Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution

Abstract

The linear sequence of DNA provides invaluable information about genes and their regulatory elements along chromosomes. However, to fully understand gene function and regulation, we need to dissect how genes physically fold in the three-dimensional nuclear space. Here we describe immuno-OligoSTORM, an imaging strategy that reveals the distribution of nucleosomes within specific genes in super-resolution, through the simultaneous visualization of DNA and histones. We combine immuno-OligoSTORM with restraint-based and coarse-grained modeling approaches to integrate super-resolution imaging data with Hi-C contact frequencies and deconvoluted micrococcal nuclease-sequencing information. The resulting method, called Modeling immuno-OligoSTORM, allows quantitative modeling of genes with nucleosome resolution and provides information about chromatin accessibility for regulatory factors, such as RNA polymerase II. With Modeling immuno-OligoSTORM, we explore intercellular variability, transcriptional-dependent gene conformation, and folding of housekeeping and pluripotency-related genes in human pluripotent and differentiated cells, thereby obtaining the highest degree of data integration achieved so far to our knowledge.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: iOS allows simultaneous labeling of genes and proteins in SR.
Fig. 2: Restraint-based model reproduces ensemble average data and cell-to-cell structural variability.
Fig. 3: Hi-C reveals changes in TAD organization between hFibs and hiPSCs.
Fig. 4: Hi-C derived models of a 2.3-Mb region of human chr12 segment reveal conformational changes between hFibs and hiPSCs.
Fig. 5: Combined iOS and Hi-C modeling in hFibs and hiPSCs.
Fig. 6: Single-cell-like deconvolution of capture MNase-seq-derived nucleosome coverage in the GAPDH-IFFO1 and NANOG regions labeled with OligoSTORM.
Fig. 7: Integrated MiOS models of GAPDH-IFFO1 and NANOG genes in hFibs and hiPSCs.

Data availability

Raw data for the capture MNase-seq (E-MTAB-10074) and Hi-C (E-MTAB-10073) sequencing experiments generated in this study were deposited at the European Nucleotide Archive under accession number PRJEB42293. Imaging and modeling datasets generated in this work are available upon request. We provide raw data related to plots and statistical source data in the Source data section provided with this paper.

Code availability

Stand-alone versions of the softwares used for chromatin coarse-grained simulations and for the fitting algorithms developed herein are available in the following repositories: Chromatin Dynamics (http://mmb.irbbarcelona.org/gitlab/juanpablo/chrom_dyn) and Chromatin Fitting (http://mmb.irbbarcelona.org/gitlab/juanpablo/fit_chrom).

References

  1. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lakadamyali, M. & Cosma, M. P. Visualizing the genome in high resolution challenges our textbook understanding. Nat. Methods 17, 371–379 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Beliveau, B. J. et al. Single-molecule super-resolution imaging of chromosomes and in situ haplotype visualization using Oligopaint FISH probes. Nat. Commun. 6, 7147 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. 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 e215 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344–350 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Boettiger, A. N. et al. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature 529, 418–422 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mateo, L. J. et al. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 568, 49–54 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362, 6413.eaau1783 (2018).

    Article  Google Scholar 

  19. Su, J. H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659.e26 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nguyen, H. Q. et al. 3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. Nat. Methods 17, 822–832 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ricci, M. A., Manzo, C., Garcia-Parajo, M. F., Lakadamyali, M. & Cosma, M. P. Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo. Cell 160, 1145–1158 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Szabo, Q. et al. Regulation of single-cell genome organization into TADs and chromatin nanodomains. Nat. Genet. 52, 1151–1157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dans, P. D., Walther, J., Gomez, H. & Orozco, M. Multiscale simulation of DNA. Curr. Opin. Struct. Biol. 37, 29–45 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Buitrago, D. et al. Impact of DNA methylation on 3D genome structure. Nat. Commun. 12, 3243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Di Stefano, M., Paulsen, J., Jost, D. & Marti-Renom, M. A. 4D nucleome modeling. Curr. Opin. Genet. Dev. 67, 25–32 (2020).

    Article  PubMed  Google Scholar 

  26. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Abbas, A. et al. Integrating Hi-C and FISH data for modeling of the 3D organization of chromosomes. Nat. Commun. 10, 2049 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Levasseur, D. N., Wang, J., Dorschner, M. O., Stamatoyannopoulos, J. A. & Orkin, S. H. Oct4 dependence of chromatin structure within the extended Nanog locus in ES cells. Genes Dev. 22, 575–580 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hawkins, R. D. et al. Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6, 479–491 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Jungmann, R. et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11, 313–318 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schnitzbauer, J., Strauss, M. T., Schlichthaerle, T., Schueder, F. & Jungmann, R. Super-resolution microscopy with DNA-PAINT. Nat. Protoc. 12, 1198–1228 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Chiariello, A. M. et al. A dynamic folded hairpin conformation is associated with α-globin activation in erythroid cells. Cell Rep. 30, 2125–2135 e2125 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Oudelaar, A. M., Beagrie, R. A., Kassouf, M. T. & Higgs, D. R. The mouse alpha-globin cluster: a paradigm for studying genome regulation and organization. Curr. Opin. Genet. Dev. 67, 18–24 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Brown, J. M. et al. A tissue-specific self-interacting chromatin domain forms independently of enhancer-promoter interactions. Nat. Commun. 9, 3849 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mizuguchi, T. et al. Cohesin-dependent globules and heterochromatin shape 3D genome architecture in S. pombe. Nature 516, 432–435 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Blinka, S., Reimer, M. H. Jr., Pulakanti, K. & Rao, S. Super-enhancers at the Nanog locus differentially regulate neighboring pluripotency-associated genes. Cell Rep. 17, 19–28 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Buitrago, D. et al. Nucleosome Dynamics: a new tool for the dynamic analysis of nucleosome positioning. Nucleic Acids Res. 47, 9511–9523 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Walther, J. et al. A multi-modal coarse grained model of DNA flexibility mappable to the atomistic level. Nucleic Acids Res. 48, e29 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Maeshima, K., Ide, S., Hibino, K. & Sasai, M. Liquid-like behavior of chromatin. Curr. Opin. Genet. Dev. 37, 36–45 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Schueder, F. et al. An order of magnitude faster DNA-PAINT imaging by optimized sequence design and buffer conditions. Nat. Methods 16, 1101–1104 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565 e557 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hua, P. et al. Defining genome architecture at base-pair resolution. Nature 595, 125–129 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Ohno, M., Ando, T., Priest, D. G. & Taniguchi, Y. Hi-CO: 3D genome structure analysis with nucleosome resolution. Nat. Protoc. 16, 3439–3469 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Tan, L., Xing, D., Chang, C. H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Beliveau, B. J. et al. OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes. Proc. Natl Acad. Sci. USA 115, E2183–E2192 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Beliveau, B. J. et al. In situ super-resolution imaging of genomic DNA with OligoSTORM and OligoDNA-PAINT. Methods Mol. Biol. 1663, 231–252 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bates, M., Huang, B., Dempsey, G. T. & Zhuang, X. Multicolor super-resolution imaging with photo-switchable fluorescent probes. Science 317, 1749–1753 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Otterstrom, J., Garcia, A. C., Vicario, C., Gomez-Garcia, P. A., Cosma, M. P. & Lakadamyali, M. Super-resolution microscopy reveals how histone tail acetylation affects DNA compaction within nucleosomes in vivo. Nucleic Acids Res. 47, 8470–8484 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gomez-Garcia, P. A., Garbacik, E. T., Otterstrom, J. J., Garcia-Parajo, M. F. & Lakadamyali, M. Excitation-multiplexed multicolor superresolution imaging with fm-STORM and fm-DNA-PAINT. Proc. Natl Acad. Sci. USA 115, 12991–12996 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Belaghzal, H., Dekker, J. & Gibcus, J. H. Hi-C 2.0: an optimized Hi-C procedure for high-resolution genome-wide mapping of chromosome conformation. Methods 123, 56–65 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cui, X. J., Li, H. & Liu, G. Q. Combinatorial patterns of histone modifications in Saccharomyces cerevisiae. Yeast 28, 683–691 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  58. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

  59. Planet, E., Attolini, C. S., Reina, O., Flores, O. & Rossell, D. htSeqTools: high-throughput sequencing quality control, processing and visualization in R. Bioinformatics 28, 589–590 (2012).

