Cited by 1,589Genomics and Chromatin DynamicsGenomic variations and chromosomal abnormalitiesNuclear Structure and Function

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Single-cell Hi-C reveals cell-to-cell variability in chromosome structure

Open access

Nagano, Takashi, Lubling, Yaniv, Stevens, Tim J., Schoenfelder, Stefan, Yaffe, Eitan, Dean, Wendy, Laue, Ernest D., Tanay, Amos, Fraser, Peter·2013·Nature

DOI 10.1038/nature12593PMC PMC3869051

Open this paper in CoChat — free account

Abstract

Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture (3C) assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single-cell Hi-C, combined with genome-wide statistical analysis and structural modelling of single-copy X chromosomes, to show that individual chromosomes maintain domain organization at the megabase scale, but show variable cell-to-cell chromosome structures at larger scales. Despite this structural stochasticity, localization of active gene domains to boundaries of chromosome territories is a hallmark of chromosomal conformation. Single-cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organization underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nature12593

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829
Capturing Chromosome Conformation
Dekker, J., Rippe, K., Dekker, M., & Kleckner, N. (2002) · Science · doi:10.1126/science.1067799 · Cited by 3,652
Spatial partitioning of the regulatory landscape of the X-inactivation centre
Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N. L., Meisig, J., Sedat, J., et al. (2012) · Nature · doi:10.1038/nature11049 · Cited by 2,895
Chromosome territories, nuclear architecture and gene regulation in mammalian cells
Cremer, T. J., & Cremer, C. G. (2001) · Nature Reviews Genetics · doi:10.1038/35066075 · Cited by 2,423
Three-dimensional folding and functional organization principles of the Drosophila genome
Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A., & Cavalli, G. (2012) · Cell · doi:10.1016/j.cell.2012.01.010 · Cited by 1,983
Self-citation
Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)
Simonis, M., Klous, P., Splinter, E., Moshkin, Y., Willemsen, R., de Wit, E., van Steensel, B., & de Laat, W. (2006) · Nature Genetics · doi:10.1038/ng1896 · Cited by 1,386
Chromosome Conformation Capture Carbon Copy (5C): A massively parallel solution for mapping interactions between genomic elementsPreprint
Dostie, J., Richmond, T., Arnaout, R., Selzer, R. R., Lee, W. L., Honan, T., Rubio, E. D., Krumm, A., Lamb, J., Nusbaum, C., et al. (2006) · Genome Research · doi:10.1101/gr.5571506 · Cited by 1,237
Beyond the Sequence: Cellular Organization of Genome Function
Misteli, T. (2007) · Cell · doi:10.1016/j.cell.2007.01.028 · Cited by 1,140
Active genes dynamically colocalize to shared sites of ongoing transcription
Osborne, C. S., Chakalova, L., Brown, K. E., Carter, D., Horton, A., Debrand, E., Goyenechea, B., Mitchell, J. A., Lopes, S. M. C. D. S., Reik, W., et al. (2004) · Nature Genetics · doi:10.1038/ng1423 · Cited by 1,080
Self-citation

