Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids

微流控 自动化 生物反应器 计算机科学 控制器(灌溉) 概念证明 工程类 控制工程 纳米技术 机械工程 材料科学 化学 生物 农学 有机化学 操作系统
作者
Hongwei Cai,Zheng Ao,Zhuhao Wu,Sunghwa Song,Ken Mackie,Feng Guo
出处
期刊:Lab on a Chip [Royal Society of Chemistry]
卷期号:21 (11): 2194-2205 被引量:52
标识
DOI:10.1039/d1lc00145k
摘要

Acoustofluidics, by combining acoustics and microfluidics, provides a unique means to manipulate cells and liquids for broad applications in biomedical sciences and translational medicine. However, it is challenging to standardize and maintain excellent performance of current acoustofluidic devices and systems due to a multiplicity of factors including device-to-device variation, manual operation, environmental factors, sample variability, etc. Herein, to address these challenges, we propose "intelligent acoustofluidics" - an automated system that involves acoustofluidic device design, sensor fusion, and intelligent controller integration. As a proof-of-concept, we developed intelligent acoustofluidics based mini-bioreactors for human brain organoid culture. Our mini-bioreactors consist of three components: (1) rotors for contact-free rotation via an acoustic spiral phase vortex approach, (2) a camera for real-time tracking of rotational actions, and (3) a reinforcement learning-based controller for closed-loop regulation of rotational manipulation. After training the reinforcement learning-based controller in simulation and experimental environments, our mini-bioreactors can achieve the automated rotation of rotors in well-plates. Importantly, our mini-bioreactors can enable excellent control over rotational mode, direction, and speed of rotors, regardless of fluctuations of rotor weight, liquid volume, and operating temperature. Moreover, we demonstrated our mini-bioreactors can stably maintain the rotational speed of organoids during long-term culture, and enhance neural differentiation and uniformity of organoids. Comparing with current acoustofluidics, our intelligent system has a superior performance in terms of automation, robustness, and accuracy, highlighting the potential of novel intelligent systems in microfluidic experimentation.
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