Enabling New Functionality, Transport Analysis, and Deep Learning in Organ-on-a-Chip Systems

皮氏培养皿发明于19世纪，至今仍是生物学研究实验室中培养活细胞的常用设备，但它并不能捕捉到人体中真实活细胞、组织和器官周围环境的许多重要元素。虽然培养皿和其他常见的实验室培养皿在过去已经导致了许多发现，但今天许多复杂的生物学问题，特别是那些与疾病过程相关的问题，涉及细胞微环境中的关键因素，如多种细胞类型之间的通信，生物力学力和附近血管中的血流，其中许多以前被忽视。芯片上器官系统（OOC）是解决这一问题的一项新技术。ooc是通过结合微加工技术、微尺度流体流动和各种生物成分（如活细胞和生物材料），在实验室中精确设计的活3D组织模型。通过适当地结合这些元素，ooc可以模拟人类组织的结构、行为和功能，并比任何其他现有的体外组织模型更准确地响应环境刺激。因此，ooc有可能彻底改变生物医学研究并加速科学发现。尽管取得了这些进展，但主要的工程挑战仍然存在，阻碍了OOCs在研究和工业中的广泛应用。这些工程挑战包括：缺乏更高效、可靠的制造方法来大规模生产ooc；每个OOC直接内置的有限功能；缺乏对ooc内部流体输送过程的了解；以及对OOC实验获得的图像和其他数据进行分析的瓶颈。

The Petri dish, which was invented in the 19th century, remains a common device for culturing living cells in the biology research lab, but does not capture many of the important elements of the environment surrounding real living cells, tissues, and organs in the body. While the Petri dish and other common lab cultureware have led to many discoveries in the past, many complex biology questions today, especially those related to disease processes, involve critical factors in the cellular microenvironment such as communication between multiple cell types, biomechanical forces, and blood flow in nearby blood vessels, many of which have been previously neglected. Organ-on-a-chip (OOC) systems (or OOCs) are a new technology that addresses this problem. OOCs are living 3D tissue models engineered precisely in the lab by combining microfabrication techniques, microscale fluid flow, and various biological components such as living cells and biomaterials. By combining these elements appropriately, OOCs can mimic human tissue structure, behaviour, and function, and respond to environmental stimuli more accurately than any other existing in vitro tissue model. Thus, OOCs have potential to revolutionize biomedical research and accelerate scientific discovery. Despite this progress, major engineering challenges exist that hinder OOCs from widespread usage in research and industry. These engineering challenges include: lack of more efficient, reliable fabrication methods to mass manufacture OOCs; limited functionality built directly into each OOC; lack of understanding of fluid transport processes within OOCs; and bottleneck in the analysis of images and other data acquired from OOC experiments. The goal of my research is to solve these issues, leading to more widespread use of this technology. The project will focus on 3 main objectives: 1) increase functionality by developing new fabrication methods, creating multi-layered device architectures, adding new on-chip sensing elements, and connecting modular OOCs together to create complex OOC multi-systems; 2) analyze fluid transport processes in various OOCs using particle-tracking methods adopted from fluid mechanics research; and 3) applying deep learning computer algorithms to “train” a computer to automatically and efficiently identify biological features from microscopy images acquired from OOCs. The project will significantly improve our understanding of OOC operation, and advance our engineering capabilities for developing next-generation OOCs. These advances will lead to new biomedical discoveries as well as novel innovations that will benefit the Canadian biotech industry, and feed the growing ecosystem of startup companies in Canada. It will also support the training of 7 highly qualified personnel and 10 additional engineering students, who will all be exposed to a highly interdisciplinary world-class research environment where they will acquire technical, transferrable, and leadership skills.