithelial tissue morphogenesis.

Cilla R, Mechery V, Hernandez de Madrid B, Del Signore S, Dotu I, Hatini V - PLoS Comput. Biol. (2015)

Establishing connectivity among AJ vertices to segment epithelial tissues.A) Voronoi regions are expanded from vertex locations to generate the Voronoi Diagram associating each voxel to the nearest AJ vertex. B) Supervertices are expanded inside Voronoi regions to link adjacent vertices. C) The AJs graph is built adding an edge between contiguous vertices through pairs of adjacent supervertices. D) Vertices, Voronoi regions and supervertices are superimposed.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401792&req=5

pcbi.1004124.g005: Establishing connectivity among AJ vertices to segment epithelial tissues.A) Voronoi regions are expanded from vertex locations to generate the Voronoi Diagram associating each voxel to the nearest AJ vertex. B) Supervertices are expanded inside Voronoi regions to link adjacent vertices. C) The AJs graph is built adding an edge between contiguous vertices through pairs of adjacent supervertices. D) Vertices, Voronoi regions and supervertices are superimposed.
Mentions: To identify edges based on segmented vertices, we devised a method based on dividing the input space into supervertices (Fig 5B) and inferring their connectivity relationships. A supervertex is the 3D region around each vertex v ∈ VA such that v is the vertex minimizing a time of travel cost function to be introduced later. We build the formal definition of a supervertex from the notion of Voronoi Region around a vertex. The Voronoi Region [43] around a vertex v ∈ VA is the subset of all the points in the 3D space that are closer to v than to any other vertex in VA:Voronoi(v)=[x∈ℝ3∣argminv′∈VA∥v′-x∥2=v](1)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental, Molecular & Chemical Biology. Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, United States of America.

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).

No MeSH data available.