Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position

Journal name:
Nature Methods
Volume:
10,
Pages:
1213–1218
Year published:
DOI:
doi:10.1038/nmeth.2688
Received
Accepted
Published online

Abstract

We describe an assay for transposase-accessible chromatin using sequencing (ATAC-seq), based on direct in vitro transposition of sequencing adaptors into native chromatin, as a rapid and sensitive method for integrative epigenomic analysis. ATAC-seq captures open chromatin sites using a simple two-step protocol with 500–50,000 cells and reveals the interplay between genomic locations of open chromatin, DNA-binding proteins, individual nucleosomes and chromatin compaction at nucleotide resolution. We discovered classes of DNA-binding factors that strictly avoided, could tolerate or tended to overlap with nucleosomes. Using ATAC-seq maps of human CD4+ T cells from a proband obtained on consecutive days, we demonstrated the feasibility of analyzing an individual's epigenome on a timescale compatible with clinical decision-making.

At a glance

Figures

  1. ATAC-seq probes open-chromatin state.
    Figure 1: ATAC-seq probes open-chromatin state.

    (a) ATAC-seq reaction schematic. Transposase (green), loaded with sequencing adaptors (red and blue), inserts only in regions of open chromatin (between nucleosomes in gray) and generates sequencing-library fragments that can be PCR-amplified. (b) Approximate reported input material and sample preparation time requirements for genome-wide methods of open-chromatin analysis. (c) Comparison of ATAC-seq to other open-chromatin assays at a locus in GM12878 lymphoblastoid cells. The lower ATAC-seq track was generated from 500 FACS-sorted cells. Bottom, composite locations of CTCF and histone modifications associated with active enhancers and promoters. DNase-seq (Duke) and FAIRE-seq (University of North Carolina, UNC) data were from the indicated ENCODE production centers.

  2. ATAC-seq provides genome-wide information on chromatin compaction.
    Figure 2: ATAC-seq provides genome-wide information on chromatin compaction.

    (a) ATAC-seq fragment sizes generated from GM12878 nuclei. Inset, log-transformed histogram shows clear periodicity persists to six nucleosomes. (b) Normalized read enrichments for seven classes of chromatin state previously defined17.

  3. ATAC-seq provides genome-wide information on nucleosome positioning in regulatory regions.
    Figure 3: ATAC-seq provides genome-wide information on nucleosome positioning in regulatory regions.

    (a) Locus containing two transcription start sites (TSSs) showing a nucleosome-free read track and calculated nucleosome track (Online Methods) as well as DNase, MNase, H3K27ac, H3K4me3 and H2A.Z tracks for comparison. DNase-seq (University of Washington, UW) data were from the indicated ENCODE production center. (b) ATAC-seq (198 million paired reads) and MNase-seq (4 billion single-end reads from ref. 23) nucleosome signal for all active TSSs (n = 64,836). TSSs are sorted by cap analysis of gene expression (CAGE) values. (c) TSSs are enriched for nucleosome-free fragments and show phased nucleosomes similar to those seen by MNase-seq at the −2, −1, +1, +2, +3 and +4 positions. (d) Relative fraction of nucleosome associated versus nucleosome-free (NFR) bases in TSS and distal sites (Online Methods). (e) Hierarchical clustering of DNA-binding factor position with respect to the nearest nucleosome dyad within accessible chromatin.

  4. ATAC-seq assays genome-wide factor occupancy.
    Figure 4: ATAC-seq assays genome-wide factor occupancy.

    (a) CTCF footprints observed in ATAC-seq and DNase-seq data, at a specific locus on chromosome 1. DNase-seq (Duke) and ChIP-seq (Broad Institute) data were from the indicated ENCODE production centers. (b) Aggregate ATAC-seq footprint for CTCF (motif shown) generated over binding sites within the genome. (c) CTCF predicted binding probability inferred from ATAC-seq data, position weight matrix (PWM) scores for the CTCF motif, and evolutionary conservation (PhyloP). Shown in the rightmost column are the CTCF ChIP-seq data (ENCODE) for this GM12878 cell line.

  5. ATAC-seq enables real-time personal epigenomics.
    Figure 5: ATAC-seq enables real-time personal epigenomics.

    (a) Workflow from standard blood draws. (b) Serial ATAC-seq data from proband T cells over 3 d. (c) Application of ATAC-seq data to prioritize candidate TF drug targets. ATAC-seq footprint prediction (green track) is compared to ChIP-seq data (blue track; data from ref. 35). Three TFs and their targeting drugs are shown. (d) Cell type–specific regulatory network from proband T cells compared with that of a GM12878 B cell line. Each row or column is the footprint profile of a TF versus that of all other TFs in the same cell type. Color indicates relative similarity (yellow) or distinctiveness (blue) in T versus B cells. The most highly differentially regulated TFs are indicated by a red box.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Kornberg, R.D. Chromatin structure: a repeating unit of histones and DNA. Science 184, 868871 (1974).
  2. Kornberg, R.D. & Lorch, Y. Chromatin structure and transcription. Annu. Rev. Cell Biol. 8, 563587 (1992).
  3. Mellor, J. The dynamics of chromatin remodeling at promoters. Mol. Cell 19, 147157 (2005).
  4. Boyle, A.P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311322 (2008).
  5. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 7582 (2012).
  6. Schones, D.E. et al. Dynamic regulation of nucleosome positioning in the human genome. Cell 132, 887898 (2008).
  7. Valouev, A. et al. Determinants of nucleosome organization in primary human cells. Nature 474, 516520 (2011).
  8. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823837 (2007).
  9. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91100 (2012).
  10. Goryshin, I.Y. & Reznikoff, W.S. Tn5 in vitro transposition. J. Biol. Chem. 273, 73677374 (1998).
  11. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).
  12. Gangadharan, S., Mularoni, L., Fain-Thornton, J., Wheelan, S.J. & Craig, N.L. DNA transposon Hermes inserts into DNA in nucleosome-free regions in vivo. Proc. Natl. Acad. Sci. USA 107, 2196621972 (2010).
  13. Song, L. & Crawford, G.E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010 pdb.prot5384 (2010).
  14. Simon, J.M., Giresi, P.G., Davis, I.J. & Lieb, J.D. Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nat. Protoc. 7, 256267 (2012).
  15. The ENCODE Project Consortium. A user's guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biol. 9, e1001046 (2011).
  16. Giresi, P.G. & Lieb, J.D. Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48, 233239 (2009).
  17. Hoffman, M.M. et al. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res. 41, 827841 (2013).
  18. Prioleau, M.-N., Nony, P., Simpson, M. & Felsenfeld, G. An insulator element and condensed chromatin region separate the chicken β-globin locus from an independently regulated erythroid-specific folate receptor gene. EMBO J. 18, 40354048 (1999).
  19. Ghirlando, R., Litt, M.D., Prioleau, M.-N., Recillas-Targa, F. & Felsenfeld, G. Physical properties of a genomic condensed chromatin fragment. J. Mol. Biol. 336, 597605 (2004).
  20. Kornberg, R.D. & Lorch, Y. Chromatin and transcription: where do we go from here. Curr. Opin. Genet. Dev. 12, 249251 (2002).
  21. Zhou, J., Fan, J.Y., Rangasamy, D. & Tremethick, D.J. The nucleosome surface regulates chromatin compaction and couples it with transcriptional repression. Nat. Struct. Mol. Biol. 14, 10701076 (2007).
  22. Chen, K. et al. DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res. 23, 341351 (2013).
  23. Kundaje, A. et al. Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements. Genome Res. 22, 17351747 (2012).
  24. Hesselberth, J.R. et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283289 (2009).
  25. Boyle, A.P. et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 21, 456464 (2011).
  26. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 8390 (2012).
  27. Pique-Regi, R. et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447455 (2011).
  28. Fraser, J.D., Irving, B.A., Crabtree, G.R. & Weiss, A. Regulation of interleukin-2 gene enhancer activity by the T cell accessory molecule CD28. Science 251, 313316 (1991).
  29. Flanagan, W.M., Corthésy, B., Bram, R.J. & Crabtree, G.R. Nuclear association of a T-cell transcription factor blocked by FK-506 and cyclosporin A. Nature 352, 803807 (1991).
  30. Lopez-Girona, A. et al. Lenalidomide downregulates the cell survival factor, interferon regulatory factor-4, providing a potential mechanistic link for predicting response. Br. J. Haematol. 154, 325336 (2011).
  31. Verstovsek, S. et al. Safety and efficacy of INCB018424, a JAK1 and JAK2 inhibitor, in myelofibrosis. N. Engl. J. Med. 363, 11171127 (2010).
  32. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory. Science 337, 11901195 (2012).
  33. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377382 (2009).
  34. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236240 (2013).
  35. Jolma, A. et al. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 20, 861873 (2010).
  36. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
  37. Guo, Y., Mahony, S. & Gifford, D.K. High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Comput. Biol. 8, e1002638 (2012).
  38. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 1486314868 (1998).

Download references

Author information

Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.

    • Jason D Buenrostro &
    • William J Greenleaf
  2. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA.

    • Jason D Buenrostro,
    • Paul G Giresi,
    • Lisa C Zaba &
    • Howard Y Chang
  3. Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California, USA.

    • Jason D Buenrostro,
    • Paul G Giresi,
    • Lisa C Zaba &
    • Howard Y Chang

Contributions

J.D.B., P.G.G. and L.C.Z. performed the research. All authors designed experiments and interpreted the data. H.Y.C. and W.J.G. wrote the paper.

Competing financial interests

Stanford University has filed a provisional patent application on the methods described, and J.D.B., P.G.G., H.Y.C. and W.J.G. are named as inventors.

Corresponding authors

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (7 MB)

    Supplementary Figures 1–12 and Supplementary Tables 1 and 2

Additional data