Functional Interaction Between Drosophila Olfactory Sensory Neurons and Their Support Cells

Insects detect volatile chemicals using antennae, which house a vast variety of olfactory sensory neurons (OSNs) that innervate hair-like structures called sensilla where odor detection takes place. In addition to OSNs, the antenna also hosts various support cell types. These include the triad of trichogen, tormogen, and thecogen support cells that lie adjacent to their respective OSNs. The arrangement of OSN supporting cells occurs stereotypically for all sensilla and is widely conserved in evolution. While insect chemosensory neurons have received considerable attention, little is known about the functional significance of the cells that support them. For instance, it remains unknown whether support cells play an active role in odor detection, or only passively contribute to homeostasis, e.g., by maintaining sensillum lymph composition. To investigate the functional interaction between OSNs and support cells, we used optical and electrophysiological approaches in Drosophila. First, we characterized the distribution of various supporting cells using genetic markers. By means of an ex vivo antennal preparation and genetically-encoded Ca2+ and K+ indicators, we then studied the activation of these auxiliary cells during odor presentation in adult flies. We observed acute responses and distinct differences in Ca2+ and K+ fluxes between support cell types. Finally, we observed alterations in OSN responses upon thecogen cell ablation in mature adults. Upon inducible ablation of thecogen cells, we notice a gain in mechanical responsiveness to mechanical stimulations during single-sensillum recording, but a lack of change to the neuronal resting activity. Taken together, these results demonstrate that support cells play a more active and responsive role during odor processing than previously thought. Our observations thus reveal that support cells functionally interact with OSNs and may be important for the extraordinary ability of insect olfactory systems to dynamically and sensitively discriminate between odors in the turbulent sensory landscape of insect flight.

Functional interaction between Drosophila olfactory sensory neurons and their support cells

Insects detect volatile chemicals using antennae, which house a vast variety of olfactory sensory neurons (OSNs) that innervate hair-like structures called sensilla where odor detection takes place. In addition to OSNs, the antenna also hosts various support cell types. These include the triad of trichogen, tormogen and thecogen support cells that lie adjacent to their respective OSNs. The arrangement of OSN supporting cells occurs stereotypically for all sensilla and is widely conserved in evolution. While insect chemosensory neurons have received considerable attention, little is known about the functional significance of the cells that support them. For instance, it remains unknown whether support cells play an active role in odor detection, or only passively contribute to homeostasis, e.g. by maintaining sensillum lymph composition. To investigate the functional interaction between OSNs and support cells, we used optical and electrophysiological approaches in Drosophila. First, we characterized the distribution of various supporting cells using genetic markers. By means of an ex vivo antennal preparation and genetically-encoded Ca2+ and K+ indicators, we then studied the activation of these auxiliary cells during odor presentation in adult flies. We observed acute responses and distinct differences in Ca2+ and K+ fluxes between support cell types. Finally, we observed alterations in OSN responses upon thecogen cell ablation in mature adults. Upon inducible ablation of thecogen cells, we notice a gain in mechanical responsiveness to mechanical stimulations during single-sensillum recording, but a lack of change to neuronal resting activity. Taken together, these results demonstrate that support cells play a more active and responsive role during odor processing than previously thought. Our observations thus reveal that support cells functionally interact with OSNs and may be important for the extraordinary ability of insect olfactory systems to dynamically and sensitively discriminate between odors in the turbulent sensory landscape of insect flight.

Additivity between Key Odorants in Pig House Air

The verification of odor abatement technologies for livestock production based on chemical odorants requires a method for conversion into an odor value that reflects the significance of the individual odorants. The aim of the present study was to compare the SOAV method (Sum of Odor Activity Values) with the odor detection threshold measured by olfactometry and to investigate the assumption of additivity. Synthetic pig house air with odorants at realistic concentration levels was used in the study (hydrogen sulfide, methanethiol, trimethylamine, butanoic acid, and 4-methylphenol). An olfactometer with only PTFE in contact with sample air was used to estimate odor threshold values (OTVs) and the odor detection threshold for samples with two to five odorants. The results show a good correlation (R2 = 0.88) between SOAV estimated based on the OTVs for panelists in the present study and values found in the literature. For the majority of the samples, the ratio between the odor detection threshold and SOAV was not significantly different from one, which indicates that the OAV for individual odorants in a mixture can be considered additive. In conclusion, the assumption of additivity between odorants measured in pig house air seems reasonable, but the strength of the method is determined by the OTV data used.

Odorization of Natural Gas: What are the Challenges?

Modern natural gas (NG) has little or no odor, so other compounds, usually mercaptans and thiols, are added as warning odorants. Federal regulations state that NG must be odorized so that it is readily detectable by people with normal senses of smell at one fifth the lower explosive limit, but regulations don't define "readily detectable" or "normal senses of smell." Methods to measure human odor detection have been available for decades. However, most previous work on NG odorants has underestimated human sensitivity, and measurements need to be repeated using the latest methods. More work is also needed to determine how odor sensitivity measured under optimal laboratory conditions is affected by real-world factors such as distraction and exposure to other odors in the environment. Regarding a "normal sense of smell," healthy people vary over orders of magnitude in the concentrations they can detect, so samples of subjects should be chosen to reflect the range of differences in the population.

Background stimulus delays detection of target stimulus in a familiar odor–odor combination

Familiarity of odor–odor combinations is enhanced through food intake in daily life. As familiarity increases, the perceptual boundary between two odors may become ambiguous; therefore, we hypothesized that exposure to one odor would delay detection of the other in a high-familiarity combination but not in a low-familiarity combination. To test this hypothesis, we measured the speed of odor detection using two types of background stimuli (black tea odor and odorless air) and two types of target stimuli (lemon odor and almond odor). For Japanese participants, the combination of black tea and lemon odor has high familiarity, whereas the combination of black tea and almond odors has low familiarity. Reaction time for detection of target stimulus was measured by inserting a pulsed target stimulus into the flow of the background stimulus (i.e., replacing the background stimulus with the target stimulus for a short time). Reaction time for detection of lemon odor was significantly longer under the black tea odor condition than under the odorless air condition. Reaction time for detection of almond odor was similar between the black tea odor and odorless air conditions. These results are in line with the hypothesis that familiarity of an odor–odor combination affects odor detection speed. Further investigations are required to reach more robust conclusions.