CAREER:Feedback-Controlled Microfluidic Chips with Integrated Sensor Networks for Blood Analysis

代谢信号与其他组织的有效交换使血液成为监测和诊断生理和病理状况的机会。在血液成分中，白细胞由于积极参与机体的免疫反应而代表着特别丰富的信息来源。因此，能够快速表征血液样本并提取可靠信息的技术在临床和基础研究应用方面的需求不断增加。这项工作的目标是开发智能微芯片，可以可靠地分析小血液样本中的白细胞，而无需任何样品准备。这些微芯片将是低成本的，一次性的，并将包括内置电极，可以将来自白细胞的化学信息转换为电子信号，由智能手机解释并传输给医疗保健提供者。因此，拟议的研究有可能通过使人们能够在家中或在移动环境中自我进行血液检查来彻底改变医疗保健服务。除了他的研究，PI完全致力于他的专业教育方面，并渴望成为下一代工程师的榜样。PI的教育目标是为学生创造以应用为中心的多学科课程、研究机会和学习经验。为此，PI建议(1)组织创新竞赛，以开发微/纳米技术来解决生物医学挑战；(2)在研究生和本科课程中实施实验室模块；(3)让本科生和研究生参与并指导他们开展本提案的研究活动；(4)指导高中教师吸引K-12和高中学生以及代表性不足的群体参与科学、技术、工程数学（STEM）教育。尽管在操纵细胞方面非常有效，但微流控装置缺乏原生传感方案，因此通常在使用实验室仪器进行定量测量之前充当上游样品制备元件。微流控操作和定量测量之间的脱节是一个重要的限制，阻碍了这些工具在学术研究实验室之外的广泛采用，例如在资源有限或在护理点环境中，它们可以真正改变医疗保健服务。PI的职业目标是开发基于聚合物的芯片实验室（LoC）平台，内置传感器网络，其简单的硬件将通过复杂的计算算法来增强，作为内容感知的自主微流体设备，用于定量细胞分析。为了实现这一目标，PI将采用高度多学科的方法，结合传统上遥远的学科，如微系统工程、信息论、数据科学和生物医学。该提案将(1)设计和制造塑料微流控芯片，连接互连的电子微传感器网络，每个传感器单独设计以产生可通过计算识别的签名响应，(2)开发计算算法，利用基于模型的信号处理和机器学习方法处理来自传感器网络的压缩数据，(3)开发开闭环控制的微流控系统，(4)将所有已开发的概念结合起来，创建一个自主的、自适应的微流体系统，该系统可以通过对白细胞进行无标记免疫分型来分析全血样本。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Efficient exchange of metabolic signals with other tissues turns the blood into an opportunity to monitor and diagnose physiological and pathological conditions. Among the constituents of blood, white blood cells represent a particularly rich source of information due to their active involvement in the immune response of the body. As such, technologies that can rapidly characterize blood samples and extract reliable information are in ever-increasing demand for both clinical and basic research applications. The proposed work aims to develop smart microchips that can reliably analyze white blood cells from small blood samples without any sample preparation. These microchips will be low-cost, disposable, and will include built-in electrodes that can convert the chemical information from white blood cells into electrical signals to be interpreted by a smartphone and transmitted to the healthcare provider. The proposed research therefore has the potential to revolutionize healthcare delivery by enabling people to self-administer blood tests at home or in mobile settings. Besides his research, the PI is fully committed to the educational aspects of his profession and aspires to be a role model for next-generation engineers. The PI's educational goal is to create application-focused multidisciplinary courses, research opportunities and learning experiences for students. To this end, the PI proposes (1) to organize innovation tournaments to develop micro/nanotechnologies for solving biomedical challenges, (2) to implement a laboratory module in the graduate- and undergraduate-level courses, (3) to involve and mentor undergraduate and graduate students in conducting the research activities of this proposal, (4) to mentor high school teachers to attract K-12 and High School Students and underrepresented groups to science, technology, engineering and mathematics (STEM) education.Despite being highly effective in manipulating cells, microfluidic devices lack native sensing schemes and hence often act as upstream sample preparation elements before quantitative measurements typically performed with a laboratory instrument. The disconnect between microfluidic manipulation and quantitative measurements is an important limitation that hampers the widespread adoption of these tools outside of academic research laboratories, for example in resource-limited or in point-of-care settings, where they can be truly transformative in healthcare delivery. The PI's career goal is to develop polymer-based lab-on-a-chip (LoC) platforms with built-in sensor networks, whose purposely simple hardware will be augmented by complex computational algorithms, to function as content-aware, autonomous microfluidic devices for quantitative cell analysis. To achieve this goal, the PI will adopt a highly multidisciplinary approach combining traditionally-distant disciplines such as microsystem engineering, information theory, data science, and biomedicine. This proposal will (1) design and fabricate plastic microfluidic chips wired with networks ofinterconnected electrical micro-sensors, each individually designed to produce a signature response that can be recognized among others through computation, (2) develop computational algorithms to process compressed data from the sensor network by utilizing both model-based signal processing and machine learning approaches, (3) develop open- and closed-loop controlled microfluidic systems, where a host ofactuators are combined with spatiotemporal data generated by networked sensors to extract biological information from the sample under test, (4) combine all of the developed concepts to create an autonomous and adaptive microfluidic system that can analyze whole blood samples by label-free immunophenotyping of white blood cells.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.