Определение летучих Обонятельные с помощью газовой хроматографии-Multi-единицы Recordings (ГКХ) в усиков насекомого Lobe
Summary
Обонятельные сигналы посредником много различных поведения у насекомых, и часто являются сложной смесью состоящей из десятков до сотен летучих соединений. Использование газовой хроматографии с многоканальной записи в усиков насекомого доли, мы опишем метод для определения биологически активных соединений.
Abstract
All organisms inhabit a world full of sensory stimuli that determine their behavioral and physiological response to their environment. Olfaction is especially important in insects, which use their olfactory systems to respond to, and discriminate amongst, complex odor stimuli. These odors elicit behaviors that mediate processes such as reproduction and habitat selection1-3. Additionally, chemical sensing by insects mediates behaviors that are highly significant for agriculture and human health, including pollination4-6, herbivory of food crops7, and transmission of disease8,9. Identification of olfactory signals and their role in insect behavior is thus important for understanding both ecological processes and human food resources and well-being.
To date, the identification of volatiles that drive insect behavior has been difficult and often tedious. Current techniques include gas chromatography-coupled electroantennogram recording (GC-EAG), and gas chromatography-coupled single sensillum recordings (GC-SSR)10-12. These techniques proved to be vital in the identification of bioactive compounds. We have developed a method that uses gas chromatography coupled to multi-channel electrophysiological recordings (termed ‘GCMR’) from neurons in the antennal lobe (AL; the insect’s primary olfactory center)13,14. This state-of-the-art technique allows us to probe how odor information is represented in the insect brain. Moreover, because neural responses to odors at this level of olfactory processing are highly sensitive owing to the degree of convergence of the antenna’s receptor neurons into AL neurons, AL recordings will allow the detection of active constituents of natural odors efficiently and with high sensitivity. Here we describe GCMR and give an example of its use.
Several general steps are involved in the detection of bioactive volatiles and insect response. Volatiles first need to be collected from sources of interest (in this example we use flowers from the genus Mimulus (Phyrmaceae)) and characterized as needed using standard GC-MS techniques14-16. Insects are prepared for study using minimal dissection, after which a recording electrode is inserted into the antennal lobe and multi-channel neural recording begins. Post-processing of the neural data then reveals which particular odorants cause significant neural responses by the insect nervous system.
Although the example we present here is specific to pollination studies, GCMR can be expanded to a wide range of study organisms and volatile sources. For instance, this method can be used in the identification of odorants attracting or repelling vector insects and crop pests. Moreover, GCMR can also be used to identify attractants for beneficial insects, such as pollinators. The technique may be expanded to non-insect subjects as well.
Protocol
Representative Results
Discussion
Насекомых обонятельные-опосредованных поведения ездить много различных процессов, в том числе воспроизводство, принимающих выбор площадки, и выявления соответствующих продовольственных ресурсов. Изучение этих процессов требует способности идентифицировать летучих вылетающих из и…
Disclosures
The authors have nothing to disclose.
Acknowledgements
Эта работа была поддержана грантом NSF IOS 1121692, а также в университете Фонд исследований в Вашингтоне.
Materials
| Name of item | Company | Catalog Number | Comments |
| Porapak Type Q 80-100 mesh | Waters | WAT027060 | |
| Reynolds Oven Bags | Reynolds | ||
| GC | Agilent | 7820A | |
| GC column | J&W Scientific, Folsom, CA, USA | DB-5 (30 m, 0.25 mm, 0.25 μm) | |
| Analytical helium carrier gas | Praxair | HE K | 1 cc/min |
| 16-channel silicon electrode | Neuronexus Technologies | a4x4-3mm50-177 | |
| Fine wire NiCr, 0.012 mm diameter) | Sandvik Kanthal HP Reid | PX000004 | For making custom tetrodes and stereotrodes |
| Pre-amplifier | Tucker-Davis System | PZ-2 | |
| Amplifier | Tucker-Davis System | RZ-2 | |
| Data acquisition system – OpenEx suite | Tucker-Davis System | ||
| Online spike-sorting software – SpikePac | Tucker-Davis System | ||
| Offline spike-sorting software – Mclust Spike-sorting toolbox | David Redish, Department of Neuroscience, University of Minnesota | Free download at http://redishlab.neuroscience.umn.edu/MClust/MClust.html | MATLAB toolbox |
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