Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing...

Area of Science:

• Cellular mechanics and biophysics
• Computational biology and image analysis

Background:

• Adherent cells generate traction forces essential for migration, tissue integrity, and morphogenesis.
• Traction force microscopy (TFM) quantifies these forces on elastic substrates, offering perturbation-free insights into cellular mechanics.
• Standard TFM algorithms rely on regularization or filtering, often requiring subjective parameter selection, leading to systematic errors in comparative studies.

Purpose of the Study:

• To systematically evaluate novel computer vision and Bayesian inference methods for solving the inverse problem in TFM.
• To compare the performance of classical L1/L2 regularization with new schemes like Elastic Net, Proximal Gradient Lasso, and Proximal Gradient Elastic Net.
• To develop and validate Bayesian methods for automatic, optimal L2 regularization in TFM.

Main Methods:

• Comparison of L1- and L2-regularization with Elastic Net, Proximal Gradient Lasso, and Proximal Gradient Elastic Net for TFM inverse problem.
• Development of Bayesian L2 regularization and Advanced Bayesian L2 regularization for automated parameter selection.
• Validation using both artificial and experimental TFM data.

Main Results:

• Elastic Net regularization demonstrated superior accuracy in traction reconstruction compared to classical L1 and L2 methods.
• The developed Bayesian L2 regularization methods enabled robust traction reconstruction without manual parameter tuning.
• Bayesian approaches significantly mitigate uncertainty when comparing cellular tractions across different experimental conditions.

Conclusions:

• Elastic Net regularization offers an improved approach for accurate traction reconstruction in TFM.
• Bayesian regularization methods provide objective and robust TFM analysis, eliminating the need for subjective parameter choices.
• These advancements enhance the reliability of comparative studies on cellular mechanics and behavior.