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

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...

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Detalles Bibliográficos
Autores principales: Morales-Navarrete, Hernán, Segovia-Miranda, Fabián, Klukowski, Piotr, Meyer, Kirstin, Nonaka, Hidenori, Marsico, Giovanni, Chernykh, Mikhail, Kalaidzidis, Alexander, Zerial, Marino, Kalaidzidis, Yannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764584/
https://www.ncbi.nlm.nih.gov/pubmed/26673893
http://dx.doi.org/10.7554/eLife.11214
Descripción
Sumario: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. DOI: http://dx.doi.org/10.7554/eLife.11214.001