Abstract
Chromatin is reprogrammed after fertilization to produce a totipotent zygote with the potential to generate a new organism1. The maternal genome inherited from the oocyte and the paternal genome provided by sperm coexist as separate haploid nuclei in the zygote. How these two epigenetically distinct genomes are spatially organized is poorly understood. Existing chromosome conformation capture-based methods2,3,4,5 are not applicable to oocytes and zygotes owing to a paucity of material. To study three-dimensional chromatin organization in rare cell types, we developed a single-nucleus Hi-C (high-resolution chromosome conformation capture) protocol that provides greater than tenfold more contacts per cell than the previous method2. Here we show that chromatin architecture is uniquely reorganized during the oocyte-to-zygote transition in mice and is distinct in paternal and maternal nuclei within single-cell zygotes. Features of genomic organization including compartments, topologically associating domains (TADs) and loops are present in individual oocytes when averaged over the genome, but the presence of each feature at a locus varies between cells. At the sub-megabase level, we observed stochastic clusters of contacts that can occur across TAD boundaries but average into TADs. Notably, we found that TADs and loops, but not compartments, are present in zygotic maternal chromatin, suggesting that these are generated by different mechanisms. Our results demonstrate that the global chromatin organization of zygote nuclei is fundamentally different from that of other interphase cells. An understanding of this zygotic chromatin ‘ground state’ could potentially provide insights into reprogramming cells to a state of totipotency.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
References
Zhou, L. Q. & Dean, J. Reprogramming the genome to totipotency in mouse embryos. Trends Cell Biol. 25, 82–91 (2015)
Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013)
Ma, W. et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat. Methods 12, 71–78 (2015)
Hsieh, T.-H. S. et al. Mapping nucleosome resolution chromosome folding in yeast by micro-C. Cell 162, 108–119 (2015)
Barutcu, A. R. et al. C-ing the genome: a compendium of chromosome conformation capture methods to study higher-order chromatin organization. J. Cell. Physiol. 231, 31–35 (2016)
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009)
Rodley, C. D. M., Bertels, F., Jones, B. & O’Sullivan, J. M. Global identification of yeast chromosome interactions using Genome conformation capture. Fungal Genet. Biol. 46, 879–886 (2009)
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014)
Nagano, T. et al. Cell cycle dynamics of chromosomal organisation at single-cell resolution. Preprint at bioRxivhttp://biorxiv.org/content/early/2016/12/15/094466 (2016)
Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017)
Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013)
Mizuguchi, T. et al. Cohesin-dependent globules and heterochromatin shape 3D genome architecture in S. pombe. Nature 516, 432–435 (2014)
Tjong, H., Gong, K., Chen, L. & Alber, F. Physical tethering and volume exclusion determine higher-order genome organization in budding yeast. Genome Res. 22, 1295–1305 (2012)
Halverson, J. D., Smrek, J., Kremer, K. & Grosberg, A. Y. From a melt of rings to chromosome territories: the role of topological constraints in genome folding. Rep. Prog. Phys. 77, 022601 (2014)
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012)
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012)
Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016)
Franke, M. et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538, 265–269 (2016)
Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015)
Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015)
Sanborn, A. L . et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl Acad. Sci. USA 112, E6456–E6465 (2015)
Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Reports 15, 2038–2049 (2016)
Miyara, F. et al. Chromatin configuration and transcriptional control in human and mouse oocytes. Mol. Reprod. Dev. 64, 458–470 (2003)
Bouniol-Baly, C. et al. Differential transcriptional activity associated with chromatin configuration in fully grown mouse germinal vesicle oocytes. Biol. Reprod. 60, 580–587 (1999)
Battulin, N. et al. Comparison of the three-dimensional organization of sperm and fibroblast genomes using the Hi-C approach. Genome Biol. 16, 77 (2015)
Ward, W. S. & Coffey, D. S. DNA packaging and organization in mammalian spermatozoa: comparison with somatic cells. Biol. Reprod. 44, 569–574 (1991)
Schwarzer, W. et al. Two independent modes of chromosome organization are revealed by cohesin removal. Preprint at bioRxivhttp://biorxiv.org/content/early/2016/12/15/094185 (2016)
