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3D structures of individual mammalian genomes studied by single-cell Hi-C
Open accessAbstract
The folding of genomic DNA from the beads-on-a-string-like structure of nucleosomes into higher-order assemblies is crucially linked to nuclear processes. Here we calculate 3D structures of entire mammalian genomes using data from a new chromosome conformation capture procedure that allows us to first image and then process single cells. The technique enables genome folding to be examined at a scale of less than 100 kb, and chromosome structures to be validated. The structures of individual topological-associated domains and loops vary substantially from cell to cell. By contrast, A and B compartments, lamina-associated domains and active enhancers and promoters are organized in a consistent way on a genome-wide basis in every cell, suggesting that they could drive chromosome and genome folding. By studying genes regulated by pluripotency factor and nucleosome remodelling deacetylase (NuRD), we illustrate how the determination of single-cell genome structure provides a new approach for investigating biological processes.
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“In regard to chromatin structure, Stevens and colleagues reported that the combination of chromosome conformation capture (3C) and tagged fluorescent imaging was able to detect the folding of a genomic sequence <100bp in a single cell (303).”
From Epigenetic Modifications in Stress Response Genes Associated With Childhood Trauma (2019) · cites this paper as [303]
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“Currently, single-cell Hi-C studies confirm the presence of TADs in individual cells but demonstrate significant variability in their shape (i.e., in the folding of DNA within TADs) [142,143,144,145].”
From Enhancer Function in the 3D Genome (2023) · cites this paper as [144]
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Citations
Cites
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Cited by
Sorted by citations
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Combining fluorescence imaging with Hi-C to study 3D genome architecture of the same single cell
Lando, D., Basu, S., Stevens, T. J., Riddell, A., Wohlfahrt, K., Cao, Y., Boucher, W., Leeb, M., Atkinson, L. P., Lee, S. F., et al. (2018) · Nature Protocols · doi:10.1038/nprot.2018.017 · Cited by 13
“Ranked chromosome pairs 0 50 100 150 Population Hi-C 4 × N 2 × N N (Stevens et al.(12))”
BackgroundSelf-citationThe 3D genome: From fundamental principles to disease and cancer
Umlauf, D., & Mourad, R. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.07.002 · Cited by 15
Live-cell 3D single-molecule tracking reveals how NuRD modulates enhancer dynamicsPreprint
Basu, S., Shukron, O., Hall, D., Parutto, P., Ponjavic, A., Shah, D., Boucher, W., Lando, D., Zhang, W., Reynolds, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.03.003178 · Cited by 14
Self-citationPolymer models are a versatile tool to study chromatin 3D organization
Esposito, A., Bianco, S., Fiorillo, L., Conte, M., Abraham, A., Musella, F., Nicodemi, M., Prisco, A., & Chiariello, A. M. (2021) · Biochemical Society Transactions · doi:10.1042/bst20201004 · Cited by 14
A (3D-Nuclear) Space Odyssey: Making Sense of Hi-C Maps
Mota-Gómez, I., & Lupiáñez, D. G. (2019) · Genes · doi:10.3390/genes10060415 · Cited by 16
“However, TAD conformations in single cells display a high degree of variability, ranging from compacted to more extended structures [70].”
BackgroundTechnologies to study spatial genome organization: beyond 3C
Übelmesser, N., & Papantonis, A. (2019) · Briefings in Functional Genomics · doi:10.1093/bfgp/elz019 · Cited by 13
The two waves in single-cell 3D genomics
Ulianov, S. V., & Razin, S. V. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.05.021 · Cited by 15
Major Reorganization of Chromosome Conformation During Muscle Development in Pig
Marti-Marimon, M., Vialaneix, N., Lahbib-Mansais, Y., Zytnicki, M., Camut, S., Robelin, D., Martine Yerle, B., & Foissac, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.748239 · Cited by 13
FISH Going Meso-Scale: A Microscopic Search for Chromatin Domains
Maslova, A., & Krasikova, A. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.753097 · Cited by 16
Mapping chromatin loops in single cells
Yu, M., Li, Y., & Hu, M. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.007 · Cited by 10
Comparison and critical assessment of single-cell Hi-C protocols
Gridina, M. M., Taskina, A., Lagunov, T. A., Nurislamov, A. R., Kulikova, T., Krasikova, A., & Fishman, V. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11023 · Cited by 12
Rubach, P., Zając, S., Jastrzębski, B., Sulkowska, J. I., & Sułkowski, P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz845 · Cited by 13
“it could be used to analyze chromatin organization (31).”
