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Imaging of Murine Whole Lung Fibrosis by Large Scale 3D Microscopy aided by Tissue Optical Clearing
Pulmonary fibrosis, characterized by excessive collagen deposition in the lungs, comprises a key and debilitating component of chronic lung diseases. Methods are lacking for the direct visualization of fibrillar collagen throughout the whole murine lung, a capability that would aid the understanding...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6127188/ https://www.ncbi.nlm.nih.gov/pubmed/30190498 http://dx.doi.org/10.1038/s41598-018-31182-2 |
Sumario: | Pulmonary fibrosis, characterized by excessive collagen deposition in the lungs, comprises a key and debilitating component of chronic lung diseases. Methods are lacking for the direct visualization of fibrillar collagen throughout the whole murine lung, a capability that would aid the understanding of lung fibrosis. We combined an optimized organ-level optical clearing (OC) approach with large-scale, label-free multiphoton microscopy (MPM) and second harmonic generation microscopy (SHGM) to reveal the complete network of fibrillar collagen in whole murine lungs. An innate inflammation-driven model based on repetitive poly(I:C) challenge was evaluated. Following OC, mosaic MPM/SHGM imaging with 3D reconstruction and whole organ quantitative analysis revealed significant differences in collagen deposition between PBS and poly(I:C) treated lungs. Airway specific analysis in whole lung acquisitions revealed significant sub-epithelial fibrosis evident throughout the proximal conductive and distal airways with higher collagen deposition in the poly(I:C) group vs PBS group. This study establishes a new, powerful approach based on OC and MPM/SHGM imaging for 3D analysis of lung fibrosis with macroscopic views of lung pathology based on microscopy and providing a new way to analyze the whole lung while avoiding regional sampling bias. |
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