Deep-learning-based 3D cellular force reconstruction directly from volumetric images.

Area of Science:

• Biomechanics
• Mechanobiology
• Cellular Biophysics

Background:

• Cellular forces in 3D environments are critical for physiological and pathological processes.
• Traditional 3D Cellular Force Microscopy (CFM) is computationally intensive and time-consuming.
• Efficient quantification of cell-matrix mechanical interactions is needed for large-scale analysis.

Purpose of the Study:

• To develop a novel, data-driven 3D-CFM method using deep neural networks.
• To reconstruct 3D cellular force fields directly from volumetric images.
• To improve the computational efficiency and robustness of 3D-CFM.

Main Methods:

• A deep-learning-based network combining deep convolutional neural networks (DCNNs) and specific function layers was developed.
• Physical information about extracellular matrix material properties was integrated into the data-driven network.
• Mini-batch stochastic gradient descent and back-propagation algorithms were used for training and efficiency.

Main Results:

• The novel deep-learning 3D-CFM can directly recover 3D cellular forces from fluorescence image pairs.
• The method demonstrates good generalization ability and robustness.
• Computational efficiency is improved by one to two orders of magnitude compared to traditional 3D-CFM.

Conclusions:

• This study presents a high-performance 3D-CFM scheme for quantitative characterization of cell-matrix mechanical interactions.
• The data-driven approach significantly accelerates force recovery and large-scale sample analysis.
• This advancement is vital for quantitative investigations in biomechanics and mechanobiology.