BackgroundRosa, A., Stefano, M. D., & Micheletti, C. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00127 · Cited by 12
TADs or no TADS: Lessons From Single-cell Imaging of Chromosome Architecture
Gizzi, A. M. C., Cattoni, D. I., & Nöllmann, M. (2020) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.12.034 · Cited by 13
“Contrasting this hypothesis, single-cell genome-wide studies have revealed a notable variability in chromosome spatial architecture both at the intra- and inter-TAD levels (Nagano et al. 2017; Stevens et al. 2017; Flyamer et al. 2017; Kind et al. 2015).”
BackgroundArchitectural proteins for the formation and maintenance of the 3D genome
Li, M., Gan, J., Sun, Y., Xu, Z., Yang, J., Sun, Y., & Li, C. (2020) · Science China Life Sciences · doi:10.1007/s11427-019-1613-3 · Cited by 15
Inferring chromosome radial organization from Hi-C data
Das, P., Shen, T., & McCord, R. P. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03841-7 · Cited by 13
Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization
Liu, W., Zhong, W., Chen, J. C., Huang, B., Hu, M., & Li, Y. (2022) · Genes · doi:10.3390/genes13040586 · Cited by 11
“…efficiency, requires high-depth Representative single-cell technologies DNA seqFISH+ [99], MERFISH [100], OligoFISSEQ [101], ORCA [102] Single-nucleus methyl-3C [40], Methyl-HiC [42], Dip-C [36], Nagano et al., 2017 [34], Flyamer et al., 2017 [35], Stevens et al., 2017 [103], sciHi-C [37]”
Gene regulatory landscape dissected by single-cell four-omics sequencing
Chen, Y., Liu, Z., Xu, H., Liu, J., Wang, M., Chi, Y., LIANG, B., Liu, M., Peng, Y., Ge, H., et al. (2026) · Nature · doi:10.1038/s41586-026-10322-z · Cited by 21
scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data
Shen, S., Zheng, Y., & Keleş, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac372 · Cited by 12
3D genomics and its applications in precision medicine
Chen, M.-J., Liu, X., Liu, Q.-Y., Shi, D., & Li, H. (2023) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-023-00428-x · Cited by 12
“3C technology-based 4C [4], 5C [7], in situ Hi-C (2013) [13], hybridization probe Capture-Hi-C and CHi-C (2014) [14, 15], DNase Hi-C (2015) [16], DNase Capture-Hi-C [16], and in situ DNase Hi-C (2016) [17] and single-cell Hi-C technology [18] and BL-Hi-C [19] have been invented.”
Shim, A. R., Frederick, J., Pujadas-Liwag, E. M., Kuo, T., Ye, I. C., Pritchard, J. A., Dunton, C. L., Gonzalez, P. C., Acosta, N., Jain, S., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301000 · Cited by 13
Dynamic changes in 3D chromatin structure during male gametogenesis in Arabidopsis thaliana
Song, Z., Xia, Q., Yang, M., Yang, T., Liu, Y., Wang, D., Shu, J., Liu, Z., Chi, Y., Xu, H., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03496-8 · Cited by 13
Zhang, W., Noa, A., Nienhaus, K., Hilbert, L., & Nienhaus, G. U. (2019) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab3200 · Cited by 12
Cohesin-Mediated Chromatin Interactions and Autoimmunity
Chandrasekaran, V., Oparina, N. Y., García-Bonete, M.-J., Wasén, C., Erlandsson, M. C., Malmhäll-Bah, E., Andersson, K., Jensen, M., Silfverswärd, S. T., Katona, G., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.840002 · Cited by 13
“org 4 identified cohesin-CTCF loops (2) interact with boundaries of the active chromatin recognized by its open conformation, presence of gene-activating histone modifications and active gene expression (23).”
BackgroundFeng, C., Wang, J., & Chu, X. (2023) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjad042 · Cited by 12
“Recently developed single-cell Hi-C technique and contact-based measurements have provided good resources for data-driven modeling of whole-genome structures ( Stevens et al., 2017 ; Tan et al., 2018 , 2021 ) , which exhibit significant cell-to-cell variations.”
Methody Cajal, S. R., Hümmer, S., Peg, V., Matías‐Guiu, X., de Torres, I. M., Castellví, J., Martínez-Sáez, E. A., & Hernández‐Losa, J. (2019) · Histopathology · doi:10.1111/his.13828 · Cited by 12
“Similarly, the accessibility of certain chromosomal regions might explain cell-type-specific chromosomal translocations such as BCR/ABL in leucocytes or VHL mutations in renal cell carcinoma.(37,38) Some mutations and genetic alterations are uniformly present in the entire organism, such as germline mutations, BRCA1 a…”
BackgroundVisualising three-dimensional genome organisation in two dimensions
Ing‐Simmons, E., & Vaquerizas, J. M. (2019) · Development · doi:10.1242/dev.177162 · Cited by 13
“These regions can be far apart or on different chromosomes, and the pairs of regions that colocalise vary from cell to cell (Nagano et al., 2013; Stevens et al., 2017; Wang et al., 2016).”
BackgroundThe non-coding genome in genetic brain disorders: new targets for therapy?
Salsench, E. M., Karkala, F., Lanko, K., & Barakat, T. S. (2021) · Essays in Biochemistry · doi:10.1042/ebc20200121 · Cited by 11
Dynamic and equilibrium properties of finite-size polymer models of chromosome folding
Conte, M., Fiorillo, L., Annunziatella, C., Esposito, A., Musella, F., Abraham, A., Bianco, S., & Chiariello, A. M. (2021) · Physical review. E · doi:10.1103/physreve.104.054402 · Cited by 14
Storage, visualization, and navigation of 3D genomics data
Waldispühl, J., Zhang, E., Butyaev, A., Nazarova, E., & Cyr, Y. (2018) · Methods · doi:10.1016/j.ymeth.2018.05.008 · Cited by 13
Modeling three-dimensional genomic organization in evolution and pathogenesis
Diament, A., & Tuller, T. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.07.008 · Cited by 11
Zhou, T., Zhang, R., Jia, D., Doty, R. T., Munday, A. D., Gao, D., Xin, L., Abkowitz, J. L., Duan, Z., & Ma, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.20.549578 · Cited by 12
Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential
zha, M., Wang, N., Zhang, C., & Wang, Z. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115914 · Cited by 12
“The single-cell Hi-C data were downloaded from [73] with file name GSM3271349_gm12878_03.clean.con.txt.gz.”
Method3D genome alterations and editing in pathology
Tiukacheva, E. A., Ulianov, S. V., Karpukhina, A. A., Razin, S. V., & Vassetzky, Y. S. (2023) · Molecular Therapy · doi:10.1016/j.ymthe.2023.02.005 · Cited by 14
Mesoscale, long-time mixing of chromosomes and its connection to polymer dynamics
Bajpai, G., & Safran, S. A. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011142 · Cited by 11
The rise of single‐cell transcriptomics in yeast
Nadal‐Ribelles, M., Solé, C., de Nadal, E., & Posas, F. (2024) · Yeast · doi:10.1002/yea.3934 · Cited by 11
“C (Stevens et al., 2017), and multiomic combinations.”
Single‐Cell Hi‐C Technologies and Computational Data Analysis
Dautle, M., & Chen, Y. (2025) · Advanced Science · doi:10.1002/advs.202412232 · Cited by 12
Developing landscapes: genome architecture during early embryogenesis
van der Weide, R. H., & de Wit, E. (2019) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2019.04.009 · Cited by 10
Machine Learning Approaches to Single-Cell Data Integration and Translation
Uhler, C., & Shivashankar, G. V. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3166132 · Cited by 11
Polymer Models of Chromatin Imaging Data in Single Cells
Conte, M., Chiariello, A. M., Abraham, A., Bianco, S., Esposito, A., Nicodemi, M., Matteuzzi, T., & Vercellone, F. (2022) · Algorithms · doi:10.3390/a15090330 · Cited by 15
“In particular, to assess whether our models do provide a bona-fide representation of the imaged chromatin structures, we used a computational method based on the root-mean-square deviation (RMSD) criterion [39,46].”
MethodBuilding Haplotype‐Resolved 3D Genome Maps of Chicken Skeletal Muscle
Li, J., Lin, Y., Li, D., He, M., Kui, H., Bai, J., Chen, Z., Gou, Y., Zhang, J., Wang, T., et al. (2024) · Advanced Science · doi:10.1002/advs.202305706 · Cited by 10
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Dharmaraj, T., Guan, Y., Liu, J. X., Badens, C., Gaborit, B., & Wilson, K. L. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00048 · Cited by 11
A minimal Gō-model for rebuilding whole genome structures from haploid single-cell Hi-C data
Wettermann, S., Brems, M. A., Siebert, J. T., Vu, G. T., Stevens, T. J., & Virnau, P. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109178 · Cited by 11
“The latter is indicated by Hi-C contact probabilities [7] and suggests there is some degree of self-similarity over a wide range of length scales with an overall compaction greater than a more extended, equilibrated polymer.”