    Article  CAS  PubMed  Google Scholar 

  60. Flores, O. & Orozco, M. nucleR: a package for non-parametric nucleosome positioning. Bioinformatics 27, 2149–2150 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Servant, N. et al. HiTC: exploration of high-throughput ‘C’ experiments. Bioinformatics 28, 2843–2844 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 19, 151 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Adhikari, B., Trieu, T. & Cheng, J. Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using distance geometry simulated annealing. BMC Genomics 17, 886 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lun, A. T. & Smyth, G. K. diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinf. 16, 258 (2015).

    Article  Google Scholar 

  67. Huang, B., Wang, W., Bates, M. & Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319, 810–813 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Case, D. A. et al. AMBER 2018 (Univ. California, 2018).

  69. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Meaburn, K. J., Misteli, T. & Soutoglou, E. Spatial genome organization in the formation of chromosomal translocations. Semin. Cancer Biol. 17, 80–90 (2007).

    Article  CAS  PubMed  Google Scholar 

  72. da Rosa, G. et al. Sequence-dependent structural properties of B-DNA: what have we learned in 40 years?. Biophys. Rev. 13, 995–1005 (2021).

    Article  CAS  PubMed  Google Scholar 

  73. Olson, W. K. et al. A standard reference frame for the description of nucleic acid base-pair geometry. J. Mol. Biol. 313, 229–237 (2001).

    Article  CAS  PubMed  Google Scholar 

  74. Wieczor, M., Hospital, A., Bayarri, G., Czub, J. & Orozco, M. Molywood: streamlining the design and rendering of molecular movies. Bioinformatics 36, 4660–4661 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Dans, P. D. et al. The static and dynamic structural heterogeneities of B-DNA: extending Calladine–Dickerson rules. Nucleic Acids Res. 47, 11090–11102 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ivani, I. et al. Parmbsc1: a refined force field for DNA simulations. Nat. Methods 13, 55–58 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge the support from the Barcelona Institute of Science and Technology (BIST) Ignite Grants (Seeding Stage 2017 and Second Phase 2018, to M.V.N. and P.D.D.); the European Union’s Horizon 2020 Research and Innovation Programme (CellViewer no. 686637 to M.L. and M.P.C.; ERC SimDNA no. 676556 to M.O.; and under the Marie Skłodowska-Curie grant agreement no. 754510 to J.P.A.); Ministerio de Ciencia e Innovación (grant no. 008506-PID2020-114080GB-I00 to M.P.C.), and an AGAUR grant from Secretaria d’Universitats i Recerca del Departament d’Empresa iConeixement de la Generalitat de Catalunya (grant no. 2017 SGR 1110 to M.O. and grant no. 006712 BFU2017-86760-P (AEI/FEDER, UE) to M.P.C.); Centro de Excelencia Severo Ochoa (grant nos. CEX2020-001049-S, MCIN/AEI/10.13039/501100011033 to CRG and CNAG authors and awarded to IRB Barcelona 2020-25); CERCA Programme/Generalitat de Catalunya (to CRG and CNAG authors); the People Program (Marie Curie Actions) FP7/2007–2013 under REA (grant no. 608959 to M.V.N.); Juan de la Cierva-Incorporación 2017 (to M.V.N.); PROBIST postdoctoral fellowship from Barcelona Institute of Science and Technology (to J.P.A.); INTREPiD Postdoctoral Programme cofunded by the European Commission (under grant agreement no. 754422 to X.G.); Grant for the recruitment of early-stage research staff FI-2020 (Operational Program of Catalonia 2014-2020 CCI grant no. 2014ES05SFOP007 of the European Social Fund to L.M.) and ‘La Caixa’ Foundation fellowship (ID 100010434 grant no. LCF/BQ/DR20/11790016 to L.M.); the Spanish Ministry of Science (for the EMBL partnership to CRG and CNAG authors and grant no. RTI2018-096704-B-100 to M.O.); Instituto de Salud Carlos Tercero (to CNAG authors; grant no. PT17/0009/0007 to M.O.); the Biomolecular and Bioinformatics Resources Platform (ISCIIIPT 13/000/0030 cofunded by the Fondo Europeo de Desarrollo Regional FEDER) (grant nos. Elixir-Excelerate: 676559; BioExcel2: 823830; and MuG: 676566 to M.O.); NIH grant nos. R01GM123289 and R01HD091797 (to J.A.A. and C.-t.W.); Bruker Inc. (to C.-t.W.); PEDECIBA (Programa de Desarrollo de las Ciencias Básicas) and SNI-ANII (Sistema Nacional de Investigadores, Agencia Nacional de Investigación e Innovación, Uruguay) (to P.D.D.); and ICREA (Institucio Catalana de Recerca i Estudis Avançats) (to M.O. and M.P.C.). We acknowledge the advanced light microscopy unit (ALMU) from CRG for their excellent technical support.

