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.

Molecular profiling of activated olfactory neurons identifies odorant receptors for odors in vivo

Nature Neuroscience volume 18, pages 1446–1454 (2015)Cite this article

Subjects

Abstract

The mammalian olfactory system uses a large family of odorant receptors (ORs) to detect and discriminate amongst a myriad of volatile odor molecules. Understanding odor coding requires comprehensive mapping between ORs and corresponding odors. We developed a means of high-throughput in vivo identification of OR repertoires responding to odorants using phosphorylated ribosome immunoprecipitation of mRNA from olfactory epithelium of odor-stimulated mice followed by RNA-Seq. This approach screened the endogenously expressed ORs against an odor in one set of experiments using awake and freely behaving mice. In combination with validations in a heterologous system, we identified sets of ORs for two odorants, acetophenone and 2,5-dihydro-2,4,5-trimethylthiazoline (TMT), encompassing 69 OR-odorant pairs. We also identified shared amino acid residues specific to the acetophenone or TMT receptors and developed models to predict receptor activation by acetophenone. Our results provide a method for understanding the combinatorial coding of odors in vivo.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Odor stimulation induces S6 phosphorylation in the mouse OE.
Figure 2: pS6-IP enriches OR mRNAs from odor stimulated OE.
Figure 3: Correlation between in vivo and in vitro responses.
Figure 4: Identification of acetophenone receptors.
Figure 5: Correlation between in vivo and in vitro sensitivities of ORs.
Figure 6: Sequence-function analysis of the identified acetophenone receptors.
Figure 7: Identification of ORs activated by TMT.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Buck, L. & Axel, R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991).

    CAS  PubMed  Google Scholar 

  2. Niimura, Y., Matsui, A. & Touhara, K. Extreme expansion of the olfactory receptor gene repertoire in African elephants and evolutionary dynamics of orthologous gene groups in 13 placental mammals. Genome Res. 24, 1485–1496 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  4. Saito, H., Chi, Q., Zhuang, H., Matsunami, H. & Mainland, J.D. Odor coding by a mammalian receptor repertoire. Sci. Signal. 2, ra9 (2009).

    PubMed  PubMed Central  Google Scholar 

  5. Peterlin, Z., Firestein, S. & Rogers, M.E. The state of the art of odorant receptor deorphanization: a report from the orphanage. J. Gen. Physiol. 143, 527–542 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  7. Touhara, K. et al. Functional identification and reconstitution of an odorant receptor in single olfactory neurons. Proc. Natl. Acad. Sci. USA 96, 4040–4045 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Bozza, T., Feinstein, P., Zheng, C. & Mombaerts, P. Odorant receptor expression defines functional units in the mouse olfactory system. J. Neurosci. 22, 3033–3043 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Saito, H., Kubota, M., Roberts, R.W., Chi, Q. & Matsunami, H. RTP family members induce functional expression of mammalian odorant receptors. Cell 119, 679–691 (2004).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  11. Grosmaitre, X. et al. SR1, a mouse odorant receptor with an unusually broad response profile. J. Neurosci. 29, 14545–14552 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Mainland, J.D. et al. The missense of smell: functional variability in the human odorant receptor repertoire. Nat. Neurosci. 17, 114–120 (2014).

    CAS  PubMed  Google Scholar 

  13. Keller, A., Zhuang, H., Chi, Q., Vosshall, L.B. & Matsunami, H. Genetic variation in a human odorant receptor alters odour perception. Nature 449, 468–472 (2007).

    CAS  PubMed  Google Scholar 

  14. Menashe, I. et al. Genetic elucidation of human hyperosmia to isovaleric acid. PLoS Biol. 5, e284 (2007).

    PubMed  PubMed Central  Google Scholar 

  15. Shirasu, M. et al. Olfactory receptor and neural pathway responsible for highly selective sensing of musk odors. Neuron 81, 165–178 (2014).

    CAS  PubMed  Google Scholar 

  16. Dong, P.Y., Gong, N.N. & Matsunami, H. Cell-based system for identification of olfactory receptors. in Bioelectronic Nose (ed. T.H. Park) 83–95 (Springer Netherlands, 2014).

  17. Knight, Z.A. et al. Molecular profiling of activated neurons by phosphorylated ribosome capture. Cell 151, 1126–1137 (2012).

    CAS  PubMed  Google Scholar 

  18. Haga, S. et al. The male mouse pheromone ESP1 enhances female sexual receptive behaviour through a specific vomeronasal receptor. Nature 466, 118–122 (2010).

    CAS  PubMed  Google Scholar 

  19. Isogai, Y. et al. Molecular organization of vomeronasal chemoreception. Nature 478, 241–245 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Chess, A., Simon, I., Cedar, H. & Axel, R. Allelic inactivation regulates olfactory receptor gene expression. Cell 78, 823–834 (1994).

