Topography of pleural epithelial structure enabled by en face isolation and machine learning

Area of Science:

• Pulmonary Physiology
• Cell Biology
• Biomechanical Engineering

Background:

• Pleural epithelial adaptations to mechanical stress are crucial for normal lung function and disease.
• Assessing regional mechanical stress in the lung is challenging due to tissue properties and large displacements.
• A reliable method for isolating pleural epithelium for structural analysis was lacking.

Purpose of the Study:

• To develop a novel method for en face harvest of murine pleural epithelium.
• To define topographic variations in pleural structure and cell morphology.
• To investigate pleural epithelial cell adaptations to mechanical stress during lung inflation.

Main Methods:

• Developed an en face harvest technique for murine pleural epithelium.
• Utilized silver-stain for light microscopy imaging of cell borders.
• Applied machine learning and watershed segmentation for quantitative cell analysis (area, perimeter).

Main Results:

• Pleural epithelial cells were significantly larger in the lung apex compared to basilar regions at residual volume.
• Apical cell distortion suggests a vertical gradient of pleural pressures.
• Epithelial cell area increased by 57% and perimeter by 27% from residual volume to total lung capacity, less than predicted by uniform expansion.

Conclusions:

• Structured analysis of pleural epithelial cells provides insights into regional mechanical stress distribution.
• The findings complement studies of pulmonary microstructure.
• The developed method enables detailed investigation of pleural epithelial adaptations to mechanical forces.