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

在器官芯片系统中实现新功能、传输分析和深度学习

• 批准号: RGPIN-2019-05885
• 负责人: Young, Edmond
• 金额: $ 2.84万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715325
• 关键词: Enabling; New; Functionality; Transport; Analysis

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.

发明于世纪的皮氏培养皿仍然是生物研究实验室中培养活细胞的常用装置，但不能捕获体内真实的活细胞、组织和器官周围环境的许多重要元素。虽然皮氏培养皿和其他常见的实验室培养器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，特别是与疾病过程相关的问题，涉及细胞微环境中的关键因素，例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了。器官芯片（OOC）系统（或OOC）是解决这个问题的新技术。OOC是在实验室中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）精确设计的活3D组织模型。通过适当地组合这些元素，OOC可以模拟人体组织结构，行为和功能，并且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，但仍存在重大的工程挑战，阻碍了OOC在研究和工业中的广泛使用。这些工程挑战包括：缺乏更有效、可靠的制造方法来大规模制造OOC;直接内置到每个OOC中的有限功能;缺乏对OOC内流体输送过程的理解;以及从OOC实验获得的图像和其他数据的分析中的瓶颈。 我的研究目标是解决这些问题，从而更广泛地使用这项技术。该项目将集中在3个主要目标：1）通过开发新的制造方法，创建多层器件架构，添加新的片上传感元件，并将模块化OOC连接在一起以创建复杂的OOC多系统来增加功能; 2）使用流体力学研究中采用的粒子跟踪方法分析各种OOC中的流体传输过程;以及3）应用深度学习计算机算法来“训练”计算机，以从OOC获取的显微镜图像中自动且有效地识别生物特征。 该项目将大大提高我们对OOC操作的理解，并提高我们开发下一代OOC的工程能力。这些进展将带来新的生物医学发现和创新，使加拿大生物技术产业受益，并为加拿大不断增长的创业公司生态系统提供支持。它还将支持7名高素质人员和10名工程专业学生的培训，他们都将接触到一个高度跨学科的世界级研究环境，在那里他们将获得技术，可转移和领导技能。