BackgroundMethodResultInfluentialSelf-citationTranscription Factors Indirectly Regulate Genes through Nuclear Colocalization
Dai, Z. (2019) · Cells · doi:10.3390/cells8070754 · Cited by 11
Gizzi, A. M. C. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.780822 · Cited by 13
“Although spatial genetics approaches established that interactions between compartments vary from cell to cell, B-B domain contact frequencies were higher than A-A domains at distances below 75 Mb but not at higher genomic distances (Su et al., 2020), consistent with Hi-C studies in mammalian (Abramo et al., 2019) or…”
InfluentialIntegrative approaches in genome structure analysis
Boninsegna, L., Yıldırım, A., Zhan, Y., & Alber, F. (2021) · Structure · doi:10.1016/j.str.2021.12.003 · Cited by 11
Burdick, R. C., Deléage, C., Duchon, A. A., Estes, J. D., Hu, W.-S., & Pathak, V. K. (2022) · mBio · doi:10.1128/mbio.03256-21 · Cited by 11
“observation that gene positions are dynamic is consistent with previous studies showing that gene positions vary substantially from cell to cell (38, 53).”
ResultTHUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data
Rowland, B., Huh, R., Hou, Z., Crowley, C., Wen, J., Shen, Y., Hu, M., Giusti‐Rodríguez, P., Sullivan, P. F., & Li, Y. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010102 · Cited by 11
Heterogeneity and Intrinsic Variation in Spatial Genome OrganizationPreprint
Finn, E. H., Pegoraro, G., Brandão, H. B., Valton, A.-L., Oomen, M. E., Dekker, J., Mirny, L. A., & Misteli, T. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/171801 · Cited by 14
Bayesian Estimation of 3D Chromosomal Structure from Single Cell Hi-C DataPreprint
Rosenthal, M., Bryner, D., Huffer, F., Evans, S. A., Srivastava, A., & Neretti, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/316265 · Cited by 11
“Thanks to recent advances in Hi-C technology we can now study long-range interactions at the single cell level [12, 22, 19, 13, 6].”
BackgroundMethodJodkowska, K., Pancaldi, V., Almeida, R., Rigau, M., Graña‐Castro, O., Fernández-Justel, J. M., Rodríguez‐Acebes, S., Rubio-Camarillo, M., Pau, E. C. D. S., Pisano, D. G., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644971 · Cited by 11
“Origin integration in alternative chromatin contact networks Finally, origin assortativity was measured in alternative chromatin contact networks generated from RNA polymerase II and SMC1 ChIA-PET datasets (Zhang et al, 2013; Dowen et al, 2014) as well as a haploid mESC HiC dataset (Stevens et al, 2017).”
MethodInfluentialHaplotype-resolved 3D chromatin architecture of the hybrid pigPreprint
Lin, Y., Li, J., Gu, Y., Jin, L. G., Bai, J., Zhang, J., Wang, Y., Liu, P., Long, K., He, M., et al. (2024) · Genome Research · doi:10.1101/gr.278101.123 · Cited by 12
“These findings agreed with human and rodent studies (Du et al. 2017; Stevens et al. 2017; Tan et al. 2021).”
Unveiling the Machinery behind Chromosome Folding by Polymer Physics Modeling
Conte, M., Esposito, A., Vercellone, F., Abraham, A., & Bianco, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043660 · Cited by 15
Zhou, Y., Tian, L., Choppavarapu, L., Fang, K., Lin, S., & Jin, V. X. (2024) · Nature Communications · doi:10.1038/s41467-024-52440-0 · Cited by 11
Advancements and future directions in single-cell Hi-C based 3D chromatin modeling
Banecki, K. H., Korsak, S., & Plewczyński, D. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.026 · Cited by 12
Image-based 3D genomics through chromatin tracing
Yang, T., & Wang, S. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00354-y · Cited by 10
Three-dimensional genome structures of single mammalian sperm
Xu, H., Chi, Y., Yin, C., Li, C., Chen, Y., Liu, Z., Liu, X., Xie, H., Chen, Z., Zhao, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59055-z · Cited by 11
“1g – i) 15,16 .”
