Uncertainty-aware traction force microscopy.

Area of Science:

• Biophysics
• Cellular Mechanics
• Image Analysis

Background:

• Traction Force Microscopy (TFM) quantifies cell forces using elastic substrates and tracked markers.
• TFM computations are ill-conditioned, requiring regularization that often relies on subjective parameter selection.
• Existing TFM methods lack robust uncertainty quantification for substrate deformation errors and their impact on traction stress.

Purpose of the Study:

• To develop an uncertainty-aware TFM technique (TFM-UQ) for reliable quantification of cell-exerted forces.
• To estimate and propagate measurement uncertainties in substrate deformation to traction stress calculations.
• To automate regularization parameter selection and improve the objectivity of TFM analysis.

Main Methods:

• Utilized a non-parametric bootstrap method to perturb Particle Image Velocimetry (PIV) cross-correlation for deformation uncertainty estimation.
• Implemented a hierarchical Bayesian TFM framework with spatially adaptive regularization to propagate uncertainty.
• Evaluated TFM-UQ using synthetic datasets with varying image quality and applied it to experimental data.

Main Results:

• TFM-UQ successfully estimates traction stress vector variability in magnitude and direction.
• The technique bypasses subjective regularization parameter selection and adapts smoothing locally.
• TFM-UQ objectively guides the selection of image analysis parameters, such as PIV window size.

Conclusions:

• TFM-UQ provides an uncertainty-aware approach to TFM, improving the reliability and interpretability of cell force measurements.
• This method outperforms traditional regularization techniques by adapting smoothing based on input data uncertainty.
• The developed tools facilitate decoupling biological heterogeneity from measurement variability and enable automated analysis of large datasets.