Uncertainty-Aware Traction Force Microscopy.

Area of Science:

• Cellular mechanics and biomechanics
• Quantitative microscopy and image analysis
• Biophysics and computational biology

Background:

• Traction Force Microscopy (TFM) quantifies cell-generated forces using substrate deformation.
• Standard TFM methods require heuristic regularization, sensitive to noise and lacking error estimation.
• Existing TFM approaches struggle to account for measurement errors in substrate deformation and their propagation to traction stress.

Purpose of the Study:

• To develop an uncertainty-aware TFM technique for robust quantification of cell-exerted forces.
• To estimate and propagate measurement uncertainties in substrate deformation to traction stress readouts.
• To enable objective parameter selection and automate TFM analysis by accounting for input data quality.

Main Methods:

• Quantification of substrate deformation and uncertainty using a non-parametric bootstrap Particle Image Velocimetry with Uncertainty Quantification (PIV-UQ).
• Implementation of a hierarchical Bayesian framework for spatially adaptive regularization, conditioned on image quality.
• Propagation of uncertainty from substrate deformation to traction stress using the TFM with Uncertainty Quantification (TFM-UQ) technique.

Main Results:

• TFM-UQ locally adapts smoothing levels, outperforming traditional regularization methods.
• The developed uncertainty-aware tools objectively guide the selection of image analysis parameters, such as PIV-UQ interrogation window size.
• Validation using synthetic and experimental datasets demonstrates the reliability and improved performance of PIV-UQ and TFM-UQ.

Conclusions:

• The uncertainty-aware TFM-UQ technique provides a more accurate and reliable method for quantifying cell-generated forces.
• This approach facilitates decoupling biological variability from measurement errors, enhancing TFM data interpretation.
• The developed tools enable parameter-free, data-driven regularization, paving the way for automated analysis of large TFM datasets.