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DamC reveals principles of chromatin folding in vivo without crosslinking and ligation

Abstract

Current understanding of chromosome folding is largely reliant on chromosome conformation capture (3C)-based experiments, where chromosomal interactions are detected as ligation products after chromatin crosslinking. To measure chromosome structure in vivo, quantitatively and without crosslinking and ligation, we implemented a modified version of DNA adenine methyltransferase identification (DamID) named DamC, which combines DNA methylation-based detection of chromosomal interactions with next-generation sequencing and biophysical modeling of methylation kinetics. DamC performed in mouse embryonic stem cells provides the first in vivo validation of the existence of topologically associating domains (TADs), CTCF loops and confirms 3C-based measurements of the scaling of contact probabilities. Combining DamC with transposon-mediated genomic engineering shows that new loops can be formed between ectopic and endogenous CTCF sites, which redistributes physical interactions within TADs. DamC provides the first crosslinking- and ligation-free demonstration of the existence of key structural features of chromosomes and provides novel insights into how chromosome structure within TADs can be manipulated.

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Fig. 1: DamC: methylation-based measurement of chromosomal interactions.
Fig. 2: Physical model of methylation dynamics.
Fig. 3: An inducible mESC line used to perform DamC and test the model predictions.
Fig. 4: DamC confirms the existence of TAD boundaries and quantitatively correlates with 4C and Hi-C.
Fig. 5: DamC-based detection of CTCF loops.
Fig. 6: Ectopic CTCF insertion leads to the formation of new loops and stripes.
Fig. 7: Scaling analysis of contact probabilities in vivo.

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Data availability

The sequencing data from this study, including bedgraph files for the visualization of DamC and 4C profiles from all samples described in the manuscript, are available at the NCBI Gene Expression Omnibus with accession code GEO GSE128017. A University of California, Santa Cruz session containing all the DamC and 4C tracks used can be found at https://genome.ucsc.edu/s/zhan/DamC_publication_2019. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE72 partner repository with the dataset identifier PXD013507. Source data for Figs. 1 and 37 and Supplementary Figs. 13, 5 and 6 are available online.

Code availability

The custom-made codes used to analyze the data are available at https://github.com/zhanyinx/NMSB_2019_redolfi_et_al.

References

  1. Denker, A. & de Laat, Wde The second decade of 3C technologies: detailed insights into nuclear organization. Genes Dev. 30, 1357–1382 (2016).

  2. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  Google Scholar 

  3. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  Google Scholar 

  4. Norton, H. K. et al. Detecting hierarchical genome folding with network modularity. Nat. Methods 15, 119–122 (2018).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Zhan, Y. et al. Reciprocal insulation analysis of Hi-C data shows that TADs represent a functionally but not structurally privileged scale in the hierarchical folding of chromosomes. Genome Res. 27, 479–490 (2017).

    Article  CAS  Google Scholar 

  10. Zuin, J. et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc. Natl Acad. Sci. USA 111, 996–1001 (2014).

    Article  CAS  Google Scholar 

  11. Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  13. Guo, Y. et al. CRISPR Inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

    Article  CAS  Google Scholar 

  14. Sanborn, A. L. et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl Acad. Sci. USA 112, E6456–E6465 (2015).

    Article  CAS  Google Scholar 

  15. Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).

    Article  CAS  Google Scholar 

  16. Gavrilov, A., Razin, S. V. & Cavalli, G. In vivo formaldehyde cross-linking: it is time for black box analysis. Brief. Funct. Genom. 14, 163–165 (2015).

    Article  CAS  Google Scholar 

  17. 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  Google Scholar 

  18. Williamson, I. et al. Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization. Genes Dev. 28, 2778–2791 (2014).

    Article  Google Scholar 

  19. Belmont, A. S. Large-scale chromatin organization: the good, the surprising, and the still perplexing. Curr. Opin. Cell Biol. 26, 69–78 (2014).

    Article  CAS  Google Scholar 

  20. Fudenberg, G. & Mirny, L. A. Higher-order chromatin structure: bridging physics and biology. Curr. Opin. Genet. Dev. 22, 115–124 (2012).

    Article  CAS  Google Scholar 

  21. Tiana, G. & Giorgetti, L. Integrating experiment, theory and simulation to determine the structure and dynamics of mammalian chromosomes. Curr. Opin. Struct. Biol. 49, 11–17 (2018).

    Article  CAS  Google Scholar 

  22. Alipour, E. & Marko, J. F. Self-organization of domain structures by DNA-loop-extruding enzymes. Nucleic Acids Res. 40, 11202–11212 (2012).

    Article  CAS  Google Scholar 

  23. Nichols, M. H. & Corces, V. G. A CTCF code for 3D genome architecture. Cell 162, 703–705 (2015).

    Article  CAS  Google Scholar 

  24. Wang, S. et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353, 598–602 (2016).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Brant, L. et al. Exploiting native forces to capture chromosome conformation in mammalian cell nuclei. Mol. Syst. Biol. 12, 891 (2016).

    Article  Google Scholar 

  27. Quinodoz, S. A. et al. Higher-order inter-chromosomal hubs shape 3D genome organization in the nucleus. Cell 174, 744–757.e24 (2018).

    Article  CAS  Google Scholar 

  28. Lebrun, E., Fourel, G., Defossez, P.-A. & Gilson, E. A methyltransferase targeting assay reveals silencer-telomere interactions in budding yeast. Mol. Cell. Biol. 23, 1498–1508 (2003).

    Article  CAS  Google Scholar 

  29. Cléard, F., Moshkin, Y., Karch, F. & Maeda, R. K. Probing long-distance regulatory interactions in the Drosophila melanogaster bithorax complex using Dam identification. Nat. Genet. 38, 931–935 (2006).

    Article  Google Scholar 

  30. Steensel, Bvan & Henikoff, S. Identification of in vivo DNA targets of chromatin proteins using tethered Dam methyltransferase. Nat. Biotechnol. 18, 424–428 (2000).

    Article  Google Scholar 

  31. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  33. Masui, O. et al. Live-cell chromosome dynamics and outcome of X chromosome pairing events during ES cell differentiation. Cell 145, 447–458 (2011).

    Article  CAS  Google Scholar 

  34. Peric-Hupkes, D. et al. Molecular maps of the reorganization of genome-nuclear lamina interactions during differentiation. Mol. Cell 38, 603–613 (2010).

    Article  CAS  Google Scholar 

  35. Kind, J. et al. Single-cell dynamics of genome-nuclear lamina interactions. Cell 153, 178–192 (2013).

    Article  CAS  Google Scholar 

  36. Cadiñanos, J. & Bradley, A. Generation of an inducible and optimized piggyBac transposon system. Nucleic Acids Res. 35, e87 (2007).

    Article  Google Scholar 

  37. Kamionka, A., Bogdanska‐Urbaniak, J., Scholz, O. & Hillen, W. Two mutations in the tetracycline repressor change the inducer anhydrotetracycline to a corepressor. Nucleic Acids Res. 32, 842–847 (2004).

    Article  CAS  Google Scholar 

  38. Giorgetti, L. et al. Predictive polymer modeling reveals coupled fluctuations in chromosome conformation and transcription. Cell 157, 950–963 (2014).

    Article  CAS  Google Scholar 

  39. Hou, C., Zhao, H., Tanimoto, K. & Dean, A. CTCF-dependent enhancer-blocking by alternative chromatin loop formation. Proc. Natl Acad. Sci. USA 105, 20398–20403 (2008).

    Article  CAS  Google Scholar 

  40. Rawat, P., Jalan, M., Sadhu, A., Kanaujia, A. & Srivastava, M. Chromatin domain organization of the TCRb Locus and its perturbation by ectopic CTCF binding. Mol. Cell. Biol. 37, e00557–16 (2017).

