Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

HiChIP: efficient and sensitive analysis of protein-directed genome architecture

Nature Methods volume 13, pages 919–922 (2016)Cite this article

Subjects

Abstract

Genome conformation is central to gene control but challenging to interrogate. Here we present HiChIP, a protein-centric chromatin conformation method. HiChIP improves the yield of conformation-informative reads by over 10-fold and lowers the input requirement over 100-fold relative to that of ChIA-PET. HiChIP of cohesin reveals multiscale genome architecture with greater signal-to-background ratios than those of in situ Hi-C.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: HiChIP discovers protein-centric in situ chromatin loops.
Figure 2: HiChIP of cohesin provides a high signal-to-background chromatin interaction map.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. Lieberman-Aiden, E. et al. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  2. Dixon, J.R. et al. Nature 485, 376–380 (2012).

    Article  CAS  Google Scholar 

  3. Jin, F. et al. Nature 503, 290–294 (2013).

    Article  CAS  Google Scholar 

  4. Naumova, N. et al. Science 342, 948–953 (2013).

    Article  CAS  Google Scholar 

  5. Rao, S.S.P. et al. Cell 159, 1665–1680 (2014).

    Article  CAS  Google Scholar 

  6. Hughes, J.R. et al. Nat. Genet. 46, 205–212 (2014).

    Article  CAS  Google Scholar 

  7. Kolovos, P. et al. Epigenetics Chromatin. 7, 10 (2014).

    Article  Google Scholar 

  8. Davies, J.O.J. et al. Nat. Methods 13, 74–80 (2016).

    Article  CAS  Google Scholar 

  9. Dekker, J. & Misteli, T. Cold Spring Harb. Perspect. Biol. 1, a019356 (2015).

    Article  Google Scholar 

  10. Fullwood, M.J. et al. Nature 462, 58–64 (2009).

    Article  CAS  Google Scholar 

  11. Tang, Z. et al. Cell 163, 1611–1627 (2015).

    Article  CAS  Google Scholar 

  12. Schmidl, C., Rendeiro, A.F., Sheffield, N.C. & Bock, C. Nat. Methods 12, 963–965 (2015).

    Article  CAS  Google Scholar 

  13. Nagano, T. et al. Genome Biol. 16, 175 (2015).

    Article  Google Scholar 

  14. Dowen, J.M. et al. Cell 159, 374–387 (2014).

    Article  CAS  Google Scholar 

  15. Heidari, N. et al. Genome Res. 24, 1905–1917 (2014).

    Article  CAS  Google Scholar 

  16. Ji, X. et al. Cell Stem Cell 18, 262–275 (2016).

    Article  CAS  Google Scholar 

  17. Servant, N. et al. Genome Biol. 16, 259 (2015).

    Article  Google Scholar 

  18. Phanstiel, D.H., Boyle, A.P., Heidari, N. & Snyder, M.P. Bioinformatics 31, 3092–3098 (2015).

    Article  CAS  Google Scholar 

  19. Zhang, Y. et al. Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  20. Ay, F., Bailey, T.L. & Noble, W.S. Genome Res. 24, 999–1011 (2014).

    Article  CAS  Google Scholar 

  21. Durand, N.C. et al. Cell Syst. 3, 95–98 (2016).

    Article  CAS  Google Scholar 

  22. Durand, N.C. et al. Cell Syst. 3, 99–101 (2016).

    Article  CAS  Google Scholar 

  23. Nora, E.P. et al. Nature 485, 381–385 (2012).

    Article  CAS  Google Scholar 

  24. Giorgetti, L. et al. Nature 535, 575–579 (2016).

    Article  CAS  Google Scholar 

  25. Kagey, M.H. et al. Nature 467, 430–435 (2010).

    Article  CAS  Google Scholar 

  26. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  27. Kheradpour, P. & Kellis, M. Nucleic Acids Res. 42, 2976–2987 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank our lab members for discussion. We thank N. Suliman for her critical reading of the manuscript. This work was supported by the Stanford Genome Training Program (NIH/NHGRI) (M.R.M.); Human Frontier Science Program, Rita Allen Foundation, Bio-X Stanford Interdisciplinary Graduate Fellowship (A.J.R.); National Institutes of Health (NIH) 1F30CA189514-01 Stanford Medical Scientist Program (R.A.F.); NIH U19AI057266 (to W.J.G.) and P50-HG007735 (to. H.Y.C. and W.J.G.); and NIH S10OD018220 to Stanford Functional Genomics Facility.

Author information

Author notes

  1. Maxwell R Mumbach, Adam J Rubin and Ryan A Flynn: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, California, USA

    Maxwell R Mumbach, Ryan A Flynn, Chao Dai, William J Greenleaf & Howard Y Chang

  2. Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California, USA

    Maxwell R Mumbach, Adam J Rubin, Ryan A Flynn, Chao Dai, Paul A Khavari & Howard Y Chang

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

    Maxwell R Mumbach & William J Greenleaf

  4. Department of Applied Physics, Stanford University, Stanford, California, USA

    William J Greenleaf

Authors
  1. Maxwell R Mumbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam J Rubin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryan A Flynn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chao Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paul A Khavari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William J Greenleaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Howard Y Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.R.M. and R.A.F. developed the method. M.R.M. performed experiments. A.J.R. and C.D. analyzed the data. M.R.M., A.J.R., R.A.F., C.D., P.A.K., W.J.G., and H.Y.C. interpreted the results and wrote the manuscript.

Corresponding author

Correspondence to Howard Y Chang.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 HiChIP provides confident 1D factor binding information.

a, Reads supporting contacts called using the Mango pipeline19 for GM12878 Smc1a HiChIP and GM12878 CTCF Advanced ChIA-PET7. Experiments were sequenced to similar depths: 644 million for HiChIP and 682 million for ChIA-PET. The FDR cutoff was set to 10-4 for the HiChIP contact calls, however was not reported for Advanced ChIA-PET. b, Overlap of cohesin ChIP peaks from GM12878 Smc1a HiChIP and ENCODE Smc3 ChIP-seq. c, MACS220 confidence scores of ENCODE Smc3 ChIP-seq peaks that overlap and do not overlap with Smc1a HiChIP peaks. Smc1a HiChIP calls a confident subset of the Smc3 ChIP-seq peaks.

Supplementary Figure 2 Cohesin HiChIP loops recapitulate known features from in situ Hi-C.

a, Overlap of global loop calls from GM12878 Smc1a HiChIP and in situ Hi-C primary + replicate library sets. b, Overlap of global loop calls from GM12878 in situ Hi-C primary and replicate library sets. c, Reproducibility of loop calls in GM12878 Smc1a HiChIP and situ Hi-C primary + replicate library sets. d, Overlap of cohesin and CTCF ChIP peaks at either one or both GM12878 Smc1a HiChIP loop anchors compared to in situ Hi-C. e, CTCF motif orientation analysis of loops in GM12878 Smc1a HiChIP compared to in situ Hi-C. f, Motif analysis at GM12878 Smc1a HiChIP loop anchors.

Supplementary Figure 3 Reproducibility analyses of the HiChIP method.

a, Reproducibility of loop calls between Smc1a HiChIP biological replicates in GM12878 and mouse embryonic stem cells. b, Reproducibility of loop calls between Smc1a HiChIP technical replicates in mouse embryonic stem cells for low cell starting material.

Supplementary Figure 4 HiChIP provides robust loop calling in lower cell number samples.

a, Reproducibility of loop calls between Smc1a HiChIP in mouse embryonic stem cells for 25 million cell starting material versus lower cell numbers.

Supplementary Figure 5 Hi-C and HiChIP-independent loops exhibit similar cohesin and CTCF binding patterns.

a, Scatter plot of number of loops called for how many reads sequenced. b, Overlap of cohesin and CTCF ChIP peaks at either one or both GM12878 loop anchors for HiChIP-only and Hi-C-only loop sets.

Supplementary Figure 6 HiChIP exhibits increased signal to background at chromatin loops relative to in situ Hi-C.

a, b, Read normalized Hi-C and HiChIP interaction maps at example loci in GM12878 cells with HiChIP – Hi-C delta heat maps. c, Read normalized mouse embryonic stem cell HiChIP interaction maps at 25 million and 1 million cells starting material compared to CH12 in situ Hi-C.

Supplementary Figure 7 Global visualization analyses of loop signal strength in HiChIP and in situ Hi-C.

a, GM12878 Smc1a HiChIP and in situ Hi-C union set loop calls sorted by interaction length distance. The center of each heatmap denotes the downstream loop anchor.

Supplementary Figure 8 Oct4 HiChIP in mouse embryonic stem cells identifies loops involved in enhancer-promoter activity.

a, Virtual 4C profile of Oct4 HiChIP in mouse embryonic stem cells at the Sox2 promoter. b, Reproducibility of loop calls between Oct4 HiChIP biological replicates in mouse embryonic stem cells. c, Overlap of loop calls from mESC Smc1a (merged) and Oct4 datasets. d, Pol II and CTCF ChIP overlap of Oct4 and cohesin-biased loop anchors. e, Virtual 4C profile of Smc1a and Oct4 HiChIP in mESC at the Smyd3 locus, representative of cohesin-associated loops with minimal Oct4 signal. f, Virtual 4C profile of Smc1a and Oct4 HiChIP in mESC at the Upf3a promoter, representative of cohesin and Oct4-assicated loops.

Supplementary Figure 9 Multiple strategies normalize for 1D ChIP fragment visibility biases in the HiChIP method.

a, Schematic of cohesin-peak filtered loops and distance-matched randomly paired loop ends used as a background set for assessment of normalization methods. b, Metaplots of virtual 4C signal surrounding downstream loop ends from upstream loop end viewpoints. VC: vanilla coverage normalization (implemented in Fit-Hi-C contact calls), KR: Knight and Ruiz matrix balancing (implemented for Juicer loop calls).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Protocol (PDF 2095 kb)

Supplementary Table 1

Comparison of sequencing and experimental statistics between recently published ChIA-PET and Capture-C data with HiChIP. (XLSX 56 kb)

Supplementary Table 2

Detailed sequencing and experimental statistics for HiChIP. (XLSX 46 kb)

Supplementary Table 3

High confidence Juicer loop calls from GM12878 Smc1a,mESC Smc1a, and mESC Oct4 HiChIP experiments. (XLSX 4371 kb)

Supplementary Table 4

PCR primer sequences and barcodes used in HiChIP experiments. (XLSX 42 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mumbach, M., Rubin, A., Flynn, R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods 13, 919–922 (2016). https://doi.org/10.1038/nmeth.3999

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3999

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research