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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.


Reconstruction of multi-scale tissue images.Tissue-level network segmentation: (A) Reconstructed image of a tissue section. Large vessels appear as empty space in the image. (B) Spatial distribution of the local maximum entropy threshold value. (C) Segmentation of large vessel in a single tissue section. Registration of high-resolution images into low-resolution ones: Representative region of a 2D plane of (D) a low-resolution (yellow) and (E) a high-resolution (red) image stained with Flk1 for sinusoids. (F) Superimposed images after the registration.DOI:http://dx.doi.org/10.7554/eLife.11214.011
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fig2s2: Reconstruction of multi-scale tissue images.Tissue-level network segmentation: (A) Reconstructed image of a tissue section. Large vessels appear as empty space in the image. (B) Spatial distribution of the local maximum entropy threshold value. (C) Segmentation of large vessel in a single tissue section. Registration of high-resolution images into low-resolution ones: Representative region of a 2D plane of (D) a low-resolution (yellow) and (E) a high-resolution (red) image stained with Flk1 for sinusoids. (F) Superimposed images after the registration.DOI:http://dx.doi.org/10.7554/eLife.11214.011

Mentions: The goal of the segmentation of tissue-level networks is to identify the volume of a sample, which is occupied by large vessels such as CV, PV, hepatic artery or bile ducts. These structures appear in the images as empty volume (Figure 2—figure supplement 2A); therefore, their segmentation is possible without using a specific staining.


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 multi-scale tissue images.Tissue-level network segmentation: (A) Reconstructed image of a tissue section. Large vessels appear as empty space in the image. (B) Spatial distribution of the local maximum entropy threshold value. (C) Segmentation of large vessel in a single tissue section. Registration of high-resolution images into low-resolution ones: Representative region of a 2D plane of (D) a low-resolution (yellow) and (E) a high-resolution (red) image stained with Flk1 for sinusoids. (F) Superimposed images after the registration.DOI:http://dx.doi.org/10.7554/eLife.11214.011
© Copyright Policy
Related In: Results  -  Collection

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

fig2s2: Reconstruction of multi-scale tissue images.Tissue-level network segmentation: (A) Reconstructed image of a tissue section. Large vessels appear as empty space in the image. (B) Spatial distribution of the local maximum entropy threshold value. (C) Segmentation of large vessel in a single tissue section. Registration of high-resolution images into low-resolution ones: Representative region of a 2D plane of (D) a low-resolution (yellow) and (E) a high-resolution (red) image stained with Flk1 for sinusoids. (F) Superimposed images after the registration.DOI:http://dx.doi.org/10.7554/eLife.11214.011
Mentions: The goal of the segmentation of tissue-level networks is to identify the volume of a sample, which is occupied by large vessels such as CV, PV, hepatic artery or bile ducts. These structures appear in the images as empty volume (Figure 2—figure supplement 2A); therefore, their segmentation is possible without using a specific staining.

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.