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Three-dimensional genome structures of single sensory neurons in mouse visual and olfactory systems

Nature Structural & Molecular Biologyvolume 26pages297307 (2019) | Download Citation

Abstract

Sensory neurons in the mouse eye and nose have unusual chromatin organization. Here we report their three-dimensional (3D) genome structure at 20-kilobase (kb) resolution, achieved by applying our recently developed diploid chromatin conformation capture (Dip-C) method to 409 single cells from the retina and the main olfactory epithelium of adult and newborn mice. The 3D genome of rod photoreceptors exhibited inverted radial distribution of euchromatin and heterochromatin compared with that of other cell types, whose nuclear periphery is mainly heterochromatin. Such genome-wide inversion is not observed in olfactory sensory neurons (OSNs). However, OSNs exhibited an interior bias for olfactory receptor (OR) genes and enhancers, in clear contrast to non-neuronal cells. Each OSN harbored multiple aggregates of OR genes and enhancers from different chromosomes. We also observed structural heterogeneity of the protocadherin gene cluster. This type of genome organization may provide the structural basis of the ‘one-neuron, one-receptor’ rule of olfaction.

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Data availability

Raw and processed data are deposited with GEO Series accession code GSE121791. All other data are available from the corresponding author upon reasonable request.

Code availability

Codes are available on GitHub as updates to the existing ‘dip-c’ and ‘hickit’ packages24: https://github.com/tanlongzhi/dip-c and https://github.com/lh3/hickit

Additional information

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

References

  1. 1.

    Solovei, I. et al. Nuclear architecture of rod photoreceptor cells adapts to vision in mammalian evolution. Cell 137, 356–368 (2009).

  2. 2.

    Falk, M. et al. Heterochromatin drives organization of conventional and inverted nuclei. Preprint at bioRxiv https://doi.org/10.1101/244038(2018).

  3. 3.

    Al Diri, I. et al. The nucleome of developing murine rod photoreceptors. Preprint at bioRxiv https://doi.org/10.1101/369702(2018).

  4. 4.

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

  5. 5.

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

  6. 6.

    Tan, L., Li, Q. & Xie, X. S. Olfactory sensory neurons transiently express multiple olfactory receptors during development. Mol. Syst. Biol. 11, 844 (2015).

  7. 7.

    Hanchate, N. K. et al. Single-cell transcriptomics reveals receptor transformations during olfactory neurogenesis. Science 350, 1251–1255 (2015).

  8. 8.

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

  9. 9.

    Clowney, E. J. et al. Nuclear aggregation of olfactory receptor genes governs their monogenic expression. Cell 151, 724–737 (2012).

  10. 10.

    Lomvardas, S. et al. Interchromosomal interactions and olfactory receptor choice. Cell 126, 403–413 (2006).

  11. 11.

    Markenscoff-Papadimitriou, E. et al. Enhancer interaction networks as a means for singular olfactory receptor expression. Cell 159, 543–557 (2014).

  12. 12.

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

  13. 13.

    Tian, X. J., Zhang, H., Sannerud, J. & Xing, J. Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design. Proc. Natl Acad. Sci. USA 113, E2889–E2898 (2016).

  14. 14.

    Serizawa, S. et al. Negative feedback regulation ensures the one receptor-one olfactory neuron rule in mouse. Science 302, 2088–2094 (2003).

  15. 15.

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

  16. 16.

    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).

  17. 17.

    Mountoufaris, G. et al. Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. Science 356, 411–414 (2017).

  18. 18.

    Guo, Y. et al. CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice. Proc. Natl Acad. Sci. USA 109, 21081–21086 (2012).

  19. 19.

    Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

  20. 20.

    Yokota, S. et al. Identification of the cluster control region for the protocadherin-β genes located beyond the protocadherin-γ cluster. J. Biol. Chem. 286, 31885–31895 (2011).

  21. 21.

    Ribich, S., Tasic, B. & Maniatis, T. Identification of long-range regulatory elements in the protocadherin-α gene cluster. Proc. Natl Acad. Sci. USA 103, 19719–19724 (2006).

  22. 22.

    Jiang, Y. et al. The methyltransferase SETDB1 regulates a large neuron-specific topological chromatin domain. Nat. Genet. 49, 1239–1250 (2017).

