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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.


Morphometric features of kidney tissue.(A) and (B) The size and volume distribution of the two cell types identified in the kidney tissue, proximal and distal tubular structures. It was observed that the two cell populations have different characteristic sizes, proximal cells were found to be larger than distal ones. (C) and (D) The distribution for the cells elongation and the number of neighbouring cells, respectively.DOI:http://dx.doi.org/10.7554/eLife.11214.030
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fig6s2: Morphometric features of kidney tissue.(A) and (B) The size and volume distribution of the two cell types identified in the kidney tissue, proximal and distal tubular structures. It was observed that the two cell populations have different characteristic sizes, proximal cells were found to be larger than distal ones. (C) and (D) The distribution for the cells elongation and the number of neighbouring cells, respectively.DOI:http://dx.doi.org/10.7554/eLife.11214.030

Mentions: To test the general applicability of the pipeline as well as the robustness of our algorithms, we applied it to two morphologically distinct tissues, lung and kidney. Lung and kidney sections were stained for nuclei (DAPI) and the cell cortex (F-actin by phalloidin). Kidney samples were additionally stained for the apical (CD13) and basal (fibronectin and laminin) cell surface. The pipeline allowed us to generate geometrical reconstructions of the tissues (Figure 6 and Videos 4 and 5, respectively) without fine-tuning of the parameters. As proof of principle, we extracted some statistics of the most relevant structures from each tissue. Structural information from both relatively large structures like alveoli in lung or glomerulus in kidney, and smaller ones like cells and nuclei were extracted from the geometrical models. Figure 6—figure supplement 1,2 show the statistical distributions of some interesting tissue features, such as cell volume and elongation, number of neighbouring cells, etc. Information about the spatial organization of the alveolar cells (i.e. their localization relative to the alveoli) in the lung was extracted as well.10.7554/eLife.11214.028Figure 6.Reconstruction of geometrical models of lung and kidney tissues.


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)

Morphometric features of kidney tissue.(A) and (B) The size and volume distribution of the two cell types identified in the kidney tissue, proximal and distal tubular structures. It was observed that the two cell populations have different characteristic sizes, proximal cells were found to be larger than distal ones. (C) and (D) The distribution for the cells elongation and the number of neighbouring cells, respectively.DOI:http://dx.doi.org/10.7554/eLife.11214.030
© Copyright Policy
Related In: Results  -  Collection

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

fig6s2: Morphometric features of kidney tissue.(A) and (B) The size and volume distribution of the two cell types identified in the kidney tissue, proximal and distal tubular structures. It was observed that the two cell populations have different characteristic sizes, proximal cells were found to be larger than distal ones. (C) and (D) The distribution for the cells elongation and the number of neighbouring cells, respectively.DOI:http://dx.doi.org/10.7554/eLife.11214.030
Mentions: To test the general applicability of the pipeline as well as the robustness of our algorithms, we applied it to two morphologically distinct tissues, lung and kidney. Lung and kidney sections were stained for nuclei (DAPI) and the cell cortex (F-actin by phalloidin). Kidney samples were additionally stained for the apical (CD13) and basal (fibronectin and laminin) cell surface. The pipeline allowed us to generate geometrical reconstructions of the tissues (Figure 6 and Videos 4 and 5, respectively) without fine-tuning of the parameters. As proof of principle, we extracted some statistics of the most relevant structures from each tissue. Structural information from both relatively large structures like alveoli in lung or glomerulus in kidney, and smaller ones like cells and nuclei were extracted from the geometrical models. Figure 6—figure supplement 1,2 show the statistical distributions of some interesting tissue features, such as cell volume and elongation, number of neighbouring cells, etc. Information about the spatial organization of the alveolar cells (i.e. their localization relative to the alveoli) in the lung was extracted as well.10.7554/eLife.11214.028Figure 6.Reconstruction of geometrical models of lung and kidney tissues.

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.