Inferring cellular contractile forces and work using deep morphology traction microscopy.

Area of Science:

• Cellular and Molecular Biology
• Biophysics
• Computational Biology

Background:

• Traction-force microscopy (TFM) is a standard method for measuring cell-generated forces and their role in cell behavior.
• Conventional TFM faces limitations due to complex experiments, specialized substrates, and noise in displacement data, complicating traction analysis.
• The ill-posed inverse problem in TFM often leads to inaccurate traction measurements.

Purpose of the Study:

• To introduce DeepMorphoTM, a deep-learning-based alternative to conventional TFM.
• To simplify the experimental methodology, imaging, and analysis for measuring cell contractility.
• To provide a more robust and accurate method for characterizing cellular traction forces.

Main Methods:

• DeepMorphoTM infers cell-induced substrate displacement from cell shape sequences.
• It computes cellular traction forces without needing specialized substrates or reference images.
• The method utilizes deep learning to analyze cell morphology and predict traction forces.

Main Results:

• DeepMorphoTM quantitatively matches results from conventional TFM.
• The method demonstrates stability against biological variability in cell contractility.
• DeepMorphoTM resolves the ill-posedness of traction computation by avoiding high-frequency noise, improving accuracy and consistency.
• The methodology shows accurate extrapolation across different cell types and substrate materials.

Conclusions:

• DeepMorphoTM offers a simpler and more capable alternative to conventional TFM for 2D cellular contractility characterization.
• The deep-learning approach enhances the accuracy, consistency, and robustness of traction force measurements.
• This technique has the potential to broaden the accessibility and application of TFM in biological research.