Epithelial tissue statistics: eliminating bias reveals morphological and morphogenetic features.

Area of Science:

• Cell biology
• Developmental biology
• Biophysics

Background:

• Geometric order in quasi-two-dimensional epithelia is crucial for understanding tissue formation (morphogenesis) and regeneration.
• Published data on cell neighbor distributions have been debated due to measurement bias.
• Key biases include apparent four-fold vertex measurement and sampling window effects on cell size.

Purpose of the Study:

• To develop a method for detecting and correcting measurement bias in epithelial tissue data.
• To enable meaningful comparisons of data from diverse experimental sources.
• To investigate the topological correlations in proliferating versus remodeling tissues.

Main Methods:

• Utilizing biased (measured) distributions to detect and correct for measurement bias without original sample knowledge.
• Applying the method to quantify apparent four-fold vertices and cell size selection bias.
• Analyzing topological correlations in Drosophila wing tissue data.

Main Results:

• Demonstrated bias detection and correction for four-fold vertices and finite sampling windows.
• Established that apparent four-fold vertices are neither randomly distributed nor oriented.
• Revealed distinct topological correlations between proliferating and remodeling tissues.
• Successfully disentangled distributional moments in Drosophila wing tissue.

Conclusions:

• The developed method provides unbiased data for robust tissue identification and classification.
• Non-random vertex distribution indicates profound differences in topological organization between tissue types.
• Accurate topological analysis is essential for understanding tissue dynamics and development.