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.

  • Review Article
  • Published:

How best to identify chromosomal interactions: a comparison of approaches

Nature Methods volume 14, pages 125–134 (2017)Cite this article

Subjects

Abstract

Chromosome conformation capture (3C) methods are central to understanding the link between nuclear structure and function, and the physical interactions between distal regulatory elements and promoters. However, no one method is appropriate to address all biological questions, as each variant differs markedly in resolution, reproducibility, throughput and biases. A thorough appreciation of the strengths and weaknesses of each technique is critical when choosing the correct method for a specific application or for gauging how best to interpret different sources of data. In addition, the analysis method must be carefully considered, as this choice can profoundly affect the output. In this Review, we describe and compare the different available 3C-based approaches, with a focus on the analysis of mammalian genomes.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Common principles in 3C-based techniques.
Figure 2: Comparison of different 3C-based methodologies.
Figure 3: Comparison of the data generated by different 3C-based methods.
Figure 4: Case study 1: regulation of the alpha-globin locus in erythoid cells.
Figure 5: Case study 2: regulation of the Sox2 locus in mouse ES (mES) cells.
Figure 6: Graphical overview of data analysis of high-throughput 3C-based technologies.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Mikkelsen, T.S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Hesselberth, J.R. et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Stamatoyannopoulos, J. Connecting the regulatory genome. Nat. Genet. 48, 479–480 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Sanyal, A., Lajoie, B.R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Bulger, M. & Groudine, M. Functional and mechanistic diversity of distal transcription enhancers. Cell 144, 327–339 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Nora, E.P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tolhuis, B., Palstra, R.J., Splinter, E., Grosveld, F. & de Laat, W. Looping and interaction between hypersensitive sites in the active beta-globin locus. Mol. Cell 10, 1453–1465 (2002).This is the first use of 3C to define interactions between regulatory elements in mammalian cells.

    CAS  PubMed  Google Scholar 

  11. Tan-Wong, S.M. et al. Gene loops enhance transcriptional directionality. Science 338, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hsieh, T.H. et al. Mapping nucleosome resolution chromosome folding in yeast by micro-C. Cell 162, 108–119 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hsieh, T.-H.S., Fudenberg, G., Goloborodko, A. & Rando, O.J. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat Methods 13, 1009–1011 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Mukherjee, S., Erickson, H. & Bastia, D. Detection of DNA looping due to simultaneous interaction of a DNA-binding protein with two spatially separated binding sites on DNA. Proc. Natl. Acad. Sci. USA 85, 6287–6291 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Mitchell, M.A. & Dervan, P.B. Interhelical DNA-DNA crosslinking. Bis(monoazidomethidium)octaoxahexacosanediamine: a probe of packaged nucleic acid. J. Am. Chem. Soc. 104, 4265–4266 (1982).

    Article  CAS  Google Scholar 

  16. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).This is the seminal paper first describing chromosome conformation capture in yeast.

    Article  CAS  PubMed  Google Scholar 

  17. Marbouty, M. et al. Condensin- and replication-mediated bacterial chromosome folding and origin condensation revealed by Hi-C and super-resolution imaging. Mol. Cell 59, 588–602 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).This paper first described the Hi-C method, and it describes large-scale organization of chromatin as a fractal globule.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Davies, J.O. et al. Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Nat. Methods 13, 74–80 (2016).This paper describes the highest resolution and sensitivity available with a one vs. all approach using NG Capture-C, which is additionally capable of high levels of multiplexing of viewpoints.

