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Boundary stacking interactions enable cross-TAD enhancer–promoter communication during limb development

Nature Genetics volume 56, pages 306–314 (2024)Cite this article

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Abstract

Although promoters and their enhancers are frequently contained within a topologically associating domain (TAD), some developmentally important genes have their promoter and enhancers within different TADs. Hypotheses about molecular mechanisms enabling cross-TAD interactions remain to be assessed. To test these hypotheses, we used optical reconstruction of chromatin architecture to characterize the conformations of the Pitx1 locus on single chromosomes in developing mouse limbs. Our data support a model in which neighboring boundaries are stacked as a result of loop extrusion, bringing boundary-proximal cis-elements into contact. This stacking interaction also contributes to the appearance of architectural stripes in the population average maps. Through molecular dynamics simulations, we found that increasing boundary strengths facilitates the formation of the stacked boundary conformation, counter-intuitively facilitating border bypass. This work provides a revised view of the TAD borders’ function, both facilitating and preventing cis-regulatory interactions, and introduces a framework to distinguish border-crossing from border-respecting enhancer–promoter pairs.

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Fig. 1: Chromatin conformation at Pitx1 domain examined by ORCA.
Fig. 2: Testing the three hypotheses regarding border-bypassing E–P interaction.
Fig. 3: Stack conformations center borders in the domain with multicontact loops, producing stripes.
Fig. 4: Modeling effects of increasing boundary strength.
Fig. 5: Boundary proximity is enriched for E–P pairs that interact across boundaries.

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Data availability

The chromosome trace data have been converted to the NIH 4DN data standard, FOF-CT (FISH-omic Format, Chromosome Tracing) and can be accessed at the 4DN data portal (https://data.4dnucleome.org/) with accession 4DNES4TC13IL. Simulation data are available on Zenodo with the following: https://doi.org/10.5281/zenodo.8148723 (ref. 89).

Code availability

Scripts for analyses and figure generation are available in our GitHub repository: https://github.com/BoettigerLab/Pitx1-ORCA-2023 (https://doi.org/10.5281/zenodo.8148745)90. Polymer simulations also require the simulation toolkit adapted from the open2c project, which is available here: https://github.com/BoettigerLab/polychrom (https://doi.org/10.5281/zenodo.7698987)91. Probe design and image analysis software is available at https://github.com/BoettigerLab/ORCA-public (https://doi.org/10.5281/zenodo.7698979)92.

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Acknowledgements

This work was supported by DP2GM132935A and U01 DK127419 from the National Institutes of Health (NIH; to A.N.B.), a Beckman Young Investigator Award and a Packard Foundation Award (to A.N.B.); a predoctoral fellowship from the Ministry of Education of Taiwan (to T.-C.H.) and a Howard Hughes Medical Institute Investigator position (to D.M.K.). We thank J. Wysocka, A. Villeneuve and members of the Boettiger and Kingsley labs for helpful comments and a critical reading of the manuscript. We thank L.J. Valencia from the Boettiger lab for technical assistance with microscopy.

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Authors and Affiliations

  1. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    Tzu-Chiao Hung, David M. Kingsley & Alistair N. Boettiger

  2. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA

    David M. Kingsley

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  1. Tzu-Chiao Hung
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Contributions

T.-C.H. performed the ORCA experiments. A.N.B. performed the simulations. T.-C.H. and A.N.B. analyzed and interpreted the data. D.M.K. conceived the genome-wide analysis and provided feedback on all aspects of the analysis. A.N.B and T.-C.H. wrote the manuscript with input from D.M.K.

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Correspondence to Alistair N. Boettiger.

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Extended data

Extended Data Fig. 1 Similarity to cHi-C data, determination of TADs, and median pairwise distance data.

a, Chromatin conformation at Pitx1 domain in forelimb and hindlimb as detected by cHi-C19, along with that in hindlimb detected by ORCA (from 33,359 individual chromosome traces). Plotted below are the directionality indices calculated from hindlimb cHi-C and ORCA used to determine TAD borders. The arrows help visualize the ‘directions’ of directionality index: TAD borders are where the index value transitions from positive to negative. The arrowhead on the contact map points to the ‘stripe’ emanating from B2. b,c, ORCA median pairwise distance of the Pitx1 chromosomal domain in hindlimb and forelimb cells, respectively. d, Difference in distance between forelimb and hindlimb cells. Red indicates that distance is smaller (closer) in hindlimb. e, Matrix of p-values for all distance differences shown in (d), using two-sided Wilcoxon rank-sum test for each pairwise distance. The colorbar turns to white at p = 0.05. Bad hybridizations (see Methods) are masked gray. n = 14,384 and 10,703 traces for forelimb and hindlimb, respectively.

