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Comparative Study
. 2017 Apr 6;544(7648):110-114.
doi: 10.1038/nature21711. Epub 2017 Mar 29.

Single-nucleus Hi-C Reveals Unique Chromatin Reorganization at Oocyte-To-Zygote Transition

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Free PMC article
Comparative Study

Single-nucleus Hi-C Reveals Unique Chromatin Reorganization at Oocyte-To-Zygote Transition

Ilya M Flyamer et al. Nature. .
Free PMC article

Abstract

Chromatin is reprogrammed after fertilization to produce a totipotent zygote with the potential to generate a new organism. 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 methods 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 method. 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.

Conflict of interest statement

Author information

Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to K.T.-K. (kikue.tachibana@imba.oeaw.ac.at) and L.A.M. (leonid@mit.edu).

Figures

Extended Data Figure 1
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 minutes) and strong (2% of formaldehyde for 15 minutes) fixation conditions. Pc(s) plot for the CH12-LX cell line is constructed using previously published in situ Hi-C data and is normalized to integrate to 1.
Extended Data Figure 2
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). a, decondensed fractal globule, b, loop extrusion model starting with fractal globule, c, loop extrusion model starting with mitotic chromosome. d, Simulations in a–c and in Fig. 4h were run for 2000 loop extrusion steps, which represents around 5 hours of real time (see Methods). In reality, zygotes spent 7–10 hours post fertilization. To ensure that Pc(s) does not change significantly over this timescale, we simulated one run for 3 times longer (6000 loop extrusion steps). Note that since 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 6000 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
Extended Data Figure 3. Quantification of average features in Hi-C maps
a, Using compartment annotation from different published datasets and quantification of compartment strength indicates highest similarity of oocyte compartments to sperm, mESCs and fibroblasts chromatin. Errorbars 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 cells. For this figure, all TADs were all computed using lavaburst algorithm described in the methods. The value plotted here is 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”. Colourmap is jet, with range from −0.5 to 0.5. c, TAD, loop and compartment strength as well as scaling steepness (definitions are in 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
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 cells (Rao, 2014) 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. , 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 heatmaps. The plot shows fraction of lavaburst boundaries that are located within a certain distance of Crane et al. boundaries; step is 40 kb. Modularity score boundaries align very well with boundaries called using (Crane et al., 2015) as seen from the figure. For example, 77% of boundaries called using modularity score were within an 80 kb of (Crane et al.,) algorithm boundaries (32% expected if boundaries were randomized by offsetting them by 1 Mb). b, same as (a), but for top two single cell in each set. c, Contact cluster calling is robust to downsampling. From each of the top 5 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 vs 1B), two maps from different cells (1A vs 2B), and each map to its randomly-shuffled control (1A vs 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 map.
Extended Data Figure 5
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 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 ref. ,. First map (top left) shows a population average contact map calculated from 2000 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 (e.g. contact probability only depends on the distance between the two regions, Pc(i,j) = Pc(abs(i–j))). 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 Figure S17 in ref. 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
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. and Methods for the description of the model. Thin gray lines denote TAD boundaries on all heatmaps. 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 (excluding first and last out of 32 blocks 0...4k, 4k..8k, 124k...128k) totalling 15,000 blocks. Single-cell map was calculated from a single randomly chosen block.
Extended Data Figure 7
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). b, Pc(s) (for cells with >30K contacts, n(NSN)=25, n(SN)=30) for oocytes staged by DIC with no DNA staining.
Extended Data Figure 8
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 >30K total contacts analysed without nuclear extraction (n(maternal)=4, n(paternal)=7). 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 cell and sperm data. c, Pc(s) for K562 cells, paternal and maternal nuclei, NSN and SN oocytes (this work), interphase cells6,8,11,15,30,36–45 and mitotic chromosomes.
Extended Data Figure 9
Extended Data Figure 9. Comparison of all mm9 datasets
a, Same as Fig. 4b, but for oocytes and zygotic nuclei together. b, Compartment strength quantified in different datasets (columns, both published and ours) based on compartment annotation from published datasets (rows). The highest values in each column represent cell types most similar to the data of interest. Note that the first 9 columns have the highest value on the main diagonal, which correspond to compartment strength evaluated using eigenvector from the same dataset. Also note that Cortex cells have similar compartment strength to oocytes and paternal zygotic nuclei.
Extended Data Figure 10
Extended Data Figure 10. Design and validation of FISH probes for quantification of compartments
a, FISH probe design for quantifying compartment segregation; left: exact locations of designed probes, right: probe locations are shown superimposed on the Hi-C data (200 kb resolution) from F123 ES cells 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’s eigenvector decomposition. c–d, top: nearest neighbour FISH distances - the same as curves in Figure 4d, but shown for (c) ES cells (n(replicate1)=87, n(replicate2)=78) and (d) maternal (n=33) and paternal (n=37) zygotic nuclei; 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 methods.
Figure 1
Figure 1. Genome-wide high-resolution single-nucleus Hi-C approach
a, snHi-C workflow for cell culture and oocytes/zygotes. b, Dependence of contact probability on genomic separation, Pc(s), for single K562 cells (n=9) with >30K total contacts (yellow), combined single-cell K562 data (orange) and published population K562 data (black). P(s) here and below were normalized to be 1 at 10 kb. c, Example contact map from a single oocyte (cell 1). Below: chromosomes 1 and 2 at 1 Mb resolution. Above: fragment of chromosome 2 at 200-kb resolution. d, Pc(s) in single oocytes with >30K total contacts (n=84) and in combined data. Black lines show slopes for Pc(s)=s−1.5 and Pc(s)=s−1.2. e, Pc(s) of oocytes (green, combined data) compared to published interphase cells (grey) with highlighted curve for ES cells (black).
Figure 2
Figure 2. SnHi-C identifies TADs and chromatin loops in individual cells
a, Contact enrichment of A/B compartments, averaging over loop and TAD positions annotated in CH12-LX cells in combined and single oocyte data. b, Scenarios leading to TADs in combined data. c, Variable contact clusters of top 4 single oocytes; first row - mESCs data, second row - combined data from all oocytes (n=120). Resolution of all maps is 40 kb. d, Superimposing contact cluster annotation from cells (n=20) compared to population Hi-C TAD annotations. e, 3D FISH in mES cells quantifies TAD boundary violations in single cells (n=211). Top: Hi-C map of tested region with FISH probe locations. Middle: Distribution of measured distances, average distances (left to right) 0.428, 0.484, and 0.646 μm, relative contact probabilities from F123 cells at 20 kb resolution are 0.0095, 0.0037, 0.0024. Wilcoxon test p-values: ** − 0.007, *** − 2.5×10−16. Bottom: Heatmap of FISH measurements with colour-coded distances. Right: representative FISH images with adjusted gamma values. Scale bar − 1 μm. Probes (yellow, magenta, green) and DAPI (blue).
Figure 3
Figure 3. Chromatin reorganization during oocyte maturation
a, Immature (Non-Surrounded Nucleolus, NSN) and mature (Surrounded Nucleolus, SN) oocytes stained with Hoechst (magenta). Scale bar, 25 μm. Images were adjusted in brightness/contrast settings in the individual channels using ImageJ. bd, Comparison of average TAD strength (b), loop strength (c) and compartment strength (d) in NSN and SN oocytes with Hoechst staining (n(NSN)=15, n(SN)=30). Error bars in (d) show standard deviation, obtained by bootstrapping. e, Pc(s) in data from combined NSN and SN oocytes, scored by Hoechst staining. fg, Pc(s) in single Hoechst-stained NSN (f, n=9) and SN oocytes (g, n=27) with >30K total contacts.
Figure 4
Figure 4. Distinct chromatin architecture in haploid nuclei of totipotent zygotes
a, Extraction of nuclei from zygotes. b, Same as Fig. 2a but for combined data from zygote nuclei. Top: maternal (n=31), Bottom: paternal (n=24). c, Comparison of compartment signal strength in combined maternal (mat) and paternal (pat) zygote nuclei, oocytes, ES cell and sperm data. Error bars as in Fig. 3d. d, 3D FISH compartment quantification in zygote nuclei. Top: Deconvolved zygote FISH image, adjusted in brightness/contrast settings, background subtracted using ImageJ. Scale bar, 5 μm. Bottom: cumulative probability density plots for nearest-neighbour distances between same compartment and different compartment probe pairs for maternal (n=33) and paternal (n=37) nuclei. Black denotes average reshuffled control with random compartment assignment; error bars show 5% and 95% percentiles. e, Pc(s) in single maternal and paternal nuclei with >30K total contacts (mat=24, pat=20). f, Pc(s) from all combined datasets with all previously published data from mammalian interphase nuclei. g, In models of maternal and paternal nuclei, loop extrusion is initialized with a model of metaphase chromosome or compact fractal globule. Colour denotes position along the chromosome. h, Model Pc(s) compared to experimental Pc(s).

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