Odor Mixtures Research Articles

Learning of a Mimic Odor within Beehives Improves Pollination Service Efficiency in a Commercial Crop

The growing global demand for pollination services leads producers to consider new strategies in pollinator management to improve its efficiency in agroecosystems [1-3]. Central place foragers, like honeybees, learn floral cues not only in the field but also inside the nest, where resource cues introduced into the hive improve foraging by guiding bees toward the learned stimuli [4]. In this regard, attempts to condition bees with crop-odor-scented food produced ambiguous results and lacked yield measurements [5-7]. To deepen our understanding of the use of odors as part of a precision pollination strategy, we developed a simple synthetic odorant mixture that bees generalized with the natural floral scent of sunflower for hybrid seed production, an economically important and highly pollinator-dependent crop [8]. Encompassing different experimental approaches, our results show that feeding colonies food scented with the sunflower mimic (SM) odor enabled the establishment of olfactory memories that biased bees to the sunflower crop. The offering of a rewarded odor mimicking the sunflower floral fragrance promoted higher foraging activity, increased the proportion of dances advertising the target inflorescences and reduced delays in dance onset, positively affected the density of bees on the crop, and increased yields from 29% to 57% in different sunflower hybrids. This study highlights the role of olfactory learning within the social context of the hive to bias foraging preferences in a novel agricultural environment and suggest that improvements in the tested parameters were due to beeś anticipated response to the sunflower scent.

Synthesis of Conserved Odor Object Representations in a Random, Divergent-Convergent Network

Animals are capable of recognizing mixtures and groups of odors as a unitary object. However, how odor object representations are generated in the brain remains elusive. Here, we investigate sensory transformation between the primary olfactory center and its downstream region, the mushroom body (MB), in Drosophila and show that clustered representations for mixtures and groups of odors emerge in the MB at the population and single-cell levels. Decoding analyses demonstrate that neurons selective for mixtures and groups enhance odor generalization. Responses of these neurons and those selective for individual odors all emerge in an experimentally well-constrained model implementing divergent-convergent, random connectivity between the primary center and the MB. Furthermore, we found that relative odor representations are conserved across animals despite this random connectivity. Our results show that the generation of distinct representations for individual odors and groups and mixtures of odors in the MB can be understood in a unified computational and mechanistic framework.

Encoding the Odor of Cigarette Smoke.

The encoding of odors is believed to begin as a combinatorial code consisting of distinct patterns of responses from odorant receptors (ORs), trace-amine associated receptors (TAARs), or both. To determine how specific response patterns arise requires detecting patterns in vivo and understanding how the components of an odor, which are nearly always mixtures of odorants, give rise to parts of the pattern. Cigarette smoke, a common and clinically relevant odor consisting of >400 odorants, evokes responses from 144 ORs and 3 TAARs in freely behaving male and female mice, the first example of in vivo responses of both ORs and TAARs to an odor. As expected, a simplified artificial mimic of cigarette smoke odor tested at low concentration to identify highly sensitive receptors evokes responses from four ORs, all also responsive to cigarette smoke. Human subjects of either sex identify 1-pentanethiol as the odorant most critical for perception of the artificial mimic; and in mice the OR response patterns to these two odors are significantly similar. Fifty-eight ORs respond to the headspace above 25% 1-pentanethiol, including 9 ORs responsive to cigarette smoke. The response patterns to both cigarette smoke and 1-pentanethiol have strongly responsive ORs spread widely across OR sequence diversity, consistent with most other combinatorial codes previously measured in vivo The encoding of cigarette smoke is accomplished by a broad receptor response pattern, and 1-pentanethiol is responsible for a small subset of the responsive ORs in this combinatorial code.SIGNIFICANCE STATEMENT Complex odors are usually perceived as distinct odor objects. Cigarette smoke is the first complex odor whose in vivo receptor response pattern has been measured. It is also the first pattern shown to include responses from both odorant receptors and trace-amine associated receptors, confirming that the encoding of complex odors can be enriched by signals coming through both families of receptors. Measures of human perception and mouse receptor physiology agree that 1-pentanethiol is a critical component of a simplified odorant mixture designed to mimic cigarette smoke odor. Its receptor response pattern helps to link those of the artificial mimic and real cigarette smoke, consistent with expectations about perceptual similarity arising from shared elements in receptor response patterns.

