TY - JOUR
T1 - A Robotic System for Long-term Personalized Automated Cultivation of Colorectal Cancer Organoids
AU - Zhu, Yibo
AU - Lin, Xiaotian
AU - Li, Zhuowei
AU - Zhou, Yanping
AU - Li, Haonan
AU - Zhuang, Songlin
PY - 2025/10/24
Y1 - 2025/10/24
N2 - Organoids are a class of popular three-dimensional in vitro models that recapitulate the structural, genetic, and functional characteristics of their native tissues, offering powerful tools for drug screening and precision medicine. While recent advances have integrated robotic automation into organoid culture systems, existing systems primarily rely on static protocols and lack adaptive, real-time feedback capabilities, thereby limiting the improvement for personalized organoid culture. A critical factor in personalized culture is the timing of culture media change, which significantly impacts organoid growth, differentiation, and overall viability. Conventional approaches depend on discrete optical evaluations coupled with subjective interpretation, rendering them incapable of dynamically adapting to variations in culture conditions. Here, we present a robotic system that can perform mechanical monitoring of organoid culture and automate culture media change for the personalized culture of colorectal cancer organoids (CRCOs). The system integrates (1) distributed ultra-thin membrane sensors for individual organoid groups by a real-time and in-situ monitoring (RISM) platform, and (2) a long-term automated culture platform (LTAC) that implements an adaptive culture media change timing control strategy based on sensed mechanical changes, enabling personalized culture optimization. In experiments, our system successfully performed CRCO culture for 7 days while continuously monitoring mechanical changes in the culture environment. In comparison to conventional manual culture employing static protocols, CRCOs cultured with our robotic system showed a significant 31% increase in CRCOs area and enhanced viability.
AB - Organoids are a class of popular three-dimensional in vitro models that recapitulate the structural, genetic, and functional characteristics of their native tissues, offering powerful tools for drug screening and precision medicine. While recent advances have integrated robotic automation into organoid culture systems, existing systems primarily rely on static protocols and lack adaptive, real-time feedback capabilities, thereby limiting the improvement for personalized organoid culture. A critical factor in personalized culture is the timing of culture media change, which significantly impacts organoid growth, differentiation, and overall viability. Conventional approaches depend on discrete optical evaluations coupled with subjective interpretation, rendering them incapable of dynamically adapting to variations in culture conditions. Here, we present a robotic system that can perform mechanical monitoring of organoid culture and automate culture media change for the personalized culture of colorectal cancer organoids (CRCOs). The system integrates (1) distributed ultra-thin membrane sensors for individual organoid groups by a real-time and in-situ monitoring (RISM) platform, and (2) a long-term automated culture platform (LTAC) that implements an adaptive culture media change timing control strategy based on sensed mechanical changes, enabling personalized culture optimization. In experiments, our system successfully performed CRCO culture for 7 days while continuously monitoring mechanical changes in the culture environment. In comparison to conventional manual culture employing static protocols, CRCOs cultured with our robotic system showed a significant 31% increase in CRCOs area and enhanced viability.
KW - Robotics and automation in life science
KW - sensor-based control
KW - robust/adaptive control
UR - https://doi.org/10.1109/LRA.2025.3625488
U2 - 10.1109/LRA.2025.3625488
DO - 10.1109/LRA.2025.3625488
M3 - Article
SN - 2377-3766
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
ER -