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.


Optimal parameter selection.(A) The mask defining the objects vicinity in the case of bile canalicular (BC) network (yellow) is shown in red and was created by applying an inflation of two voxels (~0.5 µm) to the original objects. (B) Selection of the best parameters for the ‘pure denoise’ method. We used as fixed parameter the number of cycles (10, the maximum possible). ‘Number of frames’ = 11 (the maximum available in the plug-in) shows the best results, i.e., minimum global mean square error (MSE) as well as MSE in the vicinity of the objects. (C) Selection of the best parameters for the ‘edge preserving de-noising and smoothing’ method. We used as fixed parameter the number of cycles (100). ‘Smoothing level’ = 70 corresponds the point before the MSE in the vicinity of the objects starts increasing while the global MSE remains low.DOI:http://dx.doi.org/10.7554/eLife.11214.006
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fig1s3: Optimal parameter selection.(A) The mask defining the objects vicinity in the case of bile canalicular (BC) network (yellow) is shown in red and was created by applying an inflation of two voxels (~0.5 µm) to the original objects. (B) Selection of the best parameters for the ‘pure denoise’ method. We used as fixed parameter the number of cycles (10, the maximum possible). ‘Number of frames’ = 11 (the maximum available in the plug-in) shows the best results, i.e., minimum global mean square error (MSE) as well as MSE in the vicinity of the objects. (C) Selection of the best parameters for the ‘edge preserving de-noising and smoothing’ method. We used as fixed parameter the number of cycles (100). ‘Smoothing level’ = 70 corresponds the point before the MSE in the vicinity of the objects starts increasing while the global MSE remains low.DOI:http://dx.doi.org/10.7554/eLife.11214.006

Mentions: Panel (A) shows single 2D plane projections of an artificial image of bile canalicular (BC) network (2:1 signal-to-noise ratio) after applying our de-noising algorithm as well as PD and EPDS. (B) The resulting images were analysed in terms of the global mean square error (MSE) and coefficient of correlation (CoC). (C) The same metrics evaluated only on the vicinity of the BC. Our method shows a better reduction of the noise (low global MSE and high global CoC) than the other methods. Additionally, it shows a relatively low MSE and high CoC in the vicinity of the objects. Panel (D) shows that global MSE increases with the depth of the sample for PD and EPDS, whereas it is more stable in our method. In the graph, each curve represents one independent sample. (E) Execution time of the algorithms in an Intel(R) Xeon(R) CPU E5-2620 @ 2.00 GHz. EPDS and BFBD are ~ 20 times faster than PD. The bars show the average values over three samples and the error bars correspond to standard deviations. PD and FPDS were performed using the optimal parameters shown in Figure 1—figure supplement 3. For the BFBD, we use a window of five pixels and a threshold = 1.25.


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)

Optimal parameter selection.(A) The mask defining the objects vicinity in the case of bile canalicular (BC) network (yellow) is shown in red and was created by applying an inflation of two voxels (~0.5 µm) to the original objects. (B) Selection of the best parameters for the ‘pure denoise’ method. We used as fixed parameter the number of cycles (10, the maximum possible). ‘Number of frames’ = 11 (the maximum available in the plug-in) shows the best results, i.e., minimum global mean square error (MSE) as well as MSE in the vicinity of the objects. (C) Selection of the best parameters for the ‘edge preserving de-noising and smoothing’ method. We used as fixed parameter the number of cycles (100). ‘Smoothing level’ = 70 corresponds the point before the MSE in the vicinity of the objects starts increasing while the global MSE remains low.DOI:http://dx.doi.org/10.7554/eLife.11214.006
© Copyright Policy
Related In: Results  -  Collection

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

fig1s3: Optimal parameter selection.(A) The mask defining the objects vicinity in the case of bile canalicular (BC) network (yellow) is shown in red and was created by applying an inflation of two voxels (~0.5 µm) to the original objects. (B) Selection of the best parameters for the ‘pure denoise’ method. We used as fixed parameter the number of cycles (10, the maximum possible). ‘Number of frames’ = 11 (the maximum available in the plug-in) shows the best results, i.e., minimum global mean square error (MSE) as well as MSE in the vicinity of the objects. (C) Selection of the best parameters for the ‘edge preserving de-noising and smoothing’ method. We used as fixed parameter the number of cycles (100). ‘Smoothing level’ = 70 corresponds the point before the MSE in the vicinity of the objects starts increasing while the global MSE remains low.DOI:http://dx.doi.org/10.7554/eLife.11214.006
Mentions: Panel (A) shows single 2D plane projections of an artificial image of bile canalicular (BC) network (2:1 signal-to-noise ratio) after applying our de-noising algorithm as well as PD and EPDS. (B) The resulting images were analysed in terms of the global mean square error (MSE) and coefficient of correlation (CoC). (C) The same metrics evaluated only on the vicinity of the BC. Our method shows a better reduction of the noise (low global MSE and high global CoC) than the other methods. Additionally, it shows a relatively low MSE and high CoC in the vicinity of the objects. Panel (D) shows that global MSE increases with the depth of the sample for PD and EPDS, whereas it is more stable in our method. In the graph, each curve represents one independent sample. (E) Execution time of the algorithms in an Intel(R) Xeon(R) CPU E5-2620 @ 2.00 GHz. EPDS and BFBD are ~ 20 times faster than PD. The bars show the average values over three samples and the error bars correspond to standard deviations. PD and FPDS were performed using the optimal parameters shown in Figure 1—figure supplement 3. For the BFBD, we use a window of five pixels and a threshold = 1.25.

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.