Adenot, P. G., Mercier, Y., Renard, J. P. & Thompson, E. M. Differential H4 acetylation of paternal and maternal chromatin precedes DNA replication and differential transcriptional activity in pronuclei of 1-cell mouse embryos. Development 124, 4615–4625 (1997)
Tachibana-Konwalski, K. et al. Rec8-containing cohesin maintains bivalents without turnover during the growing phase of mouse oocytes. Genes Dev. 24, 2505–2516 (2010)
Selvaraj, S., R Dixon, J., Bansal, V. & Ren, B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat. Biotechnol. 31, 1111–1118 (2013)
Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015)
Zuin, J . et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc. Natl Acad. Sci. USA 111, 996–1001 (2014)
Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013)
Wang, Z. et al. The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types. PLoS One 8, e58793 (2013)
Sofueva, S. et al. Cohesin-mediated interactions organize chromosomal domain architecture. EMBO J. 32, 3119–3129 (2013)
Zhang, Y. et al. Spatial organization of the mouse genome and its role in recurrent chromosomal translocations. Cell 148, 908–921 (2012)
McCord, R. P. et al. Correlated alterations in genome organization, histone methylation, and DNA-lamin A/C interactions in Hutchinson–Gilford progeria syndrome. Genome Res. 23, 260–269 (2013)
Lin, Y. C. et al. Global changes in the nuclear positioning of genes and intra- and interdomain genomic interactions that orchestrate B cell fate. Nat. Immunol. 13, 1196–1204 (2012)
Kalhor, R., Tjong, H., Jayathilaka, N., Alber, F. & Chen, L. Solid-phase chromosome conformation capture for structural characterization of genome architectures. Nat. Biotechnol. 30, 90–98 (2011)
Chandra, T. et al. Global reorganization of the nuclear landscape in senescent cells. Cell Reports 10, 471–483 (2015)
Acknowledgements
We thank C. Theußl for help with pronuclear extraction procedure, S. Ladstätter for assistance in scoring oocyte stages and K. Klien for experimental support and mouse colony management. We are grateful to I. Adams, S. Boyle, I. Vassias-Jossic, G. Almouzni and W. Bickmore for advice and help with FISH experiments. Illumina sequencing was performed at the VBCF NGS Unit (http://www.vbcf.ac.at) except Hi-C libraries from MEL cells, which were sequenced in the Laboratory of Evolutionary Genomics of the Faculty of Bioengineering and Bioinformatics, Moscow State University, by M. Logacheva. K562 cells were a gift from Alexander Stark laboratory. We thank the staff of the Institute of Genetics and Molecular Medicine imaging facility and Vienna Biocenter BioOptics facility for assistance with imaging and analysis. We thank all members of the K.T.-K. laboratory for discussions, Life Science Editors for editorial assistance and R. Illingworth for critically reading the manuscript. J.G. is an associated student of the DK Chromosome Dynamics supported by the grant W1238-B20 from the Austrian Science Fund (FWF). H.B.B. was partly supported by the Natural Sciences and Engineering Research Council of Canada, PGS-D. This work was funded by the Austrian Academy of Sciences and by the European Research Council (ERC-StG-336460 ChromHeritance) to K.T.-K. as well as by a grant from the Russian Science Foundation (14-24-00022) to S.V.U. and S.V.R. The work in the Mirny laboratory is supported by R01 GM114190, U54 DK107980 from the National Institute of Health, and 1504942 from the National Science Foundation.
Author information
Authors and Affiliations
Contributions
I.M.F., J.G. and M.I. contributed equally and are listed alphabetically. K.T.-K. conceived the project. I.M.F., M.I., S.V.U. and K.T.-K. conceived the method. I.M.F. developed the method. I.M.F. and J.G., supervised by K.T.-K., performed snHi-C on oocytes and zygotes. S.V.U. supervised by S.V.R. and K.T.-K. performed scHi-C on K562 cells. I.M.F. supervised by S.V.R. performed in situ Hi-C on MEL cells. I.M.F. supervised by K.T-K performed 3D FISH on ES cells. J.G. supervised by K.T-K performed 3D FISH on zygotes. N.A. developed and maintains the library ‘lavaburst’ for TAD calling. M.I. and H.B.B. supervised by L.A.M. developed and performed snHi-C data analysis. H.B.B. led FISH data analysis and performed contact cluster analysis. M.I. performed simulations, processed the data, and performed genome-wide averaging analyses. M.I., H.B.B., I.M.F. and J.G. prepared the figures. M.I., I.M.F., J.G., H.B.B., L.A.M. and K.T.-K. wrote the manuscript with input from all authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Figure 1 Comparison of conventional and strong fixation conditions for Hi-C.
Pc(s) of contact probability over genomic separation has similar shape under conventional (1% of formaldehyde for 10 min) and strong (2% of formaldehyde for 15 min) fixation conditions (one replicate). Pc(s) plot for the CH12-LX cell line is constructed using previously published in situ Hi-C data8 and is normalized to integrate to 1.
