Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Controlling airborne cues to study small animal navigation

Abstract

Small animals such as nematodes and insects analyze airborne chemical cues to infer the direction of favorable and noxious locations. In these animals, the study of navigational behavior evoked by airborne cues has been limited by the difficulty of precisely controlling stimuli. We present a system that can be used to deliver gaseous stimuli in defined spatial and temporal patterns to freely moving small animals. We used this apparatus, in combination with machine-vision algorithms, to assess and quantify navigational decision making of Drosophila melanogaster larvae in response to ethyl acetate (a volatile attractant) and carbon dioxide (a gaseous repellant).

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Apparatus design and performance.
Figure 2: Response to spatial gradients.
Figure 3: Navigation of a 2 p.p.m. cm−1 ethyl acetate concentration gradient.
Figure 4: Navigation of a 2,500 p.p.m. cm−1 CO2 concentration gradient.
Figure 5: Temporal CO2 and ethyl acetate gradients.

Similar content being viewed by others

References

  1. Brody, C.D. & Hopfield, J.J. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37, 843–852 (2003).

    Article  CAS  Google Scholar 

  2. Cleland, T.A. & Linster, C. Computation in the olfactory system. Chem. Senses 30, 801–813 (2005).

    Article  Google Scholar 

  3. Hopfield, J.J. Olfactory computation and object perception. Proc. Natl. Acad. Sci. USA 88, 6462–6466 (1991).

    Article  CAS  Google Scholar 

  4. Chalasani, S.H. et al. Dissecting a circuit for olfactory behaviour in Caenorhabditis elegans. Nature 450, 63–70 (2007).

    Article  CAS  Google Scholar 

  5. Masse, N.Y., Turner, G.C. & Jefferis, G.S. Olfactory information processing in Drosophila. Curr. Biol. 19, R700–R713 (2009).

    Article  CAS  Google Scholar 

  6. Bargmann, C.I., Hartwieg, E. & Horvitz, H.R. Odorant-selective genes and neurons mediate olfaction in C. elegans. Cell 74, 515–527 (1993).

    Article  CAS  Google Scholar 

  7. Kreher, S.A., Mathew, D., Kim, J. & Carlson, J.R. Translation of sensory input into behavioral output via an olfactory system. Neuron 59, 110–124 (2008).

    Article  CAS  Google Scholar 

  8. Louis, M., Huber, T., Benton, R., Sakmar, T.P. & Vosshall, L.B. Bilateral olfactory sensory input enhances chemotaxis behavior. Nat. Neurosci. 11, 187–199 (2008).

    Article  CAS  Google Scholar 

  9. Gomez-Marin, A., Stephens, G.J. & Louis, M. Active sampling and decision making in Drosophila chemotaxis. Nat. Commun. 2, 441 (2011).

    Article  Google Scholar 

  10. Chronis, N., Zimmer, M. & Bargmann, C.I. Microfluidics for in vivo imaging of neuronal and behavioral activity in Caenorhabditis elegans. Nat. Methods 4, 727–731 (2007).

    Article  CAS  Google Scholar 

  11. Lockery, S.R. et al. Artificial dirt: microfluidic substrates for nematode neurobiology and behavior. J. Neurophysiol. 99, 3136–3143 (2008).

    Article  CAS  Google Scholar 

  12. Albrecht, D.R. & Bargmann, C.I. High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments. Nat. Methods 8, 599–605 (2011).

    Article  CAS  Google Scholar 

  13. Jones, W.D., Cayirlioglu, P., Kadow, I.G. & Vosshall, L.B. Two chemosensory receptors together mediate carbon dioxide detection in Drosophila. Nature 445, 86–90 (2007).

    Article  CAS  Google Scholar 

  14. Cayirlioglu, P. et al. Hybrid neurons in a microRNA mutant are putative evolutionary intermediates in insect CO2 sensory systems. Science 319, 1256–1260 (2008).

    Article  CAS  Google Scholar 

  15. Luo, L. et al. Navigational decision making in Drosophila thermotaxis. J. Neurosci. 30, 4261–4272 (2010).

    Article  CAS  Google Scholar 

  16. Larsson, M.C. et al. Or83b encodes a broadly expressed odorant receptor essential for Drosophila olfaction. Neuron 43, 703–714 (2004).

    Article  CAS  Google Scholar 

  17. Vosshall, L.B. & Hansson, B.S. A unified nomenclature system for the insect olfactory coreceptor. Chem. Senses 36, 497–498. (2011).

  18. Faucher, C., Forstreuter, M., Hilker, M. & de Bruyne, M. Behavioral responses of Drosophila to biogenic levels of carbon dioxide depend on life-stage, sex and olfactory context. J. Exp. Biol. 209, 2739–2748 (2006).

    Article  CAS  Google Scholar 

  19. Kwon, J.Y., Dahanukar, A., Weiss, L.A. & Carlson, J.R. The molecular basis of CO2 reception in Drosophila. Proc. Natl. Acad. Sci. USA 104, 3574–3578 (2007).