    Article  CAS  Google Scholar 

  41. Geeven, G., Teunissen, H., de Laat, W. & de Wit, E. peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data. Nucleic Acids Res. 46, e91 (2018).

    Article  Google Scholar 

  42. Vian, L. et al. The energetics and physiological impact of cohesin extrusion. Cell 173, 1165–1178.e20 (2018).

    Article  CAS  Google Scholar 

  43. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017).

    Article  CAS  Google Scholar 

  44. Scolari, V. F., Mercy, G., Koszul, R., Lesne, A. & Mozziconacci, J. Kinetic signature of cooperativity in the irreversible collapse of a polymer. Phys. Rev. Lett. 121, 057801 (2018).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

    Article  CAS  Google Scholar 

  47. Erickson, H. P. Size and shape of protein molecules at the nanometer level determined by sedimentation, gel filtration, and electron microscopy. Biol. Proced. Online 11, 32 (2009).

    Article  CAS  Google Scholar 

  48. 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  Google Scholar 

  49. Kalhor, R., Tjong, H., Jayathilaka, N., Alber, F. & Chen, L. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30, 90–98 (2012).

    Article  CAS  Google Scholar 

  50. Rosa, A. & Everaers, R. Structure and dynamics of interphase chromosomes. PLOS Comput. Biol. 4, e1000153 (2008).

    Article  Google Scholar 

  51. La Fortezza, M. et al. DamID profiling of dynamic Polycomb-binding sites in Drosophila imaginal disc development and tumorigenesis. Epigenetics Chromatin 11, 27 (2018).

    Article  Google Scholar 

  52. Tosti, L. et al. Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo. Genome Res. 28, 592–605 (2018).

    Article  CAS  Google Scholar 

  53. Tiana, G. et al. Structural fluctuations of the chromatin fiber within topologically associating domains. Biophys. J. 110, 1234–1245 (2016).

    Article  CAS  Google Scholar 

  54. Gu, B. et al. Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements. Science 359, 1050–1055 (2018).

    Article  CAS  Google Scholar 

  55. Germier, T. et al. Real-time imaging of a single gene reveals transcription-initiated local confinement. Biophys. J. 113, 1383–1394 (2017).

    Article  CAS  Google Scholar 

  56. Urlinger, S. et al. Exploring the sequence space for tetracycline-dependent transcriptional activators: novel mutations yield expanded range and sensitivity. Proc. Natl Acad. Sci. USA 97, 7963–7968 (2000).

    Article  CAS  Google Scholar 

  57. Vogel, M. J., Peric-Hupkes, D. & van Steensel, B. Detection of in vivo protein–DNA interactions using DamID in mammalian cells. Nat. Protoc. 2, 1467–1478 (2007).

    Article  CAS  Google Scholar 

  58. Gu, H., Zou, Y.-R. & Rajewsky, K. Independent control of immunoglobulin switch recombination at individual switch regions evidenced through Cre-loxP-mediated gene targeting. Cell 73, 1155–1164 (1993).

    Article  CAS  Google Scholar 

  59. Sanulli, S. et al. Jarid2 methylation via the PRC2 complex regulates H3K27me3 deposition during cell differentiation. Mol. Cell 57, 769–783 (2015).

    Article  CAS  Google Scholar 

  60. Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011).

    Article  CAS  Google Scholar 

  61. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  Google Scholar 

  62. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteom. 13, 2513–2526 (2014).

    Article  CAS  Google Scholar 

  63. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  Google Scholar 

  64. Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “Proteomic Ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteom. 13, 3497–3506 (2014).

    Article  Google Scholar 

  65. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

  66. Splinter, E., de Wit, E., van de Werken, H. J. G., Klous, P. & de Laat, W. Determining long-range chromatin interactions for selected genomic sites using 4C-seq technology: from fixation to computation. Methods 58, 221–230 (2012).

