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A metric for odorant comparison

Abstract

In studies of vision and audition, stimuli can be systematically varied by wavelength and frequency, respectively, but there is no equivalent metric for olfaction. Restricted odorant-feature metrics such as number of carbons and functional group do not account for response patterns to odorants varying along other structural dimensions. We generated a multidimensional odor metric, in which each odorant molecule was represented as a vector of 1,664 molecular descriptor values. Revisiting many studies, we found that this metric and a second optimized metric were always better at accounting for neural responses than the specific metric used in each study. These metrics were applicable across studies that differed in the animals studied, the type of olfactory neurons tested, the odorants applied and the recording methods used. We use this new metric to recommend sets of odorants that span the physicochemical space for use in olfaction experiments.

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Figure 1: Evaluating the multidimensional metric.
Figure 2: Correlation between neuronal response pattern similarity and odorant distances calculated using three different metrics across 7 datasets.
Figure 3: Testing the predictive power of the multidimensional metric using new data.

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References

  1. Firestein, S. How the olfactory system makes sense of scents. Nature 413, 211–218 (2001).

    Article  CAS  Google Scholar 

  2. Kent, P.F. & Mozell, M. The recording of odorant-induced mucosal activity patterns with a voltage-sensitive dye. J. Neurophysiol. 68, 1804–1819 (1992).

    Article  CAS  Google Scholar 

  3. Johnson, B.A., Woo, C.C. & Leon, M. Spatial coding of odorant features in the glomerular layer of the rat olfactory bulb. J. Comp. Neurol. 393, 457–471 (1998).

    Article  CAS  Google Scholar 

  4. Zhao, H. et al. Functional expression of a mammalian odorant receptor. Science 279, 237–242 (1998).

    Article  CAS  Google Scholar 

  5. Sato, T., Hirono, J., Tonoike, M. & Takebayashi, M. Tuning specificities to aliphatic odorants in mouse olfactory receptor neurons and their local distribution. J. Neurophysiol. 72, 2980–2989 (1994).

    Article  CAS  Google Scholar 

  6. Rubin, B.D. & Katz, L.C. Optical imaging of odorant representations in the mammalian olfactory bulb. Neuron 23, 499–511 (1999).

    Article  CAS  Google Scholar 

  7. Meister, M. & Bonhoeffer, T. Tuning and topography in an odor map on the rat olfactory bulb. J. Neurosci. 21, 1351–1360 (2001).

    Article  CAS  Google Scholar 

  8. Malnic, B., Hirono, J., Sato, T. & Buck, L.B. Combinatorial receptor codes for odors. Cell 96, 713–723 (1999).

    Article  CAS  Google Scholar 

  9. Laska, M. & Teubner, P. Olfactory discrimination ability for homologous series of aliphatic alcohols and aldehydes. Chem. Senses 24, 263–270 (1999).

    Article  CAS  Google Scholar 

  10. Johnson, B.A., Woo, C.C., Hingco, E.E., Pham, K.L. & Leon, M. Multidimensional chemotopic responses to n-aliphatic acid odorants in the rat olfactory bulb. J. Comp. Neurol. 409, 529–548 (1999).

    Article  CAS  Google Scholar 

  11. Imamura, K., Mataga, N. & Mori, K. Coding of odor molecules by mitral/tufted cells in rabbit olfactory bulb. I. Aliphatic compounds. J. Neurophysiol. 68, 1986–2002 (1992).

    Article  CAS  Google Scholar 

  12. Guerrieri, F., Schubert, M., Sandoz, J.C. & Giurfa, M. Perceptual and neural olfactory similarity in honeybees. PLoS Biol. 3, e60 (2005).

    Article  Google Scholar 

  13. Araneda, R.C., Kini, A.D. & Firestein, S. The molecular receptive range of an odorant receptor. Nat. Neurosci. 3, 1248–1255 (2000).

    Article  CAS  Google Scholar 

  14. Uchida, N., Takahashi, Y.K., Tanifuji, M. & Mori, K. Odor maps in the mammalian olfactory bulb: domain organization and odorant structural features. Nat. Neurosci. 3, 1035–1043 (2000).