Extended Data Figure 2 Simulations of Pc(s) of oocytes, maternal and paternal nuclei.
a–c, Pc(s) for various polymer models. All simulated Pc(s) curves were calculated using contact radius of 10 monomer diameters (100 nm). Decondensed fractal globule (a), loop extrusion model starting with fractal globule (b), loop extrusion model starting with mitotic chromosome (c). d, Simulations in a–c and in Fig. 4h were run for 2,000 loop extrusion steps, which represents around 5 h of real time (see Supplementary Methods). In reality, zygotes spent 7–10 h after fertilization. To ensure that Pc(s) does not change greatly over this timescale, we simulated one run for three times longer (6,000 loop extrusion steps). Note that as this figure was obtained from only two simulations, and not an average of many, and therefore the Pc(s) does not exactly match Fig. 4h. Even after 6,000 loop extrusion steps, the Pc(s) curves starting with the fractal globule and with mitotic chromosome model are very distinct, and different by almost two orders of magnitude at 10 Mb.
Extended Data Figure 3 Quantification of average features in Hi-C maps.
a, We quantified compartment strengths in oocytes using compartment annotation from different published data sets and quantification of compartment strength indicates that oocyte compartments are most similar to sperm, mouse ES cells and fibroblasts chromatin. Error bars as in Fig. 3d. b, Average TADs calculated over TADs computed from various cell types. Note that high-resolution TAD calling is only available in CH12-LX cells8. For this figure, all TADs were all computed using the ‘lavaburst’ algorithm described in the Supplementary Methods. The value plotted here is the natural log of observed-over-expected of the TAD enrichment. Unlike plots in the main figures, these are true observed-over-expected probabilities, not ‘effective contact probability’. The colour map is jet, ranging from –0.5 to 0.5. c, TAD, loop and compartment strength as well as scaling steepness (definitions are in Supplementary Methods) in different classes of cells. Boxplots were generated using ‘matplotlib’ (version 1.5.1) library for Python with default parameters.
Extended Data Figure 4 Stochasticity of contact clusters and validation of contact cluster annotation algorithm.
a, For this figure, boundaries were called on population data from CH12-LX cells8 at 20 kb resolution using two different methods: lavaburst with modularity score, with an average domain size of 25 bins (500 kb), and a method from ref. 20, downloaded from https://github.com/dekkerlab/crane-nature-2015 (most recent commit, August 28, 2016). The latter method was used with default parameters, on whole-chromosome heat maps. The plot shows fraction of lavaburst boundaries that are located within a certain distance of the boundaries defined in ref. 20; step is 40 kb. Modularity score boundaries align very well with boundaries called using an algorithm from ref. 20. For example, 77% of boundaries called using modularity score were within an 80 kb of algorithm boundaries from ref. 20 (32% expected if boundaries were randomized by offsetting them by 1 Mb). b, Same as a, but for top two single cells in each set. c, Contact cluster calling is robust to downsampling. From each of the top five single-cell oocytes, we obtained two maps down-sampled by 50% (1A and 1B from cell 1, etc.). We then compared contact clusters called in the two same-cell downsampled maps to each other (1A versus 1B), two maps from different cells (1A versus 2B), and each map to its randomly-shuffled control (1A versus control). Two maps from the same cell overlap by 65–70% of domain boundaries with 80 kb error margin. Overlap between different cells is about 1.5 times less (30–40%), and overlap with the reshuffled control is about 20–30%. Displayed are the average over all chromosomes and 95% confidence intervals of the fraction of overlap. d, The Hi-C contact cluster annotation of the top four single cell K562 cells is compared with the published population Hi-C map8.
Extended Data Figure 5 Contact clusters can form in polymer models with no average structure.
This figure shows contact maps of a 10,000 monomer region in fractal globules at density 0.05 (see Supplementary Methods for the fractal globule creation descriptions). Each contact map was calculated with a contact radius of 10, at bin size of 16 monomers (approximately 10 kb, if we assume 600-bp monomers as in the other models or in refs 11, 22). First map (top left) shows a population average contact map calculated from 2,000 independent realizations. Fractal globule is a model in which monomers are all treated equally and have no specific organization; therefore, a population average contact map of the fractal globule would be completely uniform (for example, contact probability only depends on the distance between the two regions). Each of the remaining 11 maps shows a ‘single-cell’ contact map from 11 single conformations. Note the high degree of variability between single-conformation contact map, despite the complete homogeneity of the average contact map. See supplementary figure 17 in ref. 11 for similar maps from our model of mitotic chromosomes. Note that, unlike in Hi-C, where each fragment end can form only one contact, in our simulations we record all contacts happening within the contact radius 10, and each monomer can form many contacts. Thus, this map shows more contacts than a single-cell Hi-C map would, even if Hi-C had the same capture radius. The map thus shows all potential contacts that could be extracted from a single conformation if sn-HiC was ‘performed’ on the same conformation many times, each time choosing one neighbour within the contact radius of 10.
Extended Data Figure 6 TADs are not visible in single polymers undergoing loop extrusion.
Similar to Extended Data Fig. 5, but for our model of loop extrusion starting with mitotic-like conformation (maternal nuclei). In this model, a 77-Mb chromosome (600-bp monomers; 128,000 monomers) is divided into 64 blocks of 3 TADs each. TAD sizes are 300, 600, and 1,100 monomers (180 kb, 360 kb and 660 kb). See ref. 22 and Supplementary Methods for the description of the model. Thin grey lines denote TAD boundaries on all heat maps. Each panel shows a block of 6 consecutive TADs, 4,000 monomers, or 2.4 Mb. Contact map is calculated at contact radius 10, and for bin size of 6 kb (10 monomers). For a population average map, 15,000 conformations were used. From each of 50 independent runs, we sampled 10 conformation at block numbers 1,100, 1,200,..., 2,000. From each conformation, we sampled 30 non-overlapping blocks of 6 TADs (4,000 monomers) starting at monomers 4,000, 8,000, 12,000, …, 120,000) totalling 15,000 blocks. Single-cell map was calculated from a single randomly chosen block.
Extended Data Figure 7 sn-HiC results for NSN and SN oocytes sorted by DIC scoring.
Note that Hoechst staining (see Fig. 3) is necessary for proper sorting of NSN and SN oocyte populations. a, Compartment signal, average TAD, average loop in oocytes staged by DIC with no DNA staining (n (NSN) = 29, n (SN) = 40, from more than three biological replicates using 2–4 females). b, Pc(s) (for cells with >30,000 contacts, n (NSN) = 25, n (SN) = 30, from more than three biological replicates using 2–4 females) for oocytes staged by DIC with no DNA staining.
Extended Data Figure 8 Pc(s) and compartment strength in zygote nuclei in comparison to other cell types.
a, Pc(s) for maternal (mat) and paternal (pat) zygotic nuclei with >30,000 total contacts analysed without nuclear extraction (n (maternal) = 4, n (paternal) = 7, from more than two biological replicates using 4–6 females). b, Comparison of compartment signal strength in combined maternal and paternal zygote nuclei with or without using nuclear extraction, with NSN and SN oocytes (staged with Hoechst staining), and published ES cell30 and sperm25 data. c, Pc(s) for K562 cells, paternal and maternal nuclei, NSN and SN oocytes (this work), interphase cells6,8,11,15,30,31,32,33,34,35,36,37,38,39,40 and mitotic chromosomes11.
Extended Data Figure 9 Comparison of all mm9 data sets.
a, Same as Fig. 4b, but for oocytes and zygotic nuclei together. b, Compartment strength quantified in different data sets (columns, both published and from this study) on the basis of compartment annotation from published data sets (rows). The highest values in each column represent cell types that are most similar to the data of interest. Note that the first nine columns have the highest value on the main diagonal, which correspond to compartment strength evaluated using eigenvector (compartment profile) from the same data set. Also note that cortex cells have similar compartment strength to oocytes and paternal zygotic nuclei.
Extended Data Figure 10 Design and validation of FISH probes for quantification of compartments.
a, FISH probe design for quantifying compartment segregation; left: probe locations are shown superimposed on the Hi-C data (200 kb resolution from F123 ES cells30, right: exact locations of designed probes. b, Probe locations shown relative to the profile of compartment strength (200 kb resolution) as measured by the first eigenvector of the Hi-C map eigenvector decomposition. c, d, Top: nearest neighbour FISH distances—the same as curves in Fig. 4d—but shown for ES cells (n (replicate 1) = 87, n (replicate 2) = 78) (c) and maternal (n = 33) and paternal (n = 37) zygotic nuclei. Data from one biological replicate using four females. d, Bottom: z-scores showing the number of standard deviations from the expected minimum distance distribution of the control data; the control distribution was obtained from randomly reshuffling probe colours as described in the Supplementary Methods.
Supplementary information
Supplementary Information
This file contains Supplementary Methods, Supplementary References and Supplementary Table 1. (PDF 258 kb)
Source data
Rights and permissions
About this article
Cite this article
Flyamer, I., Gassler, J., Imakaev, M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017). https://doi.org/10.1038/nature21711
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature21711
This article is cited by
-
LATS1 controls CTCF chromatin occupancy and hormonal response of 3D-grown breast cancer cells
The EMBO Journal (2024)
-
Computational methods for analysing multiscale 3D genome organization
Nature Reviews Genetics (2024)
-
Emergence of replication timing during early mammalian development
Nature (2024)
-
DiffDomain enables identification of structurally reorganized topologically associating domains
Nature Communications (2024)
-
Assignment of the somatic A/B compartments to chromatin domains in giant transcriptionally active lampbrush chromosomes
Epigenetics & Chromatin (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
