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:

Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing

Nature Neuroscience volume 25, pages 484–492 (2022)Cite this article

Subjects

Abstract

The olfactory system’s ability to detect and discriminate between the vast array of chemicals present in the environment is critical for an animal’s survival. In mammals, the first step of this odor processing is executed by olfactory sensory neurons, which project their axons to a stereotyped location in the olfactory bulb (OB) to form glomeruli. The stereotyped positioning of glomeruli in the OB suggests an importance for this organization in odor perception. However, because the location of only a limited subset of glomeruli has been determined, it has been challenging to determine the relationship between glomerular location and odor discrimination. Using a combination of single-cell RNA sequencing, spatial transcriptomics and machine learning, we have generated a map of most glomerular positions in the mouse OB. These observations significantly extend earlier studies and suggest an overall organizational principle in the OB that may be used by the brain to assist in odor decoding.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: RNA sequencing of FACS-isolated OSNs reveals transcriptional heterogeneity between different types of OSNs.
Fig. 2: scRNAseq reveals that OSNs that display different ORs express unique transcriptional programs.
Fig. 3: Spatial transcriptomic based reconstruction of the mouse glomerular map.
Fig. 4: Organizing principles revealed through the reconstruction of a map of mouse glomerular positions.

Similar content being viewed by others

Data availability

All RNA sequencing data described in this manuscript are deposited at GEO accession GSE169021. The positions of the reconstructed glomerular map can be found at: https://github.com/Greerlab/OSN_2021_paper. The processed MERFISH data can be found at: https://zenodo.org/record/5745947#.YaeQ-PHMIqs Figure 1 and Extended Data Fig. 1 are associated with GSE169010; Figs. 2–4 and Extended Data Figs. 2–5,8,10 are associated with GSE169011; Fig. 3 and Extended Data Fig. 6 are associated with GSE169012.

Code availability

All the scripts used for this study can be found at:

https://github.com/Greerlab/OSN_2021_paper.

References

  1. Axel, R. The molecular logic of smell. Sci. Am. 273, 154–159 (1995).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Ressler, K. J., Sullivan, S. L. & Buck, L. B. Information coding in the olfactory system: evidence for a stereotyped and highly organized epitope map in the olfactory bulb. Cell 79, 1245–1255 (1994).

    Article  CAS  PubMed  Google Scholar 

  5. Serizawa, S. et al. Mutually exclusive expression of odorant receptor transgenes. Nat. Neurosci. 3, 687–693 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Vassar, R. et al. Topographic organization of sensory projections to the olfactory bulb. Cell 79, 981–991 (1994).

    Article  CAS  PubMed  Google Scholar 

  7. Zapiec, B. & Mombaerts, P. Multiplex assessment of the positions of odorant receptor-specific glomeruli in the mouse olfactory bulb by serial two-photon tomography. Proc. Natl Acad. Sci. USA 112, E5873–E5882 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mombaerts, P. et al. Visualizing an olfactory sensory map. Cell 87, 675–686 (1996).

    Article  CAS  PubMed  Google Scholar 

  9. Soucy, E. R., Albeanu, D. F., Fantana, A. L., Murthy, V. N. & Meister, M. Precision and diversity in an odor map on the olfactory bulb. Nat. Neurosci. 12, 210–220 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Schaefer, M. L., Finger, T. E. & Restrepo, D. Variability of position of the P2 glomerulus within a map of the mouse olfactory bulb. J. Comp. Neurol. 436, 351–362 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  12. 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).

    Article  CAS  PubMed  Google Scholar 

  13. Berkowicz, D. A., Trombley, P. Q. & Shepherd, G. M. Evidence for glutamate as the olfactory receptor cell neurotransmitter. J. Neurophysiol. 71, 2557–2561 (1994).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Belluscio, L. & Katz, L. C. Symmetry, stereotypy, and topography of odorant representations in mouse olfactory bulbs. J. Neurosci. 21, 2113–2122 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wachowiak, M., Denk, W. & Friedrich, R. W. Functional organization of sensory input to the olfactory bulb glomerulus analyzed by two-photon calcium imaging. Proc. Natl Acad. Sci. USA 101, 9097–9102 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ma, L. et al. Distributed representation of chemical features and tunotopic organization of glomeruli in the mouse olfactory bulb. Proc. Natl Acad. Sci. USA 109, 5481–5486 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kass, M. D., Rosenthal, M. C., Pottackal, J. & McGann, J. P. Fear learning enhances neural responses to threat-predictive sensory stimuli. Science 342, 1389–1392 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  21. 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  PubMed  Google Scholar 

  22. Fuss, S. H. & Korsching, S. I. Odorant feature detection: activity mapping of structure response relationships in the zebrafish olfactory bulb. J. Neurosci. 21, 8396–8407 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Galizia, C. G. & Menzel, R. Odour perception in honeybees: coding information in glomerular patterns. Curr. Opin. Neurobiol. 10, 504–510 (2000).

