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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

The developed system for the preprocessing, segmentation and tracking of epithelial cells.A) Schematic of the computational pipeline from the acquisition of 3D time lapse data to image preprocessing, cell segmentation and tracking. After segmenting the cells, we define symbolically their structure using a planar graph connecting detected AJ vertices with edges (green). Then we identify the cells in the tissue as the faces of the AJ graph and build the Cell graph to describe cell connectivity (blue). Finally, we establish correspondence between cells among frames (colored lines connecting cell centroids) obtaining cell trajectories. (B-G) Part of an epithelium of a Drosophila leg at early pupal stages. This tissue dramatically narrows and elongates at this stage to generate a narrow and hollow cylinder while the epithelium at presumptive joints invaginates. B) Maximum intensity projection of an image stack through the leg epithelium marked with E-cad∷GFP to highlight cell outlines. Distal up, narrow region—presumptive joint; wider regions part of the presumptive segment. C) Projection of the denoised and deconvoluted volume. D) The output of the filters employed to detect AJs (green) and AJ vertices (red). E) AJ graph representing the AJs structure. F) Cell graph representing neighborhood relationships among cells in the tissue. G) Polygonal representation of the cells, colored according to assigned temporal identifiers.
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pcbi.1004124.g001: The developed system for the preprocessing, segmentation and tracking of epithelial cells.A) Schematic of the computational pipeline from the acquisition of 3D time lapse data to image preprocessing, cell segmentation and tracking. After segmenting the cells, we define symbolically their structure using a planar graph connecting detected AJ vertices with edges (green). Then we identify the cells in the tissue as the faces of the AJ graph and build the Cell graph to describe cell connectivity (blue). Finally, we establish correspondence between cells among frames (colored lines connecting cell centroids) obtaining cell trajectories. (B-G) Part of an epithelium of a Drosophila leg at early pupal stages. This tissue dramatically narrows and elongates at this stage to generate a narrow and hollow cylinder while the epithelium at presumptive joints invaginates. B) Maximum intensity projection of an image stack through the leg epithelium marked with E-cad∷GFP to highlight cell outlines. Distal up, narrow region—presumptive joint; wider regions part of the presumptive segment. C) Projection of the denoised and deconvoluted volume. D) The output of the filters employed to detect AJs (green) and AJ vertices (red). E) AJ graph representing the AJs structure. F) Cell graph representing neighborhood relationships among cells in the tissue. G) Polygonal representation of the cells, colored according to assigned temporal identifiers.

Mentions: Fig 1A outlines the computational pipeline we developed to preprocess, segment and track epithelial cells. First, confocal time lapses of epithelial tissue morphogenesis (Fig 1B) are acquired. To enhance the representation of the AJs, timelapses are deconvoluted to eliminate both out-of-focus signal, and the Poisson noise introduced by the photodetector during imaging (Fig 1C). If required, the images are locally equalized to obtain an even intensity of the E-cad∷GFP signal employing Contrast Limited Adaptive Histogram Equalization (CLAHE) [28].


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)

The developed system for the preprocessing, segmentation and tracking of epithelial cells.A) Schematic of the computational pipeline from the acquisition of 3D time lapse data to image preprocessing, cell segmentation and tracking. After segmenting the cells, we define symbolically their structure using a planar graph connecting detected AJ vertices with edges (green). Then we identify the cells in the tissue as the faces of the AJ graph and build the Cell graph to describe cell connectivity (blue). Finally, we establish correspondence between cells among frames (colored lines connecting cell centroids) obtaining cell trajectories. (B-G) Part of an epithelium of a Drosophila leg at early pupal stages. This tissue dramatically narrows and elongates at this stage to generate a narrow and hollow cylinder while the epithelium at presumptive joints invaginates. B) Maximum intensity projection of an image stack through the leg epithelium marked with E-cad∷GFP to highlight cell outlines. Distal up, narrow region—presumptive joint; wider regions part of the presumptive segment. C) Projection of the denoised and deconvoluted volume. D) The output of the filters employed to detect AJs (green) and AJ vertices (red). E) AJ graph representing the AJs structure. F) Cell graph representing neighborhood relationships among cells in the tissue. G) Polygonal representation of the cells, colored according to assigned temporal identifiers.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004124.g001: The developed system for the preprocessing, segmentation and tracking of epithelial cells.A) Schematic of the computational pipeline from the acquisition of 3D time lapse data to image preprocessing, cell segmentation and tracking. After segmenting the cells, we define symbolically their structure using a planar graph connecting detected AJ vertices with edges (green). Then we identify the cells in the tissue as the faces of the AJ graph and build the Cell graph to describe cell connectivity (blue). Finally, we establish correspondence between cells among frames (colored lines connecting cell centroids) obtaining cell trajectories. (B-G) Part of an epithelium of a Drosophila leg at early pupal stages. This tissue dramatically narrows and elongates at this stage to generate a narrow and hollow cylinder while the epithelium at presumptive joints invaginates. B) Maximum intensity projection of an image stack through the leg epithelium marked with E-cad∷GFP to highlight cell outlines. Distal up, narrow region—presumptive joint; wider regions part of the presumptive segment. C) Projection of the denoised and deconvoluted volume. D) The output of the filters employed to detect AJs (green) and AJ vertices (red). E) AJ graph representing the AJs structure. F) Cell graph representing neighborhood relationships among cells in the tissue. G) Polygonal representation of the cells, colored according to assigned temporal identifiers.
Mentions: Fig 1A outlines the computational pipeline we developed to preprocess, segment and track epithelial cells. First, confocal time lapses of epithelial tissue morphogenesis (Fig 1B) are acquired. To enhance the representation of the AJs, timelapses are deconvoluted to eliminate both out-of-focus signal, and the Poisson noise introduced by the photodetector during imaging (Fig 1C). If required, the images are locally equalized to obtain an even intensity of the E-cad∷GFP signal employing Contrast Limited Adaptive Histogram Equalization (CLAHE) [28].

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