Nath, S., Dutta, S., Sarkar, S. D., Sengupta, D., Mitra, M. K., & Sengupta, K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf615 · Cited by 10
KnotGenome: a server to analyze entanglements of chromosomes
Sulkowska, J. I., Niewieczerzał, S., Jarmolinska, A. I., Siebert, J. T., Virnau, P., & Niemyska, W. H. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky511 · Cited by 10
“(19)––20 chromosomes of the length between 500 up to 2000 (beads), 190 chromosome”
BackgroundGiniQC: a measure for quantifying noise in single-cell Hi-C data
Horton, C. A., Alver, B., & Park, P. J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa048 · Cited by 10
3D genome organization in the central nervous system, implications for neuropsychological disorders
Sun, D., Weng, J., Dong, Y., & Jiang, Y. (2021) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2021.06.017 · Cited by 10
The Physics of DNA Folding: Polymer Models and Phase-Separation
Esposito, A., Abraham, A., Conte, M., Vercellone, F., Prisco, A., Bianco, S., & Chiariello, A. M. (2022) · Polymers · doi:10.3390/polym14091918 · Cited by 13
Liu, E., Lyu, H., Peng, Q., Liu, Y., Wang, T., & Han, J. (2022) · Communications Biology · doi:10.1038/s42003-022-03546-y · Cited by 9
“…in thousands of single cells 42 , and the boundaries of these TAD-like domains in single cells present a high degree of cell-to-cell variations 42 – 44 , whereas it is suggested that the hierarchical TADs in bulk Hi-C contact matrix are unlikely to be perfectly nested, but also partially…”
BackgroundRobust 3D modeling reveals spatiosyntenic properties of animal genomes
Clarence, T., Robert, N. S. M., Sarigol, F., Fu, X., Bates, P. A., & Simakov, O. (2023) · iScience · doi:10.1016/j.isci.2023.106136 · Cited by 10
Decoding Human Biology and Disease Using Single-Cell Omics Technologies
Shi, Q., Chen, X., & Zhang, Z. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.06.003 · Cited by 11
Morf, J., Basu, S., & Amaral, P. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.589413 · Cited by 10
Self-citationPrincipal curve approaches for inferring 3D chromatin architecture
Tuzhilina, E., Hastie, T. J., & Segal, M. R. (2020) · Biostatistics · doi:10.1093/biostatistics/kxaa046 · Cited by 10
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry
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Metadata
- Authors
- Stevens, T. J., Lando, D., Basu, S., Atkinson, L. P., Cao, Y., Lee, S. F., Leeb, M., Wohlfahrt, K. J., Boucher, W., O’Shaughnessy-Kirwan, A., Cramard, J., Faure, A. J., Ralser, M., Blanco, E., Morey, L., Sansó, M., Palayret, M. G. S., Lehner, B., Di Croce, L., Wutz, A., Hendrich, B., Klenerman, D., & Laue, E. D.
- Affiliations
- MRC Laboratory of Molecular Biology (GB)
- University of Cambridge (GB)
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- European Molecular Biology Laboratory (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Laue, Ernest D.
- ORCID
- Venue
- Nature
- Published
- 2017-03-13
- Volume
- 544
- Issue
- 7648
- Pages
- 59-64
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5385134
- OpenAlex
- W2597274540
- Cited by
- 832
- References
- 41
- Citations by year
- 2026: 42 · 2025: 73 · 2024: 62 · 2023: 71 · 2022: 95 · 2021: 115 · 2020: 95 · 2019: 121 · 2018: 99 · 2017: 58
- FWCI
- 41.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Wellcome Trust (082010/Z/07/Z, 277899, 206291/Z/17/Z, 082010)
- Medical Research Council (MR/M010082/1, MC_PC_12009, MR/P019471/1)
- Cancer Research UK Cambridge Institute, University of Cambridge
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Nuclear Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
- Keywords
- 3D genome organization, single-cell Hi-C, chromosome conformation capture, topologically associating domains, chromatin looping, lamina-associated domains, mouse embryonic stem cells, cell-to-cell variability, pluripotency factors, nucleosome remodeling, nuclear organization
- MeSH terms
- Mouse Embryonic Stem Cells, CCCTC-Binding Factor, Cohesins, Animals, Chromosomal Proteins, Non-Histone, DNA, Enhancer Elements, Genetic, Gene Expression Regulation, Haploidy, Models, Molecular, Molecular Conformation, Nucleosomes, Promoter Regions, Genetic, Repressor Proteins, Reproducibility of Results, G1 Phase, Genome, Cell Cycle Proteins, Chromosomes, Mammalian, Chromatin Assembly and Disassembly, Mice, Gene Regulatory Networks, Mi-2 Nucleosome Remodeling and Deacetylase Complex, Molecular Imaging, Single-Cell Analysis
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