In Silico Prediction of Cell Traction Forces

TL;DR

This paper introduces a deep learning-based method to predict cell traction forces directly from fluorescent images, reducing the need for traditional bead displacement analysis and enabling uncertainty estimation.

Contribution

It presents a novel Bayesian Neural Network approach for in silico prediction of cell forces from images, enhancing efficiency and trustworthiness of force estimation.

Findings

The method accurately predicts forces across different cells of the same strain.

It provides an uncertainty measure indicating the reliability of predictions.

The approach can potentially replace traditional TFM procedures.

Abstract

Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on gels with fluorescent beads, followed by bead displacement calculations. We present a new method allowing to predict those forces from a regular fluorescent image of the cell. Using Deep Learning, we trained a Bayesian Neural Network adapted for pixel regression of the forces and show that it generalises on different cells of the same strain. The predicted forces are computed along with an approximated uncertainty, which shows whether the prediction is trustworthy or not. Using the proposed method could help estimating forces when calculating non-trivial bead displacements and can also free one of the fluorescent channels of the microscope. Code is available at \url{https://github.com/wahlby-lab/InSilicoTFM}.