    CAS  PubMed  Google Scholar 

  21. Zhang, J., Huang, G., Dewan, A., Feinstein, P. & Bozza, T. Uncoupling stimulus specificity and glomerular position in the mouse olfactory system. Mol. Cell. Neurosci. 51, 79–88 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Oka, Y. et al. Odorant receptor map in the mouse olfactory bulb: in vivo sensitivity and specificity of receptor-defined glomeruli. Neuron 52, 857–869 (2006).

    CAS  PubMed  Google Scholar 

  23. Krautwurst, D., Yau, K.W. & Reed, R.R. Identification of ligands for olfactory receptors by functional expression of a receptor library. Cell 95, 917–926 (1998).

    CAS  PubMed  Google Scholar 

  24. McClintock, T.S. et al. In vivo identification of eugenol-responsive and muscone-responsive mouse odorant receptors. J. Neurosci. 34, 15669–15678 (2014).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhuang, H. & Matsunami, H. Evaluating cell-surface expression and measuring activation of mammalian odorant receptors in heterologous cells. Nat. Protoc. 3, 1402–1413 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Li, Y.R. & Matsunami, H. Activation state of the M3 muscarinic acetylcholine receptor modulates mammalian odorant receptor signaling. Sci. Signal. 4, ra1 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang, X. & Firestein, S. The olfactory receptor gene superfamily of the mouse. Nat. Neurosci. 5, 124–133 (2002).

    CAS  PubMed  Google Scholar 

  29. Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974).

    CAS  PubMed  Google Scholar 

  30. Bishop, C.M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer-Verlag New York, 2006).

  31. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol. 67, 301–320 (2005).

    Google Scholar 

  32. Takahashi, L.K., Nakashima, B.R., Hong, H. & Watanabe, K. The smell of danger: a behavioral and neural analysis of predator odor-induced fear. Neurosci. Biobehav. Rev. 29, 1157–1167 (2005).

    PubMed  Google Scholar 

  33. Kobayakawa, K. et al. Innate versus learned odor processing in the mouse olfactory bulb. Nature 450, 503–508 (2007).

    CAS  PubMed  Google Scholar 

  34. Weid, B. et al. In vivo identification of receptor-ligand pairs via large-scale transcriptional profiling of chemosensory neurons. Nat. Neurosci. doi:10.1038/nn.4100 (31 August 2015).

  35. Thiebaud, N. et al. Odorant metabolism catalyzed by olfactory mucosal enzymes influences peripheral olfactory responses in rats. PLoS ONE 8, e59547 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Nagashima, A. & Touhara, K. Enzymatic conversion of odorants in nasal mucus affects olfactory glomerular activation patterns and odor perception. J. Neurosci. 30, 16391–16398 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Scott, J.W., Sherrill, L., Jiang, J. & Zhao, K. Tuning to odor solubility and sorption pattern in olfactory epithelial responses. J. Neurosci. 34, 2025–2036 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Grossisseroff, R. & Lancet, D. Concentration-dependent changes of perceived odor quality. Chem. Senses 13, 191–204 (1988).

    Google Scholar 

  39. Li, J., Haddad, R., Chen, S., Santos, V. & Luetje, C.W. A broadly tuned mouse odorant receptor that detects nitrotoluenes. J. Neurochem. 121, 881–890 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Nara, K., Saraiva, L.R., Ye, X. & Buck, L.B. A large-scale analysis of odor coding in the olfactory epithelium. J. Neurosci. 31, 9179–9191 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Boyle, S.M., McInally, S. & Ray, A. Expanding the olfactory code by in silico decoding of odor-receptor chemical space. eLife 2, e01120 (2013).

    PubMed  PubMed Central  Google Scholar 

  42. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  43. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  PubMed  Google Scholar 

  45. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  46. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. McCarthy, D.J., Chen, Y. & Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  49. Crooks, G.E., Hon, G., Chandonia, J.M. & Brenner, S.E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank G. Barnea and R. Axel for antibodies to M72, H. Zhuang for some of the OR constructs, D. Marchuk for sharing of equipment, the Duke IGSP Genome Sequencing and Analysis Core Resource for providing Illumina sequencing service, and S. Mukherjee and J. Mainland for helpful discussions and comments on the manuscript. This work was supported by grants from the US National Institutes of Health (DC012095 and DC014423).

Author information

Authors and Affiliations

  1. Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA

    Yue Jiang, Naihua Natalie Gong, Xiaoyang Serene Hu, Mengjue Jessica Ni, Radhika Pasi & Hiroaki Matsunami

  2. University Program in Genetics and Genomics, Duke University Medical Center, Durham, North Carolina, USA

    Yue Jiang

  3. Department of Statistical Science, Duke University, Durham, North Carolina, USA

    Yue Jiang

  4. Department of Neurobiology, Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA

    Hiroaki Matsunami

Authors
  1. Yue Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naihua Natalie Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoyang Serene Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengjue Jessica Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Radhika Pasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroaki Matsunami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.M. supervised all of the experiments and data analysis. N.N.G., Y.J., X.S.H. and H.M. performed immunohistochemistry experiments. Y.J. and H.M. performed pS6-IP. Y.J. performed bioinformatics and statistical analysis. M.J.N. cloned some of the OR constructs. Y.J. and R.P. performed in vitro luciferase assays. Y.J., N.N.G. and H.M. wrote the paper.