    Article  CAS  PubMed  Google Scholar 

  24. Lei, H., Christensen, T. A. & Hildebrand, J. G. Spatial and temporal organization of ensemble representations for different odor classes in the moth antennal lobe. J. Neurosci. 24, 11108–11119 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Couto, A., Alenius, M. & Dickson, B. J. Molecular, anatomical, and functional organization of the Drosophila olfactory system. Curr. Biol. 15, 1535–1547 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Riabinina, O. et al. Organization of olfactory centres in the malaria mosquito Anopheles gambiae. Nat. Commun. 7, 13010 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Feinstein, P., Bozza, T., Rodriguez, I., Vassalli, A. & Mombaerts, P. Axon guidance of mouse olfactory sensory neurons by odorant receptors and the beta2 adrenergic receptor. Cell 117, 833–846 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Nakashima, A. et al. Agonist-independent GPCR activity regulates anterior-posterior targeting of olfactory sensory neurons. Cell 154, 1314–1325 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Feinstein, P. & Mombaerts, P. A contextual model for axonal sorting into glomeruli in the mouse olfactory system. Cell 117, 817–831 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Johnson, B. A. & Leon, M. Chemotopic odorant coding in a mammalian olfactory system. J. Comp. Neurol. 503, 1–34 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Prince, J. E., Brignall, A. C., Cutforth, T., Shen, K. & Cloutier, J. F. Kirrel3 is required for the coalescence of vomeronasal sensory neuron axons into glomeruli and for male–male aggression. Development 140, 2398–2408 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cho, J. H., Prince, J. E., Cutforth, T. & Cloutier, J. F. The pattern of glomerular map formation defines responsiveness to aversive odorants in mice. J. Neurosci. 31, 7920–7926 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Serizawa, S. et al. A neuronal identity code for the odorant receptor-specific and activity-dependent axon sorting. Cell 127, 1057–1069 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Imai, T., Suzuki, M. & Sakano, H. Odorant receptor-derived cAMP signals direct axonal targeting. Science 314, 657–661 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Cho, J. H., Lepine, M., Andrews, W., Parnavelas, J. & Cloutier, J. F. Requirement for Slit-1 and Robo-2 in zonal segregation of olfactory sensory neuron axons in the main olfactory bulb. J. Neurosci. 27, 9094–9104 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Takeuchi, H. et al. Sequential arrival and graded secretion of Sema3F by olfactory neuron axons specify map topography at the bulb. Cell 141, 1056–1067 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Sosulski, D. L., Bloom, M. L., Cutforth, T., Axel, R. & Datta, S. R. Distinct representations of olfactory information in different cortical centres. Nature 472, 213–216 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shykind, B. M. et al. Gene switching and the stability of odorant receptor gene choice. Cell 117, 801–815 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Graziadei, G. A., Stanley, R. S. & Graziadei, P. P. The olfactory marker protein in the olfactory system of the mouse during development. Neuroscience 5, 1239–1252 (1980).

    Article  CAS  PubMed  Google Scholar 

  42. Verhaagen, J., Oestreicher, A. B., Gispen, W. H. & Margolis, F. L. The expression of the growth associated protein B50/GAP43 in the olfactory system of neonatal and adult rats. J. Neurosci. 9, 683–691 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sautter, A., Zong, X., Hofmann, F. & Biel, M. An isoform of the rod photoreceptor cyclic nucleotide-gated channel beta subunit expressed in olfactory neurons. Proc. Natl Acad. Sci. USA 95, 4696–4701 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fuss, S. H., Omura, M. & Mombaerts, P. Local and cis effects of the H element on expression of odorant receptor genes in mouse. Cell 130, 373–384 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Lyons, D. B. et al. An epigenetic trap stabilizes singular olfactory receptor expression. Cell 154, 325–336 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Khan, M., Vaes, E. & Mombaerts, P. Regulation of the probability of mouse odorant receptor gene choice. Cell 147, 907–921 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. McInnes, L., Healy J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  48. Cutforth, T. et al. Axonal ephrin-As and odorant receptors: coordinate determination of the olfactory sensory map. Cell 114, 311–322 (2003).