    Article  CAS  Google Scholar 

  67. Gaidatzis, D., Lerch, A., Hahne, F. & Stadler, M. B. QuasR: quantification and annotation of short reads in R. Bioinformatics 31, 1130–1132 (2015).

    Article  CAS  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  Google Scholar 

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

    Article  CAS  Google Scholar 

  70. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  72. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

This work is dedicated to the memory of M. Dahan. Research in the Giorgetti laboratory is funded by the Novartis Foundation and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation (grant agreement no. 759366, ‘BioMeTre’). The Kind laboratory was funded by the ERC (grant agreement no. 678423, ‘EpiID’) and EMBO (no. LTF 1214-2016 to I.G.). R.S.G. acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 705354 and an EMBO Long-Term fellowship (no. ALTF 1086-2015). We would like to thank P. Cron for cloning TetO-piggyBac plasmids; S. Aluri and S. Thiry for assistance with high-throughput sequencing; M. Stadler for help with bioinformatics analysis; S. Grzybek and H.-R. Hotz for server supports; and E. Heard and R. Galupa (Institut Curie, PSL Research University) for kindly providing PGK cells. We are grateful to D. Schuebeler and R. Galupa for critically reading the manuscript, and to G. Fudenberg for useful comments on scaling behavior. We acknowledge The ENCODE Project Consortium and, in particular, the Ren and Hardison laboratories for ChIP-Seq datasets in ESC.

Author information

Authors and Affiliations

Authors

Contributions

J.R. generated cell lines and performed DamC experiments. Y.Z. wrote the model with assistance from G.T. and analyzed the data. C.V.-Q. performed 4C in W.dL.’s laboratory. M.K. assisted with cell culture and DamC library preparation and performed Hi-C experiments. I.G. and J.K. helped with experimental design and data analysis. V.I. performed mass spectrometry experiments and analysis. T.P. provided constructs for initial experiments and discussed the data. R.S.G. provided CTCF site sequences and tested CTCF binding in preliminary experiments. E.M. contributed to design of the initial experiments. S.A.S. developed the DamC library preparation protocol and performed piggyBac insertion mapping experiments. L.G. designed the study and wrote the paper with J.R. and Y.Z. and input from all the authors.

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Correspondence to Luca Giorgetti.

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Integrated supplementary information

Supplementary Fig. 1 Parameter study of model predictions.

a) Left: DamC enrichment is plotted as a function of the concentrations of rTetR-Dam and TetO viewpoints, imposing specific and non-specific dissociation constants to 1 nM and 80 nM respectively. Right: the rTetR-Dam concentration where the DamC enrichment is maximal is linearly correlated with the concentration of TetO viewpoint. b) DamC enrichment shows a maximum irrespective of the choice of the numerical parameters. This is exemplified by plots of DamC enrichment as a function of rTetR-Dam concentration when varying the TetO specific affinity and keeping the nonspecific affinity fixed (left panel) and vice versa (right panel).

Source data

Supplementary Fig. 2 Experimental system and optimized DamC protocol.