Flagged first, then by citations

Show all 32 references

Sorted by citations

Items 901–999 of 999

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
Inner workings of gene folding
Pierro, M. D. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900875116 · Cited by 11
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
TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data
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
Mini-review: Gene regulatory network benefits from three-dimensional chromatin conformation and structural biology
Zhu, X., Huang, Q., Luo, J., Kong, D., & Zhang, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.028 · Cited by 12
3D genomic organization in cancers
Wang, J., Tao, H., Li, H., Bo, X., & Chen, H. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0317 · 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
Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states
Li, W. V., Razaee, Z. S., & Li, J. J. (2016) · BMC Genomics · doi:10.1186/s12864-015-2303-9 · Cited by 10
Detecting Long-Range Enhancer–Promoter Interactions by Quantitative Chromosome Conformation Capture
Deng, W., & Blobel, G. A. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-4035-6_6 · Cited by 10
Comparative analysis of single-cell parallel sequencing approaches in oocyte application
Qian, Y., Liao, J., Suen, A. H. C., Lee, A. W.-T., Chung, H. S., Tang, N. L. S., Chow, K. L., Cao, Q., Yip, Y. L., Leung, T. Y., et al. (2018) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2018.12.003 · Cited by 12
Evolving methodologies and concepts in 4D nucleome research
Sparks, T. M., Harabula, I., & Pombo, A. (2020) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2020.04.005 · Cited by 10
RNA, Genome Output and Input
Morf, J., Basu, S., & Amaral, P. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.589413 · Cited by 10
Expanding studies of chromosome structure and function in the era of T2T genomics
Miga, K. H., & Sullivan, B. A. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab214 · Cited by 11
The roles of transcription, chromatin organisation and chromosomal processes in holocentromere establishment and maintenance
Wang, Y., Wu, L., & Yuen, K. W. Y. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.01.004 · Cited by 10
Biology and Model Predictions of the Dynamics and Heterogeneity of Chromatin-Nuclear Lamina Interactions
Madsen-Østerbye, J., Bellanger, A., Galigniana, N., & Collas, P. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.913458 · Cited by 9
Influence of Active Loop Extrusion on the Statistics of Triple Contacts in the Model of Interphase Chromosomes
Belan, S., & Starkov, D. (2022) · Journal of Experimental and Theoretical Physics Letters · doi:10.1134/s0021364022600896 · Cited by 10
Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data
Meluzzi, D., & Arya, G. (2019) · Methods · doi:10.1016/j.ymeth.2019.08.008 · Cited by 10
Chromatin Loops as Allosteric Modulators of Enhancer-Promoter InteractionsPreprint
Doyle, B., Fudenberg, G., Imakaev, M., & Mirny, L. A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/003087 · Cited by 11
Human Body Single-Cell Atlas of 3D Genome Organization and DNA MethylationPreprint
Zhou, J., Wu, Y., Liu, H., Tian, W., Castanon, R., Bartlett, A., Zhang, Z., Yao, G., Shi, D., Clock, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.23.644697 · Cited by 10
The spatial position effect: synthetic biology enters the era of 3D genomics
Zhou, L., Yang, H. S., Wang, Y., Chou, S., & He, J. L. (2021) · Trends in biotechnology · doi:10.1016/j.tibtech.2021.09.001 · Cited by 9
Circuit topology analysis of cellular genome reveals signature motifs, conformational heterogeneity, and scaling
Scalvini, B., Schießel, H., Golovnev, A., & Mashaghi, A. (2022) · iScience · doi:10.1016/j.isci.2022.103866 · Cited by 9
Technological advances in probing 4D genome organization
Soroczynski, J., & Risca, V. I. (2023) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2023.102211 · Cited by 9
CTPredictor: A comprehensive and robust framework for predicting cell types by integrating multi-scale features from single-cell Hi-C data
Shi, Z., & Wu, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108336 · Cited by 9
Permeable TAD boundaries and their impact on genome‐associated functions
Chang, L., & Noordermeer, D. (2024) · BioEssays · doi:10.1002/bies.202400137 · Cited by 10
Emerging methods and applications in 3D genomics
Pedrotti, S., Castiglioni, I., Estrada, C. P., Zhao, L., Chen, J., Crosetto, N., & Bienko, M. (2024) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2024.102409 · Cited by 10
Single-cell technology for drug discovery and development
Zhang, A., Zou, J., Xi, Y., Gao, L., Deng, F., Liu, Y., Gao, P., Tong, H. H. Y., Tan, L., Zou, X., et al. (2024) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2024.1459962 · Cited by 9
High-resolution detection of copy number alterations in single cells with HiScanner
Zhao, Y., Luquette, L. J., Veit, A. D., Wang, X., Xi, R., Viswanadham, V., Zhang, Y., Shao, D. D., Walsh, C. A., Yang, H. W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60446-5 · Cited by 8
Recent advances in methodologies of epigenomics
Ohishi, H., & Yeung, W. K. A. (2025) · Epigenomics · doi:10.1080/17501911.2025.2571391 · Cited by 10
Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities
Caprioli, C., Nazari, I., Milovanovic, S., & Pelicci, P. G. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.796477 · Cited by 9
SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data
Lee, L., Yu, M., Li, X., Zhu, C., Zhang, Y., Yu, H., Chen, Z., Mishra, S. V., Ren, B., Li, Y., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad315 · Cited by 10
A Lightweight Framework For Chromatin Loop Detection at the Single‐Cell Level
Wang, F., Alinejad‐Rokny, H., Lin, J., Gao, T., Chen, X., Zheng, Z., Meng, L., Li, X., & Wong, K. (2023) · Advanced Science · doi:10.1002/advs.202303502 · Cited by 9
FISH Is in the Limelight Again As More Than a Cytogenetical Technique for Metaphase Chromosomes
Matsunaga, S. (2016) · CYTOLOGIA · doi:10.1508/cytologia.81.3 · Cited by 9
Single-Cell Genomics Unravels Brain Cell-Type Complexity
Guillaumet-Adkins, A., & Heyn, H. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-53889-1_20 · Cited by 9
normGAM: an R package to remove systematic biases in genome architecture mapping data
Liu, T., & Wang, Z. (2019) · BMC Genomics · doi:10.1186/s12864-019-6331-8 · Cited by 9
Novel orthogonal methods to uncover the complexity and diversity of nuclear architecture
Tjalsma, S. J. D., & de Laat, W. L. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.10.002 · Cited by 9
HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data
Wu, H., Wang, X., Chu, M., Li, D., Cheng, L., & Zhou, K. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.064 · Cited by 9
Chromosome Folding Promotes Intrachromosomal Aberrations under Radiation- and Nuclease-Induced DNA Breakage
Eidelman, Y. A., Salnikov, I. V., Slanina, S. V., & Andreev, S. G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222212186 · Cited by 10
Stress Conditions Modulate the Chromatin Interactions Network in Arabidopsis
Yadav, V. K., Singh, S., Yadav, A., Agarwal, N., Singh, B., Jalmi, S. K., Yadav, V. K., Tiwari, V. K., Kumar, V., Singh, R. P., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.799805 · Cited by 9
Plant Epigenomics
Furci, L., Berthelier, J., Juez, O., Miryeganeh, M., & Saze, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91909-8.00007-4 · Cited by 8
A Crowdsourced nucleus: Understanding nuclear organization in terms of dynamically networked protein function
Wood, A. M., Garza-Gongora, A. G., & Kosak, S. T. (2014) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2014.01.003 · Cited by 10
Toward understanding the dynamic state of 3D genome
Shinkai, S., Onami, S., & Nakato, R. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.014 · Cited by 9
Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies
Tang, L., Hill, M., Ellinor, P., & Li, M. (2022) · Genome Biology · doi:10.1186/s13059-021-02597-4 · Cited by 8
Chromosome territory relocation paradigm during DNA damage response: Some insights from molecular biology to physics
Fatakia, S. N., Kulashreshtha, M., Mehta, I. S., & Rao, B. J. (2017) · Nucleus · doi:10.1080/19491034.2017.1313938 · Cited by 9
Easy Hi-C: A simple efficient protocol for 3D genome mapping in small cell populationsPreprint
Lu, L., Liu, X., Peng, J., Li, Y., & Jin, F. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/245688 · Cited by 9
HiCDiff: single-cell Hi-C data denoising with diffusion models
Wang, Y., & Cheng, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae279 · Cited by 10
Local volume concentration, packing domains, and scaling properties of chromatin
Carignano, M. A., Kröger, M., Almassalha, L. M., Agrawal, V., Li, W. S., Pujadas-Liwag, E. M., Nap, R. J., Backman, V., & Szleifer, I. G. (2024) · eLife · doi:10.7554/elife.97604 · Cited by 11
HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling
Otoničar, J., Lazareva, O., Mallm, J., Simovic-Lorenz, M., Philippos, G., Sant, P., Parekh, U., Hammann, L., Li, A., Yildiz, U., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03450-0 · Cited by 7
Mapping the 3D genome architecture
Tavallaee, G., & Orouji, E. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.018 · Cited by 10
Generalization of the sci-L3 method to achieve high-throughput linear amplification for replication template strand sequencing, genome conformation capture, and the joint profiling of RNA and chromatin accessibility
Chovanec, P., & Yin, Y. A. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf101 · Cited by 10
Human body single-cell atlas of three-dimensional genome organization and DNA methylation
Zhou, J., Wu, Y., Liu, H., Tian, W., Castanon, R., Bartlett, A., Zhang, Z., Yao, G., Shi, D., Clock, B., et al. (2026) · Science · doi:10.1126/science.adx0673 · Cited by 22
