Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images.

Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is much simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the microscope images, we develop a new CNN (convolutional neural network) architecture SW-UNet (small-world U-Net), which is a CNN that reflects the concept of the small-world network. The SW-UNet shows better performance in wrinkle segmentation task compared to other methods: the error (Euclidean distance) of SW-UNet is 4.9 times smaller than the 2D-FFT (fast Fourier transform) based segmentation approach, and is 2.9 times smaller than U-Net. As a demonstration, here we compare the contractile force of U2OS (human osteosarcoma) cells and show that cells with a mutation in the KRAS oncogene show larger force compared to wild-type cells. Our new machine learning based algorithm provides us an efficient, automated and accurate method to evaluate the cell contractile force.