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Single-cell analysis of olfactory neurogenesis and differentiation in adult humans

Nature Neuroscience volume 23, pages 323–326 (2020)Cite this article

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Abstract

The presence of active neurogenic niches in adult humans is controversial. We focused attention to the human olfactory neuroepithelium, an extracranial site supplying input to the olfactory bulbs of the brain. Using single-cell RNA sequencing analyzing 28,726 cells, we identified neural stem cell and neural progenitor cell pools and neurons. Additionally, we detailed the expression of 140 olfactory receptors. These data from the olfactory neuroepithelium niche provide evidence that neuron production may continue for decades in humans.

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Fig. 1: Aggregate analysis of 28,726 single cells from human olfactory cleft mucosa.
Fig. 2: Gene expression analysis of human olfactory neuroepithelium.
Fig. 3: Analysis of olfactory receptor expression in human olfactory epithelium.

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Data availability

All sequencing data generated have been deposited with the Gene Expression Omnibus under accession code GSE139522. Source data are provided for Figs. 2 and 3 and Extended Data Figs. 1 and 7.

Code availability

The code used for the olfactory receptor analysis is available at https://github.com/harbourlab/OR_SC_analysis.

References

  1. Hinds, J. W., Hinds, P. L. & McNelly, N. A. An autoradiographic study of the mouse olfactory epithelium: evidence for long-lived receptors. Anat. Rec. 210, 375–383 (1984).

    Article  CAS  Google Scholar 

  2. Schwob, J. E., Szumowski, K. E. & Stasky, A. A. Olfactory sensory neurons are trophically dependent on the olfactory bulb for their prolonged survival. J. Neurosci. 12, 3896–3919 (1992).

    Article  CAS  Google Scholar 

  3. Leung, C. T., Coulombe, P. A. & Reed, R. R. Contribution of olfactory neural stem cells to tissue maintenance and regeneration. Nat. Neurosci. 10, 720–726 (2007).

    Article  CAS  Google Scholar 

  4. Goldstein, B. J. et al. Adult c-Kit+ progenitor cells are necessary for maintenance and regeneration of olfactory neurons. J. Comp. Neurol. 523, 15–31 (2015).

    Article  CAS  Google Scholar 

  5. Fletcher, R. B. et al. Deconstructing olfactory stem cell trajectories at single-cell resolution. Cell Stem Cell 20, 817–830.e8 (2017).

    Article  CAS  Google Scholar 

  6. Brann, J. H. & Firestein, S. J. A lifetime of neurogenesis in the olfactory system. Front. Neurosci. 8, 182 (2014).

    Article  Google Scholar 

  7. Hahn, C. G. et al. In vivo and in vitro neurogenesis in human olfactory epithelium. J. Comp. Neurol. 483, 154–163 (2005).

    Article  Google Scholar 

  8. Holbrook, E. H., Wu, E., Curry, W. T., Lin, D. T. & Schwob, J. E. Immunohistochemical characterization of human olfactory tissue. Laryngoscope 121, 1687–1701 (2011).

    Article  CAS  Google Scholar 

  9. Graziadei, P. P., Karlan, M. S., Graziadei, G. A. & Bernstein, J. J. Neurogenesis of sensory neurons in the primate olfactory system after section of the fila olfactoria. Brain Res. 186, 289–300 (1980).

    Article  CAS  Google Scholar 

  10. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  11. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  12. Corada, M. et al. Sox17 is indispensable for acquisition and maintenance of arterial identity. Nat. Commun. 4, 2609 (2013).

    Article  Google Scholar 

  13. Chen, M., Reed, R. R. & Lane, A. P. Acute inflammation regulates neuroregeneration through the NF-κB pathway in olfactory epithelium. Proc. Natl Acad. Sci. USA 114, 8089–8094 (2017).

    Article  CAS  Google Scholar 

  14. Schwob, J. E., Youngentob, S. L. & Mezza, R. C. Reconstitution of the rat olfactory epithelium after methyl bromide-induced lesion. J. Comp. Neurol. 359, 15–37 (1995).

    Article  CAS  Google Scholar 

  15. Monahan, K., Horta, A. & Lomvardas, S. LHX2- and LDB1-mediated trans interactions regulate olfactory receptor choice. Nature 565, 448–453 (2019).

    Article  CAS  Google Scholar 

  16. Hirota, J. & Mombaerts, P. The LIM-homeodomain protein Lhx2 is required for complete development of mouse olfactory sensory neurons. Proc. Natl Acad. Sci. USA 101, 8751–8755 (2004).

    Article  CAS  Google Scholar 

  17. Wallrabenstein, I. et al. The smelling of Hedione results in sex-differentiated human brain activity. Neuroimage 113, 365–373 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  21. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  Google Scholar 

  22. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  Google Scholar 

  23. McInnes, L., Healy, J., Saul, N. & Grossberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  24. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Article  Google Scholar 

  25. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  Google Scholar 

  26. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  Google Scholar 

  27. Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2019).

