Employing a honeybee olfactory neural circuit as a novel gas sensor for the detection of human lung cancer biomarkers

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, for the first time, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers and their mixtures at concentration ranges relevant to human breath, parts-per-billion to parts-per-trillion. 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 could classify 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 breath, honeybee population neuronal responses were also able to classify those complex breath mixtures successfully (100% success rate with a leave-one-trial-out method). Finally, we used separate training and testing datasets containing responses to the synthetic lung cancer and healthy breath mixtures. We identified a simple metric, the peak response of the neuronal ensemble, with the ability to distinguish synthetic lung cancer breath from the healthy breath with 86.7% success rate. This study provides proof-of-concept results that a powerful biological gas sensor, the honeybee olfactory system, can be used to detect human lung cancer biomarkers and their complex mixtures at biological concentrations.