TY - JOUR TI - Robotic search for optimal cell culture in regenerative medicine AU - Kanda, Genki N AU - Tsuzuki, Taku AU - Terada, Motoki AU - Sakai, Noriko AU - Motozawa, Naohiro AU - Masuda, Tomohiro AU - Nishida, Mitsuhiro AU - Watanabe, Chihaya T AU - Higashi, Tatsuki AU - Horiguchi, Shuhei A AU - Kudo, Taku AU - Kamei, Motohisa AU - Sunagawa, Genshiro A AU - Matsukuma, Kenji AU - Sakurada, Takeshi AU - Ozawa, Yosuke AU - Takahashi, Masayo AU - Takahashi, Koichi AU - Natsume, Tohru A2 - Méndez-Ferrer, Simón A2 - Zaidi, Mone A2 - Méndez-Ferrer, Simón A2 - Sebastian, Sujith A2 - Monville, Christelle VL - 11 PY - 2022 DA - 2022/06/28 SP - e77007 C1 - eLife 2022;11:e77007 DO - 10.7554/eLife.77007 UR - https://doi.org/10.7554/eLife.77007 AB - Induced differentiation is one of the most experience- and skill-dependent experimental processes in regenerative medicine, and establishing optimal conditions often takes years. We developed a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. From 200 million possible parameter combinations, the system performed cell culture in 143 different conditions in 111 days, resulting in 88% better iPSC-RPE production than that obtained by the pre-optimized culture in terms of the pigmentation scores. Our work demonstrates that the use of autonomous robotic AI systems drastically accelerates systematic and unbiased exploration of experimental search space, suggesting immense use in medicine and research. KW - laboratory automation KW - LabDroid KW - bayesian optimization KW - regenerative medicine KW - iPS cell KW - retinal pigment epithelium JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -