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  • Review Article
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How best to identify chromosomal interactions: a comparison of approaches

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

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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.

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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.

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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).

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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

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Correspondence to Jim R Hughes.

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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)

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

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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

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