SCH: Al-driven Flexible Electronics for Cardiac Organoid Maturation

SCH：用于心脏类器官成熟的铝驱动柔性电子器件

• 批准号: 10816899
• 负责人: Jie Ding
• 金额: $ 27.85万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816899
• 关键词: 3-Dimensional; Acceleration; Algorithms; Architecture; Artificial Intelligence; Biological Models; Biophysics; Cardiac; Categories; Cell Maturation; Cells; Chronic; Computational Biology; Data; Development; E-learning; Electric Stimulation; Electronics; Evaluation; Evolution; Experimental Designs; Feedback; Gene Expression; Genes; Genetic; Goals; Human; Implant; In Situ; Intervention; Joints; Learning; Machine Learning; Maps; Measurement; Mechanics; Mission; Modality; Modeling; Molecular; Monitor; Organoids; Patients; Pharmacology; Policies; Process; Property; Psychological reinforcement; Regenerative Medicine; Regulator Genes; Research; Resolution; Rewards; Scientist; Signal Transduction; Site; Statistical Models; System; Technology; Testing; Time; Tissue Embedding; Tissue Engineering; Tissue Sample; Tissues; United States National Library of Medicine; Variant; Work; bioelectronics; biomaterial compatibility; biomedical informatics; course development; data integration; electronic sensor; empowerment; flexibility; flexible electronics; improved; in vivo; induced pluripotent stem cell; learning strategy; mathematical model; mechanical signal; miniaturize; molecular phenotype; multimodal data; multimodality; nanoelectronics; novel; pharmacologic; predictive modeling; sensor; single-cell RNA sequencing; spatiotemporal; statistical learning; stem cell derived tissues; stem cells; success; synthetic biology; tool; trustworthiness; virtual

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.

控制和监测人类诱导的多能干细胞（HIPSC）衍生的能力 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 提案提出了人工智能（AL）驱动的“半机械组织”平台，该平台整合了组织样 具有发育组织的柔性电子传感器和执行器，并提供多模式记录和 控制。将建立基于机器学习的数学模型来集成数据和组织 通过原位单细胞RNA测序读数的成熟状态。这个闭环系统将 控制整个组织范围的分布式电作用，以促进组织发育。目的是使用 HIPSC衍生的心脏器官作为模型系统，以证明这种al驱动的机器人组织 平台可以改善成熟并消除患者特异性HIPSC衍生组织的变化 样品。 具体而言，具有微型多功能传感器的灵活且可拉伸的网状纳米电子学和 电刺激器将在整个三维中完全植入，整合和分布 （3D）类器官的体积，用于连续的，多路复用的感应和致动。另外，原位 电序将用于将空间分辨的单细胞分子表型与 电子设备的功能读数。将开发一个统计学习架构，用于建模， 测试和解释多模式电活动，机械收缩，基因调节和 信号网络确定器官的功能成熟。最后，反馈控制 系统将用于实时实验设计增强，电刺激 优化和模型改进，以改善心脏器官的功能成熟。 这项工作的成功将有可能提高对遗传， 分子，电和机械过程调节了hipsc衍生心脏的成熟 器官并建立一个由Al控制的生物电子系统来感知和控制功能 用于各种再生医学和药理应用的HIPSC衍生心脏器官的成熟。该技术可能是可以推广的，以帮助科学家了解成熟和 几乎任何开发的组织和器官系统，甚至体内系统的功能。这 拟议的研究将结合AI，机器学习，计算生物学，生物医学信息学和 多模式的细胞数据以推动干细胞成熟并启用新的数据驱动发现，该发现与 国家医学图书馆的任务。