Limits...
A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture.

Morales-Navarrete H, Segovia-Miranda F, Klukowski P, Meyer K, Nonaka H, Marsico G, Chernykh M, Kalaidzidis A, Zerial M, Kalaidzidis Y - Elife (2015)

Bottom Line: We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy.Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization.The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.

ABSTRACT
A prerequisite for the systems biology analysis of tissues is an accurate digital three-dimensional reconstruction of tissue structure based on images of markers covering multiple scales. Here, we designed a flexible pipeline for the multi-scale reconstruction and quantitative morphological analysis of tissue architecture from microscopy images. Our pipeline includes newly developed algorithms that address specific challenges of thick dense tissue reconstruction. Our implementation allows for a flexible workflow, scalable to high-throughput analysis and applicable to various mammalian tissues. We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy. Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization. The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

No MeSH data available.


Reconstruction of tubular structures, nuclei and cells.Single 2D image planes are shown with contours of (A) sinusoidal and (B) and bile canalicular (BC) networks, (C) nuclei and (D) cells reconstructions overlaid on raw data. Insets show zoomed areas of the image.DOI:http://dx.doi.org/10.7554/eLife.11214.017
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fig3s4: Reconstruction of tubular structures, nuclei and cells.Single 2D image planes are shown with contours of (A) sinusoidal and (B) and bile canalicular (BC) networks, (C) nuclei and (D) cells reconstructions overlaid on raw data. Insets show zoomed areas of the image.DOI:http://dx.doi.org/10.7554/eLife.11214.017

Mentions: Finally, cells were segmented by expansion of the active mesh from the nuclei to the cell surface. The expansion was either limited to the cell cortex (i.e. the maximum density of actin) or to contacts with neighbouring cells or tubular structures (Figure 3E). The active mesh expansion was parameterized by inner pressure and mesh rigidity. However, this algorithm over-segmented bi-nucleated cells into two cells with a single nucleus. Therefore, we used phalloidin intensity and nucleus-to-nucleus distance to recognize over segmented multinuclear cells and merge them. A manual check of segmentation of 2559 cells revealed only ~2% error for hepatocyte segmentation that is a further improvement of the state-of-the-art achievements by voxel-based segmentation methods (Mosaliganti et al., 2012). The results of the segmentation of all imaged cellular and subcellular structures in the liver tissue (i.e. cells, nuclei, sinusoidal and BC networks) are presented in Figure 3E, Figure 3—figure supplement 4, and Videos 2 and 3.Video 2.Reconstruction of all imaged structures in a high-resolution image.


A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture.

Morales-Navarrete H, Segovia-Miranda F, Klukowski P, Meyer K, Nonaka H, Marsico G, Chernykh M, Kalaidzidis A, Zerial M, Kalaidzidis Y - Elife (2015)

Reconstruction of tubular structures, nuclei and cells.Single 2D image planes are shown with contours of (A) sinusoidal and (B) and bile canalicular (BC) networks, (C) nuclei and (D) cells reconstructions overlaid on raw data. Insets show zoomed areas of the image.DOI:http://dx.doi.org/10.7554/eLife.11214.017
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4764584&req=5

fig3s4: Reconstruction of tubular structures, nuclei and cells.Single 2D image planes are shown with contours of (A) sinusoidal and (B) and bile canalicular (BC) networks, (C) nuclei and (D) cells reconstructions overlaid on raw data. Insets show zoomed areas of the image.DOI:http://dx.doi.org/10.7554/eLife.11214.017
Mentions: Finally, cells were segmented by expansion of the active mesh from the nuclei to the cell surface. The expansion was either limited to the cell cortex (i.e. the maximum density of actin) or to contacts with neighbouring cells or tubular structures (Figure 3E). The active mesh expansion was parameterized by inner pressure and mesh rigidity. However, this algorithm over-segmented bi-nucleated cells into two cells with a single nucleus. Therefore, we used phalloidin intensity and nucleus-to-nucleus distance to recognize over segmented multinuclear cells and merge them. A manual check of segmentation of 2559 cells revealed only ~2% error for hepatocyte segmentation that is a further improvement of the state-of-the-art achievements by voxel-based segmentation methods (Mosaliganti et al., 2012). The results of the segmentation of all imaged cellular and subcellular structures in the liver tissue (i.e. cells, nuclei, sinusoidal and BC networks) are presented in Figure 3E, Figure 3—figure supplement 4, and Videos 2 and 3.Video 2.Reconstruction of all imaged structures in a high-resolution image.

Bottom Line: We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy.Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization.The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.

ABSTRACT
A prerequisite for the systems biology analysis of tissues is an accurate digital three-dimensional reconstruction of tissue structure based on images of markers covering multiple scales. Here, we designed a flexible pipeline for the multi-scale reconstruction and quantitative morphological analysis of tissue architecture from microscopy images. Our pipeline includes newly developed algorithms that address specific challenges of thick dense tissue reconstruction. Our implementation allows for a flexible workflow, scalable to high-throughput analysis and applicable to various mammalian tissues. We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy. Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization. The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

No MeSH data available.