Kumamoto University develops advanced cytoskeleton analysis method

A research team at Kumamoto University has created a new method for analyzing the cytoskeleton in cells using deep learning technology. This approach is faster and more accurate than traditional methods and could significantly change how scientists study cell functions in plants and other organisms. The findings were published in a journal called Protoplasma. The cytoskeleton is a network of proteins that helps maintain a cell's shape and supports its ability to grow and react to the environment. Traditionally, scientists have analyzed these structures by looking at them under a microscope, which can be slow and lead to errors. While some digital methods have helped, measuring the density of the cytoskeleton accurately has been difficult. To solve this problem, Professor Takumi Higaki and his team developed an AI-based technique that improves how researchers measure cytoskeleton density. They trained a deep learning model using many images from confocal microscopy, which allowed the system to identify cytoskeletal structures with high precision. The researchers compared their new AI method to traditional techniques. They found that while older methods could measure angles and alignment, they had trouble with density. In contrast, the deep learning model provided more reliable density measurements. The team tested the model on two important biological processes. First, they looked at how plant cells in Arabidopsis thaliana respond to environmental signals by adjusting actin filament density. Second, they studied microtubule changes during early zygote development in the same plant. Both tests showed that the AI method was effective. This new deep learning technique may benefit many fields, including plant biology and medical research. By improving and adapting this model for different cell types, researchers hope to gain new insights into cell structure and functions.