An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome


Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Paired-seq enables simultaneous profiling of accessible chromatin and gene expression in millions of single cells.
Fig. 2: Paired-seq identified major cell types in the mouse cerebral cortex.
Fig. 3: Paired-seq links candidate CREs to their putative target genes.
Fig. 4: Analysis of cellular trajectory in the developing mouse forebrain.

Code Availability

MAPS is freely available at Custom scripts used in this study can be downloaded from

Data Availability

The sequencing data obtained in this study have been deposited to the NCBI Gene Expression Omnibus (GEO) ( under accession number GSE130399. Source data for Figs. 1e–g, 2b,e,f, 3a,b and 4b–d are available with the paper online. External data sets used in this study are available from GEO: ENCODE DNase-seq (GSE37074), PolyA-RNA-seq (GSE39524) of mouse NIH/3T3 cells, sci-CAR mixed cells datasets (GSE117089), SPLiT-seq (GSE110823), sci-RNA-seq (GSE98561), Drop-seq (GSE63269), sci-ATAC-seq (GSE67446), and dscATAC-seq (GSE123581); or from the 10X genomics website, 10X scRNA-seq (, 1k_hgmm_v3_nextgem dataset). All other data are available upon reasonable request.


  1. 1.

    de Laat, W. & Duboule, D. Topology of mammalian developmental enhancers and their regulatory landscapes. Nature 502, 499–506 (2013).

  2. 2.

    Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

  3. 3.

    Crawford, G. E. et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res. 16, 123–131 (2006).

  4. 4.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. 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).

  5. 5.

    Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

  6. 6.

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

  7. 7.

    Kelsey, G., Stegle, O. & Reik, W. Single-cell epigenomics: Recording the past and predicting the future. Science 358, 69–75 (2017).

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

    Harada, A. et al. A chromatin integration labelling method enables epigenomic profiling with lower input. Nat. Cell Biol. 21, 287–296 (2019).

  13. 13.

    Hainer, S. J., Boskovic, A., McCannell, K. N., Rando, O. J. & Fazzio, T. G. Profiling of pluripotency factors in single cells and early embryos. Cell 177, 1319–1329.e1311 (2019).

  14. 14.

    Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

  15. 15.

    Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

  16. 16.

    Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

  17. 17.

    Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

  18. 18.

    Mooijman, D., Dey, S. S., Boisset, J. C., Crosetto, N. & van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34, 852–856 (2016).

  19. 19.

    Zhu, C. et al. Single-cell 5-formylcytosine landscapes of mammalian early embryos and ESCs at single-base resolution. Cell Stem Cell 20, 720–731.e725 (2017).

  20. 20.

    Wu, X., Inoue, A., Suzuki, T. & Zhang, Y. Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells. Genes Dev. 31, 511–523 (2017).

  21. 21.

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

  22. 22.

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

  23. 23.

    Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

  24. 24.

    Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

  25. 25.

    Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

  26. 26.

    Grosselin, K. et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019).

  27. 27.

    Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

  28. 28.

    Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

  29. 29.

    Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

  30. 30.

    Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).

  31. 31.

    Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).

  32. 32.

    Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).

  33. 33.

    Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203 (2017).

  34. 34.

    Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

  35. 35.

    Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019).

  36. 36.

    Li, G. et al. Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019).

  37. 37.

    Lee, D. S. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019).

  38. 38.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

  39. 39.

    Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

  40. 40.

    Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

  41. 41.

    Chen, S., Lake, B.B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. (2019).

  42. 42.

    Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

  43. 43.

    Peng, X. et al. TELP, a sensitive and versatile library construction method for next-generation sequencing. Nucleic Acids Res. 43, e35 (2015).

  44. 44.

    Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

  45. 45.

    Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

  46. 46.

    Fang, R. et al. Fast and accurate clustering of single cell epigenomes reveals cis-regulatory elements in rare cell types. Preprint at bioRxiv (2019).

  47. 47.

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

  48. 48.

    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).

  49. 49.

    Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

  50. 50.

    Lai, T. et al. SOX5 controls the sequential generation of distinct corticofugal neuron subtypes. Neuron 57, 232–247 (2008).

  51. 51.

    Sun, W. et al. SOX9 is an astrocyte-specific nuclear marker in the adult brain outside the neurogenic regions. J. Neurosci. 37, 4493–4507 (2017).

  52. 52.

