Professor Seo Dae-ha’s team at DGIST has developed new real-time microscopy technology to observe motor proteins in cells.
They used FT-pdf microscopy to study the efficient material transport strategy of cells through motor proteins and endosomes.
The microscopy technique captures scattering signals using nanoparticles, allowing real-time observation of particles’ movement and rotation.
The research team discovered temporal patterns in the rotational movements of endosomes, similar to reinforcement learning strategies in robots and internet search engines.
This study suggests that the data learning technology in human cells could be applied to understanding and diagnosing diseases in the future.
Researchers at DGIST have developed new real-time microscopy technology that allows them to observe the behavior of “motor proteins” in cells. These proteins play a crucial role in efficiently transporting materials within cells, and understanding their movement is vital for uncovering the secrets of intracellular transport strategies. By using advanced imaging techniques, the research team was able to achieve a level of accuracy comparable to electron microscopy, offering valuable insights into how cells function and how diseases may manifest.
The team’s Fourier transform-based plasmonic dark-field microscopy technique enabled them to capture the movement and rotation of endosomes, which are responsible for transporting materials to their destinations within cells. By analyzing the temporal patterns in the rotational movements of endosomes, the researchers found similarities to reinforcement learning strategies used by navigation robots and internet search engines. This breakthrough could not only help in diagnosing diseases but also shed light on how cells operate at a molecular level, potentially paving the way for more precise material transport strategies in the future.
Led by Professor Seo Dae-ha, the research was supported by several research programs and resulted in a publication in the prestigious journal Advanced Science. These findings highlight the exciting possibilities of applying advanced technology to understand cellular processes and could have significant implications for disease diagnosis and treatment in the future.
