B. Li, X. Wang, J. Liu
Carnegie Mellon University,
United States
Keywords: AI, automation, biotech, research, robotics
Summary:
Purpose of the study: Manual cell culture is a labor-intensive and error-prone bottleneck in research, and existing automation often fails research labs due to inflexibility and high cost. We aim to develop an intelligent, end-to-end platform that is compact, modular, and affordable, capable of handling both multi-well plates and common T-flasks. The system integrates natural-language control for intuitive protocol setup and uses AI-powered image analysis for automated confluency assessment and passaging decisions. This platform automates the full cell culture workflow and interoperates with other robotic systems to support downstream processes. Experimental Procedures: The system architecture integrates three core subsystems: a high-capacity automated incubator, a modular liquid handling platform, and an on-board liquid management system, all supervised by a central AI agent. The incubator is designed as a flexible, centralized cell culture core for multi-user environments, accommodating diverse labware like flasks and plates to bridge large-scale expansion and high-throughput assays. The gantry-based liquid handler enables efficient parallel processing of multiple vessels. It features modular, swappable workstations for serviceability and an access port for interoperability with external robotic arms, facilitating sample hand-off for downstream analysis. An integrated liquid management system provides on-demand, temperature-conditioned sterile reagents to the platform. The entire platform is orchestrated by an AI agent that translates natural language requests from scientists into executable robotic workflows. A digital twin allows for pre-execution simulation and protocol optimization, while the AI leverages real-time image analysis to enable automated, data-driven decisions during culture, such as initiating passaging once cells reach pre-defined biological thresholds. Summary of Data: Two functional liquid handler prototypes were developed and validated at Carnegie Mellon University. An initial version successfully integrated with a Thermo Fisher Spinnaker™ Mover for automated T-75 flask handling, demonstrating interoperability. The second prototype showcases high-throughput parallel processing, autonomously managing four T-175 flasks simultaneously. The platform has executed over 20 unattended passage and expansion protocols across five diverse cell lines (HEK293, NIH3T3, C2C12, MRC5, iPSC-MSC). The onboard image analysis model, trained on HEK293 and iPSC-MSC data, achieves over 98% accuracy in confluency and cell count analysis for these two cell lines, enabling automated, data-driven decisions. Furthermore, AI-powered natural language protocol generation has been achieved; scientists can now verbally describe an experiment, generating an executable robotic protocol in under one minute. Conclusion: This work validates an intelligent robotic platform that translates natural language commands into reproducible, parallel cell culture workflows, demonstrating a powerful and accessible end-to-end solution to eliminate critical bottlenecks in life science research. Next Steps: The automated incubator and liquid management modules are currently in prototyping, with full system integration targeted for completion within 5 months. Future experiments will validate large-scale cell expansion, harvesting, and full interoperability with other robotic systems.