a) rTetR-Dam-EGFP-ERT2 becomes increasingly localized to the nucleus upon increasing 4-OHT concentration in the culture medium, as shown by the increasingly nuclear accumulation of EGFP. Maximum intensity projections of 10 wide-field Z planes are shown. Bright spots indicate binding of rTetR-Dam-EGFP-ERT2 to the 256x TetO array on chromosome X (see Fig. 1c). b) Schematics of the strategy for measuring rTetR-Dam-EGFP-ERT2 nuclear concentrations as a function of 4-OHT concentration. After exposing the cells to different concentrations of 4-OHT, nuclei were extracted and prepared for mass spectrometry. The relative abundance of nuclear rTetR-EGFP-Dam-ERT2 was measured using parallel reaction monitoring (PRM) using two replicate samples from all 4-OHT concentrations. Absolute quantification was performed in triplicate uniquely in the 500 nM 4-OHT sample using proteomic-ruler based mass spectrometry measurements (Wiśniewski et al. Mol. Cell. Proteomics 13, 3497–3506, 2014). We then extrapolated absolute nuclear rTetR-Dam copy numbers at all concentrations of 4-OHT based on the absolute quantification at 500 nM 4-OHT and the relative PRM quantification. Finally, the nuclear concentration of Dam-fusion Protein was calculated based on the average nuclear volume determined based on DAPI staining. Contamination from cytoplasmic proteins was estimated by comparing protein copy numbers of nuclear and whole-cell extracts, and subtracted from nuclear copy numbers. c) Protein copy numbers determined in nuclear extracts at 500 nM 4-OHT using the proteomic ruler strategy (Wiśniewski et al. Mol. Cell. Proteomics 13, 3497–3506, 2014). Data from three biological replicates are plotted before correction for cytoplasmic contamination. d) Schematics of the DamC library preparation. Genomic DNA is extracted from cells expressing the Dam-fusion protein. To avoid nonspecific ligation events in step 2, DNA is treated with shrimp alkaline phosphatase prior to DpnI digestion. After digestion with DpnI, a non-templated adenine is added to the 3’ blunt end of double-stranded DNA followed by ligation of the UMI-Adapter. Next, double-stranded DNA is denatured before random annealing of the second single stranded Adapter. In step 4, a T4-DNA-Polymerase is used for removal of 3’ overhangs and synthesis in the 5´→ 3´ direction. Finally, libraries are amplified by PCR and prepared for next generation sequencing. UMI: Unique Molecular Identifier. e) The DamC sequencing library preparation protocol includes UMIs allowing to filter ~40% of duplicated reads, and increases by roughly 30% the coverage of methylated GATC sites genome-wide compared to classical DamID (Peric-Hupkes et al. Mol. Cell 38, 603–613, 2010). at the same sequencing depth. f) Median DamC enrichment at the same viewpoints used for Fig. 3d as a function of 4-OHT concentration. Significant amounts of DamC enrichment in our experimental system can be observed in a range of rTetR-Dam nuclear concentrations corresponding to 5–10 and 0.1-1 nM 4-OHT for the lines carrying 890 and 135 viewpoints, respectively.

Source data

Supplementary Fig. 3 Characterization of the TetO-piggyBac clonal cell line and saturation analysis.

a) DamC enrichment from single DpnI fragments within +/− 100 kb from individual TetO viewpoints is plotted for two biological replicates performed with 0.1-1 nM 4-OHT. The Spearman correlation coefficient between the two replicates is indicated. b) The percentage of TetO viewpoints inserted in close proximity (<1 kb) from an active promoter or enhancer, or from a CTCF site that is bound in ChIP-seq (Nora et al. Cell 169, 930-944.e22, 2017). c) 4C interaction profiles obtained using a TetO viewpoint within 2 kb from an endogenous CTCF site and the partner CTCF locus as a reverse viewpoint. d) DamC and 4C interaction profiles measured from a TetO viewpoint inserted at the 3’UTR of the Chic1 gene within the Tsix TAD in the X inactivation center. Dashed lines indicate the interactions of Chic1 with the Linx and Xite loci. e) Definition of a deviation score measuring local differences between DamC and 4C. The deviation score is defined as the average quadratic difference between the DamC and the 4C signal in a 20-restriction fragment interval, normalized by the mean of the signal in the same interval. Two intervals are shown on the right to illustrate the differences between deviation scores of ~1 and ~3. f) Left: the 10% most dissimilar 20-fragment intervals are enriched in active chromatin, based on the dominant ChromHMM state (Ernst & Kellis. Nat. Methods 9, 215–216, 2012) in the interval using four chromatin states (ChromHMM emissions) (Chi-Square Test: pvalue < 10−9). ‘Inert’ corresponds to chromatin that is not enriched in H3K9me3, H3K27m3, H3K36me3, H3K9ac, nor H3K27ac. See the Methods section for more details. Right: The distributions of deviation scores in 20-fragment intervals where the dominant ChromHMM state is either inert, repressive, polycomb-associated or active, showing that active chromatin tends to show higher local dissimilarity between 4C and DamC (p-values from Wilcoxon test, two-sided). Cf. panel f for an example of a deviation score of ~3, corresponding to the average dissimilarity at active chromatin regions. g) Left: correlation between DamC signal in the -Dox sample and DNase-seq in mESC from ENCODE datasets. Each point in the scatter plot represents the aggregated signal in 20 kb; all 20 kb intervals genome-wide are shown along with their Spearman correlation. Right: One representative megabase on Chr1 showing the high correlation between the two signals. DamC and DNase-seq data were normalized to have equal average signal over the genomic interval shown here. h) Left: Removing DNase hypersensitive GATCs (see Methods) does not lead to increased local similarity between DamC and 4C. Distributions of local deviation scores are calculated over all 130 valid profiles and deviation scores between two DamC biological replicates is shown for comparison (p-values from Wilcoxon test, one-sided).