    Article  CAS  Google Scholar 

  20. Berg, H.C. & Brown, D.A. Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature 239, 500–504 (1972).

    Article  CAS  Google Scholar 

  21. Vosshall, L.B. & Stocker, R.F. Molecular architecture of smell and taste in Drosophila. Annu. Rev. Neurosci. 30, 505–533 (2007).

    Article  CAS  Google Scholar 

  22. Baek, J.H., Cosman, P., Feng, Z., Silver, J. & Schafer, W.R. Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively. J. Neurosci. Methods 118, 9–21 (2002).

    Article  Google Scholar 

  23. Cronin, C.J., Feng, Z. & Schafer, W.R. Automated imaging of C. elegans behavior. Methods Mol. Biol. 351, 241–251 (2006).

    PubMed  Google Scholar 

  24. Swierczek, N.A., Giles, A.C., Rankin, C.H. & Kerr, R.A. High-throughput behavioral analysis in C. elegans. Nat. Methods 8, 592–598 (2011).

    Article  CAS  Google Scholar 

  25. Ramot, D., Johnson, B.E., Berry, T.L.J., Carnell, L. & Goodman, M.B. The Parallel Worm Tracker: a platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3, e2208 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

We thank E. Soucy and J. Greenwood for engineering advice and suggestions. This work was supported by a US National Institutes of Health (NIH) Pioneer award to A.D.T.S., NIH grants to J.R.C. and an NIH National Research Service award to E.A.K.

Author information

Authors and Affiliations

Authors

Contributions

M.G. designed and constructed the linear and dynamic gaseous gradient apparatus, designed and wrote MAGAT analyzer software, designed and carried out experiments, analyzed all data and assembled figures. M.B. designed and carried out experiments. D.M. and L.L. designed and carried out preliminary experiments. E.A.K. designed experiments. J.R.C. and A.D.T.S. supervised the project and designed experiments. M.G., E.A.K. and A.D.T.S. wrote the manuscript.

Corresponding author

Correspondence to Aravinthan D T Samuel.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 1 and 2, Supplementary Notes 1–3 (PDF 1917 kb)

Supplementary Video 1

Overview of video analysis steps. Video shows in sequence (1) raw image of larvae in a 2ppm cm−1 EtAc gradient (gradient increases in concentration to the right) (2) individual larvae are all tracked separately (colored tracks show movement history, circles indicate current position) (3) for each larva, we find a contour, midline, head and tail (4 larvae shown as examples) (4) from extracted position and postural features, we derive metrics (speed, body bend angle shown here) and behavioral states (1 larva shown as an example). At default playback speed of 30 frames per second (fps), video is 6× real time. (MOV 17156 kb)

Supplementary Video 2

Video complement to Figure 2a,b. Video sequence of still images depicted in Figure 2a, accompanied by navigational metrics (speed, dot product between head direction and direction of forward movement, body bend angle) presented in Figure 2b. Cyan dot on each data plot shows value associated with current frame. Text overlay on video shows elapsed time (time matches that shown in Fig. 2a,b) and behavioral state. As in Figure 2b, colored regions under data plots indicate behavioral state. At default playback speed of 10 fps, video is 2× real time. (MOV 8372 kb)

Supplementary Video 3

Extended playback of track excerpted in Supplementary Video 2. Video sequence, accompanied by navigational metrics (speed, dot product between head direction and direction of forward movement, body bend angle). Cyan dot on each data plot shows value associated with current frame. Text overlay on video shows elapsed time (time matches that shown in Fig. 2a,b) and behavioral state. As in Figure 2b, colored regions under data plots indicate behavioral state. At default playback speed of 25 fps, video is 5× real time. (MOV 25005 kb)

Supplementary Video 4

Example of runs and turns. Larva's track over video period indicated by white dots. As the video show the larva moving along the track, portions of the trajectory corresponding to runs and turns are indicated. At default playback speed of 25 fps, video is 5× real time. (MOV 702 kb)

Supplementary Video 5

Description of turn angles. The same larva and track from Supplementary Video 4 are shown. As the video plays, the prior heading angle (orange θ) and heading angle change (green Δθ) are graphically indicated for each turn. Figures 3g and 4g show distributions of Δθ, sorted according to θ. Figures 3h and 4h show the mean of Δθ versus θ. Figures 3i and 4i show the root mean square of Δθ versus θ. At default playback speed of 25 fps, video is 5× real time (except for pauses to highlight turn angles). (MOV 709 kb)

Supplementary Video 6

Example of rejected and accepted head sweeps. A portion of the video and track shown in Supplementary Videos 4, 5; the larva executes at rejected head sweep to its left followed by an accepted head sweep to its right. At default playback speed of 10 fps, video is 2× real time (except for pauses to highlight head sweeps). (MOV 232 kb)

Supplementary Software 1

Video analysis software. (ZIP 41302 kb)

Supplementary Software 2

Valve driver firmware and circuit layout. (ZIP 138 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gershow, M., Berck, M., Mathew, D. et al. Controlling airborne cues to study small animal navigation. Nat Methods 9, 290–296 (2012). https://doi.org/10.1038/nmeth.1853

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1853

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing