Abstract
Gene expression patterns are governed by the hierarchical organization of the genome. Numerous efforts, leveraging both polymer physics-based models and experimental imaging technologies, have sought to elucidate the structure-function relationship of chromatin fibers. However, a major challenge is posed by the multi-scale nature of chromatin organization. Here, we present an experimentally informed, polymer physics-based model capable of reconstructing chromatin structural ensembles by integrating low-resolution contact data with MNase-derived nucleosome positioning information. We apply our approach to multiple human genomic loci. Our analysis shows distinct structural features associated with active and inactive chromatin states, providing insights into the relationship between genomic organization and transcriptional activity. These findings offer a framework for understanding genome structure-function relationships.
Data availability
In our study, we use published datasets of the hESC cell line as input data. The Micro-C and in situ Hi-C contact map datasets from Krietenstein et al. are available on the 4D Nucleome Data Portal under accession numbers 4DNES21D8SP8 [https://data.4dnucleome.org/experiment-set-replicates/4DNES21D8SP8/] (Micro-C dataset) and 4DNES2M5JIGV [https://data.4dnucleome.org/experiment-set-replicates/4DNES2M5JIGV/] (Hi-C dataset)98. Nucleosome position data for the relevant genomic loci were obtained from the MNase-Seq H1 sample by Yazdi et al., deposited in the GEO database under accession number GSM1194220 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1194220]99. Also, all data underlying the analyses presented in this study are provided in the Source Data file. Source data are provided with this paper.
Code availability
PyMOL 2.5.0 Open-Source, 2022-03-17 is used for visualization of 3D polymer configurations. Gnuplot 5.4 patchlevel 2 and Python libraries using conda environment are used for plotting. Keynotes are used for schematic figures. A combination of C and Python codes are used for all the analyses. Python codes are available on GitHub100.
References
Forth, S. et al. Torque measurement at the single-molecule level. Annu. Rev. Biophys. 42, 583–604 (2013).
Ma, J. et al. DNA supercoiling during transcription. Biophys. Rev. 8, 75–87 (2016).
Dunaway, M. et al. Local domains of supercoiling activate a eukaryotic promoter in vivo. Nature 361, 746–748 (1993).
Mondal, A. et al. Understanding the role of DNA topology in target search dynamics of proteins. J. Phys. Chem. B 121, 9372–9381 (2017).
Bhattacherjee, A. et al. Search by proteins for their DNA target site: 1. The effect of DNA conformation on protein sliding. Nucleic Acids Res. 42, 12404–12414 (2014).
Bhattacherjee, A. et al. Search by proteins for their DNA target site: 2. The effect of DNA conformation on the dynamics of multidomain proteins. Nucleic Acids Res. 42, 12415–12424 (2014).
Wang, X. et al. Negatively charged, intrinsically disordered regions can accelerate target search by DNA-binding proteins. Nucleic Acids Res. 51, 4701–4712 (2023).
Vuzman, D. et al. DNA search efficiency is modulated by charge composition and distribution in the intrinsically disordered tail. Proc. Natl. Acad. Sci. USA 107, 21004–21009 (2010).
Sangeeta et al. Role of shape deformation of DNA-binding sites in regulating the efficiency and specificity in their recognition by DNA-binding proteins. JACS Au 4, 2640–2655 (2024).
Sangeeta et al. Nick induced dynamics in supercoiled DNA facilitates the protein target search process. J. Phys. Chem. B 128, 8246–8258 (2024).
Mondal, A. et al. Torsional behaviour of supercoiled DNA regulates recognition of architectural protein fis on minicircle DNA. Nucleic Acids Res. 50, 6671–6686 (2022).
Dey, P. et al. Structural basis of enhanced facilitated diffusion of dna-binding protein in crowded cellular milieu. Biophys. J. 118, 505–517 (2020).
Dey, P. et al. Role of macromolecular crowding on the intracellular diffusion of DNA binding proteins. Sci. Rep. 8, 844 (2018).
Mishra, S. et al. How do nucleosome dynamics regulate protein search on DNA? J. Phys. Chem. B 127, 5702–5717 (2023).
Tsompana, M. et al. Chromatin accessibility: a window into the genome. Epigenetics Chromatin 7, 33 (2014).
Hofmann, A. et al. Self-organised segregation of bacterial chromosomal origins. eLife 8, e46564 (2019).
Mondal, A. et al. Nucleosome breathing facilitates cooperative binding of pluripotency factors Sox2 and Oct4 to DNA. Biophys. J. 121, 4526–4542 (2022).
Bilokapic, S. et al. Histone octamer rearranges to adapt to DNA unwrapping. Nat. Struct. Mol. Biol. 25, 101–108 (2018).
Lee, B. et al. Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, micro-C, and promoter capture micro-C. Epigenetics Chromatin 15, 41 (2022).
Fraser, J. et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol. Syst. Biol. 11, 852 (2015).
Luger, K. et al. Crystal structure of the nucleosome core particle at 2.8 Å resolution. Nature 389, 251–260 (1997).
Mondal, A. et al. Kinetic origin of nucleosome invasion by pioneer transcription factors. Biophys. J. 120, 5219–5230 (2021).
Fierz, B. et al. Biophysics of chromatin dynamics. Annu. Rev. Biophys. 48, 321–345 (2019).
Brandani, G. et al. DNA sliding in nucleosomes via twist defect propagation revealed by molecular simulations. Nucleic Acids Res. 46, 2788–2801 (2018).
Meersseman, G. et al. Mobile nucleosomes–a general behavior. EMBO J. 11, 2951–2959 (1992).
Li, K. et al. Inter-nucleosomal potentials from nucleosomal positioning data. Eur. Phys. J. E 45, 33 (2022).
Mishra, S. et al. Superstructure detection in nucleosome distribution shows common pattern within a chromosome and within the genome. Life 12, 541 (2022).
Farr, S. et al. Nucleosome plasticity is a critical element of chromatin liquid-liquid phase separation and multivalent nucleosome interactions. Nat. Commun. 12, 2883 (2021).
Dekker, J. et al. Capturing chromosome conformation. Science 295, 1306–1311 (2002).
Bonev, B. et al. Organization and function of the 3d genome. Nat. Rev. Genet. 17, 661–678 (2016).
Sati, S. et al. Chromosome conformation capture technologies and their impact in understanding genome function. Chromosoma 126, 33–44 (2017).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Sanborn, A. 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).
Galazka, J. et al. Neurospora chromosomes are organized by blocks of importin alpha-dependent heterochromatin that are largely independent of h3k9me3. Genome Res. 26, 1069–1080 (2016).
Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqfish. Cell 174, 363–376 (2018).
Franke, M. et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538, 265–269 (2016).
Flavahan, W. et al. Insulator dysfunction and oncogene activation in idh mutant gliomas. Nature 529, 110–114 (2016).
Bonev, B. et al. Multiscale 3d genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017).
Dixon, J. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Zhang, Y. & Heermann, D. W. Loops determine the mechanical properties of mitotic chromosomes. PLoS ONE 6, 1–13 (2011).
Pope, B. et al. Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405 (2014).
Symmons, O. et al. Functional and topological characteristics of mammalian regulatory domains. Genome Res. 24, 390–400 (2014).
Ramírez, F. et al. High-resolution tads reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).
Lupiáñez, D. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).
Lupiáñez, D. et al. Breaking tads: How alterations of chromatin domains result in disease. Trends Genet. 32, 225–237 (2016).
Zhang, B. & Wolynes, P. G. Prediction of chromosome conformations with maximum entropy principle. Biophys. J. 108, 537a (2015).
Marti-Renom, M. A. & Mirny, L. A. Bridging the resolution gap in structural modeling of 3d genome organization. PLoS Comput. Biol. 7, 1–6 (2011).
Mirny, L. A. et al. The fractal globule as a model of chromatin architecture in the cell. Chromosome Res. 19, 37–51 (2011).
Polovnikov, K. E. et al. Crumpled polymer with loops recapitulates key features of chromosome organization. Phys. Rev. X 13, 041029 (2023).
Shi, G. & Thirumalai, D. A maximum-entropy model to predict 3d structural ensembles of chromatin from pairwise distances with applications to interphase chromosomes and structural variants. Nat. Commun. 14, 1150 (2023).
Lin, X., Qi, Y., Latham, A. P. & Zhang, B. Multiscale modeling of genome organization with maximum entropy optimization. J. Chem. Phys. 155, 010901 (2021).
Nguyen, H. T. & Thirumalai, D. Liquid-liquid phase separation of repeat disorder sequences leads to RNA conformational and dynamical heterogeneity. Biophys. J. 120, 108a (2021).
Zhang, B. & Wolynes, P. G. Topology, structures, and energy landscapes of human chromosomes. Proc. Natl. Acad. Sci. USA 112, 6062–6067 (2015).
Hovenga, V., Kalita, J. & Oluwadare, O. Hic-gnn: A generalizable model for 3d chromosome reconstruction using graph convolutional neural networks. Comput. Struct. Biotechnol. J. 21, 812–836 (2023).
Beagrie, R. A., Scialdone, A. et al. Complex multi-enhancer contacts captured by genome architecture mapping. Nature 543, 519–524 (2017).
Kalhor, R. et al. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30, 90–98 (2012).
Hu, M. et al. Bayesian inference of spatial organizations of chromosomes. PLoS Comput. Biol. 9, 1–14 (2013).
Le Treut, G., Képès, F. & Orland, H. A polymer model for the quantitative reconstruction of chromosome architecture from hic and gam data. Biophys. J. 115, 2286–2294 (2018).
Di Pierro, M. et al. De novo prediction of human chromosome structures: epigenetic marking patterns encode genome architecture. Proc. Natl. Acad. Sci. USA 114, 12126–12131 (2017).
Galan, S., Serra, F. et al. Identification of chromatin loops from Hi-C interaction matrices by ctcf–ctcf topology classification. NAR Genom. Bioinform. 4, lqac021 (2022).
Dugar, G. et al. A chromosomal loop anchor mediates bacterial genome organization. Nat. Genet. 54, 194–201 (2022).
Hofmann, A. et al. The role of loops on the order of eukaryotes and prokaryotes. FEBS Lett. 589, 2958–2965 (2015).
Chiariello, A. M. et al. Multiscale modelling of chromatin 4d organization in sars-cov-2 infected cells. Nat. Commun. 15, 1–12 (2024).
Forte, G. et al. Modeling the 3d spatiotemporal organization of chromatin replication. PRX Life 2, 033014 (2024).
Brandani, G. B., Gu, C., Gopi, S. & Takada, S. Multiscale Bayesian simulations reveal functional chromatin condensation of gene loci. PNAS Nexus 3, 226 (2024).
Kadam, S. et al. Predicting scale-dependent chromatin polymer properties from systematic coarse-graining. Nat. Commun. 14, 4108 (2023).
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).
Di Stefano, M. et al. Transcriptional activation during cell reprogramming correlates with the formation of 3d open chromatin hubs. Nat. Commun. 11, 2564 (2020).
Mendieta-Esteban, J., Di Stefano, M. et al. 3d reconstruction of genomic regions from sparse interaction data. NAR Genom. Bioinform. 3, lqab017 (2021).
Neguembor, M. V., Arcon, J. P. et al. Mios, an integrated imaging and computational strategy to model gene folding with nucleosome resolution. Nat. Struct. Mol. Biol. 29, 1011–1023 (2022).
Barth, R. et al. Coupling chromatin structure and dynamics by live super-resolution imaging. Sci. Adv. 6, eaaz2196 (2020).
Lorzadeh, A. et al. Nucleosome density ChIP-Seq identifies distinct chromatin modification signatures associated with MNase accessibility. Cell Rep. 17, 2112–2124 (2016).
modENCODE Consortium et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010).
Rosenbloom, K. et al. ENCODE data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res. 41, D56–D63 (2013).
