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Drosophila Olfactory Receptor Neuron Responses to 71 Odorants

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About this Collection

Drosophila Olfactory Receptor Neuron Responses to 71 Odorants


This data set is the basis of the chemical classification of volatiles described in Nowotny et al. Bioinspiration & Biomimetics (in press 2014) "Drosophila olfactory receptors as classifiers for volatiles from disparate real world applications."


Animal Physiology - Biophysics Information and Computing Sciences not elsewhere classified Sensor Technology (Chemical aspects)


https://doi.org/10.4225/08/54252553F0775


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machine olfaction feature selection olfactory receptor biosensors classification industrial chemicals wine volatiles


The physiological response to volatile stimuli was recorded from Drosophila ORNs using the same method as in Marshall et al (2009), similar to the one used previously (de Bruyne et al 1999, 2010, Hallem and Carlson 2006). A glass capillary electrode was inserted into the base of a single olfactory sensillum on the antenna or maxillary palp of a male fly (figure 1(A)) while a reference electrode was inserted into the eye. Signals were amplified 1000x via a 10x active probe fed into an AD converter with digital amplification. Action potentials, recorded from two or four neurons in a single sensillum, were sorted into neuronal types, according to their different spike amplitudes, and counted separately. There are 28 types of olfactory neurons housed in basiconic sensilla and, in this study, recordings were made from 20 of those neuronal types, equivalent to 20 different sensors. The recorded signals comprise a train of action potentials (spikes). Many of the neuronal types have a characteristic resting or basal spike frequency. Odour stimulation may therefore increase or decrease the spiking rate, with a dynamic range ≈30–300 Hz. The response for each ORN was calculated as the firing rate (spikes/s) during the period of stimulation (500 ms) minus the spontaneous activity prior to stimulation. A negative firing rate therefore indicates reduced activity (inhibition). Repetitions were always from different sensilla, either on the same or on different flies. The primary data for each ORN-odour combination was based on recordings from at least three different flies. Data are the means and standard deviations of those recordings. As olfactory stimuli we used a set of 36 aroma compounds relevant to the wine industry. All compounds were dissolved at 1% (v/v) in various solvents. A complete list of the che- micals, and solvents used and a list of odour descriptors are given in supplementary table 1. Volatiles were injected (10% v/v) from the headspace of 5 mL disposable syringes, con- taining 10 μl of the 1% dilution of the odour on filter paper, into a constant stream of clean humidified air flowing over the preparation. These procedures were the same as those used for the set of 35 chemicals in the industrial set that were tested previously in Marshall et al (2009). For stimulation with wine, we put 10 μl of undiluted Cabernet–Sauvignon wine onto the filter paper.


Data collection was funded by the Australian Research Support National Security Scheme and CSIRO's Flagship Collaboration Fund


CSIRO Data Licence


CSIRO (Australia), Monash University (Australia)


de Bruyne, Marien; Nowotny, Thomas; Berna, Amalia; Warr, Coral; Trowell, Stephen (2014): Drosophila Olfactory Receptor Neuron Responses to 71 Odorants. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/54252553F0775


All Rights (including copyright) Monash University 2014.


The metadata and files (if any) are available to the public.

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About this Project

1023.1 FF Cybernose - strategic


The aim of the CYBERNOSE® project is to incorporate olfactory biosensors into an instrument that can be used to detect, discriminate and quantify volatile odorants with similar sensitivity as an animal's nose or antennae.


Alisha Anderson


Electrophysiological recordings from Drosophila olfactory sensilla


Electrophysiological recordings from Drosophila olfactory sensilla


Experiment


Single Sensillum Recordings


Marien de Bruyne


Thomas Nowotny


Amalia Berna


Coral Warr


Stephen Trowell


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