Three Dimensional Organization of the Nucleus: adding DNA sequences to the big picture
Gilbert, D. M., & Fraser, P. (2015) · Genome biology · doi:10.1186/s13059-015-0751-9 · Cited by 8
Self-citation
The mechanisms shaping the single-cell transcriptional landscape
Martinez‐Jimenez, C. P., & Odom, D. T. (2016) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2015.11.004 · Cited by 8
In Situ HiC
Johanson, T. M., & Allan, R. S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2140-0_18 · Cited by 8
Mapping nucleosome and chromatin architectures: A survey of computational methods
Fang, K., Wang, J., Liu, L., & Jin, V. X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.037 · Cited by 9
scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data
Lyu, H., Liu, E., Wu, Z., Li, Y., Liu, Y., & Yin, X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac670 · Cited by 8
3D genome organization and its study in livestock breeding
Cheng, J., Cao, X., Wang, S., Zhang, J., Yue, B., Zhang, X., Huang, Y., Lan, X., Ren, G., & Chen, H. (2023) · Journal of Integrative Agriculture · doi:10.1016/j.jia.2023.04.007 · Cited by 9
Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection
Liu, E., Lyu, H., Liu, Y., Fu, L., Cheng, X., & Yin, X. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae138 · Cited by 8
Unveiling the role of chromosome structure morphology on gene function through chromosome conformation analysis
Zhan, Y., Yıldırım, A., Boninsegna, L., & Alber, F. (2025) · Genome biology · doi:10.1186/s13059-024-03472-8 · Cited by 8
Polymer models of the organization of chromosomes in the nucleus of cells
Chiariello, A. M., Bianco, S., Piccolo, A., Annunziatella, C., Barbieri, M., Pombo, A., & Nicodemi, M. (2015) · Modern Physics Letters B · doi:10.1142/s0217984915300033 · Cited by 8
A novel single cell method to identify the genetic composition at a single nuclear body
Anchel, D., Ching, R. W., Cotton, R., Li, R., & Bazett‐Jones, D. P. (2016) · Scientific Reports · doi:10.1038/srep29191 · Cited by 8
Can We Just Say: Transcription Second?
Krijger, P. H. L., & de Laat, W. L. (2017) · Cell · doi:10.1016/j.cell.2017.03.026 · Cited by 8
Single-nucleus Hi-C of mammalian oocytes and zygotes
Gassler, J., Flyamer, I. M., & Tachibana, K.-E. K. (2018) · Methods in cell biology · doi:10.1016/bs.mcb.2018.03.032 · Cited by 8
A case study on the detailed reproducibility of a Human Cell Atlas project
Hua, K., & Zhang, X. (2019) · Quantitative Biology · doi:10.1007/s40484-018-0164-3 · Cited by 8
Single-cell profiling lights different cell trajectories in plants
Zheng, H., Wu, F., Li, S.-M., Zhang, X. S., & Sui, N. (2021) · aBIOTECH · doi:10.1007/s42994-021-00040-7 · Cited by 8
scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders
Liu, T., & Wang, Z. (2022) · Genes · doi:10.3390/genes13061048 · Cited by 8
Dynamic chromatin architectures provide insights into the genetics of cattle myogenesis
Cheng, J., Cao, X., Wang, X., Wang, J., Yue, B., Sun, W., Huang, Y., Lan, X., Ren, G., Lei, C., et al. (2023) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-023-00855-y · Cited by 8
Genome-wide tracing to decipher nuclear organization
Flores, V., Farabella, I., & Nir, G. (2023) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2023.102175 · Cited by 8
Mapping the semi-nested community structure of 3D chromosome contact networks
Bernenko, D., Lee, S. H., Stenberg, P., & Lizana, L. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011185 · Cited by 8
Single‐cell analyses to reveal hematopoietic stem cell fate decisions
Lunger, I., Fawaz, M., & Rieger, M. A. (2017) · FEBS Letters · doi:10.1002/1873-3468.12712 · Cited by 9
Application of Single Cell Sequencing in Cancer
Yu, L., Zhao, H., Li, M., & Zhang, C. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0502-3_11 · Cited by 8
Automated Nuclear Cartography Reveals Conserved Sperm Chromosome Territory Localization across 2 Million Years of Mouse Evolution
Skinner, B. M., Bacon, J., Rathje, C. C., Larson, E. L., Kopania, E. E. K., Good, J. M., Affara, N. A., & Ellis, P. J. I. (2019) · Genes · doi:10.3390/genes10020109 · Cited by 8
Nanoscale Chromatin Imaging and Analysis (nano-ChIA) Platform Bridges 4-D Chromatin Organization with Molecular Function
Li, Y., Eshein, A., Virk, R. K. A., Eid, A., Wu, W., Frederick, J., VanDerway, D., Gladstein, S., Huang, K., Anthony, N. M., et al. (2020) · Microscopy and Microanalysis · doi:10.1017/s1431927620016785 · Cited by 7
Network-based method for regions with statistically frequent interchromosomal interactions at single-cell resolution
Bulathsinghalage, C., & Liu, L. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03689-x · Cited by 8
Simulating topological domains in human chromosomes with a fitting-free model
Brackley, C. A., Michieletto, D., Mouvet, F., Johnson, J., Kelly, S. L., Cook, P. R., & Marenduzzo, D. (2016) · Nucleus · doi:10.1080/19491034.2016.1239684 · Cited by 8
