Article | Published:

Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

Nature Biotechnology volume 36, pages 7080 (2018) | Download Citation


Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, as well as computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for the study of complex processes in the brain, such as genetic programs that coordinate adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, which provided insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

  • Subscribe to Nature Biotechnology for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus


  1. 1.

    et al. RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807 (2013).

  2. 2.

    et al. Div-Seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

  3. 3.

    et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

  4. 4.

    et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

  5. 5.

    et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

  6. 6.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  7. 7.

    et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  8. 8.

    et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

  9. 9.

    et al. Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay. Genome Biol. 17, 20 (2016).

  10. 10.

    et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

  11. 11.

    et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

  12. 12.

    et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

  13. 13.

    et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

  14. 14.

    et al. Genomic mosaicism with increased amyloid precursor protein (APP) gene copy number in single neurons from sporadic Alzheimer's disease brains. eLife 4, e05116 (2015).

  15. 15.

    et al. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013).

  16. 16.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  17. 17.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  18. 18.

    et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  19. 19.

    et al. Improved genome sequencing using an engineered transposase. BMC Biotechnol. 17, 6 (2017).

  20. 20.

    , , , & Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  21. 21.

    et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

  22. 22.

    et al. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat. Struct. Mol. Biol. 18, 1435–1440 (2011).

  23. 23.

    et al. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep. 7, 6031 (2017).

  24. 24.

    , , & Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2, 239–250 (2016).

  25. 25.

    et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

  26. 26.

    et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

  27. 27.

    et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

  28. 28.

    et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).

  29. 29.

    et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell 149, 483–496 (2012).

  30. 30.

    & Origin, lineage and function of cerebellar glia. Prog. Neurobiol. 109, 42–63 (2013).

  31. 31.

    et al. Bergmann glial AMPA receptors are required for fine motor coordination. Science 337, 749–753 (2012).

  32. 32.

    , & Development of the cerebellum: simple steps to make a 'little brain'. Development 141, 4031–4041 (2014).

  33. 33.

    et al. Non-epithelial stem cells and cortical interneuron production in the human ganglionic eminences. Nat. Neurosci. 16, 1576–1587 (2013).

  34. 34.

    et al. Subcortical origins of human and monkey neocortical interneurons. Nat. Neurosci. 16, 1588–1597 (2013).

  35. 35.

    et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110–D115 (2016).

  36. 36.

    et al. FTY720 (fingolimod) efficacy in an animal model of multiple sclerosis requires astrocyte sphingosine 1-phosphate receptor 1 (S1P1) modulation. Proc. Natl. Acad. Sci. USA 108, 751–756 (2011).

  37. 37.

    , & Fingolimod: direct CNS effects of sphingosine 1-phosphate (S1P) receptor modulation and implications in multiple sclerosis therapy. J. Neurol. Sci. 328, 9–18 (2013).

  38. 38.

    et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

  39. 39.

    et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016).

  40. 40.

    et al. Neuronal activity regulates remyelination via glutamate signalling to oligodendrocyte progenitors. Nat. Commun. 6, 8518 (2015).

  41. 41.

    , , , & Neuronal activity biases axon selection for myelination in vivo. Nat. Neurosci. 18, 683–689 (2015).

  42. 42.

    et al. Neuregulin and BDNF induce a switch to NMDA receptor-dependent myelination by oligodendrocytes. PLoS Biol. 11, e1001743 (2013).

  43. 43.

    et al. Synaptic vesicle release regulates myelin sheath number of individual oligodendrocytes in vivo. Nat. Neurosci. 18, 628–630 (2015).

  44. 44.

    , & Control of local protein synthesis and initial events in myelination by action potentials. Science 333, 1647–1651 (2011).

  45. 45.

    et al. Sox10 directs neural stem cells toward the oligodendrocyte lineage by decreasing Suppressor of Fused expression. Proc. Natl. Acad. Sci. USA 107, 21795–21800 (2010).

  46. 46.