Author information

Authors and Affiliations

Authors

Contributions

The original idea and conceptualization were by M.V.N., P.D.D., I.B.H., M.P.C. and M.O. M.P.C., M.V.N., P.D.D., J.P.A. and M.O. wrote the article with contributions from all the authors. M.V.N. produced all imaging results (iOS and MiOS) together with X.G. and L.M. R.L. produced all the capture Hi-C/MNase-seq results which were postprocessed and analyzed by D.B., under the supervision of I.B.H. J.P.A. developed and validated the restraint-based model. J.P.A. and D.B. performed Hi-C-based simulations of chromosome segments. J.W. generated the coarse-grained chromatin structures at nucleosome level, and the deconvolution of MNase-seq signals. Fitting algorithms and fitting results were generated by P.R. together with J.W. All modeling, simulations and fitting results were supervised and analyzed by P.D.D. and M.O. M.P.C. supervised the generation and analyses of all the imaging results with contribution from M.L. Design of Oligopaint probes was performed by J.A.A. and C.-t.W. M.G. and J.B. performed the sequencing of capture Hi-C/MNase-seq experiments. P.D.D. and M.V.N. integrated all the results and were the scientific coordinators of the project.

Corresponding authors

Correspondence to Maria Victoria Neguembor, Modesto Orozco, Pablo D. Dans or Maria Pia Cosma.

Ethics declarations

Competing interests

C.-t.W. holds or has patent filings pertaining to imaging, and her laboratory holds a sponsored research agreement with Bruker Inc. Although non-equity holding, C.-t.W. is a cofounder of Acuity Spatial Genomics; through personal connections to George Church, she has equity in companies associated with him, including 10x Genomics and Twist. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Structural and Molecular Biology thanks Mattia Conte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor::Sara Osman, in collaboration with the Nature Structural and Molecular Biology team.

Additional information

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

Extended data

Extended Data Fig. 1 Schematic overview of human chromosome 12 region analyzed by MiOS.

a Schematic representation of chr12:6,140,000:8,460,000 region, showing the position of genes (grey arrows), Oligopaint probes (in green for NANOG, STELLA and magenta for GAPDH-IFFO1), and capture probes (orange). The A/B compartment track shows active A (red) and repressed B (blue) compartments for hFibs (from Hi-C; taken from9. Epigenetic marks for hFibs IMR90 (blue) and hiPSCs 20-b (red) are displayed (ChIP-seq tracks taken from30,31. The positions of the regions analyzed for the target genes (from left to right: GAPDH-IFFO1, STELLA, and NANOG) are highlighted in grey. b Genomic coordinates of Oligopaint probes. c-f qRT-PCR analysis in hFibs and hiPSCs for expression of (c) GAPDH, (d) IFFO1, (e) NANOG, and (f) STELLA. Mean and standard deviation (SD) of 2^-dCt values to B-ACTIN are shown; n = 6, and n = 4, independent replicates for hFibs and hiPSCs, respectively; two-tailed unpaired t-test; p = 0.4692 (c), p = 0.0672 (d), p = 1.4e-13 (e), p = 1.02e-7 (f). g Quantification of localization precision of super-resolution images. Boxplots (median with interquartile range) and whisker plots (10–90 percentile) are shown for oligoSTORM (locus, n = 8995), DNA-PAINT (H3, n = 23023), and oligoSTORM and DNA-PAINT beads (n = 135 and n = 158 localization tracks, respectively).

Source data

Extended Data Fig. 2 Restraint-based model reproduces cell-to-cell structural variability.

a Evolution of the number of input distance restraint violations from the experimental median distance matrix (Fig. 2a, left panel) when adding subsequent modeled structures to the ensemble obtained with the restraint-based approach. b End-to-end distance distributions for the experimental (gray) and modeled (red) ensembles. The boxes highlight the first, second and third quartiles, while the whiskers extend 1.5 times the interquartile range away from the box edges. Outliers are omitted. The plots come from 3,496 experimental and 70 modeled conformations. p = 0.16, two-sided Mann-Whitney test. c Root mean square deviation (rmsd) of bead/probe positions for best fitted modeled structures against each experimental structure (red, Nexp-model = 3,496), and null distribution of rmsd values between all experimental structures, after fitted / aligned (gray, Nnull = 6,109,260). p < 1e−16, two-sided Mann-Whitney test. The boxes highlight the first, second and third quartiles, while the whiskers extend 1.5 times the interquartile range away from the box edges. Outliers are omitted. d Variance from the first 10 principal components from PCA. e Projection of the displacement vectors onto the first 2 principal components from PCA. f 3D distance matrices for single structures extracted from experimental microscopy data18 (left) and from the ensemble obtained with the restraint-based model (right). The color scale ranges from 200 nm to 850 nm.

Source data

Extended Data Fig. 3 Contact matrices and correlation analyses between capture Hi-C replicates in hFibs, hiPSCs and published datasets.

a-c Contact matrices for the region chr12:6,140,000:8,460,000 in hFibs, displayed at 5-kb resolution, for (a) Hi-C data from Rao et al. (2014), (b) capture Hi-C for replica 1, and (c) capture Hi-C for replica 2. Plotted values are log10 of iteratively corrected interaction counts scaled to sum 1 million. The position of genes GAPDH-IFFO1 (magenta) and of STELLA and NANOG (green) are marked on X and Y axes. d, e Replicates 1 and 2, respectively, of the capture Hi-C from hiPSCs. Plotted values are the same as in (a-c). f Pearson correlation coefficient of the contact matrices between every pair of experiments: Rao et al. 2014, in-house hFibs (replicates 1 and 2), and hiPSCs (replicates 1 and 2).

Extended Data Fig. 4 Parameter selection and structure overlap for restraint-based models.

a, b Tuning of α parameter used in the distance restraint-based model for hFibs (a) or hiPSCs (b). For each α, correlation between experimental Hi-C interaction matrix and the modeled contact matrix (left) and mean absolute error between Hi-C derived average distances and predicted ensemble mean distances (right). Both Spearman and stratum-adjusted (HiCRep)70,71 correlation coefficients are shown. c Representation of the 2.3 Mb region of human chr12 segment from hiPSCs (cyan) and hFibs (yellow) cells. The GAPDH-IFFO1, NANOG, and STELLA loci are colored in pink, red, and orange, respectively. d Close-up of the GAPDH-IFFO1 region. e, f Close-ups of the STELLA/NANOG region highlighting the relative location of the two genes with respect to TAD formation in hiPSCs (cyan circle) and hFibs (yellow circles) cells.

Source data

Extended Data Fig. 5 Fittings using iOS localizations, capture Hi-C contacts, and the restraint-based model of chromatin.