  28. Ligtenberg, W. reactome.db: a set of annotation maps for reactome. R package version 1.68.0 http://bioconductor.org/packages/release/data/annotation/html/reactome.db.html (2019).

  29. Fabregat, A. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    Article  CAS  Google Scholar 

  30. Goldstein, B. J. et al. Contribution of Polycomb group proteins to olfactory basal stem cell self-renewal in a novel c-KIT+ culture model and in vivo. Development 143, 4394–4404 (2016).

    Article  CAS  Google Scholar 

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Acknowledgements

We are grateful to the patients who generously contributed samples for this research. We acknowledge the assistance of the Onco-Genomics Shared Resource at the Sylvester Comprehensive Cancer Center and the University of Miami Center for Computational Science. This work was supported by funding from National Institutes of Health grant nos. DC013556 (B.J.G), DC016859 (B.J.G) and CA125970 (J.W.H.), the Triological Society/American College of Surgeons (B.J.G.), the University of Miami Sheila and David Fuente Graduate Program in Cancer Biology (M.A.D.) and the Center for Computational Science Fellowship (M.A.D.). The Sylvester Comprehensive Cancer Center also received funding from the National Cancer Institute Core Support Grant no. P30CA240139.

Author information

Author notes

  1. Bradley J. Goldstein

    Present address: Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, NC, USA

  2. These authors contributed equally: Michael A. Durante, Stefan Kurtenbach.

Authors and Affiliations

  1. Medical Scientist Training Program, University of Miami Miller School of Medicine, Miami, FL, USA

    Michael A. Durante & Rhea Choi

  2. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA

    Michael A. Durante, Stefan Kurtenbach, J. William Harbour, Rhea Choi, Sarah Kurtenbach, Garrett M. Goss & Bradley J. Goldstein

  3. Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA

    Michael A. Durante, Stefan Kurtenbach & J. William Harbour

  4. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA

    Michael A. Durante, Zoukaa B. Sargi & J. William Harbour

  5. Department of Otolaryngology, University of Miami Miller School of Medicine, Miami, FL, USA

    Zoukaa B. Sargi, Sarah Kurtenbach, Garrett M. Goss & Bradley J. Goldstein

  6. Graduate Program in Neuroscience, University of Miami Miller School of Medicine, Miami, FL, USA

    Rhea Choi & Bradley J. Goldstein

  7. Department of Molecular Genetics and Microbiology, Duke Institute for Brain Sciences, Duke University Medical Center, Durham, NC, USA

    Hiroaki Matsunami

  8. Department of Neurobiology, Duke Institute for Brain Sciences, Duke University Medical Center, Durham, NC, USA

    Hiroaki Matsunami

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  1. Michael A. Durante
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Contributions

M.A.D. and Stefan Kurtenbach analyzed and interpreted the data and wrote the manuscript. B.J.G. designed and led the project, performed the experiments, interpreted the data and wrote the manuscript. Z.B.S. provided the clinical samples and interpreted the data. J.W.H. designed the experiments and interpreted the data. R.C., G.M.G and Sarah Kurtenbach performed the experiments and interpreted the data. H.M. interpreted the data and edited the manuscript.

Corresponding author

Correspondence to Bradley J. Goldstein.

Ethics declarations

Competing interests

J.W.H. is the inventor of intellectual property related to prognostic testing for uveal melanoma. He is a paid consultant for Castle Biosciences, a licensee of this intellectual property and receives royalties from its commercialization. The other authors do not have any potential competing interests.

Additional information

Peer review information Nature Neuroscience thanks Thomas Berger, Sandrine Thuret and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 DotPlot visualization listing scRNA-seq clusters.

a, Cell phenotypes listed on y-axis, showing unbiased gene expression for the top 8 genes per cluster identified by log Fold Change; genes (features) are listed along the x-axis. Dot size reflects percentage of cells in a cluster expressing each gene; dot color reflects expression level (as indicated on legend). The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. b, DotPlot visualization of the Heatmap shown in Fig. 1d. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. c, Histogram showing the neuronal lineage cell types captured by scRNA-seq, as a percentage of the total cells analyzed per patient; see Fig. 1c.

Source data

Extended Data Fig. 2 Additional human immunohistochemistry of basal cell populations.

Co-staining for SOX2, Ki67 or the HBC marker TP63. Proliferative activity has been used as a hallmark of the globose basal cell (GBC) phenotype. We reasoned that, although some proliferating cells in the olfactory epithelium (OE) might be immune or inflammatory cells, proliferative cells within the GBC layers of the OE that are SOX2+/Ki67+/TP63- would be categorized as GBCs. a, Sustentacular cell nuclei at the top of the OE are SOX2-bright; and horizontal basal cells (HBCs) and a subset of GBCs are SOX2+, although less intensely. Arrow marks a SOX2+/KI67+ cell among the proliferative KI67+ basal region, consistent with the GBC phenotype. b, SOX2 co-localizes with TP63 in HBCs; arrows mark Sox2+/TP63- GBCs. c, TP63 + HBCs are mitotically quiescent, while many GBCs are actively proliferating, often in scattered cell clusters. Arrow marks a cluster of KI67+/TP63- basal cells. Dashed line indicates basal lamina. Immunostaining for a-c was conducted in triplicate with similar results. Scale bar, 50 µm.