    Article  CAS  PubMed  Google Scholar 

  49. McLaughlin, C. N. et al. Single-cell transcriptomes of developing and adult olfactory receptor neurons in Drosophila. eLife 10, e63856 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Godfrey, P. A., Malnic, B. & Buck, L. B. The mouse olfactory receptor gene family. Proc. Natl Acad. Sci. USA 101, 2156–2161 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Strotmann, J. et al. Small subfamily of olfactory receptor genes: structural features, expression pattern and genomic organization. Gene 236, 281–291 (1999).

    Article  CAS  PubMed  Google Scholar 

  52. Tsuboi, A. et al. Olfactory neurons expressing closely linked and homologous odorant receptor genes tend to project their axons to neighboring glomeruli on the olfactory bulb. J. Neurosci. 19, 8409–8418 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  54. Takahashi, H., Yoshihara, S., Nishizumi, H. & Tsuboi, A. Neuropilin-2 is required for the proper targeting of ventral glomeruli in the mouse olfactory bulb. Mol. Cell Neurosci. 44, 233–245 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Chae, H. et al. Mosaic representations of odors in the input and output layers of the mouse olfactory bulb. Nat. Neurosci. 22, 1306–1317 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bozza, T. et al. Mapping of class I and class II odorant receptors to glomerular domains by two distinct types of olfactory sensory neurons in the mouse. Neuron 61, 220–233 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Imai, T. et al. Pre-target axon sorting establishes the neural map topography. Science 325, 585–590 (2009).

    Article  CAS  PubMed  Google Scholar 

  58. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Schwarting, G. A. et al. Semaphorin 3A is required for guidance of olfactory axons in mice. J. Neurosci. 20, 7691–7697 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. James, G., Key, B. & Beverdam, A. The E3 ubiquitin ligase Mycbp2 genetically interacts with Robo2 to modulate axon guidance in the mouse olfactory system. Brain Struct. Funct. 219, 861–874 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Igarashi, K. M. & Mori, K. Spatial representation of hydrocarbon odorants in the ventrolateral zones of the rat olfactory bulb. J. Neurophysiol. 93, 1007–1019 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Nozawa, M., Kawahara, Y. & Nei, M. Genomic drift and copy number variation of sensory receptor genes in humans. Proc. Natl Acad. Sci. USA 104, 20421–20426 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Yablonka, A., Sobel, N. & Haddad, R. Odorant similarity in the mouse olfactory bulb. Proc. Natl Acad. Sci. USA 109, E2916–E2917 (2012). E2918-2919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Yukselen, O., Turkyilmaz, O., Ozturk, A. R., Garber, M. & Kucukural, A. DolphinNext: a distributed data processing platform for high throughput genomics. BMC Genomics 21, 310 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinf. 12, 323 (2011).

    Article  CAS  Google Scholar 

  67. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Carlson, M. org.Mm.eg.db: Genome wide annotation for Mouse. R package version 3.8.2. (2019).

  70. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: Computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 e289 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  75. Xiao, N., Cao, D. S., Zhu, M. F. & Xu, Q. S. protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 31, 1857–1859 (2015).

    Article  CAS  PubMed  Google Scholar 

  76. Monahan, K. et al. Cooperative interactions enable singular olfactory receptor expression in mouse olfactory neurons. eLife 6, e28620 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Crasto, C., Marenco, L., Miller, P. & Shepherd, G. Olfactory receptor database: a metadata-driven automated population from sources of gene and protein sequences. Nucleic Acids Res. 30, 354–360 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    Article  CAS  PubMed  Google Scholar 

  79. Saraiva, L. R. et al. Hierarchical deconstruction of mouse olfactory sensory neurons: from whole mucosa to single-cell RNA-seq. Sci. Rep. 5, 18178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Srivatsan, S. R. et al. Embryo-scale single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  PubMed  Google Scholar 