Extended Data Fig. 2 Effect of contact threshold choices.

a, Pairwise contact frequency map from hindlimb traces with different contact thresholds (n = 33,359). Dotted circle highlights Pitx1-Pen interaction. b, Correlation with cHi-C data. X-axis is in log scale. Threshold = 200 nm is highlighted. c, log2 ratio of contact frequency between Pitx1 and various loci between forelimb and hindlimb. Blue indicates more frequent interaction in forelimb, and red more frequent in hindlimb. Stars indicate that the difference is significant (p = 0.05, two-sample z-test for proportions, two-sided). For (d) and (e), refer to Fig. 2 and main text for context. d, Proportion of traces fitting each conformation for forelimb and hindlimb. e, Relative risk of E-P contact for the three conformations. Error bars denote 95% confidence interval. In most threshold conditions, relative risk does not differ much between forelimb and hindlimb. This supports a model in which hindlimb cells achieve greater E-P interaction frequency through increasing Stack formation, not through changing intrinsic properties of Stack or other conformations.

Extended Data Fig. 3 Concordance of results between independent ORCA experiments.

a, Pairwise contact frequencies for individual experiments. Across experiments, loops and stripes are always stronger in hindlimb than in forelimb. b, Pearson’s correlation coefficient between the independent experiments.

Extended Data Fig. 4 Selecting groups of molecules in ORCA data based on model criteria.

a, Illustration of how, for example, Merge traces or Stack traces were selected from all molecules. (Left) First, the trace of individual Pitx1 regulatory domains were reconstructed from raw ORCA data. (Center) Each trace was represented as a pairwise-distance map. (Right) The individual maps that matched particular criteria were sorted into the corresponding categories (for example, Merge) (see Fig. 2a–c). (Bottom) Traces that did not detect either Pitx1, B1, B2, or Pen could not be categorized as either Stack or Other and were excluded from the analysis. b, For any groups of traces, a populational representation can be obtained using a pairwise-contact frequency map, in which the value at (x, y) represents the percentage of traces having the distance between positions x and y within 200 nm.

Extended Data Fig. 5 Contribution to E-P contact by the 3 conformation categories.

a–c, Three different ways to represent the same data. a, Fractions of traces that 1) have Pitx1-Pen contact and 2) are categorized as 1 of the 3 conformations in forelimb (blue, n = 14,384) and hindlimb (red, n = 10,703). Error bars represent standard deviations. Dashed lines indicate the fraction of all traces exhibiting Pitx1-Pen interaction respectively in the two limb tissues. Differences of proportions between forelimb and hindlimb are significant for Merge (p = 5.8e-3), Stack (p = 2.0e-8), and Other (p = 8.9e-4) (two-sample z-test for proportions, two-sided). b, Same as (a), with the bars stacked on top of each other. c, Fraction of E-P contacting traces categorized as 1 of the 3 conformations in forelimb (n = 1,813) and hindlimb (n = 1,699). Note that (c) only considers E-P contacting traces. The lack of difference between forelimb and hindlimb for all 3 conformations (p > 0.05 for all 3, two-sample Z-test for proportions) may seem surprising, but it reinforces the inference that it is stacking that promotes E-P contact, not E-P contact that increases stacking.

Extended Data Fig. 6 Stripe analysis is robust to choice of threshold.

a, As in Fig. 3c, but with a 100 nm threshold. b, As in Fig. 3d (top), but with a 100 nm threshold. c, As in Fig. 3f, but with a 100 nm threshold.

