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Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin


Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1 Mb.

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Fig. 1: In silico Hi-C, SPRITE and GAM average contact maps match experimental data.
Fig. 2: Model 3D structures are a bona fide representation of chromatin conformations in single cells.
Fig. 3: Bulk Hi-C, SPRITE and GAM data are faithful overall to average 3D distances.
Fig. 4: Stochasticity of single-cell contact maps reflects the intrinsic variability of single-molecule 3D conformations.
Fig. 5: The number of cells required for replicate reproducibility differs among Hi-C, SPRITE and GAM.
Fig. 6: Noise-to-signal levels vary differently with genomic distance in Hi-C, SPRITE and GAM.

Data availability

Published Hi-C, SPRITE, GAM and microscopy data used for analysis are available at the referenced papers. The new GAM data from the F123 cell line are available on the 4D Nucleome data portal under accession no. 4DNFIFBSQ1EO.

Code availability

The codes used in our work are based on standard, publicly available software packages, as detailed in Methods. Molecular dynamics simulations use LAMMPS, v.30july2016. Analyses and plots were produced with the Anaconda package v.4.7.12. HiCRep correlations were computed with R v.3.5.1. 3D structure visualizations were produced with POV-Ray, v.3.7. The algorithms for simulation of Hi-C, SPRITE, GAM and SLICE in silico use standard routines, such as DBSCAN, and are described in full detail in Methods. The Hi-C, SPRITE and GAM algorithms are available at


  1. 1.

    Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21, 207–226 (2019).

    PubMed  Google Scholar 

  2. 2.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Oudelaar, A. M. et al. Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat. Genet. 50, 1744–1751 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565 (2020).

    CAS  Google Scholar 

  9. 9.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Bickmore, W. A. The spatial organization of the human genome. Annu. Rev. Genomics Hum. Genet. 14, 67–84 (2013).

    CAS  PubMed  Google Scholar 

  12. 12.

    Dekker, J. & Misteli, T. Long-range chromatin interactions. Cold Spring Harb. Perspect. Biol. 7, a019356 (2015).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Pombo, A. & Dillon, N. Three-dimensional genome architecture: players and mechanisms. Nat. Rev. Mol. Cell Biol. 16, 245–257 (2015).

    CAS  PubMed  Google Scholar 

  14. 14.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Dixon, J. R., Gorkin, D. U. & Ren, B. Chromatin domains: the unit of chromosome organization. Mol. Cell 62, 668–680 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Spielmann, M., Lupiáñez, D. G. & Mundlos, S. Structural variation in the 3D genome. Nat. Rev. Genet. 19, 453–467 (2018).

    CAS  PubMed  Google Scholar 

  17. 17.

    Finn, E. H. & Misteli, T. Molecular basis and biological function of variability in spatial genome organization. Science 365, eaaw9498 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

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

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Cattoni, D. I. et al. Single-cell absolute contact probability detection reveals chromosomes are organized by multiple low-frequency yet specific interactions. Nat. Commun. 8, 1753 (2017)..

  22. 22.

    Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362, eaau1783 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Cardozo Gizzi, A. M. et al. Microscopy-based chromosome conformation capture enables simultaneous visualization of genome organization and transcription in intact organisms. Mol. Cell 74, 212–222 (2019).

    CAS  PubMed  Google Scholar 

  24. 24.

    Finn, E. H. et al. Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell 176, 1502–1515 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Chiariello, A. M., Annunziatella, C., Bianco, S., Esposito, A. & Nicodemi, M. Polymer physics of chromosome large-scale 3D organisation. Sci. Rep. 6, 29775 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Bianco, S. et al. Modeling single-molecule conformations of the HoxD region in mouse embryonic stem and cortical neuronal cells. Cell Rep. 28, 1574–1583 (2019).

    CAS  PubMed  Google Scholar 

  27. 27.