Bayesian inference of the gene expression states of single cells from scRNA-seq dataPreprint
Breda, J., Zavolan, M., & van Nimwegen, E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.28.889956 · Cited by 8
SnapHiC: a computational pipeline to map chromatin contacts from single cell Hi-C dataPreprint
Yu, M., Abnousi, A., Zhang, Y.-X., Li, G., Lee, L., Chen, Z.-Y., Fang, R.-X., Wen, J., Sun, Q., Li, Y., et al. (2020) · bioRxiv · doi:10.1101/2020.12.13.422543 · Cited by 6
Communities in C. elegans connectome through the prism of non-backtracking walks
Onuchin, A. A., Chernizova, A. V., Lebedev, M., & Polovnikov, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-49503-5 · Cited by 7
Review of the geometrical developments in GEANT4-DNA: From a biological perspective
Khanna, R., Reinwald, Y., Hugtenburg, R. P., Bertolet, A., & Serjouei, A. (2025) · Reviews in Physics · doi:10.1016/j.revip.2025.100110 · Cited by 7
Application and Progress of Genomics in Deciphering the Genetic Regulation Mechanisms of Plant Secondary Metabolites
Liu, C., Xu, H., Li, Z., Wang, Y., Qiao, S., & Zhang, H. (2025) · Plants · doi:10.3390/plants14091316 · Cited by 8
Galaxy single-cell & spatial omics community update: Navigating new frontiers in 2025
Loach, M., Nilchi, A. N., Chiang, D., Howells, M., Heyl, F., Rasche, H., Jakiela, J., Tekman, M., Gamal, M., Moreno, P. A., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101005 · Cited by 7
Microfluidics-Based Chromosome Conformation Capture (3C) Technology for Examining Chromatin Organization with a Low Quantity of Cells
Sun, C., & Lu, C. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b00310 · Cited by 7
Zooming in on chromosome dynamics
Eykelenboom, J. K., & Tanaka, T. U. (2020) · Cell Cycle · doi:10.1080/15384101.2020.1757242 · Cited by 7
Bayesian methods in integrative structure modeling
Habeck, M. (2023) · Biological Chemistry · doi:10.1515/hsz-2023-0145 · Cited by 8
Single Cell Sequencing Technology and Its Application in Alzheimer’s Disease
Han, Y., Huang, C., Pan, Y., & Gu, X. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-230861 · Cited by 7
3C methods in cancer research: recent advances and future prospects
Yoon, I., Kim, U., Jung, K. O., Song, Y., Park, T., & Lee, D.-S. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01236-9 · Cited by 7
Uncovering topologically associating domains from three-dimensional genome maps with TADGATE
Dang, D., Zhang, S.-W., Dong, K., Duan, R., & Zhang, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1267 · Cited by 8
Supra-second tracking and live-cell karyotyping reveal principles of mitotic chromosome dynamics
Stamatov, R., Uzunova, S., Kicheva, Y., Karaboeva, M., Blagoev, T., & Stoynov, S. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01637-6 · Cited by 7
High-resolution single-cell 3D-models of chromatin ensembles during Drosophila embryogenesis
Sun, Q., Perez-Rathke, A., Czajkowsky, D. M., Shao, Z., & Liang, J. (2021) · RePEc: Research Papers in Economics · Cited by 7
Dive into Single, Seek Out Multiple: Probing Cancer Metastases via Single-Cell Sequencing and Imaging Techniques
Su, S., & Li, X. (2021) · Cancers · doi:10.3390/cancers13051067 · Cited by 7
Four-Dimensional Chromosome Structure Prediction
Highsmith, M. R., & Cheng, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189785 · Cited by 7
How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification
Mircea, M., & Semrau, S. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210135 · Cited by 9
Lymphocyte activation gene-3-associated protein networks are associated with HDL-cholesterol and mortality in the Trans-omics for Precision Medicine program
Manichaikul, A. W., Lin, H., Kang, C., Yang, C., Rich, S. S., Taylor, K. D., Guo, X., Rotter, J. I., Johnson, W. C., Cornell, E. S., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03304-0 · Cited by 7
Assessment of Multiway Interactions with Tri-C
Oudelaar, A. M., Downes, D. J., & Hughes, J. R. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2497-5_6 · Cited by 6
Chromosome and protein folding: In search for unified principles
Mirny, L. A. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102610 · Cited by 7
Are genes switched on when they kiss?
Fanucchi, S., Shibayama, Y., & Mhlanga, M. M. (2014) · Nucleus · doi:10.4161/nucl.28352 · Cited by 7
Hierarchical block matrices as efficient representations of chromosome topologies and their application for 3C data integration
Shavit, Y., Walker, B. J., & Lió, P. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv736 · Cited by 7
Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals
Golov, A. K., Razin, S. V., & Gavrilov, A. A. (2016) · Histochemistry and Cell Biology · doi:10.1007/s00418-016-1466-z · Cited by 7
Capturing heterogeneity: single-cell structures of the 3D genome
de Wit, E. (2017) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3404 · Cited by 7

Authors: Nagano, T., Lubling, Y., Stevens, T. J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E. D., Tanay, A., & Fraser, P.
Affiliations: Babraham Institute (GB)
Weizmann Institute of Science (IL)
University of Cambridge (GB)
Corresponding author: Laue, Ernest D.
Tanay, Amos
Fraser, Peter
ORCID: Nagano, Takashi
Lubling, Yaniv
Stevens, Tim J.
Schoenfelder, Stefan
Yaffe, Eitan
Dean, Wendy
Laue, Ernest D.
Tanay, Amos
Fraser, Peter
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2013-09-25
Volume: 502
Issue: 7469
Pages: 59-64
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature12593
PMC: PMC3869051
OpenAlex: W1965863160
Cited by: 1,589
References: 32
Citations by year: 2026: 75 · 2025: 100 · 2024: 118 · 2023: 115 · 2022: 120 · 2021: 177 · 2020: 132 · 2019: 143 · 2018: 147 · 2017: 126 · 2016: 116 · 2015: 125 · 2014: 86 · 2013: 8
FWCI: 50.06
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Apollo (University of Cambridge) (submittedVersion)
Apollo
Funders: Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBS/E/B/0000M241, BBS/E/B/000C0404, BBS/E/B/0000C151)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Nuclear Structure and Function
Keywords: single-cell Hi-C, chromosome conformation, cell-to-cell variability, chromosome structure, chromosome territories, X chromosome, 3D genome organization, chromatin interactions, interdomain contacts, nuclear organization
MeSH terms: Animals, Cell Nucleus, Chromatin, Chromosomes, Genetic Techniques, Male, Models, Molecular, Molecular Conformation, X Chromosome, Mice, Single-Cell Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Single-cell Hi-C reveals cell-to-cell variability in chromosome structure

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks