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

Area of Science:

• Cellular Biology
• Biophysics
• Machine Learning

Background:

• Cellular contractile force is crucial for biological processes.
• Quantifying cellular forces typically requires complex experimental setups.
• Developing efficient and accurate methods for force measurement is essential.

Purpose of the Study:

• To propose an image-based method for evaluating cellular contractile force using machine learning.
• To develop a novel convolutional neural network (CNN) architecture for wrinkle segmentation.
• To demonstrate the application of this method in comparing cellular forces between different cell types.

Main Methods:

• Utilized a specialized substrate that wrinkles upon cellular contraction to visualize force.
• Developed a new CNN architecture, SW-UNet (small-world U-Net), for accurate wrinkle extraction from microscope images.
• Compared the performance of SW-UNet against 2D-FFT and U-Net for wrinkle segmentation.

Main Results:

• SW-UNet demonstrated superior performance in wrinkle segmentation, with a 4.9x smaller error than 2D-FFT and 2.9x smaller error than U-Net.
• The method successfully visualized and quantified cellular contractile force magnitude and direction.
• U2OS cells with KRAS oncogene mutations exhibited significantly larger contractile forces compared to wild-type cells.

Conclusions:

• The proposed machine learning-based algorithm offers an efficient, automated, and accurate approach for evaluating cellular contractile force.
• SW-UNet provides a significant improvement in wrinkle segmentation accuracy for force evaluation.
• This method facilitates the study of cellular mechanics and the role of contractile forces in diseases like cancer.