    Article  CAS  PubMed  Google Scholar 

  21. Lucas, J.S., Zhang, Y., Dudko, O.K. & Murre, C. 3D trajectories adopted by coding and regulatory DNA elements: first-passage times for genomic interactions. Cell 158, 339–352 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  23. Gavrilov, A.A. et al. Disclosure of a structural milieu for the proximity ligation reveals the elusive nature of an active chromatin hub. Nucleic Acids Res. 41, 3563–3575 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Nagano, T. et al. Comparison of Hi-C results using in-solution versus in-nucleus ligation. Genome Biol. 16, 175 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rao, S.S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).This paper describes the highest possible resolution currently achievable with genome-wide all vs. all approaches using Hi-C.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Spilianakis, C.G. & Flavell, R.A. Long-range intrachromosomal interactions in the T helper type 2 cytokine locus. Nat. Immunol. 5, 1017–1027 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Liu, Z. & Garrard, W.T. Long-range interactions between three transcriptional enhancers, active Vkappa gene promoters, and a 3′ boundary sequence spanning 46 kilobases. Mol. Cell. Biol. 25, 3220–3231 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vernimmen, D. et al. Chromosome looping at the human alpha-globin locus is mediated via the major upstream regulatory element (HS-40). Blood 114, 4253–4260 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Vernimmen, D., De Gobbi, M., Sloane-Stanley, J.A., Wood, W.G. & Higgs, D.R. Long-range chromosomal interactions regulate the timing of the transition between poised and active gene expression. EMBO J. 26, 2041–2051 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhou, G.L. et al. Active chromatin hub of the mouse alpha-globin locus forms in a transcription factory of clustered housekeeping genes. Mol. Cell. Biol. 26, 5096–5105 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhao, Z. et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat. Genet. 38, 1341–1347 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Simonis, M. et al. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat. Genet. 38, 1348–1354 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Shore, D., Langowski, J. & Baldwin, R.L. DNA flexibility studied by covalent closure of short fragments into circles. Proc. Natl. Acad. Sci. USA 78, 4833–4837 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Stadhouders, R. et al. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nat. Protoc. 8, 509–524 (2013).

    Article  CAS  PubMed  Google Scholar 

  35. Simonis, M., Kooren, J. & de Laat, W. An evaluation of 3C-based methods to capture DNA interactions. Nat. Methods 4, 895–901 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Schwartzman, O. et al. UMI-4C for quantitative and targeted chromosomal contact profiling. Nat. Methods 13, 685–691 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Andrey, G. et al. A switch between topological domains underlies HoxD genes collinearity in mouse limbs. Science 340, 1234167 (2013).This paper uses 4C to delineate the changes in gene regulation and interaction profiles at the HoxD genes during limb development.

    Article  CAS  PubMed  Google Scholar 

  38. de Wit, E. et al. The pluripotent genome in three dimensions is shaped around pluripotency factors. Nature 501, 227–231 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Simonis, M. et al. High-resolution identification of balanced and complex chromosomal rearrangements by 4C technology. Nat. Methods 6, 837–842 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Gröschel, S. et al. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell 157, 369–381 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Dostie, J. et al. Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hughes, J.R. et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat. Genet. 46, 205–212 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Yaffe, E. & Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–1065 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hu, M. et al. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28, 3131–3133 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang, Y. et al. Spatial organization of the mouse genome and its role in recurrent chromosomal translocations. Cell 148, 908–921 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ramani, V. et al. Massively multiplex single-cell Hi-C. Preprint at http://biorxiv.org/content/early/2016/07/23/065052 (2016).

  51. Hsieh, T.S., Fudenberg, G., Goloborodko, A. & Rando, O.J. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat. Methods 13, 1009–1011 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Ma, W. et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat. Methods 12, 71–78 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Schoenfelder, S. et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genetics 47, 598–606 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. Kolovos, P. et al. Targeted chromatin capture (T2C): a novel high resolution high throughput method to detect genomic interactions and regulatory elements. Epigenetics Chromatin 7, 10 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fullwood, M.J. et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462, 58–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kieffer-Kwon, K.R. et al. Interactome maps of mouse gene regulatory domains reveal basic principles of transcriptional regulation. Cell 155, 1507–1520 (2013).

    Article  CAS  PubMed  Google Scholar 

  59. Mumbach, M.R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Brackley, C.A. et al. Predicting the three-dimensional folding of cis-regulatory regions in mammalian genomes using bioinformatic data and polymer models. Genome Biol. 17, 59 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Fraser, J. et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol. Syst. Biol. 11, 852 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. van de Werken, H.J. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. Phillips-Cremins, J.E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Klein, F.A. et al. FourCSeq: analysis of 4C sequencing data. Bioinformatics 31, 3085–3091 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. de Wit, E. et al. CTCF Binding polarity determines chromatin looping. Mol. Cell 60, 676–684 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Sauria, M.E.G., Phillips-Cremins, J.E., Corces, V.G. & Taylor, J. HiFive: a tool suite for easy and efficient HiC and 5C data analysis. Genome Biol. 16, 237 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wingett, S. et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res 4, 1310 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Durand Neva, C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Li, G. et al. ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol. 11, R22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of Hughes and Higgs groups for helpful comments and discussions. We thank our funding bodies: Wellcome Trust Strategic Award ref. 106130/Z/14/Z (J.R.H. and D.R.H.); Wellcome Trust Clinical Research Training Fellowship ref. 098931/Z/12/Z (JOJD) and Wellcome trust doctoral training programme ref. 105281/Z/14/Z (AMO).

Author information

Authors and Affiliations

  1. Medical Research Council (MRC) Molecular Hematology Unit, Weatherall Institute of Molecular Medicine, Oxford University, Oxford, United Kingdom

    James O J Davies, A Marieke Oudelaar, Douglas R Higgs & Jim R Hughes

Authors
  1. James O J Davies
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A Marieke Oudelaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Douglas R Higgs
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jim R Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jim R Hughes.

Ethics declarations

Competing interests

A patent for NG Capture-C has been filed and is pending.

Integrated supplementary information

Supplementary Figure 1 Chromatin, 4C and NG Capture-C data for the alpha globin locus in mouse erythroid cells.

The DNaseI hypersensitivity track (green), marking open chromatin, is shown at the top. Below are ChIP-seq profiles for H3K4me3, H3K4me1 and CTCF, that highlight promoters, enhancers and CTCF binding sites respectively (Hughes et al., 2014).

The raw and normalized 4Cseq profiles are shown below (van der Werken et al., 2012). The NG Capture-C profiles are an average of 4 replicates in erythroid cells and 3 ES cell replicates. The bottom track shows significant differences (-log10 p value) between the interaction profiles when the gene is active (erythroid cells) and inactive (ES cells). The data can be analysed by DESeq2, which is used for comparative analysis of several different types of count based sequencing data, particularly RNAseq.

Supplementary Figure 2 Chromatin data for the Sox2 locus in mouse ES cells.

The DNaseI hypersensitivity track, marks open chromatin and nonspecifically highlights regulatory elements. Below are ChIP-seq profiles for H3K4me1, H3K4me3, mediator (Med1) Oct-4, Nanog, and CTCF, which allow the DNAseI hypersensitive sites to be defined further.

A cluseter of hypersensitive sites 85-111kb from the promoter of the gene are defined as a superenhancer in ES cells by Whyte et al., 2013. However, the gene has several other cell type specific regulatory, which extend nearly 1Mb away from the promoter. These tend to be with CTCF sites (highlighted in purple) although there is an additional element potential regulatory element bound by Nanog and Oct4 (HS+683).

ES cell data: DNAseI-seq (ENCODE UW); ChIP-seq H3K4me1 and H3K4me3 (ENCODE/LICR); CTCF (LICR GSM918748); MED1 (Young lab GSM1038259), Sox2 (Young lab GSM1082341), Nanog (Young lab GSM1082342), Oct4 (Young lab GSM1082340)

Supplementary information

Supplementary Text and Figures

Supplementary Note 1 and Supplementary Figures 1–2 (PDF 4474 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Davies, J., Oudelaar, A., Higgs, D. et al. How best to identify chromosomal interactions: a comparison of approaches. Nat Methods 14, 125–134 (2017). https://doi.org/10.1038/nmeth.4146

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4146

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing