Albrecht Haase

Honey bees ( Apis mellifera ) offer an alternative model for investigating magnetoreception, exhibiting reliable navigation and behavioral responses to magnetic fields. Thanks to their compact brains, well-matched to the penetration depth of modern optical imaging techniques, they may offer insights into the neural and biochemical mechanisms underlying this sense, something that standard models, like migrating birds, have not so far provided. But also in honeybees, this progress requires tools capable of resolving weak, magnetically induced neural activity with high spatio-temporal precision. The approach, presented here, bridges quantum biology and neuroscience, allowing for testing the radical pair mechanism (RPM) as a potential basis for magnetic sensing. As the RPM predicts that magnetoreception is coupled to the visual system, we developed an in vivo two-photon calcium imaging approach to measure neural activity in the anterior optic tubercle, a higher-order visual center involved in chromatic processing and potentially navigation. Bees were prepared using a minimally invasive technique, in which this neuropil was retrogradely labelled with a fluorescent calcium indicator, enabling stable recording conditions over several hours. Controlled blue-light stimuli were provided by the scattered output of a fiber laser, and weak magnetic-field stimuli were applied by a shielded, three-axis Helmholtz coil system that allowed precise modulation of field strength and polarity while minimizing electromagnetic interference. Visual stimulation evoked consistent and reproducible calcium responses, validating the preparation and imaging stability. Magnetic stimulation produced small fluorescence decreases, suggesting field-dependent modulation of neural activity. The developed imaging framework shows the feasibility of detecting magnetic modulation in vision- and navigation-related brain regions, suggesting neural amplification of weak magnetic cues and providing a platform for controlled tests of RPM-specific predictions, including light dependence, polarity independence, and radiofrequency perturbation.

This chapter describes how to apply two-photon neuroimaging to study the insect olfactory system in vivo. It provides a complete protocol for insect brain functional imaging, with additional remarks on the acquisition of morphological information from the living brain. We discuss the most important choices to make when buying or building a two-photon laser scanning microscope. We illustrate different possibilities of animal preparation and brain tissue labeling for in vivo imaging. Finally, we give an overview of the main methods of image data processing and analysis, together with practical examples of pioneering applications of this imaging modality.

Drosophila suzukii severely damages the production of berry and stone fruits in large parts of the world. Unlike D. melanogaster, which reproduces on overripe and fermenting fruits on the ground, D. suzukii prefers to lay its eggs in ripening fruits still on the plants. Flies locate fruit hosts by their odorant volatiles, which are detected and encoded by a highly specialised olfactory system before being translated into behaviour. The exact information-processing pathway is not yet fully understood, especially the evaluation of odour attractiveness. It is also unclear what differentiates the brains of D. suzukii and D. melanogaster to cause the crucial difference in host selection. We hypothesised that the basis for different behaviours is already formed at the level of the antennal lobe of D. suzukii and D. melanogaster by different neuronal responses to volatiles associated with ripe and fermenting fruit. We thus investigated by 3D in vivo two-photon calcium imaging how both species encoded odours from ripe fruits, leaves, fermented fruits, bacteria, and their mixtures in the antennal lobe. We then assessed their behavioural responses to mixtures of ripe and fermenting odours. The neural responses reflect species-dependent shifts in the odour code. In addition to this, morphological differences were also observed. However, this was not directly reflected in different behavioural responses to the odours tested.

