Biological constraints on configural odour mixture perception

Recalibrating Olfactory Neuroscience to the Range of Naturally Occurring Odor Concentrations

Sensory systems enable organisms to detect and respond to environmental signals relevant for their survival and reproduction. A crucial aspect of any sensory signal is its intensity; understanding how sensory signals guide behavior requires probing sensory system function across the range of stimulus intensities naturally experienced by an organism. In olfaction, defining the range of natural odorant concentrations is difficult. Odors are complex mixtures of airborne chemicals emitting from a source in an irregular pattern that varies across time and space, necessitating specialized methods to obtain an accurate measurement of concentration. Perhaps as a result, experimentalists often choose stimulus concentrations based on empirical considerations rather than with respect to ecological or behavioral context. Here, we attempt to determine naturally relevant concentration ranges for olfactory stimuli by reviewing and integrating data from diverse disciplines. We compare odorant concentrations used in experimental studies in rodents and insects with those reported in different settings including ambient natural environments, the headspace of natural sources, and within the sources themselves. We also compare these values to psychophysical measurements of odorant detection threshold in rodents, where thresholds have been extensively measured. Odorant concentrations in natural regimes rarely exceed a few parts per billion, while most experimental studies investigating olfactory coding and behavior exceed these concentrations by several orders of magnitude. We discuss the implications of this mismatch and the importance of testing odorants in their natural concentration range for understanding neural mechanisms underlying olfactory sensation and odor-guided behaviors.

Why do we like so much the smell of roses: The recipe for the perfect flower

The rose is the most cultivated ornamental plant in the world, and one of the reasons is that its fragrance is highly pleasant to humans. This raises the question of which volatile organic compounds (VOCs) emitted by flowers are involved in a rose odor-induced positive emotional response. Here, we invited participants to smell and rate the perceptual characteristics of roses whose VOCs were quantified. We revealed that (1) the more rose-specific the flower perception, the more pleasant the smell and (2) the rosy perception is driven by ionones and to a lesser extent by oxylipins while pleasantness by balanced proportion in the mixture of ionones, oxylipins, and 2-phenylethanol and derivatives. In the mixture, the proportion of some compounds, such as aliphatics and phenolic methyl esters, impact negatively the rose scent. Thus, the pleasure that roses bring to humans could be explained by the non-conscious perception of this unique mixture of compounds.

Ex Vivo Multisite Electro-olfactogram Recordings in Rabbit Neonates

Electro-olfactogram (EOG) recording is a technique that has been used in the field of olfaction for many decades to measure the electrical activity generated by olfactory receptor neurons (ORNs) in response to odor stimuli. In the late 1980s, the development of ex vivo preparation facilitated EOG recordings in mammals. This method provides access to all olfactory turbinates, allowing for recordings lasting several hours. It has greatly contributed to our understanding of olfactory physiology and the functional links between olfaction, various behaviors, and pathologies. Although many innovative techniques are available to study olfaction in the twenty-first century, EOG recordings can still provide valuable insights, particularly when combined with other approaches. For example, the relationship between peripheral olfaction and food intake can be studied, by manipulating orexigenic and anorexigenic agents, or by modifying the animals' diet. Moreover, the connections between olfaction and the intestinal and/or nasal microbiota composition, diabetes, or peripheral plasticity resulting from learning processes can also be examined from a peripheral perspective. The ex vivo multisite EOG recording technique has been recently proved to be particularly useful in newborn rabbits. This technique has demonstrated the high native sensitivity of ORNs to a signal naturally emitted by all rabbit mothers, the mammary pheromone, throughout the olfactory mucosa. Moreover, it has also been observed that ORNs are involved in increased behavioral reactivity due to pheromone-induced odor learning, as well as in differentially processing synthetic and analytical odor mixtures. The ex vivo multisite EOG recording method is expected to remain a useful tool in future research, having in mind that it is both simple and cost-effective to implement.

Detection of odorants in odour mixtures among healthy people and patients with olfactory dysfunction

Target odorant detection in mixtures has been shown to become more difficult as the number of background odorants increases and falls below chance level in mixtures with 16 components. Our aim was to investigate target odorant detection in mixtures among healthy people and compare it between dysosmic patients and age- and gender-matched controls. Participants underwent extensive olfactory testing and performed two target odorant detection tasks. Eugenol ('clove') and phenylethanol (PEA, 'rose') were target odorants for all participants, whereas a third target was randomised. For each target odorant in task one (task two), there were four steps. Mixtures contained two (three) odorants in the first step and up to seven (eight) odorants in the fourth step. In each step, participants were asked to choose the sample with the target odorant from the three (two) jars presented. The study included 90 healthy people and 40 patients. As expected, probability of successful target odorant detection decreased as the number of odorants in the mixture increased. However, even when there were seven (eight) odorants in the mixture, around 50% (50%) of healthy people detected Eugenol and around 30% (40%) detected PEA. Furthermore, both distributions of successful target odorant detection differed from the expected binominal distribution of chance (p < 0.001). Patients performed worse at detecting Eugenol or PEA at each step than controls. Furthermore, there were significant positive correlations between task scores and olfactory function. In conclusion, target odorant detection is influenced by the target odorant, number of background odorants, and individual olfactory function.

Mixing things up! - how odor blends are processed in Drosophila

Insects have to navigate a complex and rich olfactory environment consisting of mixtures of odors at varying ratios. However, we understand little of how the olfactory system represents these complex blends. This review aims to highlight some of the recent results of studying this mixture code, in the Drosophila melanogaster olfactory system, as well as gives a short background to one of the most challenging questions in olfaction - how are mixtures encoded in the brain?