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利用蜜蜂嗅觉神经回路作为新型气体传感器,精确检测选择的人类肺癌生物标志物和细胞系。

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor.

机构信息

Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.

Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Microbiology, Genetics & Immunology, Michigan State University, East Lansing, MI, USA.

出版信息

Biosens Bioelectron. 2024 Oct 1;261:116466. doi: 10.1016/j.bios.2024.116466. Epub 2024 Jun 4.

Abstract

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

摘要

人类呼吸中包含可以作为早期疾病检测目标的生物标志物(气味物质)。众所周知,蜜蜂嗅觉灵敏,能够在低浓度下检测到各种气味。在这里,我们利用蜜蜂嗅觉神经元回路来对不同浓度的人类肺癌挥发性生物标志物及其混合物进行分类,这些混合物的浓度范围与人类呼吸中的生物标志物浓度范围相关,从十亿分之几到万亿分之几。我们还通过使用细胞培养物的“气味”来检测人类非小细胞肺癌(NSCLC)和小细胞肺癌(SCLC)细胞系,验证了这种基于大脑的传感技术。不同的肺癌生物标志物在蜜蜂触角叶神经元中引发了不同的尖峰反应动力学,表明这些神经元对生物标志物特异性信息进行了编码。通过研究蜜蜂触角叶中肺癌生物标志物诱发的群体神经元反应,我们成功地对个体人类肺癌生物标志物进行了分类(成功率为 88%)。当我们将六种不同浓度的肺癌生物标志物混合在一起,以创建“合成肺癌”与“合成健康”人类呼吸时,蜜蜂群体神经元反应能够可靠地对这些复杂的呼吸混合物进行分类,准确率极高(使用留一法分类方法,成功率为 93%-100%)。最后,我们使用该传感器来检测人类 NSCLC 和 SCLC 细胞系,结果表明,蜜蜂大脑嗅觉神经元可以区分肺癌与健康细胞系,并且可以成功区分不同的 NSCLC 和 SCLC 细胞系(分类成功率为 82%)。这些结果表明,蜜蜂嗅觉系统可以作为一种灵敏的生物气体传感器,用于检测人类肺癌。

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