  23. 23.

    Canzio, D. et al. Antisense lncRNA transcription drives stochastic Protocadherin α promoter choice. Preprint at bioRxiv https://doi.org/10.1101/360206(2018).

  24. 24.

    Tan, L., Xing, D., Chang, C. H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).

  25. 25.

    Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

  26. 26.

    Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

  27. 27.

    Blackshaw, S., Fraioli, R. E., Furukawa, T. & Cepko, C. L. Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes. Cell 107, 579–589 (2001).

  28. 28.

    Magklara, A. et al. An epigenetic signature for monoallelic olfactory receptor expression. Cell 145, 555–570 (2011).

  29. 29.

    Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

  30. 30.

    Le Gros, M. A. et al. Soft X-ray tomography reveals gradual chromatin compaction and reorganization during neurogenesis in vivo. Cell Rep. 17, 2125–2136 (2016).

  31. 31.

    Yoon, K. H. et al. Olfactory receptor genes expressed in distinct lineages are sequestered in different nuclear compartments. Proc. Natl Acad. Sci. USA 112, E2403–E2409 (2015).

  32. 32.

    Armelin-Correa, L. M., Gutiyama, L. M., Brandt, D. Y. & Malnic, B. Nuclear compartmentalization of odorant receptor genes. Proc. Natl Acad. Sci. USA 111, 2782–2787 (2014).

  33. 33.

    Solovei, I. et al. LBR and lamin A/C sequentially tether peripheral heterochromatin and inversely regulate differentiation. Cell 152, 584–598 (2013).

  34. 34.

    Tan, L. & Xie, X. S. A near-complete spatial map of olfactory receptors in the mouse main olfactory epithelium. Chem. Senses 43, 427–432 (2018).

  35. 35.

    Maass, P. G., Barutcu, A. R. & Rinn, J. L. Interchromosomal interactions: a genomic love story of kissing chromosomes. J. Cell Biol. 218, 27–38 (2019).

  36. 36.

    Sefer, E., Duggal, G. & Kingsford, C. Deconvolution of ensemble chromatin interaction data reveals the latent mixing structures in cell subpopulations. J. Comput. Biol. 23, 425–438 (2016).

  37. 37.

    Chen, C. et al. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science 356, 189–194 (2017).

  38. 38.

    Robinson, J. T. et al. Juicebox.js provides a cloud-based visualization system for Hi-C data. Cell Syst. 6, 256–258 e1 (2018).

  39. 39.

    Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

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Acknowledgements

The authors thank X. Jin for advice on mouse breeding, the Bauer Core Facility at Harvard University—in particular Z. Niziolek—for flow sorting, H. Li for updating the ‘hickit’ package and helpful discussions, A. Chapman for advice on handling 96-well plates, and S. Lomvardas and R. Cao for helpful discussions. This work was supported by Beijing Advanced Innovation Center for Genomics at Peking University, and a generous gift grant from Xianhong Wu to Harvard University.

Author information

Author notes

  1. These authors contributed equally: Longzhi Tan, Dong Xing.

Affiliations

  1. Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA, USA

    • Longzhi Tan
    • , Dong Xing
    • , Nicholas Daley
    •  & X. Sunney Xie
  2. Belmont Hill School, Belmont, MA, USA

    • Nicholas Daley
  3. Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China

    • X. Sunney Xie
  4. Biomedical Pioneering Innovation Center, Peking University, Beijing, China

    • X. Sunney Xie

Authors

  1. Search for Longzhi Tan in:

  2. Search for Dong Xing in:

  3. Search for Nicholas Daley in:

  4. Search for X. Sunney Xie in:

Contributions

L.T., D.X., N.D., and X.S.X. designed the experiments. L.T., D.X., and N.D. performed the experiments. L.T. and N.D. analyzed the data. L.T. and X.S.X. wrote the manuscript.

Competing interests

L.T., D.X., and X.S.X. are inventors on the patent WO2018217912A1 filed by President and Fellows of Harvard College that covers META and Dip-C.

Corresponding author

Correspondence to X. Sunney Xie.

