Reconstruction of Cell Focal Adhesions using Physical Constraints and Compressive Regularization

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

The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a feasible solution for quantifying the mechanical interactions, which usually regains cellular forces from deformation information of extracellular matrices embedded with fluorescent beads. Owing to computational complexity, the traditional 3D-CFM is usually extremely time-consuming, which makes it challenging for efficient force recovery and large-scale sample analysis. With the aid of deep neural networks, this study puts forward a novel data-driven 3D-CFM to reconstruct 3D cellular force fields directly from volumetric images with random fluorescence patterns. The deep learning (DL)-based network is established through stacking deep convolutional neural network (DCNN) and specific function layers. Some necessary physical information associated with constitutive relation of extracellular matrix material is coupled to the data-driven network. The mini-batch stochastic gradient descent and back-propagation algorithms are introduced to ensure its convergence and training efficiency. The network not only have good generalization ability and robustness, but also can recover 3D cellular forces directly from the input fluorescence image pairs. Particularly, the computational efficiency of the DL-based network is at least one to two orders of magnitude higher than that of the traditional 3D-CFM. This study provides a novel scheme for developing high-performance 3D cellular force microscopy to quantitatively characterize mechanical interactions between single cells and surrounding extracellular matrices, which is of vital importance for quantitative investigations in biomechanics and mechanobiology.