Source data

Supplementary Fig. 4 Additional DamC and 4C profiles from TetO viewpoints.

DamC (red) and 4C (black) profiles from forty TetO viewpoints in the pure clone with 135 TetO insertions.

Supplementary Fig. 5 TetO-piggyBac insertions do not perturb chromosome structure.

a) Insertion of TetO arrays does not perturb genome structure. Hi-C heatmaps of three different genomic locations harboring an array of 50xTetO sites and the corresponding wild-type locus are shown. Hi-C data are binned at 10 kb resolution. b) In windows of +/− 50 or +/− 200 kb surrounding the TetO integration sites, no significant changes can be detected in Hi-C at 5 and 10 kb resolution, respectively. Indeed, deviation scores between wild-type and TetO cells obtained at TetO insertion sites (green violin plot) are similar to those obtained at random wild-type genomic viewpoints (pink violin plot), and significantly smaller than those obtained by comparing virtual 4C profiles from pairs of different random genomic viewpoints (blue) (p-values are from Wilcoxon test, one-sided). c) Left: scheme of viewpoints used for the 4C experiment shown on the right. In cells harboring the TetO insertions, the ‘forward’ 4C viewpoint is within the TetO array as in main Fig. 3; in wild-type cells, the viewpoint is adjacent to the insertion genomic coordinate. The reciprocal viewpoint is the same in the two cases. Right: 4C profiles at the locus shown in panel c using the viewpoints shown on the left are indistinguishable.

Source data

Supplementary Fig. 6 Analysis of TetO-CTCF insertions.

a) Percentage of TetO-CTCF viewpoints occurring in close proximity (<1 kb) from an active promoter or enhancer, or a CTCF site that is bound in ChIP-seq (Nora et al. Cell 169, 930-944.e22, 2017). b) Distribution of peaks detected by peakC per viewpoint in TetO-CTCF (left) and TetO line (right) c) Examples of interaction profiles from TetO-CTCF viewpoints occurring in regions that are either devoid of (left) or densely bound by CTCF (right). d) Two further examples of ectopic structures formed as a consequence of the insertion of TetO-CTCF viewpoints. Hi-C data are binned at 10 kb resolution. e) Scheme of Cre-mediated excision of the ectopic CTCF cassette and genotyping. f) Genotyping PCR showing Cre-mediated excision of the CTCF cassette from the two integration sites shown in Fig. 6 in the same mESC clone (A4).

Source data

Supplementary Fig. 7 Additional DamC and 4C profiles from TetO-CTCF viewpoints.

DamC (red) and 4C (black) profiles from forty TetO-CTCF viewpoints in the pure clone with 91 TetO insertions.

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Redolfi, J., Zhan, Y., Valdes-Quezada, C. et al. DamC reveals principles of chromatin folding in vivo without crosslinking and ligation. Nat Struct Mol Biol 26, 471–480 (2019). https://doi.org/10.1038/s41594-019-0231-0

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