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Complex multi-enhancer contacts captured by genome architecture mapping


The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key features of transcriptional control and their disruption can cause disease. Here we report a genome-wide method, genome architecture mapping (GAM), for measuring chromatin contacts and other features of three-dimensional chromatin topology on the basis of sequencing DNA from a large collection of thin nuclear sections. We apply GAM to mouse embryonic stem cells and identify enrichment for specific interactions between active genes and enhancers across very large genomic distances using a mathematical model termed SLICE (statistical inference of co-segregation). GAM also reveals an abundance of three-way contacts across the genome, especially between regions that are highly transcribed or contain super-enhancers, providing a level of insight into genome architecture that, owing to the technical limitations of current technologies, has previously remained unattainable. Furthermore, GAM highlights a role for gene-expression-specific contacts in organizing the genome in mammalian nuclei.

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Figure 1: Concept of genome architecture mapping.
Figure 2: GAM independently reproduces general features of genome architecture identified by Hi-C.
Figure 3: Enhancers and active genes are enriched among specifically interacting genomic regions detected using the SLICE statistical model.
Figure 4: Super-enhancers are highly enriched among the most highly interacting TAD triplets.
Figure 5: Complex interactions between super-enhancer-containing TADs span tens of megabases.

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We thank J. Walter for critically reading the manuscript; C. Ferrai and E. Brookes for preparing mES cells; S. Robin, M.-F. Sagot and K. N. Natarajan for initial contributions to the statistical and bioinformatic analyses of GAM; T. Baslan and J. Hicks for suggestions on sequencing strategy; J. Pang from Illumina for help with HiSeq recipes; O. Clarke from Zeiss Microdissection Systems for advice on microdissection; K. Semrau from MYcroarray for advice on MYtag probes; P. A. Dear for advice about HAPPY mapping; T. Rito for help with ImageJ scripts; and D. Henrique and W. Bickmore for providing mES cell lines. The work was supported by the Medical Research Council, UK (A.P., R.A.B., M.C., S.Q.X., I.d.S., L.M.L., M.R.B., L.G., N.D.), by the Helmholtz Foundation (A.P., R.A.B., M.S., D.C.A.K., M.B.), by MRC-Technology (A.P., M.C., M.R.B.), by the Berlin Institute of Health (BIH; A.P., M.B.), by Breast Cancer Campaign (P.A.W.E.) and by Cancer Research UK (P.A.W.E.). Work by J.D. and J.F. was supported by grants from the Canadian Institutes of Health Research (CIHR) (MOP-86716, CAP-120350). Work by M.N. was supported by CINECA ISCRA grants no. HP10CYFPS5 and no. HP10CRTY8P. M.N. also acknowledges computer resources from INFN and Scope at the University of Naples.

Author information

Authors and Affiliations



A.P. and P.A.W.E. devised the GAM concept. A.P., R.A.B., M.C., L.M.L. and M.R.B. optimized the GAM experimental protocol. R.A.B. produced the GAM datasets, and with L.G. developed the sequencing strategy. M.C. produced the control WGA-amplified DNA dataset. D.C.A.K. and S.Q.X. performed the FISH experiments. A.P. and N.D. supervised the experiments. R.A.B., M.S. and I.d.S. performed the bioinformatics analyses. M.N. conceived the SLICE model concept. M.N., A.S., A.P. and R.A.B. developed the SLICE model and A.S. implemented full calculations. M.B. performed the polymer modelling analysis. A.P. and M.N. supervised computational analyses. A.P. and R.A.B. developed the radial position and compaction analyses. J.F. processed the Hi-C data, supervised by J.D. R.A.B., A.P., A.S., M.N. and M.S. wrote the paper, all authors revised the manuscript. The authors consider D.C.A.K, M.C. and S.Q.X to have contributed equally to this work.

Corresponding authors

Correspondence to Mario Nicodemi or Ana Pombo.

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Competing interests

A related patent has been filed on behalf of A.P., P.A.W.E., M.N., A.S. and R.A.B. by the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin. The other authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Limitations of current genome-wide methods for measuring chromatin interactions.

a, The table lists the current genome-wide methods for measuring chromatin interactions and compares their various limitations. CNVs, copy number variants. b, GAM has few of the limitations that affect current genome-wide methods for mapping genome architecture. c, In 3C-based methods, the presence of multiple loci in a single interaction may dilute the measured ligation frequency between any two member loci. In GAM, the measured interaction is not affected by multiplicity. d, Two interactions on different chromosomes (or distant on the same chromosome) can be correlated (occur together in the same cells), anti-correlated (one interaction occurs while the other does not) or independent (they occur either together or in different single cells randomly).

Extended Data Figure 2 Outline of the GAM method.

a, Overview of the GAM methodology. b, Ultra-thin slice through a single nucleus produced by cryosectioning (image reproduced from ref. 63, Springer-Verlag). c, Isolation of individual nuclear profiles from cryosections using laser capture microdissection. Scale bars are 30 μm. d, Whole-genome amplified DNA extracted from microdissected nuclear profiles. hgDNA, human genomic DNA. e, Identification of regions in mouse chromosomes 2, 3 and 4, present in the four nuclear profiles, by next-generation sequencing. Coverage of mouse genomic DNA (gDNA) amplified by WGA is mostly even, with a few spikes possibly due to amplification biases. Black bars under each track indicate windows called as positive by a negative binomial approach.

Extended Data Figure 3 Quality control of the GAM dataset.

a, Percentage of reads mapped to the mouse genome was reproducible between 13 independently collected batches of nuclear profiles, and a minimal threshold of 15% mapped reads was used to identify the highest quality nuclear profiles. Dashed line shows position of cut-off for low-quality samples. Histogram below shows overall distribution. b, Sequencing depth versus number of 30-kb windows identified by a negative binomial fitting approach for four individual nuclear profiles. c, Percentage of 30-kb genomic windows identified in each nuclear profile, was reproducible between collection batches. Black diamonds indicate nuclear profile samples that did not pass quality control. Histogram below shows overall distribution. d, Histogram showing the percentage of the diploid mouse genome identified in each nuclear profile. Dashed line shows maximum genomic coverage obtainable from 0.22-μm slices of a 9 μm diameter spherical nucleus. e, Box plots showing the percentage of 30-kb windows from each chromosome identified in each nuclear profile. 46C mES cells are male and therefore haploid for the X chromosome. f, Positions of three approximately 40-kb fosmid probes within the HoxB locus. g, CryoFISH experiments were carried out by hybridizing each fosmid probe to cryosections. Probes were detected using specific, fluorophore-conjugated antibodies. Arrows indicate the localization of 40-kb genomic windows only within a small proportion of nuclear profiles, as expected from their small thickness. h, Comparison of probe detection in single nuclear profiles by cryoFISH and GAM. Top row, percentage of nuclear profiles that were labelled by each probe (median of four replicate experiments in OS25 mES cells, each replicate containing 1,500–2,600 nuclear profiles). Bottom row, percentage of nuclear profiles from the mES-400 dataset in which the region encompassing each probe was positively detected (in 46C mES cells).

Extended Data Figure 4 Exploration and normalization of biases in the mES-400 dataset.

a, Normalized linkage disequilibrium effectively reduces bias in GAM datasets. 30-kb windows were divided into equal groups according to their detection frequency, GC content or mappability (grey bar plots give mean ± interquartile range, left). Mean observed over expected values (% bias) between windows in each group are shown for three different normalization schemes (heat maps, middle). Calculating the normalized linkage disequilibrium results in the lowest absolute percentage bias in all three cases (box plots, right). b, The normalized linkage disequilibrium corrects for confounding effects on co-segregation matrices caused by small differences in the detection frequency of locus pairs. c, GAM matrices are less biased than Hi-C matrices both before and after ICE normalization. Observed over expected values are given for 50-kb windows stratified by restriction site density, GC content and mappability.

Extended Data Figure 5 Four-hundred nuclear profiles are sufficient to extract most of the information about co-segregation of loci at 30 kb resolution.

a, b, Normalized linkage disequilibrium matrix for a 3 Mb (a) and a 30 Mb (b) genomic region around the Esrrb locus plotted with increasing numbers of nuclear profiles included. c, Pearson correlation coefficient between eroded datasets including only the indicated number of nuclear profiles and the full mES-400 dataset. Green line indicates correlation over all pairs of loci, and pink for only those loci within 3 Mb of each other.

Extended Data Figure 6 GAM contact matrices for all chromosomes at 1 Mb resolution.

GAM matrices of normalized linkage disequilibrium are shown for all chromosomes at 1 Mb resolution, alongside published ChIP-seq tracks for H3K27ac, H3K36me3 and H3K9me3, DNase-seq, Hi-C PCA compartments and lamina-associated domains (LADs) from mES cells (Supplementary Table 3). White lines within matrices represent genomic regions with poor mappability.

Extended Data Figure 7 GAM reproduces a significant depletion of long-range contacts around previously identified TAD boundaries.

a, TAD organization of the Xist locus (Xist highlighted in red). b, TAD organization of the Esrrb locus (Esrrb highlighted in red). c, TAD organization of the HoxA locus (HoxA gene cluster highlighted in red). d, Depletion of long-range contacts observed when a 3 × 3 window box is moved across an example TAD boundary, at an offset of 2 windows from the matrix diagonal (that is, insulation score). e, Median ratio between linkage observed at a boundary versus 150 kb upstream and downstream of the boundary was significantly lower for a previously published list of TAD boundaries in mES cells5 (purple line) than for 5,000 randomly shuffled versions of the list (permutation test, P < 2 × 10−4; black histogram). f, Average profile of long-range contacts calculated over all TAD boundaries (purple line). The average profile with the largest depletion observed after 5,000 random permutations of TAD boundaries is shown for comparison (dashed grey line).

Extended Data Figure 8 Prominent interactions identified by SLICE co-segregate frequently in raw GAM data.

a, Mean co-segregation frequency of pairs of prominently interacting windows (red line) is consistently higher than the mean co-segregation frequency of all intrachromosomal window pairs (green line ± s.d.; green area) across a wide range of genomic distances. For example, when we consider all genomic loci separated by 10 Mb, we find that they co-segregate on average much less frequently (in 5.3 out of 408 nuclear profiles; ±2.6 s.d.) than locus pairs classified as interacting (in 10.1 out of 408 nuclear profiles; ±2.2 s.d.). b, Prominent interactions identified by SLICE over the Shh, Oct4 and c-Myc loci. Also shown are ChIP-seq tracks for pluripotency transcription factors Sox2, Nanog and Oct4, and for CTCF and H3K27ac, as well as DNase-seq, positions of predicted enhancers and topological domains and published Hi-C data at 50 kb resolution. c, Number of prominent interactions by overlapping feature present in each window. d, Genomic distances between pairs of prominently interacting windows by overlapping feature. e, Enrichment of genomic features overlapped by prominently interacting 30-kb windows. As for Fig. 3c, but excluding windows overlapping more than one feature (for example, both an inactive gene and an active gene). f, As for Fig. 3c, except enrichments are calculated for the top 5% most interacting pairs of 50-kb windows at each genomic distance ranked by normalized linkage disequilibrium (that is, GAM data before SLICE analysis). g, Enrichments calculated from independent 200 nuclear profile subsamples of the mES-400 dataset (n = 10, mean ± s.d.). h, A small proportion of 30-kb windows within the broadly inactive compartment B (calculated from Hi-C at 100 kb resolution) overlap active genes or enhancers. i, Prominent interactions involving active and enhancer windows are enriched irrespective of A or B compartmentalization, demonstrating that observed enrichments between active regions and enhancers are not a trivial consequence of nuclear compartmentalization. j, Average linkage from 5-kb windows overlapping an enhancer to prominently interacting 30-kb active windows (orange), enhancer windows (purple) or non-interacting active windows (control windows; grey).

Extended Data Figure 9 GAM also provides information about locus radial positioning and compaction.

a, A locus positioned centrally within the nucleus is more frequently found in equatorial nuclear profiles, which have a larger volume. By contrast, a locus positioned close to the nuclear periphery is more frequently found in apical sections, which have a smaller volume. b, The mean percentage of the genome covered per nuclear profile (as a proxy for nuclear profile volume) is negatively correlated with radial positioning in the five mouse autosomes for which radial position data are available35. c, A more de-compacted locus with a larger volume is intersected more frequently (that is, is detected in more nuclear profiles) than a corresponding compacted locus with a smaller volume. d, 30-kb windows in the highest quartiles of detection frequency also show a higher coverage by DNase-seq26 (top panel) and GRO-seq33 (bottom panel), indicating a greater level of active transcription. This is consistent with a general de-compaction of actively transcribed chromatin regions, leading to a volume-induced increase in detection frequency.

Extended Data Figure 10 TAD triplet enrichment analysis.