    , , & Sox9 and Sox10 influence survival and migration of oligodendrocyte precursors in the spinal cord by regulating PDGF receptor alpha expression. Development 135, 637–646 (2008).

  47. 47.

    et al. Dual regulatory switch through interactions of Tcf7l2/Tcf4 with stage-specific partners propels oligodendroglial maturation. Nat. Commun. 7, 10883 (2016).

  48. 48.

    , , , & MEF2C haploinsufficiency syndrome: report of a new MEF2C mutation and review. Eur. J. Med. Genet. 59, 478–482 (2016).

  49. 49.

    et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

  50. 50.

    et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

  51. 51.

    et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

  52. 52.

    et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).

  53. 53.

    et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).

  54. 54.

    , & Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

  55. 55.

    et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

  56. 56.

    et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  57. 57.

    Pathways towards and away from Alzheimer's disease. Nature 430, 631–639 (2004).

  58. 58.

    et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

  59. 59.

    et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

  60. 60.

    et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580 (2016).

  61. 61.

    , & ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

  62. 62.

    et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

Download references


Flow cytometry was done at both the UCSD Human Embryonic Stem Cell Core and the TSRI Flow Cytometry Core. We thank T.F. Osothprarop and M.M. He (Illumina, San Diego, California, USA) for providing Tn5059 transposase; N. Salathia for assistance in sequencing; Y. Wu, T. Pakozdi, and Z. Chiang for assistance in sequencing analysis; and G. Kennedy for help on RNAscope. Funding support was provided by the NIH Common Fund Single Cell Analysis Program (1U01MH098977 to K.Z. and J.C.). T.E.D. is a fellow in the Pediatric Scientist Development Program and is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (award no. K12-HD000850). G.E.K. was supported by a Neuroplasticity of Aging Training Grant (5T32AG000216-24). P.V.K. was supported by the NIH Centers for Excellence in Genomic Science (P50MH106933), NIH grant 1R01HL131768, and an NSF CAREER award (NSF-14-532). J.F. was supported by NIH grant F31 CA206236. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The GTEx samples used for the analyses described in this study were obtained from the University of Miami Brain Endowment Bank.

Author information

Author notes

    • Blue B Lake
    • , Song Chen
    • , Brandon C Sos
    •  & Jean Fan

    These authors contributed equally to this work.


  1. Department of Bioengineering, University of California San Diego, La Jolla, California, USA.

    • Blue B Lake
    • , Song Chen
    • , Brandon C Sos
    • , Thu E Duong
    • , Derek Gao
    •  & Kun Zhang
  2. UC San Diego School of Medicine, La Jolla, California, USA.

    • Brandon C Sos
    •  & Gwendolyn E Kaeser
  3. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Jean Fan
    •  & Peter V Kharchenko
  4. Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA.

    • Gwendolyn E Kaeser
    • , Yun C Yung
    •  & Jerold Chun
  5. Department of Pediatric Respiratory Medicine, University of California San Diego, La Jolla, California, USA.

    • Thu E Duong


  1. Search for Blue B Lake in:

  2. Search for Song Chen in:

  3. Search for Brandon C Sos in:

  4. Search for Jean Fan in:

  5. Search for Gwendolyn E Kaeser in:

  6. Search for Yun C Yung in:

  7. Search for Thu E Duong in:

  8. Search for Derek Gao in:

  9. Search for Jerold Chun in:

  10. Search for Peter V Kharchenko in:

  11. Search for Kun Zhang in:


K.Z., P.V.K., and J.C. oversaw the study. B.B.L., S.C., B.C.S., G.E.K., Y.C.Y., T.E.D., and D.G. performed experiments. J.F., B.B.L., S.C., and B.C.S. performed bioinformatics analyses. B.B.L., S.C., B.C.S., J.F., J.C., P.V.K., and K.Z. wrote the manuscript, with input from all other co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jerold Chun or Peter V Kharchenko or Kun Zhang.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–14

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Tables

    Supplementary tables 1–13

About this article

Publication history





Rights and permissions

To obtain permission to re-use content from this article visit RightsLink.