    Gorkin, D. U. et al. An atlas of dynamic chromatin landscapes in the developing mouse fetus. Nature (in the press).

  53. 53.

    Yu, M. & Ren, B. The three-dimensional organization of mammalian genomes. Annu. Rev. Cell Dev. Biol. 33, 265–289 (2017).

  54. 54.

    Fang, R. et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 26, 1345–1348 (2016).

  55. 55.

    Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

  56. 56.

    Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

  57. 57.

    Martynoga, B., Drechsel, D. & Guillemot, F. Molecular control of neurogenesis: a view from the mammalian cerebral cortex. Cold Spring Harb. Perspect. Biol. 4, a008359 (2012).

  58. 58.

    Mulqueen, R. M. et al. Improved single-cell ATAC-seq reveals chromatin dynamics of in vitro corticogenesis. Preprint at bioRxiv (2019).

  59. 59.

    Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871.e858 (2018).

  60. 60.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  61. 61.

    Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinformatics 51, 11.14.1–11.14.19 (2015).

  62. 62.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  63. 63.

    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).

  64. 64.

    Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

  65. 65.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  66. 66.

    Subelj, L. & Bajec, M. Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 83, 036103 (2011).

  67. 67.

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

  68. 68.

    Juric, I. et al. MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments. PLoS Comput. Biol. 15, e1006982 (2019).

Download references


We thank B. Li for bioinformatic support and S. Kuan for sequencing. We thank the QB3 MacroLab for purifying the Tn5 enzyme. We thank D. U. Gorkin (UC San Diego) for sharing the frozen archived mouse fetal brain tissues. We thank S. Preissl, R. Fang, X. Hou, J. Song, Y. Li, Y. Zhang, and Y. Qiu for discussion. This study was funded by grants 1U19 MH114831, U54 HG006997 and the Ludwig Institute for Cancer Research (to B.R.).

Author information




B.R. and C.Z. conceived and designed the study and wrote the manuscript. C.Z. performed the Paired-seq experiments and data analysis. M.Y. and R.H. performed PLAC-seq experiments. H.H. prepared the nuclei. I.J., A.A., and M.H. performed PLAC-seq data analysis. J.L. and M.M.B. harvested adult mouse cerebral cortex tissues. All authors discussed results and edited the manuscript.

Corresponding author

Correspondence to Bing Ren.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Quality control for Paired-seq libraries.

a, Sequence of Paired-seq products illustrating the structure of DNA barcode combinations. b, Paired-seq DNA profiles are enriched around the TSSs, whereas (e) RNA profiles are enriched at the TTSs in NIH/3T3 cells. As comparison, DNA and RNA profiles from sci-CAR are also plotted. c, Proportions of DNA and RNA reads in both libraries are shown, n = 3 independent experiments. d,e, Scatter plots showing the correlation of reads from two replicates of Paired-seq (d) DNA profiles or (e) RNA profiles. f,g, Box plots showing (f) the fraction of reads around TSS (-1000 to + 500 bp) and (g) the faction of reads inside known peaks (GSE:49847) of Paired-seq DNA profiles from HEK293T, HepG2 and NIH/3T3 cells. sci-CAR40 datasets (GSE117089) from the same cell types were also used for comparison. h,i, Scatter plot showing the proportion of human and mouse reads in each cell in Paired-seq (h) DNA and (i) RNA profiles. j, Scatter plot showing the proportions of both DNA and RNA reads mapped to genomes in the same single cells. Cells with more than 80% reads mapped to human and mouse genome were colored in red and blue, respectively. k,l, UMAP visualization of HepG2 and HEK293T cells based on (k) DNA and (l) RNA reads. Cells were colored by density-based clustering from each profile and cell identities. The clustering results were also projected to each other. In box plots, center lines indicate the median, box limits indicate the first and third quartiles, and whiskers indicate 1.5× IQR. The sample sizes are provided in the Source Data with this paper online. Source data

Extended Data Fig. 2 Integrative analysis of Paired-seq DNA and RNA profiles from mouse adult cerebral cortex.