a, Ranking of candidate triplet TADs on the same autosomal chromosome by their mean Pi3 at a spatial distance of <100 nm and position of the cut-off for the top 2% selected for further analysis. b, Classification of TADs. TADs overlapping super-enhancers are designated SE. Non-super-enhancer–TADs are designated low-transcription when their GRO-seq coverage is in the bottom 25% quartile, or high-transcription when it is in the top 25%. Remaining TADs are classified as medium-transcription TADs. c, Genomic span of top 2% triplet interactions by TAD class. d, Enrichment analysis as in Fig. 4c additionally showing triplets containing medium transcription TADs. e, Enrichment analysis as in Fig. 4c, except TADs are classified according to whether they overlap super-enhancers, typical enhancers (TE) or no enhancers (None). f, Enrichment of TAD triplet classes calculated from independent 200 nuclear profile subsamples of the mES-400 dataset (n = 10, mean ± s.d.). g, TAD classification stratified by overlap with A and B compartments calculated from Hi-C at 1 Mb resolution. h, Enrichments calculated between sets of three super-enhancer-containing TADs or three highly transcribed TADs, stratified by their overlap with PCA compartments. i, Scheme for testing whether within an interacting triplet, two super-enhancer-containing TADs preferentially contact the 40 kb window overlapping the super-enhancer of the third super-enhancer-containing TAD. j, Average co-segregation between a 40 kb window directly overlapping a super-enhancer and two other super-enhancer-containing TADs in a triplet (purple line), two highly expressed TADs in a triplet (orange line), or two super-enhancer-containing TADs not in a triplet (dashed line). The 40-kb windows overlapping super-enhancers co-segregate more frequently with the two other super-enhancer-containing TADs in their triplet than 40-kb windows located 120 kb upstream or downstream (paired t-test, P < 10−6). A lower, yet significant enrichment was also found for one super-enhancer-containing TAD interacting with two highly transcribed TADs (P < 10−10), while no significant enrichment was detected between super-enhancer-containing TADs that did not form top triplets (P = 0.68). k, Percentage of TADs in each class that overlap LADs. l, Highly transcribed and super-enhancer-containing TADs that form the least triplet contacts more frequently overlap or are closer to LADs compared with TADs that form the most triplet contacts. Therefore, proximity to the nuclear lamina appears to curb the formation of higher complexity contacts involving highly transcribed TADs, either by restricting access to more central enhancer clusters or by limiting the surface available for the formation of multiple contacts.

Extended Data Figure 11 Model for chromatin organization in mES cell nuclei.

a, b, Polymer modelling performed using the SBS model under different conditions. We sampled the ensemble of (1) polymers in the coil thermodynamics state, equivalent to the random-open conformation of a SAW model; (2) polymers in the compact state, where binder-specific interactions prevail and fold the polymer in closed conformations; and (3) mixtures of the SBS polymers in coil and compact states. From these in silico models, we calculated the co-segregation frequency of polymer bead pairs (a) or triplets (b) for a wide range of genomic lengths (from 0.5 up to 20 Mb). The long-range decay of co-segregation probability observed in the mES-400 dataset (blue line and surface) is not consistent with the SAW model that lacks specific interactions (grey line and surface). Instead, the observed decay of pairs or triplets are closely matched by a 40:60 mixture of coil/compact SBS polymers (best-fit SBS model: red line, RSS = 1%; red surface, RSS = 2%), consistent with pair and triplet contacts being abundant features of chromatin folding across large genomic distances. c, Comparison of GAM normalized linkage and SLICE Pi between pairs of TADs tested by cryoFISH (see Fig. 5). *The predicted Pi for SE4/Low2 falls just below the significance threshold at their genomic distance. d, Distribution of inter-TAD distances obtained from cryoFISH data. Grey shading and dashed black line respectively give the estimated range and median of distance expected between non-interacting TADs at different linear separations (see Methods). e, The chromatin fibre is organized in TADs. Inactive TADs often coincide with LADs and are therefore generally associated with either the nuclear lamina or the surface of the nucleoli. Highly transcribed TADs and TADs containing strong enhancers form clusters away from the nuclear periphery. Inset, contacts within and between highly transcribed or super-enhancer-containing TADs are nucleated by active genes and enhancer elements.

Supplementary information

Supplementary Information

This file contains Supplementary Notes 1-3. (PDF 1458 kb)

Supplementary Table 1

Details of sequencing datasets generated for this study (NPs, negative and positive control), including experimental batch, sequencing depth, and all metrics used to judge the quality of each NP. (XLSX 140 kb)

Supplementary Table 2

This table contains a list of TADs forming the top 2% most interacting TAD triplets, including their transcriptional/se class. (XLSX 4118 kb)

Supplementary Table 3

Details of published genome-wide datasets used in this study, including accession numbers. (XLSX 8 kb)

Supplementary Table 4

Details of FISH probes used for validating long-range interactions. (XLSX 8 kb)

Supplementary Table 5

Measurements of distances between the edges of two FISH probe signals. (XLSX 26 kb)

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Beagrie, R., Scialdone, A., Schueler, M. et al. Complex multi-enhancer contacts captured by genome architecture mapping. Nature 543, 519–524 (2017).

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