A Handheld Microchip for GC Analysis of Breath to Screen for COVID-19

The COVID-19 pandemic has caused unprecedented societal suffering and economic disruption. Although current COVID-19 diagnostic testing technologies are critical for curbing the spread of the virus and preventing future outbreaks, they are not practical for field use. Current diagnostic tests are cumbersome to perform because they use aqueous solutions, require multiple steps, and hours-to-days to obtain results. Therefore, there is an urgent need to develop a diagnostic approach that is non-invasive, portable, and can rapidly provide test results. The overall goal of the project is to develop a mobile breath analysis technology for rapid screening for COVID-19 using a handheld breath collection tool and a portable GC with a photoionization detector (PID). The handheld tool will be a closed system for trapping target volatile organic compounds (VOCs) on a microfabricated chip. The captured VOCs will be eluted with a solvent and then analyzed using a portable GC-PID instrument. Artificial intelligence (AI) and machine learning algorithms will be applied to recognize the VOC pattern that correlates with COVID-19 infection. The central innovation is the microfabricated chip that captures VOCs in exhaled breath and thus serves as a preconcentrator, which enables analysis of the captured VOCs by the portable GC-PID. The hypothesis is that VOC metabolome in exhaled breath is directly related to the body’s reaction to tSARS-CoV-2 infection, and the changes of VOC profile in exhaled breath relative to healthy controls can be used to detect both symptomatic and asymptomatic COVID-19 patients.
The University of Louisville is uniquely suited to rapidly transition the microchip technology to field use because of the PI and Co-PI’s experience in breath analysis and translational research, and the project team’s experience in virology, infectious diseases, biostatistics, and artificial intelligence as well as the state-of-the-art facilities that include a MicroNano Technology Center, Biosafety Level 3 Regional Biocontainment Lab, and an NIH-funded REACH program.

新冠疫情（COVID-19 pandemic）造成了前所未有的社会苦难与经济冲击。尽管当前的新冠诊断检测技术是遏制病毒传播、防范后续疫情暴发的关键，但它们并不适用于现场应用。当前的诊断检测操作繁琐，因其使用水溶液、需多步操作，且需数小时至数天才能获得检测结果。因此，亟需开发一种无创、便携且可快速出具检测结果的诊断方法。本项目的总体目标是开发一款用于新冠快速筛查的移动式呼吸分析技术，采用手持式呼吸采集工具与搭载光离子化检测器（Photoionization Detector, PID）的便携式气相色谱仪（Gas Chromatography, GC）。该手持式工具将作为一套封闭系统，在微加工芯片（microfabricated chip）上捕获目标挥发性有机化合物（Volatile Organic Compounds, VOCs）。捕获的挥发性有机化合物将通过溶剂洗脱，随后借助便携式GC-PID设备进行分析。本研究将应用人工智能（Artificial Intelligence, AI）与机器学习算法，识别与新冠感染相关的挥发性有机化合物特征模式。本项目的核心创新点在于该微加工芯片，其可捕获呼出气中的挥发性有机化合物，充当预浓缩器，从而使便携式GC-PID设备能够对捕获的挥发性有机化合物进行分析。本项目的研究假说为：呼出气中的挥发性有机化合物代谢组与机体对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的反应直接相关，且呼出气中挥发性有机化合物谱相较于健康对照的变化，可用于检测有症状与无症状新冠感染者。
路易斯维尔大学（University of Louisville）具备快速将该微芯片技术转化为现场应用的独特优势：项目负责人（Principal Investigator, PI）与共同负责人（Co-Principal Investigator, Co-PI）在呼吸分析与转化研究领域拥有丰富经验，项目团队涵盖病毒学、传染病学、生物统计学与人工智能等多领域专业人才，同时拥有尖端科研设施，包括微纳技术中心、3级生物安全区域生物遏制实验室，以及由美国国立卫生研究院（National Institutes of Health, NIH）资助的REACH项目平台。