CAREER: Design and validation of a novel part-brain-part-engineered gas sensor for noninvasive detection of lung cancer

职业：设计和验证一种新型半脑半工程气体传感器，用于肺癌无创检测

• 批准号: 2238686
• 负责人: Debajit Saha
• 金额: $ 55万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238686&HistoricalAwards=false
• 关键词: CAREER; Design; validation; novel; part

It is well known that the presence of cancer changes the volatile chemical composition of human exhaled breath (i.e., changes its “smell”), and this can be used to detect cancer noninvasively. This project aims to develop a lung cancer detection device based on a biological chemical sensory array and biological neural circuitries from insects. Neuronal voltage responses evoked by the smell of lung cancer will be used to classify lung cancer vs. noncancer and to differentiate between different types of lung cancers. This study will be performed using lung cancer cell cultures over multiple days. When completed, this novel gas sensor for noninvasive detection of lung cancer will add a powerful new dimension to biosensing. This project will advance the science of complex ‘smell’ processing in the brain and has potential to improve human health by advancing the development of biosensors for diverse medical applications. This project will also address the critical need to provide early and authentic research exposure to historically underrepresented students by providing research exposure to high school students and personalized research experiences to freshman undergraduates. The investigator's long-term career goal is to employ insect brain-based sensors in clinical settings for early detection of different cancers from exhaled breath samples. Towards this goal, the objective of this CAREER project is to develop an olfactory neuronal response-based gas sensor for sensitive, robust, and real-time detection of lung cancer. An antennae-attached ex vivo insect (locust) brain will constitute the central gas sensing device, which will be coupled with a miniaturized and implantable electrode array, a multi-channel amplifier, and biological neural computation schemes for developing the part-brain, part-engineered gas sensor. This gas sensor will utilize cancer volatiles-evoked neuronal spiking (voltage) responses for classifying human lung cancer from noncancer. The sensor’s ability to distinguish between human small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cell lines will be systematically tested using volatile organic compound (VOC) signatures of individual cell cultures. The sensor’s performance will be compared with the detection performance of gas chromatography mass spectrometry (GC-MS) technology. Finally, this sensor will be optimized for one-shot and real-time detection of human lung cancer by increasing the recording electrode numbers and by implementing a recurrent neural network (RNN) model for data analysis. When completed, this novel technology will incorporate fully functional biological chemosensory arrays, neuronal signal transduction, and neural circuit computations all together in one single VOC sensing device for sensitive and real-time detection of lung cancer.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.

众所周知，癌症的存在改变了人呼出气的挥发性化学成分（即，改变其“气味”），这可以用于非侵入性地检测癌症。本项目旨在开发一种基于生物化学传感阵列和昆虫生物神经回路的肺癌检测设备。由肺癌气味引起的神经元电压反应将被用来对肺癌与非癌进行分类，并区分不同类型的肺癌。本研究将在多天内使用肺癌细胞培养物进行。完成后，这种用于肺癌非侵入性检测的新型气体传感器将为生物传感增添一个强大的新维度。该项目将推进大脑中复杂的“气味”处理科学，并有可能通过推进用于各种医疗应用的生物传感器的开发来改善人类健康。该项目还将解决迫切需要提供早期和真实的研究接触历史上代表性不足的学生，通过提供研究接触高中学生和个性化的研究经验，大一本科生。研究人员的长期职业目标是在临床环境中使用昆虫大脑传感器，从呼出气体样本中早期检测不同的癌症。为了实现这一目标，本CAREER项目的目标是开发一种基于嗅觉神经元响应的气体传感器，用于灵敏、鲁棒和实时检测肺癌。一个天线连接的离体昆虫（蝗虫）的大脑将构成中央气体传感装置，这将是一个小型化和可植入的电极阵列，多通道放大器，和生物神经计算方案开发的部分大脑，部分工程气体传感器。这种气体传感器将利用癌症挥发物诱发的神经元尖峰（电压）的反应，从非癌症分类人类肺癌。传感器区分人类小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）细胞系的能力将使用单个细胞培养物的挥发性有机化合物（VOC）特征进行系统测试。将该传感器的性能与气相色谱-质谱（GC-MS）技术的检测性能进行比较。最后，通过增加记录电极数量和实施用于数据分析的递归神经网络（RNN）模型，该传感器将被优化用于一次性和实时检测人类肺癌。完成后，这项新技术将把功能齐全的生物化学传感器阵列、神经元信号转导和神经电路计算整合在一个单一的VOC传感设备中，用于灵敏和实时检测肺癌。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。