    Bianco, S. et al. Polymer physics predicts the effects of structural variants on chromatin architecture. Nat. Genet. 50, 662–667 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Conte, M. et al. Polymer physics indicates chromatin folding variability across single-cells results from state degeneracy in phase separation. Nat. Commun. 11, 3289 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene–enhancer interactions. Cell 161, 1012–1025 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Andrey, G. et al. A switch between topological domains underlies HoxD genes collinearity in mouse limbs. Science 340, 1234167 (2013).

  31. 31.

    Noordermeer, D. et al. The dynamic architecture of Hox gene clusters. Science 334, 222–225 (2011).

    CAS  PubMed  Google Scholar 

  32. 32.

    Li, Q. et al. The three-dimensional genome organization of Drosophila melanogaster through data integration. Genome Biol. 18, 145 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Serra, F. et al. Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors. PLoS Comput. Biol. 13, e1005665 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Nir, G. et al. Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling. PLoS Genet. 14, e1007872 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Lin, D., Bonora, G., Yardimci, G. G. & Noble, W. S. Computational methods for analyzing and modeling genome structure and organization. Wiley Interdiscip. Rev. Syst. Biol. Med. 11, e1435 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Tjong, H. et al. Population-based 3D genome structure analysis reveals driving forces in spatial genome organization. Proc. Natl Acad. Sci. USA 113, E1663–E1672 (2016).

    CAS  PubMed  Google Scholar 

  37. 37.

    Bohn, M. & Heermann, D. W. Diffusion-driven looping provides a consistent provides a consistent framework for chromatin organization. PLoS ONE 5, e12218 (2010).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Barbieri, M. et al. Complexity of chromatin folding is captured by the strings and binders switch model. Proc. Natl Acad. Sci. USA 109, 16173–16178 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Brackley, C. A., Taylor, S., Papantonis, A., Cook, P. R. & Marenduzzo, D. Nonspecific bridging-induced attraction drives clustering of DNA-binding proteins and genome organization. Proc. Natl Acad. Sci. USA 110, E3605–E3611 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jost, D., Carrivain, P., Cavalli, G. & Vaillant, C. Modeling epigenome folding: formation and dynamics of topologically associated chromatin domains. Nucleic Acids Res. 42, 9553–9561 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Di Pierro, M., Zhang, B., Aiden, E. L., Wolynes, P. G. & Onuchic, J. N. Transferable model for chromosome architecture. Proc. Natl Acad. Sci. USA 113, 12168–12173 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Buckle, A., Brackley, C. A., Boyle, S., Marenduzzo, D. & Gilbert, N. Polymer simulations of heteromorphic chromatin predict the 3D folding of complex genomic loci. Mol. Cell 72, 786–797 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Fiorillo, L. et al. A modern challenge of polymer physics: novel ways to study, interpret, and reconstruct chromatin structure. Wiley Interdiscip. Rev. Comput. Mol. Sci. 10, e1454 (2019).

    Google Scholar 

  46. 46.

    Shi, G., Liu, L., Hyeon, C. & Thirumalai, D. Interphase human chromosome exhibits out of equilibrium glassy dynamics. Nat. Commun. 9, 3161 (2018)..

  47. 47.

    Nicodemi, M. & Prisco, A. Thermodynamic pathways to genome spatial organization in the cell nucleus. Biophys. J. 96, 2168–2177 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Fiorillo, L. et al. Inference of chromosome 3D structures from GAM data by a physics computational approach. Methods 181–182, 70–79 (2020).

    PubMed  Google Scholar 

  49. 49.

    Barbieri, M. et al. Active and poised promoter states drive folding of the extended HoxB locus in mouse embryonic stem cells. Nat. Struct. Mol. Biol. 24, 515–524 (2017).

    CAS  PubMed  Google Scholar 

  50. 50.

    Kragesteen, B. K. et al. Dynamic 3D chromatin architecture contributes to enhancer specificity and limb morphogenesis. Nat. Genet. 50, 1463–1473 (2018).

    CAS  PubMed  Google Scholar 

  51. 51.

    Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Dekker, J. et al. The 4D nucleome project. Nature 549, 219–226 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

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

    CAS  Google Scholar 

  56. 56.

    Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Lando, D., Stevens, T. J., Basu, S. & Laue, E. D. Calculation of 3D genome structures for comparison of chromosome conformation capture experiments with microscopy: an evaluation of single-cell Hi-C protocols. Nucleus 9, 190–201 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Díaz, N. et al. Chromatin conformation analysis of primary patient tissue using a low input Hi-C method. Nat. Commun. 9, 4938 (2018)..

  61. 61.

    Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).

    CAS  Google Scholar 

  62. 62.

    Kremer, K. & Grest, G. S. Dynamics of entangled linear polymer melts: a molecular-dynamics simulation. J. Chem. Phys. 92, 5057–5086 (1990).

    CAS  Google Scholar 

  63. 63.

    Gribnau, J., Hochedlinger, K., Hata, K., Li, E. & Jaenisch, R. Asynchronous replication timing of imprinted loci is independent of DNA methylation, but consistent with differential subnuclear localization. Genes Dev. 17, 759–773 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

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

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Bystricky, K., Heun, P., Gehlen, L., Langowski, J. & Gasser, S. M. Long-range compaction and flexibility of interphase chromatin in budding yeast analyzed by high-resolution imaging techniques. Proc. Natl Acad. Sci. USA 101, 16495–16500 (2004).

    CAS  PubMed  Google Scholar 

  66. 66.

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

    CAS  PubMed  Google Scholar 

  67. 67.

    Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining (eds Simoudis, E. et al.) 226–231 (AAAI Press, 1996).

  68. 68.

    Tahara, M. et al. Cell diameter measurements obtained with a handheld cell counter could be used as a surrogate marker of G2/M arrest and apoptosis in colon cancer cell lines exposed to SN-38. Biochem. Biophys. Res. Commun. 434, 753–759 (2013).

    CAS  PubMed  Google Scholar 

  69. 69.

    Yang, F. et al. Dielectrophoretic separation of colorectal cancer cells. Biomicrofluidics 4, 13204 (2010).

    PubMed  Google Scholar 

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We received no specific funding for this work. M.N. acknowledges support from CINECA ISCRA (nos. HP10CYFPS5 and HP10CRTY8P), Einstein BIH Fellowship Award (nos. EVF-BIH-2016 and 2019), Regione Campania SATIN Project 2018–2020 and computer resources from INFN, CINECA, ENEA CRESCO/ENEAGRID and Scope/ReCAS/Ibisco at the University of Naples. A. Pombo thanks the Helmholtz Association (Germany) for support. M.N. and A. Pombo acknowledge support from the National Institutes of Health Common Fund 4D Nucleome Program (grant nos. 1U54DK107977-01 and 1UM1HG011585-01) and the EU H2020 Marie Curie ITN (no. 813282). I.I.-A. acknowledges support from the Federation of European Biochemical Societies (FEBS Long-Term Fellowship). A.M.C. and S.B. acknowledge support from CINECA ISCRA (grant no. HP10CCZ4KN).

Author information




M.N. designed the project with input from A. Pombo and A. Prisco. L.F., F.M. and M.C. developed the modeling. L.F. and F.M. ran computer simulations and performed data analyses with help from M.C., A.M.C., S.B., A.E. and A.A. R.K., A.K. and I.I.-A. produced and normalized the GAM dataset. M.N., L.F., F.M., A. Pombo and A. Prisco wrote the manuscript, with input from all the authors.

Corresponding author

Correspondence to Mario Nicodemi.

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Competing interests

A. Pombo and M.N. hold a patent on ‘Genome Architecture Mapping’: Pombo, A., Edwards, P. A. W., Nicodemi, M., Beagrie, R. A. & Scialdone, A. Patent no. PCT/EP2015/079413 (2015).

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Peer review information Nature Methods thanks Zhijun Duan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Table 1 and Figs. 1–16.

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Fiorillo, L., Musella, F., Conte, M. et al. Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin. Nat Methods 18, 482–490 (2021).

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