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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.


Performance assessment of the automated detection of vertices, cell-cell contacts and cell tracks.A-D) Evaluation of the vertex detector, E-H) edge detector, and I-J) cell tracker. C-D), G-H) Green—true detections, blue—missed detections, red—false detections. A) Precision-Recall curve for AJ vertex detection. B) Variation of the F1 score of the vertex detector relative to changes in detection threshold TV. Vertex detection of C) Notum and D) Leg datasets. Vertex location accuracy highly depends on properly tuning up Tv. E) Precision-Recall curve for AJ edge detection. F) Variation of the F1 score relative to changes in propagation threshold TE. Edge detection in G) Notum and H) Leg datasets. Edge detection is more robust than vertex detection. I), J), K) and L) 2D projection of the trajectories respectively found for the cells in E) Notum, F) Leg, G) Mitosis in notum and H) Apoptosis in notum datasets. The system recovers accurate cell trajectories in different scenarios.
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pcbi.1004124.g004: Performance assessment of the automated detection of vertices, cell-cell contacts and cell tracks.A-D) Evaluation of the vertex detector, E-H) edge detector, and I-J) cell tracker. C-D), G-H) Green—true detections, blue—missed detections, red—false detections. A) Precision-Recall curve for AJ vertex detection. B) Variation of the F1 score of the vertex detector relative to changes in detection threshold TV. Vertex detection of C) Notum and D) Leg datasets. Vertex location accuracy highly depends on properly tuning up Tv. E) Precision-Recall curve for AJ edge detection. F) Variation of the F1 score relative to changes in propagation threshold TE. Edge detection in G) Notum and H) Leg datasets. Edge detection is more robust than vertex detection. I), J), K) and L) 2D projection of the trajectories respectively found for the cells in E) Notum, F) Leg, G) Mitosis in notum and H) Apoptosis in notum datasets. The system recovers accurate cell trajectories in different scenarios.

Mentions: Fig 4A shows Precision-Recall curves generated for the different datasets at different threshold detection levels. Briefly, Precision measures the number of truly detected vertices found with a given threshold level, while Recall measures the number of vertices undetected for that threshold level. The curve for the Notum dataset has an anomalous behavior for low recall values that arises from the very high values of the Vertexness function at sensory bristle cell locations that are mistaken for AJ locations. S6A Fig presents an example of this effect. Another common vertex location error that is produced at AJs are indentations between adjacent vertices as shown in S6B Fig. These are likely to arise by displacement of cell contacts by contractile forces generated by the actomyosin cytoskeleton. Vertex locations errors are more common in areas where cells are more parallel to the Z axis, where they are more difficult to locate due to voxel anisotropy and in areas with a low signal to noise ratio (Fig 4C and 4D).


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)

Performance assessment of the automated detection of vertices, cell-cell contacts and cell tracks.A-D) Evaluation of the vertex detector, E-H) edge detector, and I-J) cell tracker. C-D), G-H) Green—true detections, blue—missed detections, red—false detections. A) Precision-Recall curve for AJ vertex detection. B) Variation of the F1 score of the vertex detector relative to changes in detection threshold TV. Vertex detection of C) Notum and D) Leg datasets. Vertex location accuracy highly depends on properly tuning up Tv. E) Precision-Recall curve for AJ edge detection. F) Variation of the F1 score relative to changes in propagation threshold TE. Edge detection in G) Notum and H) Leg datasets. Edge detection is more robust than vertex detection. I), J), K) and L) 2D projection of the trajectories respectively found for the cells in E) Notum, F) Leg, G) Mitosis in notum and H) Apoptosis in notum datasets. The system recovers accurate cell trajectories in different scenarios.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4401792&req=5

pcbi.1004124.g004: Performance assessment of the automated detection of vertices, cell-cell contacts and cell tracks.A-D) Evaluation of the vertex detector, E-H) edge detector, and I-J) cell tracker. C-D), G-H) Green—true detections, blue—missed detections, red—false detections. A) Precision-Recall curve for AJ vertex detection. B) Variation of the F1 score of the vertex detector relative to changes in detection threshold TV. Vertex detection of C) Notum and D) Leg datasets. Vertex location accuracy highly depends on properly tuning up Tv. E) Precision-Recall curve for AJ edge detection. F) Variation of the F1 score relative to changes in propagation threshold TE. Edge detection in G) Notum and H) Leg datasets. Edge detection is more robust than vertex detection. I), J), K) and L) 2D projection of the trajectories respectively found for the cells in E) Notum, F) Leg, G) Mitosis in notum and H) Apoptosis in notum datasets. The system recovers accurate cell trajectories in different scenarios.
Mentions: Fig 4A shows Precision-Recall curves generated for the different datasets at different threshold detection levels. Briefly, Precision measures the number of truly detected vertices found with a given threshold level, while Recall measures the number of vertices undetected for that threshold level. The curve for the Notum dataset has an anomalous behavior for low recall values that arises from the very high values of the Vertexness function at sensory bristle cell locations that are mistaken for AJ locations. S6A Fig presents an example of this effect. Another common vertex location error that is produced at AJs are indentations between adjacent vertices as shown in S6B Fig. These are likely to arise by displacement of cell contacts by contractile forces generated by the actomyosin cytoskeleton. Vertex locations errors are more common in areas where cells are more parallel to the Z axis, where they are more difficult to locate due to voxel anisotropy and in areas with a low signal to noise ratio (Fig 4C and 4D).

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.