Analyzing force measurements of multi-cellular clusters comprising indeterminate geometries

Area of Science:

• Biomaterials Science
• Biophysics
• Cell Biology

Background:

• Quantifying mechanical properties of multi-cellular biomimetic models is vital for biomedical applications.
• Heterogeneous geometries in models like tumor spheroids challenge traditional analysis methods.
• Linear fitting is commonly used but may be inaccurate for non-planar contact geometries.

Purpose of the Study:

• To introduce and validate the Integrated Elasticity (IE) regression for analyzing force-displacement data in multi-cellular clusters.
• To compare the precision and accuracy of IE regression against traditional linear fitting for tumor spheroid mechanical property quantification.
• To address the limitations of linear assumptions in analyzing complex, non-planar geometries.

Main Methods:

• Development of the Integrated Elasticity (IE) regression based on established elastic theories.
• Application of IE regression and traditional linear regression to force-displacement data from tumor spheroid compression measurements.
• Image analysis to assess contact geometry deviation from planarity.
• Comparative analysis of elastic moduli predicted by both regression methods.

Main Results:

• IE regression demonstrated excellent precision even with non-planar contact geometries.
• Linear fittings, while seemingly satisfactory, predicted significantly smaller elastic moduli compared to IE regression.
• IE regression results align with previous findings indicating underestimation of elastic constants by linear methods.

Conclusions:

• IE regression provides a more accurate and reliable method for quantifying mechanical properties of biomimetic models with complex geometries.
• Traditional linear regression may underestimate the true elastic constants of biological samples.
• IE regression is recommended as a simple, free, and optimal alternative for analyzing multi-cellular clusters, especially those with concave geometries.