  81. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  82. Hara, T. et al. Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma. Cancer Cell 39, 779–792.e11 (2021).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Davis, M. Lee, A. Byrne, J. Bikoff, J. Lieberman, T. Tsukahara, D. Brann, B. Datta and members of the Greer laboratory for helpful comments on the manuscript. We thank the Bio Imaging Group and Felipe Henriques for microscopy assistance and the Flow Cytometry Core for aid in running the FACS experiments. We thank S. Becker for assistance in performing MERFISH. We thank B. Datta and G. Barnea for providing mouse lines. P.L.G. is supported by fellowships from the Smith Family Foundation, the Searle Scholars Program, the Rita Allen Foundation, the Whitehall Foundation and by grants DP2 OD027719-01 and NIH 5 KL2 TR001455-04 from the National Institutes of Health (NIH). Z.W. and G.A. are partially supported by NIH grant HG009446. D.M.B is a Biogen Fellow of the Life Sciences Research Foundation and an Interdisciplinary Scholar of the Wu Tsai Neurosciences Institute at Stanford University. Production of Slideseq data was supported by NIH grant RF1MH124598 to E.Z.M. and F.C. D.P.S. and C.E.B. are supported by the Massachusetts Life Sciences Center (MLSC), and D.P.S. is supported by NIH/NIMH R01 MMH113743, and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Evan Murray, Greg Andrews, Hao-Ching Jiang, Sung Jin Park, Elisa Donnard.

Authors and Affiliations

  1. Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA

    I-Hao Wang, Hao-Ching Jiang, Sung Jin Park, Manuel Garber & Paul L. Greer

  2. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Evan Murray, Fei Chen & Evan Z. Macosko

  3. Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA

    Greg Andrews, Elisa Donnard, Manuel Garber & Zhiping Weng

  4. Department of Neurobiology and Brudnick Neuropsychiatric Research Institute, University of Massachusetts Medical School, Worcester, MA, USA

    Violeta Durán-Laforet, Travis E. Faust & Dorothy P. Schafer

  5. Department of Psychology, Stanford University, Palo Alto, CA, USA

    Daniel M. Bear

  6. Wu Tsai Neurosciences Institute, Stanford University, Palo Alto, CA, USA

    Daniel M. Bear

  7. Sanderson Center for Optical Imaging and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA

    Christina E. Baer

  8. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Fei Chen

  9. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Evan Z. Macosko

Authors
  1. I-Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evan Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Greg Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao-Ching Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sung Jin Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elisa Donnard
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Violeta Durán-Laforet
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel M. Bear
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Travis E. Faust
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Manuel Garber
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Christina E. Baer
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dorothy P. Schafer
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhiping Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Evan Z. Macosko
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Paul L. Greer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

I.-H.W. and P.L.G. designed the study and oversaw all data analysis and interpretation. I.-H.W., H.-C.J., S.J.P., and P.L.G. performed the OSN FACS-isolated RNAseq. I.-H.W. analyzed the RNAseq data. I.-H.W. and P.L.G. performed the scRNAseq. I.-H.W. and E.D. analyzed the scRNAseq data. G.A., I.-H.W., and D.M.B. performed the OR identity prediction analysis. E.M. performed the Slide-seqV2 experiments. I.-H.W. analyzed the Slide-seqV2 data. G.A. and I.-H.W. performed the glomerular position prediction analysis. I.-H.W. analyzed the reconstructed glomerular map. I.-H.W., H.-C.J., and S.J.P. performed the light-sheet microscopic imaging. V.D-L. performed the MERFISH. I.-H.W. and T.E.F. analyzed the MERFISH data. The work was supervised by M.G., C.E.B., D.P.S., Z.W., F.C., E.Z.M. and P.L.G, and I.-H.W. and P.L.G. wrote the manuscript.

Corresponding author

Correspondence to Paul L. Greer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Dinu Albeanu, Omer Ali Bayraktar, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 FACS isolation of specific OR-expressing OSNs using OR-IRES-GFP/mCherry knockin mice.

a, Scatter plots of FACS-isolated, dissociated olfactory epithelial cells from mice harboring the Olfr160-IRES-tauCherry (top), the Olfr73-IRES-GFP (middle), or the Olfr1507-IRES-GFP (bottom) alleles. The gate used to isolate ~100% pure populations of fluorescent OSNs is indicated. b, Mature OSN markers (for example, Omp and Cnga2) but not immature OSN markers (for example, Gap43) are detected in FACS isolated samples. c, Each OSN population selectively expresses its corresponding OR.

Extended Data Fig. 2 scRNAseq reveals diverse cell types in the main olfactory epithelium.

a, Schematic representation of the workflow used to perform scRNAseq on cellular populations isolated from the mouse main olfactory epithelium. b, The distribution of different cell types on the UMAP projection plots. HBC: horizontal basal cell; GBC: Globose basal cell; INP: Immediate neuronal precursor; iOSN: immature olfactory sensory neuron; OSN: Olfactory sensory neuron; MV: Microvillar cell; SUS: Sustentacular cell; BG: Bowman’s gland; OEC: Olfactory ensheathing cell; EC: Ependymal cell; MC: myeloid cell; MP: Macrophage; BC: B cell; TC: T cell; NP: Neutrophil; NKN: Unknown. c, Violin plots of the genes used to assign cell types. d, Dot plot of a subset of the differentially expressed genes between cell types. For each gene depicted, the size of the circle corresponds to the percentage of sequenced cells in which transcripts that map to that gene were detected, and the color reflects the average expression level of the gene within those cells.

Extended Data Fig. 3 Quality control analysis of OSN scRNAseq.

a, Histogram of the number of distinct OR expressed by each of the mature OSNs captured in scRNAseq experiments. b, Histogram of the number of UMIs detected by each of the single OR expressing OSNs captured in scRNAseq experiments, the cells with 15000 > UMI > 2600 were used for the future analyses (blue area). c, Plot of the number of cells sequenced that express each distinct OR, the OR transcript detected by Slide-seqV2 are labeled. Data are plotted as mean + /- SD from six independent experiments. d, Schematic of each cluster of OR genes and their chromosomal location and how many cells were detected that express each of these ORs.

Extended Data Fig. 4 UMAP projections reveal distinct transcriptomes expressed by different types of OSN.

a, Normalized expression of the Acsm4, Nrp2, Plxna1, and Nrp1 genes on UMAP projection plots of OSN scRNAseq data. b, UMAP plots of OSNs were regenerated with all OR gene expression information removed. OSNs expressing each of ten random ORs selected in Fig.2a are pseudocolored in the UMAP plot, revealing that cells displaying the same OR cluster together even in the absence of OR gene expression information. c, UMAP plots were regenerated in a pairwise manner using each of the groups of OSNs expressing the ten most frequently occurring ORs with OR gene expression information removed prior to clustering. In each pairwise analysis all of the OSNs expressing one OR are colored red, and all of the OSNs expressing the other OR are colored blue to make it easier to visualize their separation in UMAP space. d, ‘Shuffled’ control for the pairwise OSN UMAP segregation analysis. The analysis was performed as in c, except OR identity was randomly assigned prior to analysis. e, Analysis of all of the differentially expressed genes identified in a pairwise manner for each of the 654 types of OSN. For each differentially expressed gene, we determined for each of the 654 types of OSN in how many of those types of OSN is that gene detected in at least 10% of the sequenced cells.

Extended Data Fig. 5 Pairwise analysis reveals OSN features that correlate with transcriptomic similarity.

a, Analysis of the ability to predict OR identity using different GO biological pathway term gene sets. The boxplots depict the bootstrapped data from 100 iterations of the prediction for each GO term, The top 30 terms are ordered from the highest mean accuracy to the lowest. In addition to the GO terms found in the axon guidance biological category, we hand curated a list of axon guidance genes and adhesion molecules (Supplementary Table 2), removing transcription factors and odorant receptor genes and included this refined set of axon guidance/adhesion molecule genes in the analysis. b, Analysis of the ability to predict OR identity using different GO molecular pathway term gene sets. The boxplots depict the bootstrapped data from 100 iterations of the prediction for each GO term, The top 30 terms are ordered from the highest mean accuracy to the lowest. c, Incorrectly predicted ORs are likely to possess similar protein sequence to the correct OR. The OR protein similarity scores of incorrectly predicted OR pairs from Fig.2d were calculated and binned (N = 100 independent tests). The boxplot depicts the likelihood of a given OR protein similarity score distribution as the fold change relative to random OR pairs. d, The transcriptomic correlation between a pair of OSNs positively correlates with the protein sequence similarity of the ORs they express. Transcriptomic correlation and OR protein sequence similarity of all the pairwise combination of 654 types of OSN were calculated and binned. The plot depicts the mean + /- SEM for each protein similarity score bin. e, Heat map of each set of OSNs expressing a specific OR reveals high transcriptional correlation (upper triangle) and OR protein sequence similarity (lower triangle) between OSNs expressing ORs located within the same genomic cluster. OSNs are ordered by genomic location and the color bar indicates the genomic cluster. Boxplots in this figure represent Q1-1.5*IQR, Q1, median, Q3, and Q3 + 1.5*IQR, data beyond the whisker were plotted as individual dots.

Extended Data Fig. 6 Slide-seqV2 enables high quality spatial transcriptomic analysis of the mouse OB.

a, Plot of the number of genes (left panel) or UMIs (right panel) detected per bead on each slide of the Slide-seqV2 experiments (N ranges from 30041 to 56016, the exact number of beads on each individual slide is provided in Supplementary data 1), Boxplots indicate Q1-1.5*IQR, Q1, median, Q3, and Q3 + 1.5 *IQR; data beyond the whisker were plotted as individual dots. b, Mitochondrial transcript levels as a marker to define layer organization in the OB. Scale bar: 500 μm. c, Schematic of automated calling of glomerular positions in Slide-seqV2 experiments. d, Slide-seqV2 identified glomerular positions match glomerular positions that have been empirically determined. Scale bar: 500 μm. e, The glomerular positions of 14 ORs detected in two replicates of 20 Slide-seqV2 experiments exhibit high spatial correlation. 13 of the 14 glomerular positions identified in two, independent biological replicates were used as the anchors to align the two OBs. The held-out glomerulus was then transformed based on the anchors and the position was recorded. This process was iterated for all 14 glomeruli. The correlation between the position of the held-out glomeruli on the A-P and D-V axes across both replicates is shown. Data were fitted with a regression line (blue), +/- 95% CI (light blue).

Extended Data Fig. 7 Validation of the predicted glomerular map.

a, Light-sheet microscopic images of intrinsic fluorescent signals from the OBs of three independent mice harboring the Olfr73-IRES-GFP, Olfr160-IRES-tauCherry, or the Olfr1507-IRES-GFP alleles. Similar results were observed from each mouse line with at least 3 biological repeats per line. Scale bar: 500 μm. b, Reconstruction of the signals detected in a. The distance between the centroid positions of the same glomerulus detected in independent biological replicates is depicted. c, Example signal captured by MERFISH for each of the ORs depicted. Each spot represents an individual mRNA molecule that was detected by MERFISH in these experiments. From the three sections analyzed, OR signal was reliably detected in a total of 11 glomeruli, which is approximately the number expected given that ~300 OR probes were used in these experiments and the fact that sectioning the entire olfactory bulb at this thickness would result in ~300 sections. Scale bar: 500 μm.

Extended Data Fig. 8 An axon guidance gene expression program strongly correlates with glomerular positioning.

a, GO analysis of the genes that most contribute to the ability of the model to predict glomerular position. The top ten GO terms in both the dorsal–ventral and anterior–posterior axes are shown. b, Scaled expression of axon guidance genes obtained from the scRNAseq data for each of the OSNs that form glomeruli in our model.

Extended Data Fig. 9 OSN types associated with the same genomic cluster and their distribution on the predicted glomeruli map.

OSNs expressing ORs from the same genomic cluster form glomeruli in nearby locations within the OB.

Extended Data Fig. 10 Correlation between glomerular location and odor detecting properties.

a, Only glomeruli formed by OSNs expressing class II ORs were used for this analysis (N = 601). The data are presented as the mean with +/− bootstrapped 95% CI. Data were fitted with an exponential model (blue). OR proteins share substantial similarity with one another to facilitate common signaling pathways. The majority of the differences plotted here occur in the ligand binding regions of the ORs. b, The distribution of the glomeruli responding to the indicated ligands on the predicted glomerular map. The data presented in Fig. 4h are excluded here. c, Dendrogram of the OSN transcriptome. The coordinate of the predict glomerular positions on the A-P and D-V axis and the chemical responses to fatty acid odorants and ketones in Fig. 4h are indicated d, Volcano plot of the differentially expressed genes between OSNs that respond to the fatty acids and ketones indicated in Fig. 4h, the labeled genes are genes involved in lipid metabolism, two-sided likelihood-ratio test, Bonferroni correction for multiple comparisons.

Supplementary information

Supplementary Information

Supplementary Data 1.

Reporting Summary

Supplementary Tables

Supplementary Table 1. The differentially expressed genes between OSNs expressing Olfr73, Olfr160 or Olfr1507. Supplementary Table 2. The list of axonal guidance genes used in the OSN transcriptome identity prediction analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, IH., Murray, E., Andrews, G. et al. Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing. Nat Neurosci 25, 484–492 (2022). https://doi.org/10.1038/s41593-022-01030-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01030-8

This article is cited by

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