Corresponding author

Correspondence to Hiroaki Matsunami.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 pS6 induction in the OE following exposure to M72 agonists and control odors.

(a) In vitro responses of Olfr160 (M72) to various odors used in Fig. 1 b and Fig. S1b. Responses are scaled to acetophenone. The maximum response to acetophenone is defined as 100.

(b) pS6 immunostaining in the OE following exposure to M72 agonists and control odors. Green: Antibody staining for pS6. Magenta: Antibody staining for a known acetophenone receptor, M72. Blue: Bisbenzimide staining showing the nuclei. Arrowheads indicate colocalization of M72 and pS6 signals.Scale bar, 50 μm.

Supplementary Figure 2 Characterization of pS6 induction in the OE for known OR–odorant pairs.

(a) pS6 induction in the OE for 5 known OR–odorant pairs. Green: Antibody staining for pS6. Magenta: RNA FISH for ORs. Blue: Bisbenzimide staining showing the nuclei. Arrowheads indicate colocalization of OR and pS6 signals when stimulated with the cognate ligands. Scale bar, 25 μm.

(b) Time course of pS6 induction following odor exposure. Green: Antibody staining for pS6. Blue: Bisbenzimide staining showing the nuclei. Scale bar, 50 μm.

Supplementary Figure 3 pS6 induction in the OE for newly identified acetophenone ORs.

(a) Representative figures showing pS6 induction in the OE for newly identified acetophenone ORs and a control OR (Olfr1132) following 1% and 100% acetophenone stimulation for 1 hour. Green: Antibody staining for pS6. Magenta: RNA FISH for ORs. Blue: Bisbenzimide staining showing the nuclei. Scale bar, 25 μm.

(b) Quantification of pS6 staining intensity following 1% and 100% acetophenone stimulation.

Supplementary Figure 4 Sequence comparison of 48 identified acetophenone ORs and all mouse ORs.

Heights of letters indicate residue abundance for a given location in the alignment. The positions that are more conserved with the acetophenone ORs than random are marked with stars. Predicted transmembrane domains are labeled in orange.

Supplementary Figure 5 Dose response curves of human ORs to acetophenone.

Dose response curves of 27 human ORs used for external validation of the model.

Supplementary Figure 6 pS6–IP identifies ORs activated by TMT.

(a) Scatter plot comparing immunoprecipitated mRNA counts from stimulated sample (100% TMT) versus unstimulated sample. X–axis: mean read counts of genes in unstimulated IP samples (n=4). Y–axis: mean read counts of genes in TMT stimulated IP samples (n=4). Red dots represent ORs. Gray dots represent non–OR genes. Broken line: unit–slope.

(b) Differential enrichment calling of OR mRNA. 43 ORs are enriched in the 100% TMT stimulated group with p–value smaller than 0.05, after adjusting for multiple comparisons across the detected OR repertoire. Broken line: unit–slope.

(c) Differential enrichment calling of OR mRNA. 4 ORs are enriched in the 1% acetophenone stimulated group with p–value smaller than 0.05, after adjusting for multiple comparisons across the detected OR repertoire. Broken line: unit–slope.

(d) Scatter plot comparing p–values of enrichment in 100% TMT versus 1% TMT stimulated samples. Red dashed line: p=0.001. Blue dashed line: p=0.05. Note the absence of ORs in the bottom right corner.

(e) Localization of ORs that were identified to be activated by TMT via RNA in situ hybridization. 20 μm tissue sections of the OE were marked with digoxigenin (DIG)-labeled RNA probe complementary to specific OR mRNA transcripts. Scale bar = 100 μm and applies to all panels.

Supplementary Figure 7 Comparison with acetophenone ORs identified by DREAM.

ROC curves illustrating performance of classifiers using in vivo enrichment p–values to predict whether the OR is called as acetophenone OR by DREAM. Area Under Curve: 0.860, p=4×10−9, Wilcoxon rank–sum test (one tailed against H0: Classifier performance no better than random).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1513 kb)

Supplementary Methods Checklist (PDF 385 kb)

Statistics for comparing pS6 signal intensity.

Comparison of pS6 signal intensities at different odorant concentrations. One-way ANOVA followed by Dunnett's multiple comparisons test. (XLS 44 kb)

Data sets for in vivo and in vitro OR activation measures.

Differential expression analysis results for 100% acetophenone, 1% acetophenone, EC50 values and fold of induction for in vitro luciferase assay, summary of identified acetophenone ORs, differential expression analysis results for 100% TMT, 1% TMT, and list of identified TMT ORs. (XLS 997 kb)

Comparison with the DREAM method.

In vivo and in vitro data summary for the 22 ORs for acetophenone identified by the DREAM method. (XLSX 58 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Y., Gong, N., Hu, X. et al. Molecular profiling of activated olfactory neurons identifies odorant receptors for odors in vivo. Nat Neurosci 18, 1446–1454 (2015). https://doi.org/10.1038/nn.4104

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4104

Search

Advanced 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