SCH: Al-driven Flexible Electronics for Cardiac Organoid Maturation

The ability to control and monitor the maturation of human-induced pluripotent stem cell (hiPSC)-derived tissues is critical for tissue engineering, regenerative medicine, pharmacology, and synthetic biology. This proposal presents an artificial intelligence (Al)-driven "cyborg tissue" platform that integrates tissue-like flexible electronic sensors and actuators with developing tissues and provides multimodal recording and control. Machine learning-based mathematical models will be built to integrate the data and tissue maturation status readout through the in situ single-cell RNA sequencing. This closed-loop system will control the tissue-wide distributed electrical actuations to promote tissue development. The aim is to use hiPSC-derived cardiac organoids as a model system to demonstrate that this Al-driven cyborg tissue platform can improve the maturation and eliminate the variations in patient-specific hiPSC-derived tissue samples. Specifically, flexible and stretchable mesh nanoelectronics with miniaturized multifunctional sensors and electrical stimulators will be fully implanted, integrated, and distributed across the entire three-dimensional (3D) volume of organoids for continuous, multiplexed sensing and actuation. Additionally, in situ electro-sequencing will be used to combine spatially resolved single-cell molecular phenotypes with the functional readouts from the electronics. A statistical learning architecture will be developed for modeling, testing, and interpreting multimodal electrical activities, mechanical contractile, gene regulatory, and signaling networks to determine the functional maturation of the organoids. Finally, a feedback control system will be implemented for real-time experimental design enhancement, electrical stimulation optimization, and model refinement to improve the functional maturation of cardiac organoids. The success of this work will potentially provide an improved mechanistic understanding of how genetic, molecular, electrical, and mechanical processes regulate the maturation of the hiPSC-derived cardiac organoids and establish an Al-controlled bioelectronics system to sense and control the functional maturation of hiPSC-derived cardiac organoids for various regenerative medicine and pharmacological applications. The technology is likely to be generalizable to help scientists understand the maturation and functions of virtually any kind of developing tissue and organoid systems and even in vivo systems. This proposed research will combine AI, machine learning, computational biology, biomedical informatics and multimodal cell data to advance stem cell maturation and enable new data-driven discovery, which aligns with the mission of the National Library of Medicine.