This study focuses on automating the culture of iPS cells. Our goal is to reduce the variability due to manual experimentations and human labor, by utilizing smaller and more affordable equipment, which leads to enhance the reproducibility, and provide opportunities for more researchers who are even unfamiliar with iPS cells. Several types of automated culture machines have emerged, but most of them are still large, costly, and can only perform partial tasks.
The simpler structure of the working urn has reduced the size and cost of the equipment. In addition, the tasks from cell maintenance to differentiation induction can be performed automatically, with only prior material preparation, and task setting on software. Machine repetition is remarkably accurate compared to human repetition.
The use of such machines not only improves repeat usability, but also reduce human labor. Furthermore, if the parameters in each protocol are shared, researchers unfamiliar with iPS cells will be able to achieve similar results as well-experienced researchers, making it possible for them to easily enter into research using iPS cells. We are exploring the possibility of incorporating an AI-based image judgment system to automate the selection of the next work schedule, setting date, and work content from the learning of the experiment results.
We also anticipate that allowing researchers to tailor their working protocols to their preferences, and share them online, will not only enhance reproducibility among the world researchers, but also encourage global research collaboration.