The role of papillary skin glands in guiding mouse pups to the nipple.

The nipple odor of lactating mice (Mus musculus) plays a crucial role in attracting newborn pups and motivating them to suck milk. The characteristic odor of a lactating murine nipple is assumed to be a mixture of multiple odorous substrates, that is, milk, dam's and pups' saliva, skin glands' secretions, and amniotic fluid. The present study aimed to characterize the behavioral activity of the original odor mixture that develops over the nipples in the first 2days postpartum. We extracted this odor mixture in water and evaluated its attractive and appetitive potencies using two behavioral assays (viz., relative attraction and oral activation assays). It resulted that the so-called nipple wash was as appetitive as fresh milk, and even more attractive than it. The behavioral potency of the nipples was shown to be specific to lactating nipples (relative to nulliparous nipples) and to be preserved for 2weeks when stored at -80°C. Finally, we perfected a nipple deodorization procedure by inactivating the nipples' behavioral potency. We observed that such altered appetitive potency was fully restored 30min after its washing, but without any maternal self-licking and pups' sucking, indicating that the secretions of the nipple skin glands' were sufficient to explain the success of neonatal guidance to the nipple.

Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice

Odor landscapes contain complex blends of molecules that each activate unique, overlapping populations of olfactory sensory neurons (OSNs). Despite the presence of hundreds of OSN subtypes in many animals, the overlapping nature of odor inputs may lead to saturation of neural responses at the early stages of stimulus encoding. Information loss due to saturation could be mitigated by normalizing mechanisms such as antagonism at the level of receptor-ligand interactions, whose existence and prevalence remains uncertain. By imaging OSN axon terminals in olfactory bulb glomeruli as well as OSN cell bodies within the olfactory epithelium in freely breathing mice, we find widespread antagonistic interactions in binary odor mixtures. In complex mixtures of up to 12 odorants, antagonistic interactions are stronger and more prevalent with increasing mixture complexity. Therefore, antagonism is a common feature of odor mixture encoding in OSNs and helps in normalizing activity to reduce saturation and increase information transfer.

Widespread Inhibition, Antagonism, and Synergy in Mouse Olfactory Sensory Neurons In Vivo

Sensory information is selectively or non-selectively enhanced and inhibited in the brain, but it remains unclear whether and how this occurs at the most peripheral level. Using invivo calcium imaging of mouse olfactory bulb and olfactory epithelium in wild-type and mutant animals, we show that odors produce not only excitatory but also inhibitory responses in olfactory sensory neurons (OSNs). Heterologous assays indicate that odorants can act as agonists to some but inverse agonists to other odorant receptors. We also demonstrate that responses to odor mixtures are extensively suppressed or enhanced in OSNs. When high concentrations of odors are mixed, widespread antagonism suppresses the overall response amplitudes and density. In contrast, a mixture of low concentrations of odors often produces synergistic effects and boosts the faint odor inputs. Thus, odor responses are extensively tuned by inhibition, antagonism, and synergy at the most peripheral level, contributing to robust sensory representations.

Odorant Receptor Inhibition Is Fundamental to Odor Encoding

Most natural odors are complex mixtures of volatile components, competing to bind odorant receptors (ORs) expressed in olfactory sensory neurons (OSNs) of the nose. To date, surprisingly little is known about how OR antagonism shapes neuronal representations in the detection layer of the olfactory system. Here, we investigated its prevalence, the degree to which it disrupts OR ensemble activity, and its conservation across phylogenetically related ORs. Calcium imaging microscopy of dissociated OSNs revealed significant inhibition, often complete attenuation, of responses to indole-a commonly occurring volatile associated with both floral and fecal odors-by a set of 36 tested odorants. To confirm an OR mechanism for the observed inhibition, we performed single-cell transcriptomics on OSNs exhibiting specific response profiles to a diagnostic panel of odorants and identified three paralogous receptors-Olfr740, Olfr741, and Olfr743-which, when tested invitro, recapitulated OSN responses. We screened ten ORs from the Olfr740 gene family with ∼800 perfumery-related odorants spanning a range of chemical scaffolds and functional groups. Over half of these compounds (430) antagonized at least one of the ten ORs. OR activity fitted a mathematical model of competitive receptor binding and suggests normalization of OSN ensemble responses to odorant mixtures is the rule rather than the exception. In summary, we observed OR antagonism occurred frequently and in a combinatorial manner. Thus, extensive receptor-mediated computation of mixture information appears to occur in the olfactory epithelium prior to transmission of odor information to the olfactory bulb.

