The ability to determine and control the maturation of human-induced pluripotent stem cell (hiPSC) derived tissues is critical to tissue engineering, regenerative medicine, pharmacology, and synthetic biology, which requires the interrogation and intervention of cellular activities across the three-dimensional (3D) volume of tissues and over the time course of tissue development at cellular resolution. This proposal aims to build an AI-enabled cyber-physical-biological system to monitor and control the maturation of hiPSC derived cardiomyocyte (hiPSC-CM) organoids during development. The proposed research will develop “tissue-like†nanoelectronics that can be integrated into the developing cardiac organoids, distributing the electronic sensor and actuator network throughout the entire 3D volume of the tissue and enabling tissue-level recording and control over the entire time course of development at single-cell resolution. In situ single-cell RNA sequencing will be used to integrate gene expression data with continuous physical sensing data. Machine learning and statistical models will be built for interpreting the online sensing data, and cyber-control methods will be developed for the closed-loop online control of the cardiac organoid maturation. The developed hardware and software can be applied to virtually any current biological systems, in which the change of cellular states can be reliably recorded and controlled through the electronic sensors and actuators. The success of this proposal will further merge the field of AI, nanoelectronics, and biology, bringing unlimited opportunities for access and control to biological and biomedical engineering. The multidisciplinary teamwork will represent a successful case that schools of thought from diverse fields including bioengineering, machine learning, statistics, control theory, etc. inspire and complement each other to create state-of-the-art research results in each field. The research team will also collaborate with internal and external partners to launch educational and societal activities for students from diverse backgrounds, such as providing e-seminars, workshops and new courses for undergraduate students on advanced nanoelectronics fabrication, and workshops and tours for local K-12 students to explore stem cell culture, online videos to disseminate new research in genomics, mathematical and computational modeling, integration of AI, nanoelectronics, and biology.
We propose to develop a seamless integration of cyber-physical systems with biological systems, enabling a closed-loop control, capable of real-time, bidirectionally, and long-term stably interrogating and intervening cellular activities across the 3D volume of tissue networks at single-cell resolution. As a demonstration, we will apply this cyber-physical-biological system to the hiPSC-CM organoids, promoting and accelerating their maturation. We will achieve our goal through the following 4 technical innovations: (A) developing technologies to integrate stretchable mesh nanoelectronics with multifunctional sensors and actuators to the cardiac organoids, enabling real-time monitoring and control of organoid development; (B) precisely registering electronic sensors during in situ single-cell RNA sequencing to determine the molecular maturation of cardiac organoids and correlate spatial gene expression profiling with sensing data at single-cell resolution; (C) developing novel machine learning models and tools to identify the statistical interference between gene expression and organoid-wide electrical and mechanical recording and also building online predictive models to real-time determine the maturation of cardiac organoids; (D) developing effective and scalable Reinforcement Learning (RL) methods to determine optimized electrical activation patterns to promote the maturation of cardiac organoids and to test its performance in patient-specific cardiac organoids.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