Extended Data Fig. 7 Stripes at the single-cell level.

a, Two models to explain stripes. The ‘Reel-in’ model predicts that the stripe anchor slides sequentially along the stripe, contacting one element at a time, due to uni-directional loop extrusion by a cohesin attached at the stripe anchor where it loaded. The stripe seen in aggregated population data emerges from a series of single-point contacts in individual traces. An alternative model proposes that stripe anchors make multiple contacts per trace, and represent a rosette of loops within the domain. Loop extrusion and thermal fluctuation help bring peripheral regions in contact with the loop anchors. Note that in the ‘Reel-in’ model, thermal fluctuation can also bring additional contacts, but its effect beyond local contribution is not essential to the model. b, Comparison of cHi-C data and ORCA data; a stripe anchored at B2 is highlighted in the box. The ORCA data is shown at a 100 nm cut-off, to complement the 200 nm cut-off shown in Fig. 1, and illustrate that stripes are not only observed with the more generous distance threshold. c, Contact maps from single traces from ORCA data, with contact thresholded at 100 nm. The stripe shown in (b) is highlighted with a black outline, and the number of contacts made with the stripe anchor is indicated in text. d, Comparison of bulk Hi-C data showing a stripe (black outline) in embryonic stem cells, with pseudo-bulk scHi-C data for the same region, showing the same stripe (though a bit more noisy). e, Individual single cell maps for the scHi-C data in (d); the stripe is highlighted with a black outline. The number of contacts the anchor makes with other elements in the domain per cell is indicated.

Extended Data Fig. 8 Centrality and multiple contact in simulation.

Plots characterizing the 10,000 polymers, each of length 200, from the result of the strong border condition of the base model simulation. a, Median number of contacts along the polymer. b, Median distance from center along the polymer. Bars, which might be difficult to spot due to their shortness, indicate 95% confidence interval as obtained by bootstrapping 1,000 times. c, The relationship between centrality within a trace and contact frequency with other parts of the trace, shown by the 2,000,000 segments from all the simulated polymers. Each dot represents a segment of a polymer with 200 segments. The X axis denotes distance from the center. The Y axis denotes the number of segments, including oneself, within the same polymer contacting this segment. The r and p-value of the log-log Pearson correlation are shown. Since the dots are too dense, the density of the dots is represented by color. The polymers were down-sampled by a factor of 5 (from length 1,000 to 200).

Extended Data Fig. 9 Effect of model parameters.

In different rows are the contact frequency maps for the strong border condition, maps for the weak border condition, difference between strong and weak maps, plots of the relative risk for E-P contact among the 3 conformation categories (error bars represent 95% confidence intervals), and plots of the fraction of traces in each conformation in the two conditions (error bars represent standard deviations). Across the columns are simulations with different parameter sets (n = 5,000 for each set); a, base model (as in Fig. 4), b, ½ LEF and c, ¼ LEF (reduced LEF density relative to base model), d, ½ LEF lifetime (reduces LEF’s time attached to the polymer), e, unidirectional (borders can only stop LEF from the direction indicated; right, left, left, left for the 4 borders), and f, reduced border diff (reduces the border strength difference between strong and weak by increasing the border strength of weak).

Extended Data Fig. 10 Modeling TAD border bypass without loop extrusion.

a, Schematic illustration of simulated 5 TAD forming domains (colored lines) and the interacting TAD border regions (colored circles). Relative interaction strengths are indicated by the weight of the lines (0.2 between TADs, 0.3 within TADs, 0.8 between P/B1/B2/E). The simulation explores the effect of strengthening the TAD borders by removing the inter-TAD affinity. Example polymers from the simulation are shown, illustrating the tendency of the TADs to separate. b, Contact frequency maps (log scale) for the two simulation conditions shown in (a). A threshold of 5 monomer diameters was used to define Hi-C/ORCA like ‘contact’. c, Difference of the contact frequency maps shown in (b); d, schematic and e, contact frequency map for a simulation illustrating the effect of increasing the affinity of the cross-TAD P/B1/B2/E interactions. f, Difference of the contact frequency maps shown in (e). Red indicates increased contact following increase in affinity among P/B1/B2/E. Note the inter-TAD regions change subtly but are red rather than blue. Intra-TAD interactions at the border elements decrease (blue) as a consequence of being sterically displaced by the increased long-range contact among border elements.

Supplementary information

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Supplementary Table 1.

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Supplementary Data

Fasta file of probes used in the ORCA.

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Hung, TC., Kingsley, D.M. & Boettiger, A.N. Boundary stacking interactions enable cross-TAD enhancer–promoter communication during limb development. Nat Genet 56, 306–314 (2024). https://doi.org/10.1038/s41588-023-01641-2

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