Mixture and concentration effects on odorant receptor response patterns in vivo.

Natural odors are mixtures of volatile chemicals (odorants). Odors are encoded as responses of distinct subsets of the hundreds of odorant receptors and trace amine-associated receptors expressed monogenically by olfactory sensory neurons. This is an elegantly simple mechanism for differentially encoding odors but it is susceptible to complex dose-response relationships and interactions between odorants at receptors, which may help explain olfactory phenomena such as mixture suppression, synthetic versus elemental odor processing, and poorly predictable perceptual outcomes of new odor mixtures. In this study in vivo tests in freely behaving mice confirm evidence of a characteristic receptor response pattern consisting of a few receptors with strong responses and a greater number of weakly responding receptors. Odorant receptors responsive to an odor are often unrelated and widely divergent in sequence, even when the odor consists of a single species of odorant. Odorant receptor response patterns to a citrus odor broaden with concentration. Some highly sensitive receptors respond only to a low concentration but others respond in proportion to concentration, a feature that may be critical for concentration-invariant perception. Other tests find evidence of interactions between odorants in vivo. All of the odorant receptor responses to a moderate concentration of the fecal malodor indole are suppressed by a high concentration of the floral odorant, α-ionone. Such suppressive effects are consistent with prior evidence that odorant interactions at individual odorant receptors are common.

Habituation as a neural algorithm for online odor discrimination

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

Odor Mixtures in Identification Testing Using Sniffin\u2019 Sticks: The SSomix Test

Clinical tests assessing olfactory performance have become indispensable for diagnosing olfactory dysfunction. As time and personnel resources are limited, it would be advantageous to have shorter protocols focusing on singular aspects of olfactory performance, such as odor identification. However, such a unidimensional approach is often inconclusive and needs further tests (and tools). Hence, new testing methods with high levels of sensitivity, specificity, and reproducibility are required for clinical practice. Here, we developed a Sniffin’ Sticks odor mixture identification test method (SSomix), with emphasis on resource efficiency and simplicity of administration. SSomix consists of mixtures of two and three odors applied onto a piece of paper using 11 out of 16 items from the original Sniffin’ Sticks identification test kit. A total of 66 healthy subjects and 22 patients with olfactory dysfunction were included in the study. SSomix showed good to excellent test-retest reliability and validity. The area under the receiver operating characteristics curves indicated good diagnostic accuracy in identifying patients with reduced and severely impaired olfactory function. SSomix was a suitable downsizing of the original kit, especially regarding resource efficiency.

Widespread receptor-driven modulation in peripheral olfactory coding.

Olfactory responses to single odors have been well characterized but in reality we are continually presented with complex mixtures of odors. We performed high-throughput analysis of single-cell responses to odor blends using Swept Confocally Aligned Planar Excitation (SCAPE) microscopy of intact mouse olfactory epithelium, imaging ~10,000 olfactory sensory neurons in parallel. In large numbers of responding cells, mixtures of odors did not elicit a simple sum of the responses to individual components of the blend. Instead, many neurons exhibited either antagonism or enhancement of their response in the presence of another odor. All eight odors tested acted as both agonists and antagonists at different receptors. We propose that this peripheral modulation of responses increases the capacity of the olfactory system to distinguish complex odor mixtures.

The perception of complex scents

Neuroscience It is generally assumed that olfactory receptors faithfully report information to the brain in the form of a linear, additive code. However, under realistic conditions, the olfactory system handles a far more complex input, usually mixtures of odors. Xu et al. found that when we smell scents, the nasal olfactory sensory neurons relay a more complex pattern of signals to the brain than previously thought. The responses of individual neurons within the peripheral olfactory epithelium were either amplified or attenuated by the presence of other odors, which could explain the common perception of one odor in a mixture dominating over others. This effect occurs within the peripheral sensory organ's receptors and not within the brain. Science , this issue p. [eaaz5390][1] [1]: /lookup/doi/10.1126/science.aaz5390

Superior Identification of Component Odors in a Mixture Is Linked to Autistic Traits in Children and Adults.