Honey bees Apis mellifera follow the day-night cycle for their foraging activity, entering rest periods during darkness. Despite considerable research on sleep behaviour in bees, its underlying neurophysiological mechanisms are not well understood, partly due to the lack of brain imaging data that allow for analysis from a network- or system-level perspective. This study aims to fill this gap by investigating whether neuronal activity during rest periods exhibits stereotypic patterns comparable to sleep signatures observed in vertebrates. Using two-photon calcium imaging of the antennal lobes (AL) in head-fixed bees, we analysed brain dynamics across motion and rest epochs during the nocturnal period. The recorded activity was computationally characterised, and machine learning was applied to determine whether a classifier could distinguish the two states after motion correction. Out-of-sample classification accuracy reached 93%, and a feature importance analysis suggested network features to be decisive. Accordingly, the glomerular connectivity was found to be significantly increased in the rest-state patterns. A full simulation of the AL using a leaky spiking neural network revealed that such a transition in network connectivity could be achieved by weakly correlated input noise and a reduction of synaptic conductance of the inhibitive local neurons (LNs) which couple the AL network nodes. The difference in the AL response maps between awake- and sleep-like states generated by the simulation showed a decreased specificity of the odour code in the sleep state, suggesting reduced information processing during sleep. Since LNs in the bee brain are GABAergic, this suggests that the GABAergic system plays a central role in sleep regulation in bees as in many higher species including humans. Our findings provide the first evidence that sleep-related network modulation mechanisms may be conserved throughout evolution, highlighting the bee’s potential as an invertebrate model for studying sleep at the level of single neurons.

The fly Drosophila suzukii , a close relative of D. melanogaster severely damages the production of berry and stone fruits in large parts of the world. Unlike D. melanogaster , which reproduces on overripe and fermenting fruits on the ground, D. suzukii prefers to lay its eggs in ripening fruits still on the plants. Flies locate fruit hosts by their odorant volatiles, which are detected and encoded by a highly specialized olfactory system before being translated into behaviour. The exact information processing pathway is not yet fully understood, especially the evaluation of odour attractiveness. It is also unclear what differentiates the brains of D. suzukii and D. melanogaster to cause the crucial difference in host selection. We hypothesized that the basis for different behaviours is already formed at the level of the antennal lobe of D. suzukii and D. melanogaster, by different neuronal responses to volatiles associated with ripe and fermenting fruit. We thus investigated by 3D in vivo two-photon calcium imaging how both species encoded odours from ripe fruits, leaves, fermented fruits, bacteria, and their mixtures in the antennal lobe. We then assessed their behavioural responses to mixtures of ripe and fermenting odours. The neural responses reflect species-dependent shifts in the odour code. In addition to this, morphological differences were also observed. Yet this was not directly reflected in different behavioural responses to the odours tested.

In this work, we identified the trail pheromone of the ant Crematogaster scutellaris. We combined gas chromatography–mass spectrometry analysis of extracts from the hind tibia, the location of the respective glands, with automated trail following assays. The study found tridecan-2-ol to be the strongest discriminator between hind tibia and other body part extracts. Tridecan-2-ol elicited trail-following behaviour at concentrations of 1 ng/µL. A separation of the enantiomers showed responses to (R)-tridecan-2-ol already at 0.001 ng/µL and only at a 1000-fold higher concentration for (S)-tridecan-2-ol, suggesting that only the R enantiomer is used by C. scutellaris in its natural environment. We also found strong behavioural responses to 2-dodecanol, a substance that was not detectable in the hind tibia extract of C. scutellaris, but which has been reported to be the trail pheromone of the related species C. castanea. We discuss the contribution of these results to the 'dissection and reconstruction' of strategies and mechanisms underlying the social organization of ants.

The proboscis extension response (PER) has been widely used to evaluate honeybees’ (Apis mellifera) learning and memory abilities, typically by using odors and visual cues for the conditioned stimuli. Here we asked whether honeybees could learn to distinguish between different magnitudes of the same type of stimulus, given as two speeds of air flux. By taking advantage of a novel automated system for administering PER experiments, we determined that the bees were highly successful when the lower air flux was rewarded and less successful when the higher flux was rewarded. Importantly, since our method includes AI-assisted analysis, we were able to consider subthreshold responses at a high temporal resolution; this analysis revealed patterns of rapid generalization and slowly acquired discrimination between the rewarded and unrewarded stimuli, as well as indications that the high air flux may have been mildly aversive. The learning curve for these mechanosensory stimuli, at least when the lower flux is rewarded, more closely mimics prior data from olfactory PER studies rather than visual ones, possibly in agreement with recent findings that the insect olfactory system is also sensitive to mechanosensory information. This work demonstrates a new modality to be used in PER experiments and lays the foundation for deeper exploration of honeybee cognitive processes when posed with complex learning challenges.