Two specific cells with a given distance between both gene regions (GAPDH-IFFO1 and NANOG) are shown. a Structure from the simulated ensemble (distance GAPDH-IFFO1 to NANOG fixed at 1.191 µm) that best fits the iOS localizations within the confocal plane (considering a depth of 0.260 µm) of one hFib cell. Note that this single structure of the chr12 segment connecting the genes of interest fit to 52.8% of the iOS localizations (5-kb beads fitted are shown as orange spheres) and fulfills 43.9% of the Hi-C contacts simultaneously. Zoom-in of the genes showing the beads fitted to iOS localizations. b Same as (a) for a hiPS cell, where the GAPDH-IFFO1 to NANOG distance was fixed to 1.082 µm, fulfilling 72.7% of iOS localizations and 42.4% of the Hi-C contacts.

Extended Data Fig. 6 Nucleosome positioning in hiPSC and hFibs cells determined from capture MNase-seq.

a–e Comparison of fuzziness score obtained with nucleR (0:well-positioned - 1:fuzzy nucleosome) between hiPSCs and hFibs for nucleosomes detected in the complete captured region (chr12:6140000–8460000) (a) and at the individual genes (b–e); Replica 1 (R1) and 2 (R2) are shown. Box plots include a marker for the median of the data and a box indicating the interquartile range. Whiskers show minimum and maximum values. Wilcoxon rank sum test; (a) p < 2.22e-16 (188 vs 172 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (b) R1: p = 0.17, R2: p = 0.0012 (35 vs 34 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively) (c) R1: p = 4.8e-8, R2: p = 4.2e-10 (92 vs 83 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (d) R1: p = 0.04, R2: p = 0.0069 (32 vs 29 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (e) R1: p = 0.00022, R2: p = 0.0014 (29 vs 26 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively). f–i Nucleosome positioning around the following genes: (f) GAPDH, (g) IFFO1, (h) NANOG, and (i) STELLA. Black lines represent normalized (0–1) nucleosome coverage. Blue boxes are the nucleosome positions detected by nucleR60. Changes in nucleosome organization from hiPSCs to hFibs, detected with NucDyn41 are represented as color-coded boxes for inclusion (green), eviction (red), positive shifts (purple), and negative shifts (yellow).

Source data

Extended Data Fig. 7 Amplified views of the bottom-up first-principle coarse-grained model of the nucleosome fiber and the distribution of fitting values when the sampled conformations are confronted to iOS localizations.

a A representative folded GAPDH gene is amplified until consecutives centroids are shown. Each individual DNA centroid is located at the base pair reference frame (BPRF) following Cambridge and Tsukuba conventions72,73,74. These centroids represent the monomer length defined in our implementation, whose arbitrariness was based on a detailed knowledge on the structure and dynamics of B-DNA at the atomistic level42,75,76. In the first amplification, each DNA centroid is roughly represented considering its spherical exclusion volume (radius of van der Waals) centered at the BPRF. In the last two amplifications, the DNA-excluded volume is no longer depicted, and only the center of the BPRF is shown. Note that on average, being B-DNA, the distance between two consecutives base pairs is ~3.3 Å, although the experimentally falsifiable resolution of our predictions ranges from nucleosome clutches to near single nucleosome particles. b Distribution of the fitting values obtained from the filtered ensembles of GAPDH and NANOG folded conformations in hFibs and hiPSCs when confronted to iOS localizations. The top 10 structures with the highest fitting numbers, for which physical descriptors were computed and reported in Fig. 7 and Supplementary Table 2, are found at the right of the vertical dashed lines.

Source data

Supplementary information

Supplementary Information

Supplementary Note (related to Methods) and Tables 1, 2, 4 and 5.

Reporting Summary

Supplementary Video 1

Video of the overlap of structures obtained with the restraint-based simulations. Representation of the 2.3-Mb region of human chr12 segment from hiPSCs (cyan) and hFibs (yellow) cells. The GAPDH-IFFO1, NANOG and STELLA loci are colored in pink, red and orange, respectively. The video was made using the Molywood tool76.

Supplementary Table 3

List of primary probes Oligopaints.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Neguembor, M.V., Arcon, J.P., Buitrago, D. et al. MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution. Nat Struct Mol Biol 29, 1011–1023 (2022). https://doi.org/10.1038/s41594-022-00839-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41594-022-00839-y

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing