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Topography of pleural epithelial structure enabled by en face isolation and machine learning
Authors:Betty S Liu  Cristian D Valenzuela  Katherine L Mentzer  Willi L Wagner  Hassan A Khalil  Zi Chen  Maximilian Ackermann  Steven J Mentzer
Institution:1. Laboratory of Adaptive and Regenerative Biology and Division of Thoracic Surgery, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;2. Institute for Computational and Mathematical Engineering, Department of Management Science & Engineering, Stanford University, Stanford, California, USA;3. Department of Diagnostic and Interventional Radiology, Translational Lung Research Center, University of Heidelberg, Heidelberg, Germany;4. Institute of Functional and Clinical Anatomy and Department of Molecular Pathology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
Abstract:Pleural epithelial adaptations to mechanical stress are relevant to both normal lung function and parenchymal lung diseases. Assessing regional differences in mechanical stress, however, has been complicated by the nonlinear stress–strain properties of the lung and the large displacements with ventilation. Moreover, there is no reliable method of isolating pleural epithelium for structural studies. To define the topographic variation in pleural structure, we developed a method of en face harvest of murine pleural epithelium. Silver-stain was used to highlight cell borders and facilitate imaging with light microscopy. Machine learning and watershed segmentation were used to define the cell area and cell perimeter of the isolated pleural epithelial cells. In the deflated lung at residual volume, the pleural epithelial cells were significantly larger in the apex (624 ± 247 μm2) than in basilar regions of the lung (471 ± 119 μm2) (p < 0.001). The distortion of apical epithelial cells was consistent with a vertical gradient of pleural pressures. To assess epithelial changes with inflation, the pleura was studied at total lung capacity. The average epithelial cell area increased 57% and the average perimeter increased 27% between residual volume and total lung capacity. The increase in lung volume was less than half the percent change predicted by uniform or isotropic expansion of the lung. We conclude that the structured analysis of pleural epithelial cells complements studies of pulmonary microstructure and provides useful insights into the regional distribution of mechanical stresses in the lung.
Keywords:epithelium  lung  machine learning  morphometry  pleura
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