Integration of advanced imaging and deep learning reveals structural adaptations in hygrosensilla, suggesting mechanical transduction in humidity detection.
Understanding how organisms detect environmental humidity remains a fundamental problem in sensory biology. While specialised sensory neurons in insect antennae can detect changes in humidity, the mechanism underlying this ability is not fully understood. Here, we present an integrated approach combining precise humidity control, rapid cryo-preservation, and serial block-face scanning electron microscopy (SBF-SEM) to investigate the ultrastructure of hygrosensilla in the vinegar fly Drosophila melanogaster . We developed a deep learning-based segmentation pipeline to analyse three-dimensional structural features of sensilla exposed to different humidity conditions at stable temperature. Our analysis reveals consistent differences in sensilla width between high and low humidity conditions across all chambers of the sacculus. Additionally, we identified chamber-specific patterns in sensilla tapering, indicating specialised structural adaptations across different sensilla populations. The observed structural differences suggest a potential role for mechanical transduction in humidity sensing. This study establishes a technical framework for high-resolution analysis of sensory organs while providing new insights into the structural basis of humidity detection. Our findings advance our understanding of how specialised sensory organs might transduce environmental signals into neural responses.
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Giri et al. (2025) studied this question.
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