Extended Data Fig. 3 Additional human immunohistochemistry (IHC) of immature and mature olfactory sensory neurons (OSN) populations.

a, Co-staining for LHX2 and OMP demonstrates many LHX2+/OMP- neurons, distributed in deeper layers of the OE, which are immature OSNs; OMP is a marker for fully differentiated OSNs, while LHX2 expression in differentiating OSNs orchestrates OR receptor expression. b, DCX was identified by scRNA-seq here as enriched in immature OSNs (see Fig. 2a). IHC confirms scattered DCX+ neuronal somata and dendrites in the OE. c, Similarly, the bHLH transcription factor OLIG2 was identified to be enriched in immature OSNs; IHC confirms nuclear expression in the deeper OSN layers of OE tissue. Dashed line indicates basal lamina. Immunostaining for a–c was conducted in triplicate with similar results. Scale bar, 25 µm.

Extended Data Fig. 4 Analysis of immune cell populations.

Feature plots indicate expression of inflammatory cell markers in human nasal biopsy samples. UMAP clustering identifies lymphocyte populations; a, Cytotoxic T cell markers co-localize including CD8A, PRF1, GZMA and GZMB. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. b, Within the monocyte/macrophage populations (CD14+, CD68+ cells), markers for activated M2 macrophages, such as CD163 and IL10, are indicated. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. c, DotPlot visualization of additional immune cell gene expression from combined aggregate samples. Cell cluster identity is listed on the y-axis, genes (Features) are listed on x-axis. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. d, Immune cell DotPlots showing the contribution of cell types and gene expression patterns by individual patient sample. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients.

Extended Data Fig. 5 Focused UMAP plot of OE neuronal lineage populations, with cell phenotype assignments indicated.

Compare with gene expression feature plots in Figs. 1e and 2f. The plot depicts clusters from 694 GBCs, immature olfactory neurons and mature olfactory neurons, n = 4 patients.

Extended Data Fig. 6 Gene set enrichment analysis on differential expression data from selected cell clusters.

The top 50 Reactome pathways ranked by adjusted P value were plotted in the visualization. a, mOSNs versus GBCs; many top terms involve neuronal, transduction and synapse function. The differential expression was calculated in Seurat from 222 mOSNs and 115 GBCs, n = 4 patients. The default two-sided enrichment p-value with Benjamini–Hochberg correction from the fgsea package was utilized. b, GBCs versus olfactory HBCs; top terms include cell cycle or neurogenesis functions. The differential expression was calculated in Seurat from 115 GBCs and 2,182 olfactory HBCs, n = 4 patients. The default two-sided enrichment p-value with Benjamini–Hochberg correction from the fgsea package was utilized.

Extended Data Fig. 7 OR gene expression in human olfactory neurons.

a, Range of gene expression in our datasets (binned to 0.01). We identified 4.80 × 107 observations (gene expression measurements) expressing > 0. Genes with no expression ( = 0, n = 532063621) were excluded in this plot. The distribution plot shows that choosing a cutoff of 0.5 (red vertical dotted line). b, Doublet analysis. Box plots depicting the number of UMIs (“nCount_RNA”, left plot), and genes (“nFeature_RNA”, right plot) in immature and mature neurons expressing 1 or 2 ORs.

Source data

Extended Data Fig. 8 Principal component determination analysis.

The 4-patient combined data set was analyzed in Seurat to explore principal components (PCs) contributing to heterogeneity, and to determine an appropriate PC selection. a, Using the JackStraw approach, approximately 100 PCs had low a p-value. The plot depicts PC calculated from from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. The jackstraw test implemented in Seurat was used to calculate p-values of PCs. b, To select a suitable number of PCs for downstream analysis, we used the elbow plot heuristic approach, indicating that beyond 20–30 PCs, very little additional variation is explained. Therefore, for downstream analysis we chose to include 30 PCs.

Extended Data Fig. 9 scRNA-seq quality control plots.

a, Number of genes (nFeature_RNA) per cluster. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. b, Number of UMIs (nCount_RNA) per cluster. The plot depicts clusters from 28,726 combined olfactory and respiratory mucosal cells, n = 4 patients. Cluster cell type identities are listed along the x-axis. Violin plot widths are proportional to the density of the distribution.

Supplementary information

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Supplementary Tables 1 and 11.

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Supplementary Tables

Supplementary Tables 2–10.

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Source Data Fig. 2

Statistical Source Data

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 7

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Durante, M.A., Kurtenbach, S., Sargi, Z.B. et al. Single-cell analysis of olfactory neurogenesis and differentiation in adult humans. Nat Neurosci 23, 323–326 (2020). https://doi.org/10.1038/s41593-020-0587-9

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