    Article  CAS  Google Scholar 

  15. Hallem, E.A. & Carlson, J.R. The spatial code for odors is changed by conditioning. Neuron 42, 359–361 (2004).

    Article  CAS  Google Scholar 

  16. Hallem, E.A. & Carlson, J.R. Coding of odors by a receptor repertoire. Cell 125, 143–160 (2006).

    Article  CAS  Google Scholar 

  17. de Bruyne, M., Foster, K. & Carlson, J.R. Odor coding in the Drosophila antenna. Neuron 30, 537–552 (2001).

    Article  CAS  Google Scholar 

  18. de Bruyne, M., Clyne, P.J. & Carlson, J.R. Odor coding in a model olfactory organ: the Drosophila maxillary palp. J. Neurosci. 19, 4520–4532 (1999).

    Article  CAS  Google Scholar 

  19. Wachowiak, M. & Cohen, L.B. Representation of odorants by receptor neuron input to the mouse olfactory bulb. Neuron 32, 723–735 (2001).

    Article  CAS  Google Scholar 

  20. Sachse, S., Rappert, A. & Galizia, C.G. The spatial representation of chemical structures in the antennal lobe of honeybees: steps towards the olfactory code. Eur. J. Neurosci. 11, 3970–3982 (1999).

    Article  CAS  Google Scholar 

  21. Johnson, B.A. et al. Functional mapping of the rat olfactory bulb using diverse odorants reveals modular responses to functional groups and hydrocarbon structural features. J. Comp. Neurol. 449, 180–194 (2002).

    Article  Google Scholar 

  22. Takahashi, Y.K., Kurosaki, M., Hirono, S. & Mori, K. Topographic representation of odorant molecular features in the rat olfactory bulb. J. Neurophysiol. 92, 2413–2427 (2004).

    Article  CAS  Google Scholar 

  23. Manzini, I., Brase, C., Chen, T. & Schild, D. Response profiles to amino acid odorants of olfactory glomeruli in larval Xenopus laevis. J. Physiol. (Lond.) 581, 567–579 (2007).

    Article  Google Scholar 

  24. Cleland, T., Morse, A., Yue, E. & Linster, C. Behavioral models of odor similarity. Behav. Neurosci. 116, 222–231 (2002).

    Article  Google Scholar 

  25. Laska, M., Galizia, C.G., Giurfa, M. & Menzel, R. Olfactory discrimination ability and odor structure-activity relationships in honeybees. Chem. Senses 24, 429–438 (1999).

    Article  CAS  Google Scholar 

  26. Davison, I.G. & Katz, L.C. Sparse and selective odor coding by mitral/tufted neurons in the main olfactory bulb. J. Neurosci. 27, 2091–2101 (2007).

    Article  CAS  Google Scholar 

  27. Kaluza, J.F. & Breer, H. Responsiveness of olfactory neurons to distinct aliphatic aldehydes. J. Exp. Biol. 203, 927–933 (2000).

    CAS  PubMed  Google Scholar 

  28. Friedrich, R.W. & Korsching, S.I. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging. Neuron 18, 737–752 (1997).

    Article  CAS  Google Scholar 

  29. Tetko, I.V. et al. Virtual computational chemistry laboratory–design and description. J. Comput. Aided Mol. Des. 19, 453–463 (2005).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank A. Elite.

Author information

Authors and Affiliations

Authors

Contributions

R.H., R.K., D.H. and N.S. are authors of the concept. R.H. performed the analysis; R.H., N.S. and D.H. wrote the manuscript; Y.K.T. and K.M. provided data post-hoc for the blind test.

Corresponding author

Correspondence to Rafi Haddad.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Table 1–3, Supplementary Methods (PDF 312 kb)

Supplementary Data 1

The predicted and observed values used for the blind test. The distances were calculated by using the multidimensional optimized metric. (XLS 440 kb)

Supplementary Data 2

Interactive tool to calculate odorant distances between any two odorants out of more than 400 commonly used odorants. The distance is calculated by using the multidimensional optimized metric. (XLS 323 kb)

Supplementary Data 3

A suggested 8 clusters with different granularity of more than 400 odorants using the optimized multidimensional metric. Each clustering can be used to select odorant that span the odor space in a more systematic way. (XLS 124 kb)

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Haddad, R., Khan, R., Takahashi, Y. et al. A metric for odorant comparison. Nat Methods 5, 425–429 (2008). https://doi.org/10.1038/nmeth.1197

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