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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.


Related in: MedlinePlus

Analysis of Drosophila notum morphogenesis.A) A Maximum intensity projection of an image stack through the mid-scutum marked with E-cad∷GFP to highlight cell outlines. Anterior to the right. B) Maximum intensity projection of the output of the filters employed to detect the AJs (green) and AJ vertices (red). C) AJ graph (green) and Cell graph (blue) symbolically represent the cell outlines and their connectivity, respectively. D) Projection of the 3D centroid trajectories recovered after tracking the motion of cells. Note that motion of cell centroid varies depending on the position of the cell across the tissue. E) Evolution of cell strain rate parameters (, , , ) computed from Kalman smoothed trajectories of cell centroids over time. F) Mean velocities (, ) of the cell centroid trajectories over time. Time evolution of G) expansion coefficient ℰ, and H) rotation coefficient θ. See text for further detail.
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pcbi.1004124.g003: Analysis of Drosophila notum morphogenesis.A) A Maximum intensity projection of an image stack through the mid-scutum marked with E-cad∷GFP to highlight cell outlines. Anterior to the right. B) Maximum intensity projection of the output of the filters employed to detect the AJs (green) and AJ vertices (red). C) AJ graph (green) and Cell graph (blue) symbolically represent the cell outlines and their connectivity, respectively. D) Projection of the 3D centroid trajectories recovered after tracking the motion of cells. Note that motion of cell centroid varies depending on the position of the cell across the tissue. E) Evolution of cell strain rate parameters (, , , ) computed from Kalman smoothed trajectories of cell centroids over time. F) Mean velocities (, ) of the cell centroid trajectories over time. Time evolution of G) expansion coefficient ℰ, and H) rotation coefficient θ. See text for further detail.

Mentions: We illustrate the usage of the system to study the morphogenesis of a region of a Drosophila notum 24 hours after pupariation (apf) exploring the collective cell behaviors that contribute to tissue deformation. The timelapse captured the dynamics of cells in an area around the midline of the mid-scutum. We have employed the system to recover AJ graphs and cell graphs (Fig 3B and 3C), identifying the cells in the tissue and establishing the temporal correspondence among them (Fig 3D). The output of the system included some error that we corrected manually with validation tools that we developed to obtain accurate data. A visual inspection of the recovered cell trajectories shown in Fig 3D reveals a velocity gradient, increasing from posterior (left) to anterior (right). To understand the reason for this, we have built a model to quantify the process that contributes to the global deformation of the tissue at this developmental stage.


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)

Analysis of Drosophila notum morphogenesis.A) A Maximum intensity projection of an image stack through the mid-scutum marked with E-cad∷GFP to highlight cell outlines. Anterior to the right. B) Maximum intensity projection of the output of the filters employed to detect the AJs (green) and AJ vertices (red). C) AJ graph (green) and Cell graph (blue) symbolically represent the cell outlines and their connectivity, respectively. D) Projection of the 3D centroid trajectories recovered after tracking the motion of cells. Note that motion of cell centroid varies depending on the position of the cell across the tissue. E) Evolution of cell strain rate parameters (, , , ) computed from Kalman smoothed trajectories of cell centroids over time. F) Mean velocities (, ) of the cell centroid trajectories over time. Time evolution of G) expansion coefficient ℰ, and H) rotation coefficient θ. See text for further detail.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004124.g003: Analysis of Drosophila notum morphogenesis.A) A Maximum intensity projection of an image stack through the mid-scutum marked with E-cad∷GFP to highlight cell outlines. Anterior to the right. B) Maximum intensity projection of the output of the filters employed to detect the AJs (green) and AJ vertices (red). C) AJ graph (green) and Cell graph (blue) symbolically represent the cell outlines and their connectivity, respectively. D) Projection of the 3D centroid trajectories recovered after tracking the motion of cells. Note that motion of cell centroid varies depending on the position of the cell across the tissue. E) Evolution of cell strain rate parameters (, , , ) computed from Kalman smoothed trajectories of cell centroids over time. F) Mean velocities (, ) of the cell centroid trajectories over time. Time evolution of G) expansion coefficient ℰ, and H) rotation coefficient θ. See text for further detail.
Mentions: We illustrate the usage of the system to study the morphogenesis of a region of a Drosophila notum 24 hours after pupariation (apf) exploring the collective cell behaviors that contribute to tissue deformation. The timelapse captured the dynamics of cells in an area around the midline of the mid-scutum. We have employed the system to recover AJ graphs and cell graphs (Fig 3B and 3C), identifying the cells in the tissue and establishing the temporal correspondence among them (Fig 3D). The output of the system included some error that we corrected manually with validation tools that we developed to obtain accurate data. A visual inspection of the recovered cell trajectories shown in Fig 3D reveals a velocity gradient, increasing from posterior (left) to anterior (right). To understand the reason for this, we have built a model to quantify the process that contributes to the global deformation of the tissue at this developmental stage.

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.


Related in: MedlinePlus