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.


Cell classification accuracy.Confusion matrixes obtained with the (A) linear discriminant analysis and (B) the Bayesian network classifier. The instances (e.g. nuclei) in each predicted class are represented in the columns of the matrix, while the instances in an actual class (manually identified) are represented in the rows. 3D representation of the different nuclei types identified in a representative sample of liver tissue: (C) hepatocytes, (D) sinusoidal endothelial cells (SECs), (E) stellate and (F) Kupffer cells.DOI:http://dx.doi.org/10.7554/eLife.11214.016
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4764584&req=5

fig3s3: Cell classification accuracy.Confusion matrixes obtained with the (A) linear discriminant analysis and (B) the Bayesian network classifier. The instances (e.g. nuclei) in each predicted class are represented in the columns of the matrix, while the instances in an actual class (manually identified) are represented in the rows. 3D representation of the different nuclei types identified in a representative sample of liver tissue: (C) hepatocytes, (D) sinusoidal endothelial cells (SECs), (E) stellate and (F) Kupffer cells.DOI:http://dx.doi.org/10.7554/eLife.11214.016

Mentions: The performance of the classifiers was measured using the leave-one-out cross-validation method on the training set. Both classifiers recognized hepatocytes with ~100% accuracy, thus further improving the previous performance (O'Gorman et al., 1985). The overall cell-type classification yielded 95.4% and 92.6% accuracy for the LDA and BNC, respectively. Although discriminating non-parenchymal cells is difficult even for a person skilled in the art, our algorithms achieved accuracy higher than 90%. The predictive performance of the classifiers is shown in Figure 3—figure supplement 3A,B. As expected, the first largest population of cells corresponds to hepatocytes (44.6% ± 2.7%, mean ± SEM) followed by sinusoidal endothelial cells (29.8% ± 2.5%). Surprisingly, we found important quantitative differences for Kupffer and stellate cells. The percentage of Kupffer cells (8.7% ± 0.7%) was lower than that of stellate cells (11.2% ± 1.0%), against previous estimates on 2D images (Baratta et al., 2009). The percentage of other cells was 5.7% ± 0.8%. A 3D visualization of the localization of the nuclei of the different cell types is shown in Figure 3—figure supplement 3C–F.


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)

Cell classification accuracy.Confusion matrixes obtained with the (A) linear discriminant analysis and (B) the Bayesian network classifier. The instances (e.g. nuclei) in each predicted class are represented in the columns of the matrix, while the instances in an actual class (manually identified) are represented in the rows. 3D representation of the different nuclei types identified in a representative sample of liver tissue: (C) hepatocytes, (D) sinusoidal endothelial cells (SECs), (E) stellate and (F) Kupffer cells.DOI:http://dx.doi.org/10.7554/eLife.11214.016
© Copyright Policy
Related In: Results  -  Collection

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

fig3s3: Cell classification accuracy.Confusion matrixes obtained with the (A) linear discriminant analysis and (B) the Bayesian network classifier. The instances (e.g. nuclei) in each predicted class are represented in the columns of the matrix, while the instances in an actual class (manually identified) are represented in the rows. 3D representation of the different nuclei types identified in a representative sample of liver tissue: (C) hepatocytes, (D) sinusoidal endothelial cells (SECs), (E) stellate and (F) Kupffer cells.DOI:http://dx.doi.org/10.7554/eLife.11214.016
Mentions: The performance of the classifiers was measured using the leave-one-out cross-validation method on the training set. Both classifiers recognized hepatocytes with ~100% accuracy, thus further improving the previous performance (O'Gorman et al., 1985). The overall cell-type classification yielded 95.4% and 92.6% accuracy for the LDA and BNC, respectively. Although discriminating non-parenchymal cells is difficult even for a person skilled in the art, our algorithms achieved accuracy higher than 90%. The predictive performance of the classifiers is shown in Figure 3—figure supplement 3A,B. As expected, the first largest population of cells corresponds to hepatocytes (44.6% ± 2.7%, mean ± SEM) followed by sinusoidal endothelial cells (29.8% ± 2.5%). Surprisingly, we found important quantitative differences for Kupffer and stellate cells. The percentage of Kupffer cells (8.7% ± 0.7%) was lower than that of stellate cells (11.2% ± 1.0%), against previous estimates on 2D images (Baratta et al., 2009). The percentage of other cells was 5.7% ± 0.8%. A 3D visualization of the localization of the nuclei of the different cell types is shown in Figure 3—figure supplement 3C–F.

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.