Most familiar odors are complex mixtures of volatile molecules, which the olfactory system automatically synthesizes into a perceptual whole. However, odors are rarely encountered in isolation; thus, the brain must also separate distinct odor objects from complex and variable backgrounds. In vision, autistic traits are associated with superior performance in tasks that require focus on the local features of a perceptual scene. The aim of the present study was to determine whether the same advantage was observed in the analysis of olfactory scenes. To do this, we compared the ability of 1) 40 young adults (aged 16-35) with high (n = 20) and low levels of autistic traits and 2) 20 children (aged 7-11), with (n = 10) and without an autism spectrum disorder diagnosis, to identify individual odor objects presented within odor mixtures. First, we used a 4-alternative forced choice task to confirm that both adults and children were able to reliably identify 8 blended fragrances, representing food-related odors, when presented individually. We then used the same forced choice format to test participants' ability to identify the odors when they were combined in either binary or ternary mixtures. Adults with high levels of autistic traits showed superior performance on binary but not ternary mixture trials, whereas children with an autism spectrum disorder diagnosis outperformed age-matched neurotypical peers, irrespective of mixture complexity. These findings indicate that the local processing advantages associated with high levels of autistic traits in visual tasks are also apparent in a task requiring analytical processing of odor mixtures.

Pleasantness of Binary Odor Mixtures: Rules and Prediction

Pleasantness is a major dimension of odor percepts. While naturally encountered odors rely on mixtures of odorants, few studies have investigated the rules underlying the perceived pleasantness of odor mixtures. To address this issue, a set of 222 binary mixtures based on a set of 72 odorants were rated by a panel of 30 participants for odor intensity and pleasantness. In most cases, the pleasantness of the binary mixtures was driven by the pleasantness and intensity of its components. Nevertheless, a significant pleasantness partial addition was observed in 6 binary mixtures consisting of 2 components with similar pleasantness ratings. A mathematical model, involving the pleasantness of the components as well as τ-values reflecting components' odor intensity, was applied to predict mixture pleasantness. Using this model, the pleasantness of mixtures including 2 components with contrasted intensity and pleasantness could be efficiently predicted at the panel level (R2 > 0.80, Root Mean Squared Error < 0.67).

Visual Analysis of Odor Interaction Based on Support Vector Regression Method.

The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.

Contribution of Volatile Odorous Terpenoid Compounds to Aged Cognac Spirits Aroma in a Context of Multicomponent Odor Mixtures.

Cognac spirit aromas result from the presence of a wide variety of volatile odorous compounds associated with the modalities of spirit distillation and aging. However, very few studies have been carried out on aging notes of Cognac spirits. An HPLC fractionation approach was used in order to evidence fractions of interest recalling the specific aromatic nuances of aged Cognac. Then, a GC-O/MS analysis of the selected fractions allowed one to detect odorous zones and identify several volatile compounds. Among them, various well-known volatile compounds representative of the terpenoid family were highlighted, such as geraniol, α-terpinene, nerol, α-terpineol, 1,8-cineole (eucalyptol) and, particularly, piperitone, santalol, and α-campholenal, which have not previously been cited in Cognac. These compounds were quantitated and their detection thresholds were determined. Geraniol, α-terpinene, α-terpineol, and 1,8-cineole concentrations increased while spirits were more aged, while nerol tends to decrease. A sensory contribution of terpenes was observed through perceptual synergic effects, along with β-damascenone and whisky lactone.

Modulation of the combinatorial code of odorant receptor response patterns in odorant mixtures

The perception of odors relies on combinatorial codes consisting of odorant receptor (OR) response patterns to encode odor identity. Modulation of these patterns by odorant interactions at ORs potentially explains several olfactory phenomena: mixture suppression, unpredictable sensory outcomes, and the perception of odorant mixtures as unique objects. We determined OR response patterns to 4 odorants and 3 binary mixtures in vivo in mice, identifying 30 responsive ORs. These patterns typically had a few strongly responsive ORs and a greater number of weakly responsive ORs. ORs responsive to an odorant were often unrelated sequences distributed across several OR subfamilies. Mixture responses predicted pharmacological interactions between odorants, which were tested in vitro by heterologous expression of ORs in cultured cells, providing independent evidence confirming odorant agonists for 13 ORs and identifying both suppressive and additive effects. This included 11 instances of antagonism of ORs by an odorant, 1 instance of additive responses to a binary mixture, 1 instance of suppression of a strong agonist by a weak agonist, and the discovery of an inverse agonist for an OR. Interactions between odorants at ORs are common even when the odorants are not known to interact perceptually in humans, and in some cases interactions at mouse ORs correlate with the ability of humans to perceive an odorant in a mixture.

The contribution of temporal coding to odor coding and odor perception in humans.

Whether neurons encode information through their spike rates, their activity times or both is an ongoing debate in systems neuroscience. Here, we tested whether humans can discriminate between a pair of temporal odor mixtures (TOMs) composed of the same two components delivered in rapid succession in either one temporal order or its reverse. These TOMs presumably activate the same olfactory neurons but at different times and thus differ mainly in the time of neuron activation. We found that most participants could hardly discriminate between TOMs, although they easily discriminated between a TOM and one of its components. By contrast, participants succeeded in discriminating between the TOMs when they were notified of their successive nature in advance. We thus suggest that the time of glomerulus activation can be exploited to extract odor-related information, although it does not change the odor perception substantially, as should be expected from an odor code per se.

Association between human olfactory performance and ability to detect single compounds in complex chemical mixtures.

Humans can accurately discern thousands of odorants, although there is a considerable inter-individual variability. Individuals can be classified as normosmic, hyposmic or anosmic, depending on their olfactory sensitivity or blindness. In this research we studied the olfactory sensitivity to banana head-space as a complex odor mixture in a group of 53 subjects classified for their olfactory status, by means of the "Sniffin' Sticks" extended test. Using the coupled Gas Chromatography-Mass Spectrometry/ Olfactometry (GC–MS/O) technique, the single components of the banana flavor mixture were separated, identified and verbally evaluated by each subject. For each compound both the “odor type” (i.e., odor quality: fruity, floral, green, etc.) and “odor descriptor” (i.e., name used by subjects for odor identification) were reported, so that we could identify molecules that were defined as smelling of banana. The results show that: (a) the threshold olfactory performance is linearly correlated with the number of odor-active compounds (total or smelling of banana) for each subject; (b) the intensity reported by each subject during the sniffing of the pen containing the banana aroma in the identification test is positively correlated both with its hedonic valence and the number of odor-active compounds smelling of banana. In conclusion, our findings show that human perception of single compounds is conditioned by the threshold olfactory performance of the subject and that his/her ability to detect single molecular components, which smell as the mixture, affects the intensity and hedonism for the complex aroma.

Configural perception of a binary olfactory mixture in honey bees, as in humans, rodents and newborn rabbits.

How animals perceive and learn complex stimuli, such as mixtures of odorants, is a difficult problem, for which the definition of general rules across the animal kingdom remains elusive. Recent experiments conducted in human and rodent adults as well as newborn rabbits suggested that these species process particular odor mixtures in a similar, configural manner. Thus, the binary mixture of ethyl isobutyrate (EI) and ethyl maltol (EM) induces configural processing in humans, who perceive a mixture odor quality (pineapple) that is distinct from the quality of each component (strawberry and caramel). Similarly, rabbit neonates treat the mixture differently, at least in part, from its components. In the present study, we asked whether the properties of the EI.EM mixture extend to an influential invertebrate model, the honey bee Apis mellifera. We used appetitive conditioning of the proboscis extension response to evaluate how bees perceive the EI.EM mixture. In a first experiment, we measured perceptual similarity between this mixture and its components in a generalization protocol. In a second experiment, we measured the ability of bees to differentiate between the mixture and both of its components in a negative patterning protocol. In each experimental series, the performance of bees with this mixture was compared with that obtained with four other mixtures, chosen from previous work in humans, newborn rabbits and bees. Our results suggest that when having to differentiate mixture and components, bees treat the EI.EM in a robust configural manner, similarly to mammals, suggesting the existence of common perceptual rules across the animal kindgdom.