Using Histologic Image Analysis to Understand Biophysical Regulations of Epithelial Cell Morphology

Area of Science:

• Epithelial mechanics
• Mechanobiology
• Biophysics education

Background:

• Epithelial mechanics and mechanobiology are crucial for understanding biological processes like organ development and disease.
• Current educational approaches lack hands-on modules to connect physics principles with cell biology.
• There is a need for accessible tools to teach cell shape and mechanics.

Purpose of the Study:

• To develop a hands-on, low-cost educational module for studying epithelial cell mechanics.
• To engage students in observing cell morphology and relating it to fundamental physics.
• To provide a scalable learning tool for various educational levels and outreach programs.

Main Methods:

• Utilized commercial frog skin histology slides for studying epithelial cells.
• Employed artificial intelligence-based image-segmentation tools for high-throughput quantification of cell morphology.
• Integrated a light microscope and computer for data acquisition and analysis.

Main Results:

• Successfully quantified diverse cell morphologic features using AI tools.
• Enabled students to reproduce key findings in cell mechanics, including cell aspect ratio distribution and nuclear-to-cellular area ratio.
• Demonstrated the module's low cost, portability, and scalability.

Conclusions:

• The developed module offers an engaging and accessible method for teaching epithelial tissue mechanics.
• It effectively connects fundamental physics principles to cell biology through hands-on activities.
• The workflow supports science education and outreach by making complex topics readily observable.