a, UMAP visualization of co-clustering of nuclei from two replicates. b, Comparison of DNA-based, RNA-based, and integrated clustering results. Cells were colored based on unsupervised clustering from integrated clustering and colored the same as Fig. 2b. c, Promoter accessibility and gene expression of several marker genes in the nine major groups. Relative promoter accessibilities and gene expressions are indicated in the size and the color of circles. d, Expression levels of genes of all clusters are plotted in a box plot for each quantile of promoter accessibility. e, For each cell cluster, expression levels of genes are plotted in a box plot for each quantile of promoter accessibility. In box plots center lines indicate the median, box limits indicate the first and third quartiles, and whiskers indicate 1.5× IQR. Source data

Extended Data Fig. 3 Co-clustering of Paired-seq datasets from mouse E12.5, E16.5 forebrain, and adult cerebral cortex.

a, UMAP visualization of Paired-seq data from two replicates of both mouse E12.5 and E16.5 forebrains showing clustering of cells based on cell types, not replicates. b, UMAP visualization of Paired-seq data of mouse E12.5 and E16.5 forebrains and adult cerebral cortex showing clustering of cells based on cell type, not batches. c, Aggregate chromatin accessibility (blue) and gene expression (green) profiles for each cell clusters at several marker gene loci. Source data

Extended Data Fig. 4 Paired-seq facilitates the linking of candidate CREs to putative target genes in mouse fetal forebrains.

a, Bar charts show the numbers of gene−CRE links identified in mouse E12.5 and E16.5 forebrain and adult cerebral cortex data sets. b,c, Bar charts show the fractions of gene−CRE pairs (b) identified by Paired-seq and supported by PLAC-seq or (c) identified by PLAC-seq and supported by Paired-seq. P value, two-sided Fisher’s exact test. do, Number of identified CREs linked to each gene, number of identified genes linked to each CRE, number of CREs between CREs and their linked genes, and number of genes between CREs and their linked genes in (dg) E12.5, (hk) E16.5 forebrain and (lo) adult cerebral cortex. Source data

Extended Data Fig. 5 Dynamics of gene−CRE pairing during mouse brain development.

a,b, Box plots showing the number of linked CREs for genes of each group of (a) E12.5 to E16.5 and (b) E16.5 to adult. P value, two-sided Kolmogorov–Smirnov test test. Genes were classified according the number of linked candidate CREs: genes with a gain of CREs (log2(fold-change) > 3), genes with unchanged number of linked CREs (−1 < log2(fold-change) < 1) and genes with a loss of linked CREs (log2(fold-change) < -3). c, DAVID GO analysis of genes with more than ten linked CREs. d, Top 20 TF genes with the highest number of linked CREs. e, The predicted gene−CRE pair for Dlx1 gene in the dIn2 cluster. The common links shared by two stages of development are shown in gray, and the stage-specific links were shown in light and dark violet red. In the close-up view, the positions of stage-specific CREs ae indicated by a red dashed box. In box plots. center lines indicate the median, box limits indicate the first and third quartiles and whiskers indicate 1.5× interquartile range IQR. Source data

Extended Data Fig. 6 Analysis of cellular trajectory of developing mouse forebrain.

ac, Diffusion map showing the single-cell trajectories of neurogenesis toward (a) GABAergic neurons, (b) glutamatergic neurons, and (c) astrogenesis. d, The combined diffusion map corresponding to Fig. 4a is also shown. The cells were colored by stages and clusters, respectively. e, Heat map shows the ordering of the chromVAR TF motif enrichments across astrogenesis. The relative expression and promoter accessibility of corresponding TF genes are also shown. f, Line plots showing the relative enrichment of TF motifs, gene expression, and promoter accessibility for STAT3, NFKB1, and SP1 according to the diffusion pseudotime for astrogenesis. The estimated time-of-gain and time-of-loss of TF motifs are indicated by red and green rectangles below. g, Pie charts showing the fraction of TFs with the TF gene promoters became accessible before (TF gene first), synchronized with, or after (Motif first) the TF motifs became accessible, for neurogenesis towards GABAergic neurons, glutamatergic neurons and astrogenesis. Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Paired-seq primer sequences

Supplementary Table 2

Paired-seq barcode oligo sequences

Supplementary Table 3

Summary of sequenced nuclei

Supplementary Table 4

Identified gene−CRE pairs in mouse fetal forebrains

Supplementary Table 5

Identified gene−CRE pairs in adult cerebral cortex

Source data

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 2

Statistical Source Data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 1

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 3

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 6

Statistical Source Data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhu, C., Yu, M., Huang, H. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 26, 1063–1070 (2019).

Download citation

Further reading