Integrated supplementary information

  1. Supplementary Figure 1 Representative single-cell contact maps.

    a, Histogram of the numbers of chromatin contacts per cell. The bin size is 10 k. Note that the number of contacts is affected by the method of whole-genome amplification (META is roughly two times homemade Nextera), the cell type, and in some cases, recent lots of Qiagen protease that may damage DNA. b, Contact maps from 4 single cells of different types. Cells are chosen randomly.

  2. Supplementary Figure 2 Representative single-cell contact maps after haplotype imputation.

    Similar to Supplementary Fig. 1, but after resolving the two haplotypes of each chromosome. Note that imputation is inefficient for chromosome X in Cell 136, because SNPs are sparse on chromosome X in the mouse strain B6D2F1/J.

  3. Supplementary Figure 3 Additional information about principle component analysis (PCA) of single-cell chromatin compartment values.

    a, Percentage of explained variance for the first 35 principal components (PCs). The fact that the top few PCs only explained a relatively small fraction of the total variance is consistent with previous observations that chromatin compartment is intrinsically heterogeneous even within the same cell type (Science. 43, 924–928, 2018; Nature. 547, 61–67, 2017). b, PCA is not qualitatively affected by down-sampling contacts, excluding cells, and changing the bin size. Similar to Fig. 1b, but down-sampled to 20 k (left) or 100 k (right) contacts per cell before performing PCA. In the right panels, cells with < 100 k contacts (60 out of 409, or 15%) were excluded. In the bottom right panel, compartment values were calculated per 100-kb in rather than 1-Mb.

  4. Supplementary Figure 4 Average compartment values near cell-type-specific genes.

    In Fig. 1d, the average compartment value was calculated from the 1-Mb bins that contain midpoints of cell-type-specific genes. Here each 1-Mb bin was shifted a certain amount (x axis), upstream or downstream, from gene midpoints. The median values of 5 cell types are shown.

  5. Supplementary Figure 5 Cross sections of all rods and retinal precursors, colored by CpG frequency.

    Similar to Fig. 2a, but for all rods and retinal precursors.

  6. Supplementary Figure 6 Large-scale domains of radial positioning are consistent with large-scale patterns in bulk Hi-C.

    a, Similar to Fig. 3c top (mature OSNs), but with intrachromosomal contact maps from bulk Hi-C data (red heatmap; normalization: balanced). b, c, Zoom-in views of two chromosomes.

  7. Supplementary Figure 7 Quantification of OR-OR contact strengths is not affected if ORs near enhancers are excluded.

    Similar to Fig. 4a and Fig. 4b but excluding ORs within 200 kb or 500 kb of any enhancers.

  8. Supplementary Figure 8 Stochastic aggregation of a subset of ORs and enhancers in each mature OSN and unknown MOE cell.

    a, The number of enhancers (left), the number of enhancers from other chromosomes (middle), and the number of chromosomes of enhancers (right) within 2.5 particle radii (~150 nm) of each enhancer. In each heat map, rows and columns are sorted by their average values. b, Similar to a but near each OR. c, d, Similar to a and b (on the same scales) for non-neuronal MOE cells.

  9. Supplementary Figure 9 Most quantification of OR-OR and enhancer-enhancer aggregation is not affected by the distance threshold.

    Similar to Fig. 5a and Fig. 5c, but with a distance threshold of 1.5 (~100 nm; left), 5.0 (~300 nm; middle), or 10.0 (~600 nm; right) particle radii instead of 2.5 particle radii (~150 nm).

  10. Supplementary Figure 10 Interchromosomal interactions of the clustered protocadherin locus.

    a, Probability for each 200-kb bin along the genome to be within 7.5 particle radii (~450 nm) from the centroid (chromosome 18: 37,431,420 bp) of clustered protocadherin promoters. For each bin, 3D distances from the two parental alleles were considered as two separate data points. b, Zoom-in views of two chromosomes.

Supplementary information

  1. Supplementary Figures, Supplementary Table and Supplementary Notes

    Supplementary Figures 1–10, Supplementary Table 1 and Supplementary Notes 1–3

  2. Reporting Summary

  3. Supplementary Dataset 1

    Information about each single cell

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https://doi.org/10.1038/s41594-019-0205-2