A joint research group led by Genki Kanda of the RIKEN Center for Biosystems Dynamics Research (BDR) has developed an artificial intelligence (AI) robotic system to autonomously determine the optimal conditions for growing replacement retinal layers needed for vision. The AI controlled a trial and error process that spanned 200 million possible conditions and managed to improve cell culture recipes used in regenerative medicine. This achievement, published in the scientific journal eLife on June 28, is just one example of how the automated design and conduct of science experiments can increase the efficiency and speed of life science research in general.
Research in regenerative medicine often requires numerous experiments that are both time-consuming and labor-intensive. In particular, creating specific tissue from stem cells – a process called induced cell differentiation – requires months of work and the degree of success depends on a wide variety of variables. Finding the optimal type, dose and timing of reagents, as well as optimal physical variables such as pipette strength, cell transfer time and temperature is difficult and requires an enormous amount of trial and error. As Kanda explains, “because minute differences in physical conditions have a significant impact on quality, and because inducing cell differentiation takes weeks to months in culture, the impact of a small difference in timing on day 3 may not be able to impact for several months.” are detected.”
To make this process more efficient and practical, the BDR team set out to develop an autonomous experimental system that can determine the optimal conditions and grow functional retinal pigment layers from stem cells. Retinal pigment epithelial cells (RPE) were chosen because degeneration of these cells is a common age-related condition that prevents people from seeing. Equally importantly, transplanted RPE retinal layers are already having some clinical success.
For autonomous experiments to succeed, the robot must repeatedly produce the same series of precise movements and manipulations, and the AI must be able to evaluate the results and formulate the next experiment. The new system achieves these goals using a general-purpose humanoid robot – called Maholo – capable of highly accurate experimental behavior in the life sciences. Maholo is controlled by AI software that uses a newly designed optimization algorithm to determine which parameters to change and how to change them to improve differentiation efficiency in the next round of experiments.
Researchers are introducing necessary protocols for generating RPE cells from stem cells in Maholo. Although RPE cells were successfully generated in all experiments, the efficiency was only 50%. So for every 100 stem cells there were only about 50 RPE cells. After establishing this baseline, the AI started the optimization process to determine the best conditions between all chemical and physical parameters. What would have taken humans over two and a half years to complete, the robotic AI system took just 185 days and resulted in a differentiation efficiency of 90%. In practice, these cells exhibited many of the typical biological markers that would make them suitable for transplantation into an eye with a damaged RPE cell layer.
The success of the new system goes beyond the immediate results. “We chose to differentiate RPE cells from stem cells as a model,” Kanda says, “but in principle, combining a precision robot with the optimization algorithms will enable autonomous trial-and-error experiments in many areas of the brain.” life sciences.”
However, the researchers emphasize that the aim of the study is not to replace human lab workers with robots. “Using robots and AI to conduct experiments will be of great interest to the public,” Kanda says. “However, it is a mistake to see them as substitutes. Our vision is that people do what they do best, which is creative. We can use robots and AI for the trial-and-error parts of experiments that require repeatable precision. and take a lot of time, but which does not require any thinking.”
Reference: Kanda GN, Tsuzuki T, Terada M, et al. Robotic quest for optimal cell culture in regenerative medicine. eLife† 2022;11:e77007. doi: 10.7554/eLife.77007
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