SCH: Al-driven Flexible Electronics for Cardiac Organoid Maturation

控制和监测人诱导的多能干细胞（hiPSC）衍生的细胞的成熟的能力 组织对于组织工程、再生医学、药理学和合成生物学至关重要。这 一项提案提出了一种人工智能（AI）驱动的“半机械组织”平台，该平台集成了组织样 柔性电子传感器和致动器与发展中的组织，并提供多模式记录， 控制将建立基于机器学习的数学模型来整合数据和组织 通过原位单细胞RNA测序读出成熟状态。该闭环系统将 控制组织范围的分布式电致动以促进组织发育。目的是利用 hiPSC衍生的心脏类器官作为模型系统，以证明这种Al驱动的半机械人组织 该平台可以改善成熟并消除患者特异性hiPSC衍生组织中的变异 样品 具体而言，具有小型化多功能传感器的柔性和可拉伸网状纳米电子器件， 电刺激器将被完全植入，集成，并分布在整个三维空间 (3D)用于连续、多路复用感测和致动的类器官体积。此外，在现场 电测序将用于联合收割机将空间分辨的单细胞分子表型与 电子设备的功能读数将为建模开发一个统计学习架构， 测试和解释多模态电活动、机械收缩、基因调节，以及 信号网络来确定类器官的功能成熟。最后，反馈控制 系统将实施实时实验设计增强，电刺激 优化和模型细化，以改善心脏类器官的功能成熟。 这项工作的成功将有可能提供一个更好的机制理解如何遗传， 分子、电和机械过程调节hiPSC衍生的心脏细胞的成熟。 类器官，并建立一个铝控制的生物电子系统，以感测和控制功能 hiPSC衍生的心脏类器官的成熟用于各种再生医学和药理学应用。这项技术很可能是可推广的，以帮助科学家了解成熟， 几乎任何种类的发育组织和类器官系统甚至体内系统的功能。这 拟议的研究将结合联合收割机人工智能，机器学习，计算生物学，生物医学信息学和 多模式细胞数据，以促进干细胞成熟，并实现新的数据驱动的发现，这与 国家医学图书馆的使命。

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.