Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq)1, we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2–4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6–8 h after egg laying (predominantly stage 10–11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10–12 h after egg laying (predominantly stage 13), when each of the embryo’s more than 20,000 cells are undergoing terminal differentiation. Our results show that there is spatial heterogeneity in the accessibility of the regulatory genome before gastrulation, a feature that aligns with future cell fate, and that nuclei can be temporally ordered along developmental trajectories. During mid-embryogenesis, tissue granularity emerges such that individual cell types can be inferred by their chromatin accessibility while maintaining a signature of their germ layer of origin. Analysis of the data reveals overlapping usage of regulatory elements between cells of the endoderm and non-myogenic mesoderm, suggesting a common developmental program that is reminiscent of the mesendoderm lineage in other species2,3,4. We identify 30,075 distal regulatory elements that exhibit tissue-specific accessibility. We validated the germ-layer specificity of a subset of these predicted enhancers in transgenic embryos, achieving an accuracy of 90%. Overall, our results demonstrate the power of shotgun single-cell profiling of embryos to resolve dynamic changes in the chromatin landscape during development, and to uncover the cis-regulatory programs of metazoan germ layers and cell types.

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This work was technically supported by the EMBL Advanced Light Microscopy, Genomics and Flow Cytometry Facilities. We thank D. Prunkard and L. Gitari in the UW-Pathology Flow Cytometry Facility for their assistance with sorting, and all members of the Furlong and Shendure laboratories for discussions and comments. This work was financially supported by BMBF (TransDiag-2) funds to E.E.M.F., and NIH (DP1HG007811 and R01HG006283) and the Paul G. Allen Family Foundation funds to J.S. D.A.C. was partly supported by T32HL007828 from the National Heart, Lung, and Blood Institute. J.S. is a Howard Hughes Medical Institute Investigator.

Author information

Author notes

    • David A. Garfield

    Present address: IRI Life Sciences, Humboldt Universität zu Berlin, Berlin, Germany.

    • Darren A. Cusanovich
    • , James P. Reddington
    •  & David A. Garfield

    These authors contributed equally to this work.

    • Jay Shendure
    •  & Eileen E. M. Furlong

    These authors jointly supervised this work.


  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    • Darren A. Cusanovich
    • , Riza M. Daza
    • , Delasa Aghamirzaie
    • , Hannah A. Pliner
    • , Xiaojie Qiu
    • , Cole Trapnell
    •  & Jay Shendure
  2. European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany

    • James P. Reddington
    • , David A. Garfield
    • , Raquel Marco-Ferreres
    •  & Eileen E. M. Furlong
  3. Illumina, San Diego, California, USA

    • Lena Christiansen
    •  & Frank J. Steemers
  4. Howard Hughes Medical Institute, Seattle, Washington, USA

    • Jay Shendure


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D.A.C., J.P.R., D.A.G., J.S. and E.E.M.F. designed the study, explored results and prepared the manuscript, with contributions from all authors. D.A.C. and R.M.D. developed and optimized sci-ATAC-seq, with assistance from L.C. and F.J.S. J.P.R. and D.A.G. led sample preparation and biological validations, with assistance from R.M.-F. D.A.C., J.P.R. and D.A.G. led data analysis, with assistance on specific analyses from D.A., H.A.P., C.T. and X.Q. J.S. and E.E.M.F. supervised the study.

Competing interests

L.C. and F.J.S. own shares in and are employed by Illumina, Inc. transduction.

Corresponding authors

Correspondence to Jay Shendure or Eileen E. M. Furlong.

Reviewer Information Nature thanks M. Bulyk, S. Gisselbrecht and B. Gottgens 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.

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    Supplementary Data

    This file contains Supplementary Tables 1-3, 5-13 and a Supplementary Table Guide.

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    Supplementary Table 4

    Enrichment analyses for t-SNE-defined clades. This data file contains sheets for the total enrichment, for enrichment of proximal elements (within 500bp of an annotated transcription start site) and distal (>500bp from an annotated TSS). Annotation datasets are described in “Gene Expression, Enhancer Expression, and TF Binding Data” in Methods for details. The term ‘custom’ refers to our database of TF ChIP peaks, while ‘stark’ refers to a subset of the CAD database from7 that are active at 2-4hr using terms that are specific to early development that are missing from the primary CAD database. These terms were calculate for all time points, but were only used to annotate clusters at 2-4hr. Excel file with multiple sheets. Statistical significance for each test was determined by a two-sided Fisher’s Exact Test with the number of significant and tested peaks for each category given in the supplementary table. Only statistically significant categories are listed. A full list of significant and tested elements for each cluster and each time point can be found in Table S1. A list of all tested categories can be found in Table S13.

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