Wiese, O. et al. Nucleosome positions alone can be used to predict domains in yeast chromosomes. Proc. Natl. Acad. Sci. USA 116, 17307–17315 (2019).
Consortium, R. E. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Ernst, J. & Kellis, M. Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc. 12, 2478–2492 (2017).
Roy et al. Identification of functional elements and regulatory circuits by Drosophila modencode. Science 330, 1787–1797 (2010).
Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).
Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662–D669 (2015).
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).
Ricci, M. et al. Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo. Cell 160, 1145–1158 (2015).
Zufferey, M. et al. Comparison of computational methods for the identification of topologically associating domains. Genome Biol. 19, 217 (2018).
Chen, F. et al. Hicdb: a sensitive and robust method for detecting contact domain boundaries. Nucleic Acids Res. 46, 11239–11250 (2018).
Ester, M. et al. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96, 226–231 (1996).
Gamby, A. et al. Convex-hull algorithms: implementation, testing, and experimentation. Algorithms 11, 195 (2018).
Pownall, M. E. et al. Chromatin expansion microscopy reveals nanoscale organization of transcription and chromatin. Science 381, 92–100 (2023).
Amadei, A. et al. Essential dynamics of proteins. Proteins 17, 412–425 (1993).
Di Pierro, M. et al. Transferable model for chromosome architecture. Proc. Natl. Acad. Sci. USA 113, 12168–12173 (2016).
Cui, Y. & Bustamante, C. Pulling a single chromatin fiber reveals the forces that maintain its higher-order structure. Proc. Natl. Acad. Sci. USA 97, 127–132 (2000).
Arbona, J. et al. Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations. Genome Biol. 18, 81 (2017).
Leidescher, S. et al. Spatial organization of transcribed eukaryotic genes. Nat. Cell Biol. 24, 327–339 (2022).
Mergell, B., Everaers, R. & Schiessel, H. Nucleosome interactions in chromatin: fiber stiffening and hairpin formation. Phys. Rev. E 70, 011915 (2004).
Zeng, Y. et al. Lin28a binds active promoters and recruits Tet1 to regulate gene expression. Mol. Cell 61, 153–160 (2016).
Chen, K. et al. Danpos: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res. 23, 341–351 (2013).
Kumari, K. et al. Computing 3d chromatin configurations from contact probability maps by inverse Brownian dynamics. Biophys. J. 118, 2193–2208 (2020).
Carignano, M. A. et al. Local volume concentration, packing domains, and scaling properties of chromatin. eLife 13, RP97604 (2024).
Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565.e7 (2020).
Yazdi, P. et al. Nucleosome organization in human embryonic stem cells. PLoS ONE 10, e0136314 (2015).
Mittal, R., Heermann, D. W. & Bhattacherjee, A. An experimentally-informed polymer model reveals high resolution organization of genomic loci. Scor8R/MultiScale_Chromosome_Organisation, https://doi.org/10.5281/zenodo.17913622 (2025).
Acknowledgements
We gratefully acknowledge the financial support from DST India (CRG/2023/000636), DBT India (BT/PR46247/BID/7/1015/2023), DBT CoE research grant and JNU ANRF PAIR grant (ANRF/PAIR/2025/000029/PAIR-A). A.B. gratefully acknowledges support from the Alexandar von Humboldt Foundation, Germany. D.H. gratefully acknowledges the support from Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster). R.M. acknowledges the financial support from the Council of Scientific & Industrial Research (CSIR), Govt. of India for Senior Research Fellowship (File No: 09/0263(11815)/2021-EMR-I).
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Contributions
A.B. conceived the idea. R.M. and A.B. designed the model and algorithms. R.M. collected the relevant datasets and performed the simulations. R.M. and A.B. wrote the manuscript. D.H. provided feedback regarding further improvement of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Andrea Chiariello and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Mittal, R., Heermann, D.W. & Bhattacherjee, A. An experimentally-informed polymer model reveals high resolution organization of genomic loci. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68928-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-68928-w
