Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos

Article

Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos Graphical Abstract

Authors Mouse oogenesis

PGC GO I Conventional TAD Compartment A/B

GO II

FGO

GVBD

MII oocyte

Zhenhai Du, Hui Zheng, Yumiko K. Kawamura, ..., € Tachibana, Kikue Antoine H.F.M. Peters, Wei Xie

Correspondence

PAD

[email protected] (A.H.F.M.P.), [email protected] (W.X.)

In Brief

TAD

PAD-PAD interaction PAD

PAD TAD iPAD Co

TAD

Gene

P

PAD

H3K27me3

P P

P

Co P

Cohesin Polycomb

Highlights d

Hi-C analysis of meiotic chromatin architecture during mouse oocyte development

d

Late-stage mouse oocytes show unique H3K27me3-marked Polycomb-associating domains

d

PADs disassemble upon meiotic resumption but briefly reappear in early embryos

d

PADs are regulated by Polycomb proteins and independent of cohesin

Du et al., 2020, Molecular Cell 77, 1–15 February 20, 2020 ª 2019 Elsevier Inc. https://doi.org/10.1016/j.molcel.2019.11.011

In mammals, germ cells undergo a series of events to give rise to haploid gametes. The dynamics of meiotic chromatin architecture still remain poorly understood. By applying Hi-C to mouse oocytes, Du et al. reveal a unique Polycomb-regulated chromatin architecture, which adds a new perspective to the non-canonical oocyte epigenomes.

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

Molecular Cell

Article Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos Zhenhai Du,1,11 Hui Zheng,1,11 Yumiko K. Kawamura,2,11 Ke Zhang,1,11 Johanna Gassler,3 Sean Powell,3 Qianhua Xu,1 Zili Lin,1 Kai Xu,1 Qian Zhou,4 Evgeniy A. Ozonov,2 Nathalie Ve´ron,2 Bo Huang,1 Lijia Li,1 Guang Yu,1 Ling Liu,1 Wan Kin Au Yeung,5 Peizhe Wang,6 Lei Chang,7 Qiujun Wang,1 Aibin He,8 Yujie Sun,7 Jie Na,6 Qingyuan Sun,4 € Tachibana,3,9 Antoine H.F.M. Peters,2,10,* and Wei Xie1,12,* Hiroyuki Sasaki,5 Kikue 1Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China 2Friedrich Miescher Institute for Biomedical Research, Basel 4058, Switzerland 3Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter (VBC), 1030 Vienna, Austria 4State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China 5Division of Epigenomics and Development, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan 6Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, 100084, China 7State Key Laboratory of Membrane Biology, Biodynamic Optical Imaging Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100871, China 8Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China 9Department of Totipotency, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany 10Faculty of Sciences, University of Basel, Basel 4056, Switzerland 11These authors contributed equally 12Lead Contact *Correspondence: [email protected] (A.H.F.M.P.), [email protected] (W.X.) https://doi.org/10.1016/j.molcel.2019.11.011

SUMMARY

INTRODUCTION

In mammals, chromatin organization undergoes drastic reorganization during oocyte development. However, the dynamics of three-dimensional chromatin structure in this process is poorly characterized. Using low-input Hi-C (genome-wide chromatin conformation capture), we found that a unique chromatin organization gradually appears during mouse oocyte growth. Oocytes at late stages show self-interacting, cohesin-independent compartmental domains marked by H3K27me3, therefore termed Polycomb-associating domains (PADs). PADs and inter-PAD (iPAD) regions form compartment-like structures with strong inter-domain interactions among nearby PADs. PADs disassemble upon meiotic resumption from diplotene arrest but briefly reappear on the maternal genome after fertilization. Upon maternal depletion of Eed, PADs are largely intact in oocytes, but their reestablishment after fertilization is compromised. By contrast, depletion of Polycomb repressive complex 1 (PRC1) proteins attenuates PADs in oocytes, which is associated with substantial gene de-repression in PADs. These data reveal a critical role of Polycomb in regulating chromatin architecture during mammalian oocyte growth and early development.

In eukaryotes, linear DNA in the nucleus is packaged into highly organized chromatin fibers (Bickmore, 2013; Gibcus and Dekker, 2013). The proper folding of chromatin fiber is crucial for fundamental cell processes, including gene expression and cell division (Van Bortle and Corces, 2012). The recent development of chromosome conformation capture (3C)-based technologies and imaging methods has revealed key principles underlying chromatin fiber folding (Davies et al., 2017). For instance, different regions with similar chromatin states tend to interact, forming chromatin compartments. Two major compartments, A and B, are frequently observed, which correspond to megabase gene-rich regions and gene desert regions, respectively (Lieberman-Aiden et al., 2009). On the other hand, topologically associating domains (TADs) were discovered in many species as large self-interacting domains (Dixon et al., 2012; Hou et al., 2012; Nora et al., 2012; Sexton et al., 2012). In mammals, TADs are frequently flanked by convergent CTCF sites and are believed to restrict promoter-enhancer interactions within the domains (Flavahan et al., 2016; Guo et al., 2015; Jin et al., 2013; Lupia´n˜ez et al., 2015; Rao et al., 2014; Tang et al., 2015; Valton and Dekker, 2016). Both compartments and TADs are mainly found in interphase, but not metaphase, when chromatin instead forms linearly compressed axis-loop structure (Gibcus et al., 2018; Nagano et al., 2017; Naumova et al., 2013). Mammalian gametogenesis and early development are accompanied by drastic chromatin reorganization (Zheng and Xie, 2019). In mice, primordial germ cells (PGCs) first emerge

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around embryonic day 7.25 (E7.25) (Chiquoine, 1954; Ginsburg et al., 1990). At E13.5, PGCs in female embryos initiate entry into prophase of meiosis I, with chromatin undergoing assembly of the synaptonemal complex, DNA recombination, and disassembly of the synaptonemal complex (Handel and Schimenti, 2010). The resulting primary oocytes are arrested at the diplotene stage in prophase I of meiosis for an extended period of time (up to months in mice and decades in humans) (Hilscher et al., 1974; Speed, 1982). Upon hormone stimulation, a limited number of primordial follicles enter the growth phase and increase in sizes (Bachvarova, 1985). During this period, oocytes accumulate proteins and RNAs for embryonic development, transiting from growing oocytes (GOs) to full-grown oocytes (FGOs) (Bachvarova, 1985; Li et al., 2010; Veselovska et al., 2015). These oocytes are also referred as germinal vesicle (GV; nucleus) oocytes. FGOs include two major types with distinct nuclear organization: the NSN (non-surrounded nucleolus) type with active transcription and the SN (surrounded nucleolus) type with silenced transcription (Bouniol-Baly et al., 1999; Miyara et al., 2003). Upon releasing from diplotene arrest, oocytes resume meiosis I and are subsequently rearrested at metaphase II, awaiting fertilization. During gametogenesis and early embryogenesis, extensive epigenetic reprogramming occurs for histone modifications and DNA methylation (Eckersley-Maslin et al., 2018; Stewart et al., 2016; Xu and Xie, 2018). In addition, TADs, compartments, and loop structures are found in mouse NSN FGOs, which become weaker in SN FGOs (Flyamer et al., 2017). These structures are then absent in mouse MII oocytes, which adopt a mitotic chromatin-like organization (Du et al., 2017; Ke et al., 2017). After fertilization, chromatin organization becomes surprisingly relaxed in zygotes, followed by a slow and prolonged reestablishment process during preimplantation development (Du et al., 2017; Ke et al., 2017). Nevertheless, a comprehensive study of chromatin architecture refolding during oogenesis is still lacking. Here, using a low-input Hi-C (sisHi-C) method we reported previously (Du et al., 2017), we systematically investigated the dynamics of three-dimensional (3D) chromatin structure in mouse PGCs and oocytes at several growth stages. Our results revealed highly dynamic regulation of chromatin architecture during oogenesis and a unique chromatin organization in late-stage oocytes that is regulated by Polycomb proteins. RESULTS Mapping Global Chromatin Organization during Mouse Oogenesis To examine the dynamics of 3D chromatin structure in female germ cell development, we collected PGCs at E11.5 and E13.5 (with male and female PGCs separated), stage I GOs (GOs I; postnatal days 7 or 10 [P7 or P10], with diameters of 25–70 mm), stage II GOs (GOs II; P14, with diameters of 50–70 mm), and FGOs (postnatal week 8, with diameters >70 mm) (STAR Methods). These samples were analyzed using a small-scale in situ Hi-C (sisHi-C) method we developed previously (Du et al., 2017) (STAR Methods). We also included Hi-C data of metaphase II (MII) oocytes from our previous study (Du et al., 2017) for comparative analyses (STAR Methods). It is worth noting that both PGCs and

2 Molecular Cell 77, 1–15, February 20, 2020

GOs can contain heterogeneous subpopulations with different developmental stages, and thus, the Hi-C data represent population averages. The GV oocytes (including GO I, GO II, and FGO) are arrested at the diplotene stage of prophase in meiosis I. MII oocytes are arrested at the metaphase in meiosis II. By conducting sisHi-C for two or three biological replicates for each stage, we obtained 140–193 million pairwise chromatin contacts for each stage (Table S1). These data are at levels comparable to previous low-input Hi-C studies (on average 180 million contact reads for each stage; Du et al., 2017; Ke et al., 2017). The interaction intensities across the genome are highly consistent between the replicates (Figure S1A). Interestingly, oocytes have much fewer inter-chromosomal read pairs than mouse embryonic stem cells (mESCs) (grown in serum/LIF [leukemia inhibitor factor]) and PGCs, and the ratios decrease as oocytes grow (Figure S1B). This is perhaps related to increasing sizes of oocyte nuclei during follicle growth which leads to further separation of chromosomes. Globally, the interaction heatmaps show that PGCs have relatively similar patterns compared to mESCs both for compartments and TADs (Figure 1A). This is also true between E11.5 PGCs and E13.5 PGCs (Figures S1C and S1D). Surprisingly, oocytes at late stages, including GOs II, FGOs, and MII oocytes, show distinct chromatin organization. The most evident feature is the depletion of distal interactions and plaid patterns (compartments) across the chromosomes (Figure 1A). At local levels, their chromatin organizations are also distinct from those in mESCs and PGCs (Figure 1A, zoomed-in heatmaps). GOs II and FGOs appear to have a local compartment-like structure (described below), while MII oocytes entirely lack defined compartments but instead show a uniform chromatin interaction profile as reported before (Du et al., 2017; Ke et al., 2017). GOs I appear to be in a transitionary state between PGCs and GOs II/FGOs (Figure 1A). These results were confirmed by a global hierarchical clustering analysis (Figure S1E; STAR Methods). We also examined how the chromatin contact probability (denoted by P(s)) depends on genomic distance (denoted by s) (Naumova et al., 2013). Consistently, PGCs have similar P(s) as mESCs but show somewhat stronger interactions at ultra-long distances (>50 Mb) (Figures 1A, 1B, and S1F). By contrast, late-stage oocytes (GOs II, FGOs, and MII oocytes) displayed stronger mid-range interactions (1–10 Mb) but comparable or weaker extra-long (>50 Mb) distance interactions compared to mESCs (Figures 1B, S1F, and S1G). Consistently, chromatin interactions in late-stage oocytes are largely centered on the diagonal in 2D chromatin interaction heatmaps (Figure 1A). Thus, these results indicate oocytes have highly dynamic higher-order chromatin states (see below). Establishment of Polycomb-Associating Domains (PADs) during Follicle Growth We then focused on the dynamics of chromatin architecture in growing and mature oocytes. The depletion of distal plaid patterns in late-stage oocytes indicates that the conventional compartments A and B may be significantly weakened. To quantify such differences, we attempted to identify compartments A and B based on principal-component analysis (PCA) (Lieberman-Aiden et al., 2009). This analysis yielded largely similar compartments in mESCs and GOs I (Figure S1H, principal

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

A

Chromatin interaction heatmap mESC

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Figure 1. Mapping Global Chromatin Organization during Mouse Oogenesis Using sisHi-C (A) Heatmaps showing normalized Hi-C interaction frequencies (100-kb bin, chromosome 6) in mESCs, PGCs, and oocytes (pooled data from two or three biological replicates). Zoomed-in views (40-kb bin) are also shown. (B) The chromatin contact probabilities relative to genomic distance (P(s) curves, chromosome 1) are shown for mESCs, PGCs, and oocytes (pooled data from two or three biological replicates). P(s) ~s
0.5 and P(s) ~s
1 are also shown. See also Figure S1 and Table S1.

component 1 [PC1]). By contrast, compartmentalization in GOs II is nearly undetectable according to the PCA and correlation heatmap (Lieberman-Aiden et al., 2009) (Figure S1H). Unexpectedly, a new compartment appears in FGOs, as shown by both PC1 and correlation heatmap analyses (Figure S1H), which correlates with a plaid pattern that is restricted to local regions along

the diagonal of interaction heatmap (Figures 1A, 2A, and S1I, zoomed-in views). Such unique local plaid patterns are highly reproducible between FGO replicates (Figure S2A) and are also confirmed in pooled single-nucleus FGOs Hi-C data (Flyamer et al., 2017) (Figure S2B). In fact, these local plaid patterns can already be observed in some regions in GOs II

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A mESC

Chromatin interaction heatmap GO I

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chr2:63,480,000-68,520,000

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mCG/CG H3K4me3 iPAD

H3K27me3

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mESC

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1 -1 0 Normalized RPKM

Figure 2. Establishment of PADs during Follicle Growth (A) Heatmaps showing normalized Hi-C interaction frequencies (40-kb bin) for a region in chromosome 15 in mESCs, PGCs, and oocytes (pooled data from two or three biological replicates). (B) Correlation heatmap (20-kb bin) of a region on chromosome 2 in FGOs. Interaction frequency heatmap is also shown on the left. Tracks of DNA methylation (Shirane et al., 2013), H3K4me3, H3K27me3 (Zhang et al., 2016; Zheng et al., 2016), and local PC1 values are also shown on top. A zoomed-in view of the tracks is shown on the right. PADs and iPADs are marked by dark red and dark blue bars. One self-interacting PAD is indicated with an arrow in the heatmaps. (legend continued on next page)

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(Figure S1I, arrows). Thus, our analyses suggest a transition from conventional compartments to local chromatin compartments during oocyte growth. We then sought to identify the locations of these local compartments in FGOs. An initial investigation revealed that conventional PCA (Lieberman-Aiden et al., 2009) can accurately identify compartment A- and B-like structures but only partially capture the local compartments in FGOs (data not shown). This is presumably because the conventional PCA focuses on whole-chromosome interactions. To capture the local plaid patterns, we optimized PCA to use only local chromatin interactions (within 10 Mb) (STAR Methods). The resulting PC1 values (termed local PC1 values) correlated well with the local plaid patterns in FGOs (Figure 2B, ‘‘local PC1’’). This analysis successfully separated the genome into two classes. One class includes regions that show strong self-interactions and also inter-domain interactions, forming compartmental domains (Figure 2B, white arrow). The other class includes regions interspaced between the compartmental domains. As validation, the majority of these domains (84.6%, compared to 44.3% of random) were also recaptured in single-nucleus FGO data (Flyamer et al., 2017) (Figure S2C). To understand the nature of these local compartments, we compared their locations to reported epigenetic features in FGOs, including DNA methylation and histone modifications (Dahl et al., 2016; Hanna et al., 2018; Kobayashi et al., 2012; Shirane et al., 2013; Stewart et al., 2015; Xu et al., 2019; Zhang et al., 2016; Zheng et al., 2016). Strikingly, we found the self-interacting compartmental domains well match H3K27me3 marked regions in FGOs (Figures 2B, 2C, and S2D). As control, such strong correlation was not found in mESCs (Figure S2E). For convenience, we termed these H3K27me3 marked self-interacting domains as Polycomb-associating domains (PADs) and regions interspacing PADs as inter-PAD (iPAD) regions (Table S2). We also refer to the local plaid patterns in FGOs, which reflect interactions among PADs and/or iPADs, as PAD/iPAD compartmental interactions. Globally, PAD-PAD shows the strongest interactions, followed by iPAD-iPAD and then PAD-iPAD (Figure 2D). We also examined the relationship of PAD/iPADs with other epigenetic modifications. In mouse FGOs, transcribing regions are nearly always marked by high levels of DNA methylation due to H3K36me3-dependent methylation mechanism (Kobayashi et al., 2012; Shirane et al., 2013; Stewart et al., 2015; Xu et al., 2019). The rest of the non-transcribed regions are primarily marked by H3K4me3 or H3K27me3, which exists as non-canonical broad domains (Zhang et al., 2016; Zheng et al., 2016). In fact, H3K27me3 first appears in nearly all non-transcribed regions in GOs I (Zheng et al., 2016). During the maturation of oocytes, a portion of H3K27me3 is replaced by newly acquired non-canonical H3K4me3 (ncH3K4me3) (Zheng et al., 2016). Such ncH3K4me3 occupies a large number of distal loci and is surprisingly linked to genome silencing (Andreu-Vieyra et al., 2010; Hanna et al., 2018; Zhang et al., 2016). Consistently, our analysis showed that PADs exclusively cover H3K27me3-

marked regions, while iPADs include regions marked by either DNA methylation (indicating either current or past transcription) or ncH3K4me3 (non-transcribed regions) in FGOs (Figures 2B and S2F). We then compared PADs/iPADs to previously reported chromatin architecture features. While both PAD/iPADs and compartments A/B (Lieberman-Aiden et al., 2009) lead to bipartite spatial segregation of the genome, differences clearly exist between them. First, PADs and their compartmental interactions only occur in GOs II and FGOs, while compartments A and B are evident in most cell types despite becoming weaker for late stage oocytes (Figure S3A). Second, compared to compartments A and B, PAD/iPAD exist in larger numbers in the genome with much shorter average lengths (Figure S3B). Third, compartmental interactions are also restricted to local regions for PADs and iPADs, while those for compartments A and B often extend to the entire chromosome (Figure 1A). Fourthly, unlike chromatin compartments A and B, which differ in GC content and gene densities (Lieberman-Aiden et al., 2009), PAD and iPAD show comparable GC levels and smaller differences in gene density (Figure S3B). Notably, long-range interactions of H3K27me3 targets have been previously observed in other cell types such as mESCs and forebrain (Bantignies et al., 2011; Denholtz et al., 2013; Noordermeer et al., 2011; Tolhuis et al., 2011; Vieux-Rochas et al., 2015). However, H3K27me3-marked regions and the associated interactions in these cells are relatively rare in the genome and are typically limited to developmental genes rather than being the dominant organization of the entire genome (see Figure S3C for comparison). In fact, PADs occupy nearly half (45.4%) of the genome in mouse FGOs. Finally, transcription-correlated compartmental domains are widely observed in fly and other species (Rowley et al., 2017). Similar patterns were also found in mammalian cells, although they are obscured by CTCF and cohesin-mediated chromatin looping (Rao et al., 2017; Rowley and Corces, 2016; Sanborn et al., 2015; Schwarzer et al., 2017). However, PADs and iPADs do not simply segregate based on transcribed regions, as they both occupy part of non-transcribed regions (H3K27me3-marked and noncanonical H3K4me3-marked regions, respectively) as described above (Figure 2B). Taken together, our data reveal a unique PAD chromatin organization in late-stage oocytes that is distinct from conventional compartments A and B, typical Polycomb target interactions, and transcription-correlated compartmental domains. PADs Do Not Require Cohesin Cohesin is essential for CTCF-anchored loop and TAD formation presumably via loop extrusion, whereby cohesin extrudes chromatin until it is blocked by CTCF (Fudenberg et al., 2016; Gassler et al., 2017; Haarhuis et al., 2017; Rao et al., 2017; Sanborn et al., 2015; Schwarzer et al., 2017; Wutz et al., 2017). These cohesin-mediated domains appear to antagonize chromatin compartmentalization (Mirny et al., 2019; Nora et al.,

(C) Heatmap showing the enrichment of H3K27me3 (normalized RPKM) around PADs and iPADs in FGOs. (D) Boxplots showing the intra-chromosomal interaction frequencies between different PADs (dark red) and different iPADs (dark green) and between PADs and iPADs (dark blue). p values calculated by Wilcoxon rank-sum test (two tailed) are also shown. Those with relatively high levels of significance are marked in red. See also Figure S2 and Table S2.

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Average insulation score analysis TAD-specific boundary (inside iPAD)

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Figure 3. Dynamics of TADs and PADs during Oocyte Development (A) The average insulation scores at PAD-specific boundaries or TAD-specific boundaries (inside iPADs) and nearby regions are shown for Scc1fl/fl and Scc1
/
FGOs. (B) Heatmaps showing normalized Hi-C interaction frequencies (300-kb bin) for a region in chromosome 10 in Scc1fl/fl and Scc1
/
FGOs. PAD positions (defined in WT FGOs) are also indicated. (legend continued on next page)

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2017; Nuebler et al., 2018; Rao et al., 2017; Schwarzer et al., 2017; Wutz et al., 2017). Interestingly, while PADs show compartmental interactions, these regions also form self-interacting domains like TADs. To investigate the relationship between cohesin and PADs, we depleted Scc1, an essential subunit of cohesin, in oocytes using Scc1fl/fl;Zp3-Cre mice (Ladstatter and Tachibana-Konwalski, 2016) and performed Hi-C as previously described (Flyamer et al., 2017). Globally, the Scc1
/
FGOs showed increased long-distance chromatin interactions at the chromosome level (Figures S3D and S3E). As also observed in somatic cells and zygotes (Gassler et al., 2017; Rao et al., 2017; Schwarzer et al., 2017; Wutz et al., 2017), conventional TADs (identified in mESCs, inside iPAD [see below]) are largely lost as evidenced by much weaker boundary insulation (Figure 3A; STAR Methods). Intriguingly, PAD-PAD interactions are evidently strengthened in Scc1
/
FGOs (Figure 3B), as also confirmed by quantitative analyses using observed/expected Hi-C interactions (with distance effect normalized) (Figure S3F). In addition, PAD-specific boundaries (excluding conventional TAD boundaries [see below]) show strengthened insulation in Scc1
/
FGOs (Figure 3A). Notably, the loss of TADs in Scc1
/
FGOs is unlikely resulted from the loss of cohesion between bivalent chromosomes or sister chromatids, which is primarily executed by Rec8 in mouse oocytes, a meiosis-specific cohesin subunit (Tachibana-Konwalski et al., 2010). Hence, cohesin is dispensable for PADs in oocytes but may antagonize PADs, suggesting PADs and TADs are regulated by distinct mechanisms. Dynamics of TADs and PADs during Oocyte Development An interesting question is whether conventional TADs are still present in late-stage oocytes. TADs are evident in GOs I, which resemble those in PGCs and mESCs (Figure 3C). However, a portion of TADs appears to be significantly weakened or even lost in GOs II and FGOs (Figure 3C, see below for details). To systematically investigate the presence of TADs in FGOs, we classified all mESC TAD boundaries identified using insulation scores (Crane et al., 2015) into three groups (Table S3) (similar results were obtained using PGC TADs [data not shown]). The first group co-localize with PAD boundaries (‘‘TAD/PAD shared boundaries’’). These boundaries are present in GOs and FGOs, as shown by their strong chromatin insulation (Figure 3D). The second group of TAD boundaries is located inside iPADs, which show reduced insulation in late-stage oocytes (Figure 3E). Finally, the third group of TAD boundaries is located inside PADs. Surprisingly, the insulation of these boundaries is weaker in GOs I and GOs II and becomes further attenuated in FGOs (Figure 3G). By contrast, the strengths of PAD-specific bound-

aries (which do not overlap with TAD boundaries) gradually increased from GOs I to FGOs (Figure 3F). In sum, PADs and TADs co-exist in late-stage oocytes, with TAD boundaries being resolved when they fall inside PADs. Notably, we cannot exclude the possibility that TAD boundaries may be masked by the strong PAD intra-domain interactions. Transcription Inhibition Does Not Recapitulate PAD Establishment in Oocytes During maturation, FGOs switch from the NSN stage to the SN stage, which is accompanied by transcription silencing and chromatin compaction (Bouniol-Baly et al., 1999; Miyara et al., 2003). This raises the interesting question of whether the formation of PADs is linked to transcription silencing and/or chromatin compaction during this transition. However, an analysis of single-nucleus Hi-C datasets (Flyamer et al., 2017) identified PADs in both NSN and SN oocytes (Figure S2B). Interestingly, it was reported that transcription inhibition in GOs can induce a SN-stage-like chromatin state (Abe et al., 2010; Bouniol-Baly et al., 1999; Liu et al., 2012). Therefore, we isolated GOs II (from P14 mice) and treated them with alpha-amanitin for 24 h, as in previous studies (Abe et al., 2010; Bouniol-Baly et al., 1999; Liu et al., 2012) (STAR Methods; Figure S4A). A Hi-C analysis revealed strengthened local interactions in alpha-amanitintreated oocytes (Figure S4B, green arrow). However, we did not find clear PADs in these cells (Figure S4B, black arrows, and Figure S4C). Thus, transcription silencing and associated chromatin compaction in oocytes do not recapitulate the establishment of PADs. PADs Are Disassembled after Exiting from Diplotene Arrest but Briefly Reappear in Preimplantation Embryos To determine if PADs still exist once oocytes exit from diplotene arrest, we collected oocytes that resume meiosis and undergo germinal vesicle (nucleus) breakdown (GVBD) (Figure S4D; STAR Methods). Hi-C analysis showed that PADs are largely absent in oocytes undergoing GVBD (Figures S4E and S4F). Similar to TADs and chromatin compartments (Du et al., 2017; Ke et al., 2017), PADs are also not found in MII oocytes (Figure 1A). Notably, our previous work (Zheng et al., 2016) showed that distal H3K27me3 in mouse oocytes is inherited after fertilization and persists until blastocyst. Thus, we asked if PADs may be present in early embryos after fertilization by reexamining Hi-C data in mouse preimplantation embryos (Du et al., 2017). Interestingly, PADs (Figure 4A, boxes) and their compartmental interactions (Figure 4A, arrow) appear to emerge in early 2-cell embryos and become further evident in late 2-cell embryos. PADs begin to fade away in the 8-cell embryos and become undetectable in inner cell mass (ICM).

(C) WashU EpiGenome Browser view shows the Hi-C interaction heatmaps (40-kb bin) in mESCs, E11.5 PGCs, and oocytes at different stages (pooled data from two or three biological replicates). TADs identified in mESCs, insulation scores (ISs) in mESCs and FGOs, H3K27me3 enrichment in FGOs, and PADs identified in FGOs are also shown. Examples of interactions between different PADs are indicated by black arrows. (D–G) Top, zoomed-in views of Hi-C interaction heatmaps (40-kb bin) in mESCs and FGOs are shown for four blue shaded regions in Figure 3C corresponding to TAD/PAD shared boundaries (n = 1,221, D), TAD-specific boundaries inside iPADs (n = 531, E), PAD specific boundaries (n = 3,828, F), and TAD-specific boundaries inside PADs (n = 154, G). ISs in mESCs and FGOs, as well as PADs, are also shown. Examples for four different types of boundaries are marked with boxes of different colors. Bottom: the average ISs at each class of boundaries as well as their nearby regions are shown in mESCs, PGCs, and oocytes. See also Figure S3 and Table S3.

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Figure 4. Brief Reappearance of PADs after Fertilization

chr10:88,680,000-113,680,000 Maternal FGO

Paternal Sperm

Gametes

PN5 zygote

Early 2-cell

Late 2-cell

8-cell

ICM

Interaction frequency Low

B

(A) WashU EpiGenome Browser views show the Hi-C interaction heatmaps (40-kb bin) in mouse FGOs, sperm, and maternal and paternal alleles of preimplantation embryos. Two example PAD regions are marked by blue boxes, and the interactions between these two PADs are indicated by black arrows. (B) WashU EpiGenome Browser view shows the differential Hi-C interaction heatmap (maternalpaternal) of late 2-cell stage. H3K27me3 enrichment on two parental alleles is also shown. Examples of interactions between different PADs are indicated by arrows. (C) The average insulation scores at PAD-specific boundaries and nearby regions are shown for maternal and paternal alleles in early embryos. See also Figure S4.

High

Differential Hi-C heatmap (Maternal-Paternal) of late 2-cell chr10:88,680,000-113,680,000

PADs in Oocytes and Early Embryos Depend on Polycomb Proteins PAD Given the strong correlation of PADs Differential Hi-C heatmap with H3K27me3, we asked if Polycomb proteins may play a role in shaping the oocyte and early embryo 3D genome. We knocked out Eed, a core component of PRC2 that deposits H3K27me3 (Cao et al., 2002; Margueron and Reinberg, 2011; Whitcomb et al., 2007), in oocytes using Eedfl/fl mice and Differential Gdf9-Cre mice (STAR Methods). Immuinteraction frequency nostaining showed that H3K27me3 is Paternal Maternal nearly undetectable in Eed
/
FGOs C (Figure S5A). sisHi-C analysis revealed PAD specific boundary Maternal Paternal that Eed
/
and Eedfl/fl FGOs share glob0.15 0.15 PN5 zygote ally similar chromatin contact probability 0.1 0.1 Early 2-cell (Figure S5B). Unexpectedly, PADs are 0.05 0.05 Late 2-cell largely unaffected in Eed
/
FGOs 0 0 8-cell compared to control (Figures 5A, S5C– -0.05 -0.05 ICM S5F). Cleavage under targets and release -0.1 -0.1 -500kb Boundary +500kb using nuclease (CUT&RUN) (Skene and -500kb Boundary +500kb Henikoff, 2017) detected strongly reduced but evident residual H3K27me3 Remarkably, such reappearance of PADs and PAD/iPAD in Eed
/
GOs II with the genome-wide pattern retained (Figcompartmental interactions at the 2-cell stage only occurs on ure S5G). H3K27me3 further decreased in Eed
/
FGOs, the maternal, but not paternal, allele (Figures 4A and 4B). We although weak signals could still be observed in selected regions confirmed the brief reappearance of PADs and their interac- (Figure S5G, shaded). Quantitative measurement using spike-in tions using insulation score analysis at PAD-specific bound- DNA confirmed decreased total H3K27me3 in GOs II (to 20% of aries (to eliminate possible confounding effects from TADs) control) and FGOs (to 10%–20% of control, with background (Figure 4C) and quantitative measurement of PAD/iPAD possibly increased as H3K27me3 decreased). Thus, we compartmental interactions (Figure S4G). In fact, insulation of conclude that the H3K27me3 is strongly decreased in Eed
/
PAD-specific boundaries can be observed as early as in zy- GOs II and FGOs. Nevertheless, we cannot exclude the possibilgotes (Figure 4C). Notably, the brief maternal appearance of ity that PAD may be established before the substantial loss of PADs is in contrast to the gradual consolidation of TADs H3K27me3 and that its maintenance does not require (Figure S4H) on both alleles (Du et al., 2017; Ke et al., 2017) H3K27me3 in these diplotene-arrested oocytes. To determine if PRC2/H3K27me3 may be involved in the rees(see Discussion). Taken together, these data revealed PADs tablishment of PADs in early embryos, we crossed female briefly appear on the maternal allele after fertilization. Insulation score

Late 2-cell

P H3K27me3 M

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Figure 5. PADs Are Compromised EedmNull 2-Cell Embryos

Interaction heatmap FGO

Late 2-cell

Eedfl/fl

Eed-/-

Eedfl/wt

WT ICM

EedmNull

107Mb

chr10

Maternal

96

PAD

Interaction frequency 0

chr10

Late 2-cell PAD/iPAD compartmental interactions

B

5.90e-62

12

H3K27me3

0.28 0.0004

0.78

10

Differential heatmap (late 2-cell maternal)

C PAD-PAD

P M

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PAD-iPAD iPAD-iPAD

8 6 4 2

EedmNull - Eedfl/wt

Interaction frequency

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107Mb

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0 Maternal

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Insulation score

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0

-0.1

-0.1

-0.2

-0.2

-0.3 -0.3 -500kb Boundary +500kb -500kb Boundary +500kb Eedfl/wt late 2-cell

(A) Heatmaps showing normalized Hi-C interaction frequencies (100-kb bin) for a region in chromosome 10 in Eedfl/fl and Eed
/
FGOs, maternal and paternal alleles of Eedfl/WT, EedmNull 2-cell embryos, and WT ICM embryos. Examples of weakened PAD-PAD interactions are indicated by black arrows. PAD positions (defined in WT FGOs) are also indicated. (B) Boxplots showing the intra-chromosomal interaction frequencies between different PADs (dark red) and different iPADs (dark green) and between PADs and iPADs (dark blue) for Eedfl/WT and EedmNull 2-cell embryos. p values calculated by Wilcoxon rank-sum test (two tailed) are also shown. Those with relatively high levels of significance are marked in red. PAD positions (defined in WT FGOs) are also indicated. (C) Heatmaps showing the differential Hi-C interaction frequency (EedmNull - Eedfl/WT) in the maternal allele of late 2-cell embryos. H3K27me3 enrichment on two parental alleles of WT 2cell embryos is also shown. Examples of interactions between different PADs are indicated by arrows. (D) The average ISs at PAD-specific boundaries and nearby regions are shown for maternal and paternal alleles in Eedfl/WT and EedmNull 2-cell embryos. See also Figures S5 and S6.

Differential interaction frequency Eedfl/wt

D

107Mb

in

EedmNull late 2-cell

Eedfl/fl;Gdf9-Cre mice with WT male mice (PWK/PhJ background) and performed sisHi-C for the resulting (Eed maternal null, or EedmNull) late 2-cell embryos (where PADs are the most evident in early embryos). We confirmed substantial loss of H3K27me3 in EedmNull 2-cell embryos by immunostaining (Figure S6A). We then assigned Hi-C sequencing reads to their parental origins based on SNPs present between the two strains as previously described (Du et al., 2017). Strikingly, PAD-PAD interactions are clearly attenuated in the maternal allele of EedmNull 2-cell embryos compared to control (Eedfl/WT) (Figures 5A, 5B, and S6B). PAD-PAD interactions become comparable to PADiPAD interactions in the mutant embryos (Figure 5B). These results are also supported by a differential heatmap analysis (Figures 5C and S6C) and quantitative analyses of insulation scores at PAD-specific boundaries (Figures 5D and S6D). As control, PADs are absent in the paternal genome of both control and mutant 2-cell embryos (Figures 5A and S6B), and TAD boundaries are largely not affected in mutants (Figure S6D). These

EedmNull

data suggest that PRC2 or H3K27me3 is required for the reestablishment of PADs in early embryos. In addition to PRC2, PRC1 is also responsible for long-range interactions between Polycomb targets in mESCs and differentiated cells (Bonev et al., 2017; Rhodes et al., 2019). Thus, we asked whether PRC1 regulates PAD. Previous work showed that RING1 and RNF2, two components of PRC1, serve redundant functions in mouse oogenesis and are essential for early embryo development (Posfai et al., 2012). Therefore, we performed Hi-C analysis in control (Ring1
/
Rnf2fl/fl) and Ring1
/
Rnf2fl/fl;Zp3-Cre (Ring1/Rnf2 dKO) FGOs for three replicates. We confirmed the lack of histone H2A mono-ubiquitination, the catalytic product of PRC1 (Simon and Kingston, 2009), in these mutant oocytes (Figure S7A). Strikingly, PAD boundaries, but not TAD boundaries, become less insulated (Figures 6A, 6B, S7B, and S7C). PAD-PAD interactions are clearly attenuated in Ring1/Rnf2 dKO FGOs (Figures 6A, 6C, and S7B, green arrows). Interestingly, such impacts are much more pronounced for longer-distance PAD-PAD interactions (2–5 Mb) but less so for short-distance PAD-PAD interactions (400 kb to 2 Mb) (Figure S7D). Notably, Ring1/Rnf2 dKO FGOs generally have significantly fewer long-distance interactions (Figure S7E). However, the impacts on longer-distance PAD-PADs are apparent even after normalizing distance effects by using observed/expected Hi-C interactions (STAR

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A

FGO

Ring1-/-Rnf2fl/fl

PAD

B

Ring1/Rnf2 dKO

Average insulation score analysis TAD-specific boundary (inside iPAD)

Interaction heatmap

PAD specific boundary

chr10:82

88

82

Insulation score

0

88Mb

PAD Differential heatmap

-0.1

-0.1 -0.2

-0.3

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Ring1-/-Rnf2fl/fl

chr10:82

mESC WT FGO

Ring1-/-Rnf2fl/fl Ring1/Rnf2 dKO

Differential interaction frequency Ring1/Rnf2 dKO

88Mb

Ring1/Rnf2 dKO - Ring1-/-Rnf2fl/fl

C

D

RNA-seq analysis (FGO)

PAD/iPAD compartmental interactions 5

<1e-300

PAD-PAD PAD-iPAD iPAD-iPAD

Ring1-/-Rnf2fl/fl

Eedfl/fl

0.126

10

0.429

Eed-/-

3

1.04e-20

<1e-300

0.15

2

5 4 3 2.38e-25 2

1

2.97e-7

Log (FPKM+1)

6

4 Log (FPKM+1)

Interaction frequency

Ring1/Rnf2 dKO

T W

2 nf /R g1 in R

0.397

Pc (n G t = a no 1,3 rge 4 t nPc 7) (n G t =5 ar ,9 ge G en 61) t e (n s in =1 i 4, PA 46 D 4)

FG

O dK

nf R

-/-

g1 in R

0.056

Pc (n G t = a no 1,3 rge 4 t nPc 7) G (n t =5 ar ,9 ge G en 61) t es (n in =1 i 4, PA 46 D 4)

O

l

2 fl/f

C ES

4

0

0

m

6

2

1

0

8

Genes in PAD

Genes in PAD

Figure 6. PAD Boundaries and PAD-PAD Interactions Are Attenuated in Ring1/Rnf2 dKO FGOs (A) Heatmaps showing normalized Hi-C interaction frequencies (40-kb bin) for a region in chromosome10 in Ring1
/
Rnf2fl/fl and Ring1/Rnf2 dKO FGOs. Differential heatmap shows the differential Hi-C interaction frequency (Ring1/Rnf2 dKO - Ring1
/
Rnf2fl/fl) in the FGOs. Examples of weakened PAD-PAD interactions and PAD boundary are indicated by green and black arrows, respectively. PAD positions (defined in WT FGOs) are also indicated. (B) The average ISs at PAD specific boundaries and nearby regions are shown for mESCs, WT FGOs, and Ring1
/
Rnf2fl/fl and Ring1/Rnf2 dKO FGOs. (C) Boxplots showing the intra-chromosomal interaction frequencies between different PADs (dark red), different iPADs (dark green), and between PADs and iPADs (dark blue) for mESCs, WT FGOs, Ring1
/
Rnf2fl/fl, and Ring1/Rnf2 dKO FGOs. p values calculated by Wilcoxon rank-sum test (two tailed) are also shown. Those with relatively high levels of significance are marked in red. (D) Boxplots showing the transcription levels of classic PcG target genes and non-PcG target genes in PADs or genes in iPADs in Ring1
/
Rnf2fl/fl, Ring1/Rnf2 dKO FGOs, Eedfl/fl and Eed
/
FGOs. p values calculated by t test (two tailed) are also shown. Those with relatively high levels of significance are marked in red. See also Figure S7.

Methods; Figure S7D). Overall, these data possibly reflect distinct roles for PRC1 in controlling long- versus short-range PAD interactions. While the long-distance PAD interactions may require PRC1, the short-distance interactions may be mediated by alternative local chromatin segregation mechanisms. The impact of PRC1 deficiency on PADs is stronger than that of Eed deficiency (Figures S5B–S5F) in FGOs, which echoes the notion that PRC1 directly functions in chromatin compaction and gene repression (Kundu et al., 2017; Schoenfelder et al., 2015). Notably, Ring1/Rnf2 dKO FGOs suffer from

10 Molecular Cell 77, 1–15, February 20, 2020

defects of meiosis, and the derived embryos after fertilization are arrested at early 2-cell stage (Posfai et al., 2012), precluding the analysis to test the role of PRC1 on the reestablishment of PADs in early embryos. To assess the relationship between PAD and gene expression, we performed total RNA sequencing (RNA-seq) in both control and Ring1/Rnf2 dKO FGOs. Genes located in PADs are normally repressed but are moderately de-repressed in Ring1/Rnf2 dKO FGOs (Figure 6D). Importantly, such de-repression is found for both classic Polycomb-group proteins (PcG) target genes

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

A

A model for chromatin architecture dynamics in mouse oogenesis and early development Fertilization

Postnatal

PGC

GO I

GO II

FGO

GVBD MII/Sperm Zygote

2-cell

8-cell

ICM

PAD TAD Compartment A/B

PAD TAD Compartment A/B

B

TAD vs. PAD PGC

FGO H3K27me3

H3K27me3

iPAD TADs

PAD

Low

TAD

PAD

High

Interaction intensity

PAD

TADs

H3K27me3

PAD-PAD interaction

Transcribing

Figure 7. Dynamic Reprogramming of Chromatin Architecture during Oogenesis and Early Development (A) A schematic model showing the dynamic changes of chromatin organization in mouse oogenesis and early development. PGC and GOs I have both conventional TADs and compartments A and B similar to mESCs. In GOs II, PADs begin to emerge while conventional compartments A and B become weaker. During further maturation of oocytes, conventional compartments A and B are largely replaced by PAD/iPAD, and some TADs are altered by PADs. In MII oocytes, uniform chromatin architecture exists, which lacks TADs and compartments (A/B and PAD/iPAD). PADs then briefly reappear specifically on the maternal allele as early as the zygote stage, are most evident at the 2-cell stage, and become undetectable in ICM. (B) Schematics showing the reorganization of chromatin structure and histone modifications from PGCs to FGOs. In PGCs, H3K27me3 exists as sharp peaks enriched at inactive promoters. TADs at this stage are also similar to mESCs. During oogenesis, H3K27me3 becomes broader and covers a large portion of nontranscribed regions on the genome. PADs and related compartmental interactions gradually appear in H3K27me3-marked regions. Certain TADs become undetectable once they overlap with PADs.

(identified by mESC H3K27me3) and non-PcG target genes (Figure 6D). By contrast, the de-repression in PADs is not evident in Eed
/
FGOs (Figure 6D), suggesting that gene silencing in PADs is independent of EED and H3K27me3. Gene repression is correlated with the induced PAD-PAD interaction, as PADs with gene de-repression in Ring1/Rnf2 dKO FGOs showed more reduced PAD-PAD interactions compared to those without gene de-repression (Figure S7F; STAR Methods). This is true after normalizing distance effects (Figure S7F, right) and also for both short- (400 kb to 2 Mb) and long-range (2–5 Mb) PADPAD interactions (data not shown). Taken together, these data support the possibility that PADs may play a role in gene repression in FGOs. Nevertheless, at this moment, we cannot exclude the possibility that gene repression in PADs may be directly caused by PRC1 binding, which warrants future investigations.

DISCUSSION Chromatin undergoes drastic reorganization during oogenesis. However, little is known about the molecular basis for the reprogramming of chromatin higher-order organization in this process. Here, using a low-input Hi-C (sisHi-C) approach, we examined 3D chromatin architecture in mouse oogenesis. Our study not only provides a spatiotemporal view of highly dynamic chromatin structure during female germ cell development but also reveals a surprising chromatin configuration PAD in late-stage oocytes that is distinct from other known chromatin organizations thus far (Figures 7A and 7B). One interesting question is why PADs are uniquely present in mouse oocytes. Notably, broad H3K27me3 domains are prevalent in the mouse oocyte genome (Zheng et al., 2016).

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Furthermore, it has been reported that lamina-associated domains cannot be detected at this stage (Borsos et al., 2019). It is tempting to speculate that the conventional compartments A and B are weakened in mouse oocytes, allowing chromatin to better segregate according to their chromatin state. Our data indicate that the deficiency in PRC1, but not PRC2 or H3K27me3, affects PADs and gene repression in mouse oocytes. Nevertheless, we could not exclude the possibility that residual H3K27me3 is still sufficient for the maintenance of PAD structures or that PAD may be established before substantial loss of H3K27me3 in oocytes. Importantly, our data suggest that PRC2 or H3K27me3 is likely important for the establishment of PADs, at least in embryos. Thus, both PRC1 and PRC2 complexes may play important roles in regulating PADs. Notably, CBX2, a component of PRC1 that is also expressed during oogenesis, was reported to mediate condensates through phase separation (Plys et al., 2019; Tatavosian et al., 2019). Future studies are warranted to test these models. The reappearance of PADs after fertilization raises the interesting question of whether such a structure is maternally inherited in early embryos. While PADs are virtually absent in MII oocytes, one possibility is that the persisting maternal H3K27me3 may act as ‘‘seeds’’ that enable the formation of PADs perhaps through PRC1, which can recognize H3K27me3 (Bernstein et al., 2006; Puschendorf et al., 2008; Tardat et al., 2015). Another intriguing question is why PADs are strongest around the 2-cell stage. We confirmed that zygotic genome activation at this stage is not required for PADs by reanalyzing Hi-C data of early embryos in the presence of a transcription inhibitor (Du et al., 2017) (data not shown). Notably, both the TAD boundary (Figure S4H) and PAD boundary (Figure 4C) can be detected as early as the 1-cell stage. Previous work showed that TADs are highly relaxed right after fertilization (Du et al., 2017; Ke et al., 2017). Therefore, PADs may be less evident at the 1-cell stage possibly for similar reasons as those for TADs. The disappearance of PADs after the 8-cell stage could be related to the dilution or degradation of PAD regulators. One important question is whether PADs are functional for oocyte and embryo development. In Ring1/Rnf2 dKO oocytes, PADs are significantly attenuated, along with evident gene derepression in PADs. These data support the possibility that perhaps PADs separate the oocyte genome into defined compartments to ensure proper gene repression. In mouse embryos, oocyte-inherited H3K27me3 domains play critical roles in instructing the allele-specific transcription of Xist gene and several other imprinted genes (Inoue et al., 2017a; Inoue et al., 2017b). Whether PADs play a role in these events remains to be explored, although PADs appear to be fairly weak after the 2-cell stage. Taken together, our study unveiled highly dynamic chromatin reorganization during female gametogenesis. Future studies are warranted to identify additional regulating factors and functions of PADs in gametogenesis and early development. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following:

12 Molecular Cell 77, 1–15, February 20, 2020

d d d

d

d d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS B PGC and oocyte collection B Collection of transcription inhibited oocytes B Collection of Scc1 cKO oocytes B Collection of Eed cKO oocytes B Collection of Ring1 KO and Rnf2 cKO oocytes METHOD DETAILS B EU incorporation B Immunostaining B CUT&RUN library generation and sequencing B CUT&RUN data mapping B sisHi-C library generation and sequencing B snHi-C library generation and sequencing B RNA-seq library generation and sequencing B Hi-C data mapping B RNA-seq data processing B Allele assignment of sequencing reads B Hi-C interaction heatmap, differential interaction heatmap, observed/expected heatmap and correlation heatmap B Comparison of interaction frequencies between different stages B Hierarchical clustering analysis B The P(s) analysis B Identification of topologically associating domains (TADs) B Identification of conventional chromatin compartments and Polycomb Associating Domains (PADs) B Analysis of cis-interactions among PADs/iPADs B Comparison of insulation scores between different stages B Analysis of compartmental interactions B Analysis of gene density, GC content in PAD/iPAD and compartments A/B B Analysis of gene expression in PADs QUANTIFICATION AND STATISTICAL ANALYSIS DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. molcel.2019.11.011. ACKNOWLEDGMENTS We thank Prof. Fei Gao (IOZ, Beijing, China) for providing Gdf9-Cre mice, Dr. Miguel Vidal (CSIC, Madrid, Spain) for Ring1 deficient mice, Prof. Maarten van Lohuizen (NKI, Amsterdam, the Netherlands) for Rnf2fl/fl mice, and Prof. Barbara Knowles (JAX, Bar Harbor, ME, USA) for Zp3-Cre transgenic mice. We are grateful to Rui Ma, Hui Quan, Xianglin Zhang, Zhaofeng Ye, Yuanyuan Li, Wenhao Zhang, Xiaowo Wang, Jianyang Zeng, Yiqin Gao, and Juntao Gao for discussions. We thank Drs. Haruhiko Koseki, Yi Zhang, and Inoue Azusa for sharing reagents or protocols. We appreciate members of the Xie laboratory for comments during preparation of the manuscript. This work is supported by funding provided by the National Natural Science Foundation of China (grants 31422031 and 31725018 to W.X.); the National Basic Research Program of China (grant 2015CB856201 to W.X.); the Beijing Municipal Science & Technology Commission (grant Z181100001318006 to W.X.); the THU-PKU

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

Center for Life Sciences (W.X.); the Novartis Research Foundation (N.V. and A.H.F.M.P.); the European Research Council (grant ERC 695288–Totipotency to Y.K.K., E.A.O., and A.H.F.M.P.); and the JSPS (KAKENHI JP18H05214 to H.S.). Research in the laboratory of K.T. is supported by the Austrian Academy of Sciences, the FWF and Herzfelder foundation (P30613-B21), a European Research Council grant (ERC-StG-336460 ChromHeritance), and an HFSP project grant (PGP0057-2018). Z.D., H.Z., and Q.X. are supported by THU-PKU Center for Life Sciences postdoctoral fellowships. W.X. is an HHMI international research scholar. J.G. is supported by the European Research Council and the L’Ore´al Austria Fellowship for Women in Science and is an associated student of the DK Chromosome Dynamics (W1238B20) supported by the Austrian Science Fund (FWF). AUTHOR CONTRIBUTIONS Z.D., H.Z., and W.X. conceived the project. Z.D. conducted the sisHi-C experiments. Z.L., Q.X., B.H., K.X., Z.D., Y.K.K., J.G., and K.Z. performed the mouse PGCs, oocytes, and embryo experiments. H.Z. and Z.D. performed bioinformatics analysis. L. Li and G.Y. performed the CUT&RUN experiments. J.G. performed the single-nucleus Hi-C experiments. S.P. processed the singlenucleus Hi-C data. L.C., P.W., L. Liu, E.A.O., N.V., Q.Z., and W.K.A.Y. helped with various experiments or analyses. Q.W. performed next-generation sequencing (NGS). A.H., Y.S., J.N., Q.S., H.S., K.T., A.H.F.M.P., and W.X. supervised the project or related experiments. Z.D., H.Z., K.Z., A.H.F.M.P., and W.X. wrote the manuscript, with help from other authors. DECLARATION OF INTERESTS The authors declare no competing interests. Received: August 15, 2019 Revised: September 3, 2019 Accepted: November 8, 2019 Published: December 11, 2019 REFERENCES Abdennur, N., and Mirny, L.A. (2019). Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics. https://doi.org/10. 1093/bioinformatics/btz540. Abe, K., Inoue, A., Suzuki, M.G., and Aoki, F. (2010). Global gene silencing is caused by the dissociation of RNA polymerase II from DNA in mouse oocytes. J. Reprod. Dev. 56, 502–507. Akdemir, K.C., and Chin, L. (2015). HiCPlotter integrates genomic data with interaction matrices. Genome Biol. 16, 198. Andreu-Vieyra, C.V., Chen, R., Agno, J.E., Glaser, S., Anastassiadis, K., Stewart, A.F., and Matzuk, M.M. (2010). MLL2 is required in oocytes for bulk histone 3 lysine 4 trimethylation and transcriptional silencing. PLoS Biol. 8, e1000453. Bachvarova, R. (1985). Gene expression during oogenesis and oocyte development in mammals. Dev. Biol. 1, 453–524. Bantignies, F., Roure, V., Comet, I., Leblanc, B., Schuettengruber, B., Bonnet, J., Tixier, V., Mas, A., and Cavalli, G. (2011). Polycomb-dependent regulatory contacts between distant Hox loci in Drosophila. Cell 144, 214–226. Bernstein, E., Duncan, E.M., Masui, O., Gil, J., Heard, E., and Allis, C.D. (2006). Mouse polycomb proteins bind differentially to methylated histone H3 and RNA and are enriched in facultative heterochromatin. Mol. Cell. Biol. 26, 2560–2569. Bickmore, W.A. (2013). The spatial organization of the human genome. Annu. Rev. Genomics Hum. Genet. 14, 67–84. Bonev, B., Mendelson Cohen, N., Szabo, Q., Fritsch, L., Papadopoulos, G.L., Lubling, Y., Xu, X., Lv, X., Hugnot, J.P., Tanay, A., and Cavalli, G. (2017). Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24.

Borsos, M., Perricone, S.M., Schauer, T., Pontabry, J., de Luca, K.L., de Vries, S.S., Ruiz-Morales, E.R., Torres-Padilla, M.E., and Kind, J. (2019). Genomelamina interactions are established de novo in the early mouse embryo. Nature 569, 729–733. Bouniol-Baly, C., Hamraoui, L., Guibert, J., Beaujean, N., Szo¨llo¨si, M.S., and Debey, P. (1999). Differential transcriptional activity associated with chromatin configuration in fully grown mouse germinal vesicle oocytes. Biol. Reprod. 60, 580–587. Brind’Amour, J., Kobayashi, H., Richard Albert, J., Shirane, K., Sakashita, A., Kamio, A., Bogutz, A., Koike, T., Karimi, M.M., Lefebvre, L., et al. (2018). LTR retrotransposons transcribed in oocytes drive species-specific and heritable changes in DNA methylation. Nat. Commun. 9, 3331. Cao, R., Wang, L., Wang, H., Xia, L., Erdjument-Bromage, H., Tempst, P., Jones, R.S., and Zhang, Y. (2002). Role of histone H3 lysine 27 methylation in Polycomb-group silencing. Science 298, 1039–1043. Chiquoine, A.D. (1954). The identification, origin, and migration of the primordial germ cells in the mouse embryo. Anat. Rec. 118, 135–146. Crane, E., Bian, Q., McCord, R.P., Lajoie, B.R., Wheeler, B.S., Ralston, E.J., Uzawa, S., Dekker, J., and Meyer, B.J. (2015). Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244. Dahl, J.A., Jung, I., Aanes, H., Greggains, G.D., Manaf, A., Lerdrup, M., Li, G., Kuan, S., Li, B., Lee, A.Y., et al. (2016). Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition. Nature 537, 548–552. Davies, J.O.J., Oudelaar, A.M., Higgs, D.R., and Hughes, J.R. (2017). How best to identify chromosomal interactions: a comparison of approaches. Nat. Methods 14, 125–134. Denholtz, M., Bonora, G., Chronis, C., Splinter, E., de Laat, W., Ernst, J., Pellegrini, M., and Plath, K. (2013). Long-range chromatin contacts in embryonic stem cells reveal a role for pluripotency factors and polycomb proteins in genome organization. Cell Stem Cell 13, 602–616. Dixon, J.R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J.S., and Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380. Dixon, J.R., Jung, I., Selvaraj, S., Shen, Y., Antosiewicz-Bourget, J.E., Lee, A.Y., Ye, Z., Kim, A., Rajagopal, N., Xie, W., et al. (2015). Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336. Du, Z., Zheng, H., Huang, B., Ma, R., Wu, J., Zhang, X., He, J., Xiang, Y., Wang, Q., Li, Y., et al. (2017). Allelic reprogramming of 3D chromatin architecture during early mammalian development. Nature 547, 232–235. Eckersley-Maslin, M.A., Alda-Catalinas, C., and Reik, W. (2018). Dynamics of the epigenetic landscape during the maternal-to-zygotic transition. Nat. Rev. Mol. Cell Biol. 19, 436–450. Flavahan, W.A., Drier, Y., Liau, B.B., Gillespie, S.M., Venteicher, A.S., Stemmer-Rachamimov, A.O., Suva`, M.L., and Bernstein, B.E. (2016). Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114. Flyamer, I.M., Gassler, J., Imakaev, M., Branda˜o, H.B., Ulianov, S.V., Abdennur, N., Razin, S.V., Mirny, L.A., and Tachibana-Konwalski, K. (2017). Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-tozygote transition. Nature 544, 110–114. Fudenberg, G., Imakaev, M., Lu, C., Goloborodko, A., Abdennur, N., and Mirny, L.A. (2016). Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049. €tter, S., Gassler, J., Branda˜o, H.B., Imakaev, M., Flyamer, I.M., Ladsta Bickmore, W.A., Peters, J.M., Mirny, L.A., and Tachibana, K. (2017). A mechanism of cohesin-dependent loop extrusion organizes zygotic genome architecture. EMBO J. 36, 3600–3618. Gibcus, J.H., and Dekker, J. (2013). The hierarchy of the 3D genome. Mol. Cell 49, 773–782. Gibcus, J.H., Samejima, K., Goloborodko, A., Samejima, I., Naumova, N., Nuebler, J., Kanemaki, M.T., Xie, L., Paulson, J.R., Earnshaw, W.C., et al. (2018). A pathway for mitotic chromosome formation. Science 359, eaao6135.

Molecular Cell 77, 1–15, February 20, 2020 13

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

Ginsburg, M., Snow, M.H., and McLaren, A. (1990). Primordial germ cells in the mouse embryo during gastrulation. Development 110, 521–528.

Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079.

Guo, Y., Xu, Q., Canzio, D., Shou, J., Li, J., Gorkin, D.U., Jung, I., Wu, H., Zhai, Y., Tang, Y., et al. (2015). CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910.

Li, L., Zheng, P., and Dean, J. (2010). Maternal control of early mouse development. Development 137, 859–870.

Haarhuis, J.H.I., van der Weide, R.H., Blomen, V.A., Yanez-Cuna, J.O., Amendola, M., van Ruiten, M.S., Krijger, P.H.L., Teunissen, H., Medema, R.H., van Steensel, B., et al. (2017). The cohesin release factor WAPL restricts chromatin loop extension. Cell 169, 693–707.e614. Handel, M.A., and Schimenti, J.C. (2010). Genetics of mammalian meiosis: regulation, dynamics and impact on fertility. Nat. Rev. Genet. 11, 124–136. Hanna, C.W., Taudt, A., Huang, J., Gahurova, L., Kranz, A., Andrews, S., Dean, W., Stewart, A.F., Colome´-Tatche´, M., and Kelsey, G. (2018). MLL2 conveys transcription-independent H3K4 trimethylation in oocytes. Nat. Struct. Mol. Biol. 25, 73–82. €mer, U., Birke, A., Pelzer, H., €lthoff-Ohnolz, B., Kra Hilscher, B., Hilscher, W., Bu and Gauss, G. (1974). Kinetics of gametogenesis. I. Comparative histological and autoradiographic studies of oocytes and transitional prospermatogonia during oogenesis and prespermatogenesis. Cell Tissue Res. 154, 443–470.

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). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293. Liu, Y.J., Nakamura, T., and Nakano, T. (2012). Essential role of DPPA3 for chromatin condensation in mouse oocytogenesis. Biol. Reprod. 86, 40. Lupia´n˜ez, D.G., Kraft, K., Heinrich, V., Krawitz, P., Brancati, F., Klopocki, E., Horn, D., Kayserili, H., Opitz, J.M., Laxova, R., et al. (2015). Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025. Margueron, R., and Reinberg, D. (2011). The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349. Mirny, L.A., Imakaev, M., and Abdennur, N. (2019). Two major mechanisms of chromosome organization. Curr. Opin. Cell Biol. 58, 142–152.

Hou, C., Li, L., Qin, Z.S., and Corces, V.G. (2012). Gene density, transcription, and insulators contribute to the partition of the Drosophila genome into physical domains. Mol. Cell 48, 471–484.

Miyara, F., Migne, C., Dumont-Hassan, M., Le Meur, A., Cohen-Bacrie, P., Aubriot, F.X., Glissant, A., Nathan, C., Douard, S., Stanovici, A., and Debey, P. (2003). Chromatin configuration and transcriptional control in human and mouse oocytes. Mol. Reprod. Dev. 64, 458–470.

Imakaev, M., Fudenberg, G., McCord, R.P., Naumova, N., Goloborodko, A., Lajoie, B.R., Dekker, J., and Mirny, L.A. (2012). Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003.

Nagano, T., Lubling, Y., Va´rnai, C., Dudley, C., Leung, W., Baran, Y., Mendelson Cohen, N., Wingett, S., Fraser, P., and Tanay, A. (2017). Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67.

Inoue, A., Jiang, L., Lu, F., Suzuki, T., and Zhang, Y. (2017a). Maternal H3K27me3 controls DNA methylation-independent imprinting. Nature 547, 419–424.

Naumova, N., Imakaev, M., Fudenberg, G., Zhan, Y., Lajoie, B.R., Mirny, L.A., and Dekker, J. (2013). Organization of the mitotic chromosome. Science 342, 948–953.

Inoue, A., Jiang, L., Lu, F., and Zhang, Y. (2017b). Genomic imprinting of Xist by maternal H3K27me3. Genes Dev. 31, 1927–1932.

Noordermeer, D., Leleu, M., Splinter, E., Rougemont, J., De Laat, W., and Duboule, D. (2011). The dynamic architecture of Hox gene clusters. Science 334, 222–225.

Jin, F., Li, Y., Dixon, J.R., Selvaraj, S., Ye, Z., Lee, A.Y., Yen, C.A., Schmitt, A.D., Espinoza, C.A., and Ren, B. (2013). A high-resolution map of the threedimensional chromatin interactome in human cells. Nature 503, 290–294. Ke, Y., Xu, Y., Chen, X., Feng, S., Liu, Z., Sun, Y., Yao, X., Li, F., Zhu, W., Gao, L., et al. (2017). 3D chromatin structures of mature gametes and structural reprogramming during mammalian embryogenesis. Cell 170, 367–381.e320. Kim, D., Pertea, G., Trapnell, C., Pimental, H., Kelley, R., and Salzberg, S.L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome biology 14, https://doi.org/10. 1186/gb-2013-14-4-r36. Kobayashi, H., Sakurai, T., Imai, M., Takahashi, N., Fukuda, A., Yayoi, O., Sato, S., Nakabayashi, K., Hata, K., Sotomaru, Y., et al. (2012). Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 8, e1002440. Kundu, S., Ji, F., Sunwoo, H., Jain, G., Lee, J.T., Sadreyev, R.I., Dekker, J., and Kingston, R.E. (2017). Polycomb repressive complex 1 generates discrete compacted domains that change during differentiation. Mol. Cell 65, 432– 446.e435. Ladstatter, S., and Tachibana-Konwalski, K. (2016). A surveillance mechanism ensures repair of DNA lesions during zygotic reprogramming. Cell 167, 1774– 1787.e1713. Lan, Z.J., Xu, X., and Cooney, A.J. (2004). Differential oocyte-specific expression of Cre recombinase activity in GDF-9-iCre, Zp3cre, and Msx2Cre transgenic mice. Biol. Reprod. 71, 1469–1474. Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359.

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). Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385. Nora, E.P., Goloborodko, A., Valton, A.L., Gibcus, J.H., Uebersohn, A., Abdennur, N., Dekker, J., Mirny, L.A., and Bruneau, B.G. (2017). Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e922. Nuebler, J., Fudenberg, G., Imakaev, M., Abdennur, N., and Mirny, L.A. (2018). Chromatin organization by an interplay of loop extrusion and compartmental segregation. Proc. Natl. Acad. Sci. USA 115, E6697–E6706. Paradis, E., Claude, J., and Strimmer, K. (2004). APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289–290. Plys, A.J., Davis, C.P., Kim, J., Rizki, G., Keenen, M.M., Marr, S.K., and Kingston, R.E. (2019). Phase separation of Polycomb-repressive complex 1 is governed by a charged disordered region of CBX2. Genes Dev. 33, 799–813. Posfai, E., Kunzmann, R., Brochard, V., Salvaing, J., Cabuy, E., Roloff, T.C., Liu, Z., Tardat, M., van Lohuizen, M., Vidal, M., et al. (2012). Polycomb function during oogenesis is required for mouse embryonic development. Genes Dev. 26, 920–932. Puschendorf, M., Terranova, R., Boutsma, E., Mao, X., Isono, K., Brykczynska, U., Kolb, C., Otte, A.P., Koseki, H., Orkin, S.H., et al. (2008). PRC1 and Suv39h specify parental asymmetry at constitutive heterochromatin in early mouse embryos. Nat. Genet. 40, 411–420.

Lewandoski, M., Wassarman, K.M., and Martin, G.R. (1997). Zp3-cre, a transgenic mouse line for the activation or inactivation of loxP-flanked target genes specifically in the female germ line. Curr. Biol. 7, 148–151.

Rao, S.S., Huntley, M.H., Durand, N.C., Stamenova, E.K., Bochkov, I.D., Robinson, J.T., Sanborn, A.L., Machol, I., Omer, A.D., Lander, E.S., and Aiden, E.L. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., and Durbin, R.; 1000 Genome Project Data Processing

Quinlan, A.R., and Hall, I.M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842.

14 Molecular Cell 77, 1–15, February 20, 2020

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

Rao, S.S.P., Huang, S.C., Glenn St Hilaire, B., Engreitz, J.M., Perez, E.M., KiefferKwon, K.R., Sanborn, A.L., Johnstone, S.E., Bascom, G.D., Bochkov, I.D., et al. (2017). Cohesin loss eliminates all loop domains. Cell 171, 305–320.e324. Rhodes, J., Feldmann, A., Herna´ndez-Rodrı´guez, B., Dı´az, N., Brown, J., Fursova, N., Blackledge, N., Prathapan, P., Dobrinic, P., Huseyin, M., et al. (2019). Cohesin disrupts polycomb-dependent chromosome interactions. bioRxiv. https://doi.org/10.1101/593970. Rowley, M.J., and Corces, V.G. (2016). The three-dimensional genome: principles and roles of long-distance interactions. Curr. Opin. Cell Biol. 40, 8–14. Rowley, M.J., Nichols, M.H., Lyu, X., Ando-Kuri, M., Rivera, I.S.M., Hermetz, K., Wang, P., Ruan, Y., and Corces, V.G. (2017). Evolutionarily conserved principles predict 3D chromatin organization. Mol. Cell 67, 837–852.e837. Saldanha, A.J. (2004). Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248. Sanborn, A.L., Rao, S.S., Huang, S.C., Durand, N.C., Huntley, M.H., Jewett, A.I., Bochkov, I.D., Chinnappan, D., Cutkosky, A., Li, J., et al. (2015). Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl. Acad. Sci. USA 112, E6456–E6465. Schoenfelder, S., Sugar, R., Dimond, A., Javierre, B.M., Armstrong, H., Mifsud, B., Dimitrova, E., Matheson, L., Tavares-Cadete, F., Furlan-Magaril, M., et al. (2015). Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome. Nat. Genet. 47, 1179–1186. Schwarzer, W., Abdennur, N., Goloborodko, A., Pekowska, A., Fudenberg, G., Loe-Mie, Y., Fonseca, N.A., Huber, W., Haering, C.H., Mirny, L., and Spitz, F. (2017). Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56. Seitan, V.C., Hao, B., Tachibana-Konwalski, K., Lavagnolli, T., MiraBontenbal, H., Brown, K.E., Teng, G., Carroll, T., Terry, A., Horan, K., et al. (2011). A role for cohesin in T-cell-receptor rearrangement and thymocyte differentiation. Nature 476, 467–471. Servant, N., Varoquaux, N., Lajoie, B.R., Viara, E., Chen, C.J., Vert, J.P., Heard, E., Dekker, J., and Barillot, E. (2015). HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259. Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A., and Cavalli, G. (2012). Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472. Shirane, K., Toh, H., Kobayashi, H., Miura, F., Chiba, H., Ito, T., Kono, T., and Sasaki, H. (2013). Mouse oocyte methylomes at base resolution reveal genome-wide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet. 9, e1003439. Simon, J.A., and Kingston, R.E. (2009). Mechanisms of polycomb gene silencing: knowns and unknowns. Nat. Rev. Mol. Cell Biol. 10, 697–708. Skene, P.J., and Henikoff, S. (2017). An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856. Speed, R.M. (1982). Meiosis in the foetal mouse ovary. I. An analysis at the light microscope level using surface-spreading. Chromosoma 85, 427–437. Stewart, K.R., Veselovska, L., Kim, J., Huang, J., Saadeh, H., Tomizawa, S., Smallwood, S.A., Chen, T., and Kelsey, G. (2015). Dynamic changes in histone modifications precede de novo DNA methylation in oocytes. Genes Dev. 29, 2449–2462. Stewart, K.R., Veselovska, L., and Kelsey, G. (2016). Establishment and functions of DNA methylation in the germline. Epigenomics 8, 1399–1413. Tachibana-Konwalski, K., Godwin, J., van der Weyden, L., Champion, L., Kudo, N.R., Adams, D.J., and Nasmyth, K. (2010). Rec8-containing cohesin maintains bivalents without turnover during the growing phase of mouse oocytes. Genes Dev. 24, 2505–2516. Tang, Z., Luo, O.J., Li, X., Zheng, M., Zhu, J.J., Szalaj, P., Trzaskoma, P., Magalska, A., Wlodarczyk, J., Ruszczycki, B., et al. (2015). CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627.

Tardat, M., Albert, M., Kunzmann, R., Liu, Z., Kaustov, L., Thierry, R., Duan, S., Brykczynska, U., Arrowsmith, C.H., and Peters, A.H. (2015). Cbx2 targets PRC1 to constitutive heterochromatin in mouse zygotes in a parent-oforigin-dependent manner. Mol. Cell 58, 157–171. Tatavosian, R., Kent, S., Brown, K., Yao, T., Duc, H.N., Huynh, T.N., Zhen, C.Y., Ma, B., Wang, H., and Ren, X. (2019). Nuclear condensates of the Polycomb protein chromobox 2 (CBX2) assemble through phase separation. J. Biol. Chem. 294, 1451–1463. Tolhuis, B., Blom, M., Kerkhoven, R.M., Pagie, L., Teunissen, H., Nieuwland, M., Simonis, M., de Laat, W., van Lohuizen, M., and van Steensel, B. (2011). Interactions among Polycomb domains are guided by chromosome architecture. PLoS Genet. 7, e1001343. Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D.R., Pimentel, H., Salzberg, S.L., Rinn, J.L., and Pachter, L. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578. Valton, A.L., and Dekker, J. (2016). TAD disruption as oncogenic driver. Curr. Opin. Genet. Dev. 36, 34–40. Van Bortle, K., and Corces, V.G. (2012). Nuclear organization and genome function. Annu. Rev. Cell Dev. Biol. 28, 163–187. Veselovska, L., Smallwood, S.A., Saadeh, H., Stewart, K.R., Krueger, F., Maupetit-Me´houas, S., Arnaud, P., Tomizawa, S., Andrews, S., and Kelsey, G. (2015). Deep sequencing and de novo assembly of the mouse oocyte transcriptome define the contribution of transcription to the DNA methylation landscape. Genome Biol. 16, 209. Vieux-Rochas, M., Fabre, P.J., Leleu, M., Duboule, D., and Noordermeer, D. (2015). Clustering of mammalian Hox genes with other H3K27me3 targets within an active nuclear domain. Proc. Natl. Acad. Sci. USA 112, 4672–4677. Whitcomb, S.J., Basu, A., Allis, C.D., and Bernstein, E. (2007). Polycomb group proteins: an evolutionary perspective. Trends Genet. 23, 494–502. Wutz, G., Va´rnai, C., Nagasaka, K., Cisneros, D.A., Stocsits, R.R., Tang, W., Schoenfelder, S., Jessberger, G., Muhar, M., Hossain, M.J., et al. (2017). Topologically associating domains and chromatin loops depend on cohesin and are regulated by CTCF, WAPL, and PDS5 proteins. EMBO J. 36, 3573–3599. Xia, W., Xu, J., Yu, G., Yao, G., Xu, K., Ma, X., Zhang, N., Liu, B., Li, T., Lin, Z., et al. (2019). Resetting histone modifications during human parental-to-zygotic transition. Science 365, 353–360. Xie, H., Xu, J., Hsu, J.H., Nguyen, M., Fujiwara, Y., Peng, C., and Orkin, S.H. (2014). Polycomb repressive complex 2 regulates normal hematopoietic stem cell function in a developmental-stage-specific manner. Cell Stem Cell 14, 68–80. Xu, Q., and Xie, W. (2018). Epigenome in early mammalian development: inheritance, reprogramming and establishment. Trends Cell Biol. 28, 237–253. Xu, Q., Xiang, Y., Wang, Q., Wang, L., Brind’Amour, J., Bogutz, A.B., Zhang, Y., Zhang, B., Yu, G., Xia, W., et al. (2019). SETD2 regulates the maternal epigenome, genomic imprinting and embryonic development. Nat. Genet. 51, 844–856. Zhang, B., Zheng, H., Huang, B., Li, W., Xiang, Y., Peng, X., Ming, J., Wu, X., Zhang, Y., Xu, Q., et al. (2016). Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557. Zheng, H., and Xie, W. (2019). The role of 3D genome organization in development and cell differentiation. Nat. Rev. Mol. Cell Biol. 20, 535–550. Zheng, H., Huang, B., Zhang, B., Xiang, Y., Du, Z., Xu, Q., Li, Y., Wang, Q., Ma, J., Peng, X., et al. (2016). Resetting epigenetic memory by reprogramming of histone modifications in mammals. Mol. Cell 63, 1066–1079. Zhou, X., Lowdon, R.F., Li, D., Lawson, H.A., Madden, P.A., Costello, J.F., and Wang, T. (2013). Exploring long-range genome interactions using the WashU Epigenome Browser. Nat. Methods 10, 375–376. Zuin, J., Dixon, J.R., van der Reijden, M.I., Ye, Z., Kolovos, P., Brouwer, R.W., van de Corput, M.P., van de Werken, H.J., Knoch, T.A., van IJcken, W.F.J., et al. (2014). Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc. Natl. Acad. Sci. USA 111, 996–1001.

Molecular Cell 77, 1–15, February 20, 2020 15

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STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

H3K27me3 antibody (oocyte immunostaining)

Diagenode

Cat # C15410069; RRID: AB_2814977

H3K27me3 antibody (embryos immunostaining)

Cell Signaling Technology

Cat # 9733; RRID: AB_2616029

H3K27me3 antibody (CUT&RUN)

Active Motif

Cat # 61017; RRID: AB_2614987

H2AK119ub antibody (oocyte immunostaining)

Cell Signaling Technology

Cat # 8240; RRID: AB_10891618

Antibodies

Biological Samples Primordial germ cell (PGC)

This study

N/A

Oocytes

This study

N/A

Embryos

This study

N/A

Chemicals, Peptides, and Recombinant Proteins a-amanitin

Sigma-Aldrich

Cat # A2263

M2 medium

Sigma-Aldrich

Cat # M7167

Hyaluronidase

Sigma-Aldrich

Cat # A5177

Milrinone

Sigma-Aldrich

Cat # M4659

Acidic Tyrode’s solution

Sigma-Aldrich

Cat # T1788

KSOM medium

Millipore

Cat # MR-121-D

Biotin-14-dCTP

Thermo Fisher

Cat # 19518018

MboI

New England Biolabs

Cat # R0147M

T4 DNA ligase

New England Biolabs

Cat # M0202L

T4 DNA Polymerase

New England Biolabs

Cat # M0203L

T4 Polynucleotide Kinase

New England Biolabs

Cat # M0201L

Klenow Fragment (3 /5 exo-)

New England Biolabs

Cat # M0212L

DNA Polymerase I, Large (Klenow) Fragment

New England Biolabs

Cat # M0210L

Proteinase K

Roche

Cat # 10910000

DAPI

Invitrogen

Cat # D1306

AMPure XP beads

Beckman Coulter

Cat # A63881

Dynabeads MyOne Streptavidin C1 beads

Thermo Fisher

Cat # 65001

0.25% trypsin-EDTA

Thermo Fisher

Cat # 25200-072

0

0

DNA suspension Buffer

TEKnova

Cat # PNT0221

DpnII

New England Biolabs

Cat # P0543

Mineral oil

Signa-Aldrich

Cat # M8410

HaltTM Protease Inhibitor Cocktail

Thermo Scientific

Cat # 78430

BSA

New England Biolabs

Cat # B9000S

TRIzol

Thermo

Cat # 10296028

Concanavalin A beads

Polysciences

Cat # 86057

5% Digitonin

Thermo

Cat # BN2006

Cell-LightTM EU Apollo 567 In Vitro Imaging Kit

RiboBio

Cat # C10316-1

Quick Ligation Kit

New England Biolabs

Cat # M2200

RNAEasy Micro Kit

QIAGEN

Cat # 74004

Ovation v2 Kit

NuGen

Cat # 7102

Truseq DNA LT kit

Illumina

Cat # FC-121-2001

NEB Next rRNA Depletion Kit

New England Biolabs

Cat # E6310S

NEB Next first-strand synthesis module

New England Biolabs

Cat # E7771S

Critical Commercial Assays

(Continued on next page)

e1 Molecular Cell 77, 1–15.e1–e7, February 20, 2020

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

Continued REAGENT or RESOURCE

SOURCE

IDENTIFIER

NEB Next second-strand synthesis module

New England Biolabs

Cat # E7550S

NEBNext Ultra II DNA Library Prep Kit for Illumina

New England Biolabs

Cat # E7645S

Deposited Data Hi-C data from staged PGCs, oocytes and embryos

This Study

GEO: GSE118263

RNA-seq data from Eedfl/fl and Eed
/
oocytes

This Study

GEO: GSE118263

RNA-seq data from Ring1
/
Rnf2fl/fl and Ring1/Rnf2 dKO oocytes

This Study

GEO: GSE132156

Hi-C data from Scc1fl/fl and Scc1
/
oocytes

This Study

GEO: GSE136093

Early embryo Hi-C Datasets

Du et al., 2017

GEO: GSE82185

SN and NSN oocyte Hi-C datasets

Flyamer et al., 2017

GEO: GSE80006

Mouse oocyte and embryo H3K27me3 Dataset

Zheng et al., 2016

GEO: GSE76687

Mouse oocyte and embryo H3K4me3 Dataset

Zhang et al., 2016

GEO: GSE71434

Mouse oocyte DNA Methylation Dataset

Shirane et al., 2013

DDBJ/GenBank/EMBL: DRA000570

OG2 mice

Jackson Laboratory

Cat # 004654

PWK/PhJ mice

Jackson Laboratory

Cat # 003715

C57BL/6N mice

Beijing Vital River Laboratory Animal Technology Co., Ltd.

Stock number 213

Experimental Models: Organisms/Strains

Eedfl mice

Xie et al., 2014

N/A

Gdf9-Cre mice

Lan et al., 2004

N/A

Ring1
/
Rnf2fl mice

Posfai et al., 2012

N/A

Zp3-Cre mice

Lan et al., 2004; Lewandoski et al., 1997

N/A

Scc1fl mice

Seitan et al., 2011

N/A

Bowtie2 (v2.2.2)

Langmead and Salzberg, 2012

http://bowtie-bio.sourceforge.net/bowtie2/index.shtml

HiC-Pro (v2.7.1b)

Servant et al., 2015

https://github.com/nservant/HiC-Pro

Bedtools

Quinlan and Hall, 2010

http://bedtools.readthedocs.io/en/latest/

Samtools

Li et al., 2009

https://github.com/samtools/samtools

R package: ape

Paradis et al., 2004

https://cran.r-projsect.org/web/packages/ape/index. html

Java TreeView

Saldanha, 2004

http://jtreeview.sourceforge.net/

C-world (Hi-C analysis software)

Job Dekker lab

https://github.com/dekkerlab/cworld-dekker

HiCPlotter (v0.6.05.compare)

Akdemir and Chin, 2015

https://github.com/kcakdemir/HiCPlotter

WashU Eepigenome Browser

Zhou et al., 2013

http://epigenomegateway.wustl.edu/

TopHat2 (version 2.0.11)

Kim et al., 2013

https://ccb.jhu.edu/software/tophat/index.shtml

Cufflinks (version 2.0.2)

Trapnell et al., 2012

http://cole-trapnell-lab.github.io/cufflinks

HiC-Lib

Imakaev et al., 2012

https://bitbucket.org/mirnylab/hiclib

Mirnylib

Imakaev et al., 2012

https://bitbucket.org/mirnylab/mirnylib

Cooler v0.7.2

Abdennur and Mirny, 2019

https://github.com/mirnylab/cooler; https://zenodo. org/record/826873

Software and Algorithms

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Wei Xie ([email protected]).

Molecular Cell 77, 1–15.e1–e7, February 20, 2020 e2

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

EXPERIMENTAL MODEL AND SUBJECT DETAILS PGC and oocyte collection To isolate PGCs, the OG2 strain with Oct4-GFP reporter (Jackson Laboratory, Cat # 004654, a gift from Prof. Jie Na lab) was crossed with the PWK/PhJ female mice (Jackson Laboratory, Cat # 003715). Germinal ridges were isolated from E11.5 and E13.5 embryos. To obtain the single cell suspension, the germinal ridges were digested with 0.25% trypsin-EDTA (Thermo Fisher, Cat # 25200-072) at 37 C for 10min. For E13.5 embryos, the germinal ridges were digested separately based on the gender. GFP-positive PGCs were sorted and collected by FACS. All analyzed wild-type oocytes were collected from C57BL/6N females (Beijing Vital River Laboratory Animal Technology Co., Ltd.). Briefly, GOs were isolated by mechanical dissection using a 26 gauge needle in M2 medium (Sigma, Cat # M7167). GOs I were isolated from 7 or 10d old mice and GOs II were from 14d old mice. FGOs were isolated from superovulated 8-week-old mice 46 h after PMSG injection. The whole ovaries were clipped mechanically with a razor blade. To remove the granulosa cells, the dissociated oocyte-granulosa cell complexes were transferred into M2 medium containing 0.1% hyaluronidase (Sigma, Cat # A5177) and digested for 5 min. Oocytes were then crosslinked with 1% formaldehyde after removing the zona pellucida by acidic Tyrode’s solution (Sigma, Cat # T1788) treatment. For oocytes undergoing germinal vesicle break down (GVBD), the FGOs with granulosa cells removed were first cultured in M2 medium supplemented with 5 mM milrinone (Sigma, Cat # M4659). Then half of the FGOs were washed out of milrinone and cultured at 37 C to allow synchronized resumption of meiosis. After 2-4hours of incubation, oocytes that exit meiosis arrest and lose the GV were collected. The other half of the FGOs cultured with milrinone were collected at the same time as control. Oocytes were then crosslinked with 1% formaldehyde after removing the zona pellucida by acidic Tyrode’s solution treatment. Collection of transcription inhibited oocytes To inhibit transcription in oocytes, GOs II were cultured in KSOM medium (Millipore, Cat # MR-121-D) supplemented with 250 mg/ml alpha-amanitin (Sigma, Cat # A2263) for 24 hours as previously described (Abe et al., 2010; Bouniol-Baly et al., 1999; Liu et al., 2012). As a control, half of the GOs II were cultured in KSOM medium with DMSO. Collection of Scc1 cKO oocytes Generation of Scc1fl (Seitan et al., 2011) mice was previously described. Experimental mice were obtained by mating of homozygous floxed females to homozygous floxed males carrying Zp3-Cre (Lan et al., 2004; Lewandoski et al., 1997). Scc1fl/fl and Scc1
/
FGOs (including SN and NSN oocytes) were collected from 2-3 month old Scc1fl/fl and Scc1fl/fl;Zp3-Cre mice separately as previously described (Flyamer et al., 2017). Collection of Eed cKO oocytes Eedfl (Xie et al., 2014) and Gdf9-Cre (Lan et al., 2004) alleles were described previously. Eedfl/fl FGOs and Eed
/
FGOs were collected from 8-week-old Eedfl/fl and Eedfl/fl;Gdf9-Cre mice separately. Eedfl/wt and EedmNull late 2-cell embryos were harvested by mating Eedfl/fl or Eedfl/fl;Gdf9-Cre females with PWK/PhJ males. Collection of Ring1 KO and Rnf2 cKO oocytes Ring1
/
Rnf2fl (Posfai et al., 2012) and Zp3-Cre (Lan et al., 2004; Lewandoski et al., 1997) mice were generated as previously described. Ring1
/
Rnf2fl/fl FGOs and Ring1/Rnf2 dKO FGOs were collected from 8-week-old Ring1
/
Rnf2fl/fl and Ring1
/
Rnf2fl/fl; Zp3-Cre mice separately. METHOD DETAILS EU incorporation GOs II before treatment, treated with DMSO, and treated with a-amanitin were harvested and cultured in KSOM medium containing 500 mM EU for 2 h at 37 C prior to the fixation. Detection of incorporated EU was achieved by using Cell-LightTM EU Apollo 567 In Vitro Imaging Kit (RiboBio, Cat # C10316-1) according to the manufacturer’s protocol. Immunostaining Oocytes or embryos were treated briefly with acidic Tyrode’s solution to remove zona pellucida. They were then fixed in 4% PFA for 1 hour at RT, permeabilized with 0.5% Triton X-100 in PBS at room temperature for 30 min and blocked with 1% BSA in PBS at 4 C overnight. Oocytes or embryos were incubated with the H3K27me3 antibody (Diagenode, Cat # C15410069, 1:500 for oocytes) (Cell Signaling Technology, Cat # 9733, 1:500 for embryos) or H2AK119ub antibody (Cell Signaling Technology, Cat # 8240, 1:500) at 4 C overnight. Samples were washed with PBST (PBS with 0.1% Triton X-100) three times and incubated with secondary antibodies (Alexa fluor 488 goat anti-rabbit or Alexa fluor 569 donkey anti-rabbit, 1:500) for 1 h at RT. DNA was stained with DAPI. After staining, embryos were mounted on glass slides in VECTASHIELD Antifade mounting medium (Vector Laboratories). Images were acquired on a Zeiss 780 confocal microscope or on an Axio Imager M2 + Yokogawa CSU W1 Dual camera T2 sinning disk confocal scanning unit.

e3 Molecular Cell 77, 1–15.e1–e7, February 20, 2020

Please cite this article in press as: Du et al., Polycomb Group Proteins Regulate Chromatin Architecture in Mouse Oocytes and Early Embryos, Molecular Cell (2019), https://doi.org/10.1016/j.molcel.2019.11.011

The same parameters were used for individual channel of all groups of samples. The fluorescence intensity of H3K27me3 and H2AK119ub was quantified using ImageJ software. CUT&RUN library generation and sequencing CUT&RUN libraries of Eedfl/fl and Eed
/
oocytes were conducted as previously described (Skene and Henikoff, 2017). Briefly, oocytes were resuspended by washing buffer (HEPES-KOH, pH = 7.5, 20mM; NaCl, 150mM; Spermidine, 0.5mM and with Roche complete protease inhibitor) and incubated with concanavalin-coated magnetic beads (Polyscience, 86057) at 23 C for 10 mins. Then the samples were resuspended by antibody buffer (washing buffer plus digitonin (Thermo, Cat # BN2006), freshly pre-heated, 0.005%0.01%, tested for each batch; EDTA, pH = 8.0, 2mM) with H3K27me3 antibody (Active motif, Cat #61017) diluted at ratio of 1:100. After being incubated at 4 C for overnight, the samples were resuspended by washing buffer with pA-MNase (to a final concentration of 700ng/mL) and incubated at 4 C for 3 hours. Sequentially, the targeted digestion was performed with CaCl2 treatment and quenched by stop buffer. Purified DNA was subjected to Tru-seq library construction using NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, E7645S). The products were purified and size-selected with AMPure XP beads (Beckman Coulter, Cat # A63881). About 0.2pg yeast DNA was added to each reaction as spike-in DNA. CUT&RUN data mapping As described previously (Xia et al., 2019), the pair-end CUT&RUN reads were aligned to mm9 and sacCer3 reference genome with random chromosome cleaned by Bowtie (version 2.2.2) (Langmead and Salzberg, 2012) under the parameters –t –q –N 1 –L 25. The paired-end CUT&RUN reads were aligned with the parameters: -t –q –N 1 –L 25 –X 1000–no-mixed–no-discordant. All unmapped reads, non-uniquely mapped reads, reads with low mapping quality (MAPQ < 20) and PCR duplicates were removed. We normalized the mouse profiles to the number of yeast reads. To visualize the CUT&RUN signals in the UCSC genome browser, we generated the spike-in DNA normalized read counts. sisHi-C library generation and sequencing sisHi-C libraries were produced as described (Du et al., 2017). Briefly, PGCs, oocytes or embryos were cross-linked with 1% formaldehyde at room temperature (RT) for 10 min. Next, formaldehyde was quenched with glycine for 10 min at RT. After being washed with 1 3 PBS, the samples were incubated in lysis buffer (10mM Tris-HCl pH7.4, 10mM NaCl, 0.1mM EDTA, 0.5% NP-40 and proteinase inhibitor) on ice for 50 min and the chromatin was solubilized with 0.5% SDS. The nuclei were digested with MboI at 37  C overnight. After fill-in with biotin-14-dCTP, the fragments were ligated in a small volume. Reversal of cross-linking, DNA purification, and sonication were done sequentially. The biotin-labeled DNA was then pulled down with Dynabeads MyOne Streptavidin C1 (Thermo Fisher, Cat # 65001). The fragments that included a ligation junction were subjected to Illumina library preparation. 9-15 cycles of PCR amplification were performed with Extaq (Takara), and the products were purified and size-selected with AMPure XP beads (Beckman Coulter, Cat # A63881). All libraries were sequenced on Illumina HiSeq2500 or HiSeq XTen according to the manufacturer’s instruction. snHi-C library generation and sequencing Single-nucleus Hi-C (snHi-C) libraries were produced as described (Flyamer et al., 2017). Briefly, Scc1fl/fl and Scc1
/
FGOs were fixed in 2% formaldehyde for 15 min and then lysed on ice in lysis buffer (10 mM Tris–HCl pH 8.0, 10 mM NaCl, 0.5% NP-40 substitute, 1% Triton X-100, proteinase inhibitor) for at least 15 min. The oocytes were washed once through PBS and 1X NEB 3 buffer with 0.6% SDS, in which they were then incubated at 37 C for 2 h with shaking. Oocytes were washed once in 1X DpnII buffer with 2 3 BSA (NEB, Cat # B9000S), and then, chromatin was digested overnight with 5 U DpnII (NEB, Cat # P0543). The nuclei were then washed once through PBS, then through 1X T4 ligase buffer. The nuclei were incubated in the same buffer but with 5U T4 DNA ligase (Thermo Scientific) at 16 C for 4.5 h, and then for 30 min at room temperature. Whole-genome amplification was performed and the products were purified and size-selected with AMPure XP beads (Beckman Coulter, Cat # A63881). The libraries were sequenced on Illumina HiSeq 2500 v4 according to the manufacturer’s instruction. Sequencing was performed at the VBCF NGS Unit (https://www. viennabiocenter.org/facilities). RNA-seq library generation and sequencing RNA-seq libraries of Ring1
/
Rnf2fl/fl and Ring1/Rnf2 dKO oocytes were prepared as follows. Cumulus oocyte complexes were isolated from ovaries of approx. 3 months old females. Granulosa cells were removed by repeated pipetting using a fine-pulled capillary glass needle in M2 medium. Oocytes were washed twice in M2 medium and subsequently washed twice in DNA suspension Buffer (TEKnova, Cat # PNT0221). For RNA isolation, 15 GV oocytes from one mouse were pooled and RNA was extracted using the RNAEasy Micro Kit (QIAGEN,Cat # 74004) according to the manufacturer’s instructions. RNA was amplified and converted to cDNA using the Ovation v2 Kit (NuGen, Cat # 7102). Sequencing libraries were prepared with the Truseq DNA LT kit (Illumina, Cat # FC-121-2001). RNA-seq libraries of Eedfl/fl and Eed
/
oocytes were prepared as previously described (Brind’Amour et al., 2018). Total RNA was isolated from about 25 oocytes with TRIzol (Thermo, 10296028), and ribosomal RNA was depleted using the NEB Next rRNA Depletion Kit (NEB, E6310S) according to the manufacturer’s instruction. Double-stranded cDNA was synthesized using NEB

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Next first-strand (NEB, E7771S) and second-strand synthesis modules (NEB, E7550S). Library preparation was performed with NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, E7645S) according to the manufacturer’s instruction. Hi-C data mapping Paired end raw reads of Hi-C libraries were aligned, processed and iteratively corrected using HiCPro (version 2.7.1b) as described (Servant et al., 2015). Briefly, sequencing reads are first independently aligned to the mouse reference genome (mm9) using the bowtie2 end-to-end algorithm and ‘‘-very-sensitive’’ option. To rescue the chimeric fragments spanning the ligation junction, the ligation site was detected and the 50 fraction of the reads was aligned back to the reference genome. Unmapped reads, multiple mapped reads and singletons were then discarded. Pairs of aligned reads were then assigned to MboI restriction fragments. Read pairs from uncut DNA, self-circle ligation and PCR artifacts were filtered out and the valid read pairs involving two different restriction fragments were used to build the contact matrix. Valid read pairs are then binned at a specific resolution by dividing the genome into bins of equal size. We chose 100-kb bin size for examination of global interaction patterns of the genome, and 40-kb bin size to show local interactions and to perform TAD calling. Next, the binned interaction matrices were normalized using the iterative correction method (Imakaev et al., 2012; Servant et al., 2015) to correct the biases such as GC content, mappability and effective fragment length in HiC data. Single-nucleus Hi-C data is processed as previous described (Flyamer et al., 2017). HiC-Lib (Imakaev et al., 2012), Mirnylib (Imakaev et al., 2012), and Cooler (v0.7.2) (Abdennur and Mirny, 2019) were used for data processing. Single oocyte Hi-C data were combined and used in the analysis. RNA-seq data processing All RNA-seq data were mapped to the mouse reference genome (mm9) by TopHat2 (version 2.0.11) (Kim et al., 2013). The gene expression level was calculated by Cufflinks (version 2.0.2)(Trapnell et al., 2012) using the refFlat database from the UCSC genome browser. Samtools (Li et al., 2009) and Bedtools (Quinlan and Hall, 2010) were used for downstream analyses. Allele assignment of sequencing reads Allelic interaction frequency matrices were generated with HiC-Pro (Servant et al., 2015) using the SNPs between two mouse strains (C57BL/6N and PWK/PhJ). Briefly, for allele-specific analysis, the paired end reads are first aligned to a modified mm9 genome where all polymorphic sites were N-masked. Then the polymorphic sites were detected on the aligned reads. Reads without SNP information or containing conflicting allelic polymorphic sites are classified as unassigned. All the read pairs for which both reads are assigned to the same parental allele or for which one read is assigned to one parental allele and the other is unassigned were classifying as allele-specific. Hi-C interaction heatmap, differential interaction heatmap, observed/expected heatmap and correlation heatmap To generate the contact frequency (interaction) heatmap, all the whole chromosome scale Hi-C interaction frequency heatmaps and the zoom-in views in this study were generated using HiCPlotter version 0.6.05.compare, a Hi-C data visualization tool (Akdemir and Chin, 2015). The triangle interaction heatmaps were generated with WashU Epigenome Browser (Zhou et al., 2013). The interactions between loci were shown on 2D heatmaps along a color scale using the normalized contact matrices. To demonstrate the interaction between PADs in the maternal allele of early embryos, differential Hi-C interaction heatmaps were calculated as previously described (Du et al., 2017). Briefly, the normalized interaction matrices (40-kb bin) of paternal allele of late 2-cell embryos were subtracted from the maternal allele. In the differential matrices, the positive values indicate the interaction frequency at maternal allele is higher than the paternal allele, and vice versa. The observed/expected matrices were calculated as previously described (Lieberman-Aiden et al., 2009). Heatmaps were generated with Java TreeView (Saldanha, 2004). To generate the correlation heatmap, the correlation matrices for each stage were generated as previously described(LiebermanAiden et al., 2009). The heatmap was generated with Java TreeView (Saldanha, 2004). Comparison of interaction frequencies between different stages To compare interaction frequencies between different PGCs or oocytes stages, we first combined the Hi-C data of replicates of each stage. The interaction matrices were then normalized for sequencing depth as previously described (Zuin et al., 2014) by making the sum of all interaction frequencies for a given chromosome at each stage equal to that of an arbitrarily selected ‘‘standard stage’’ (E13.5 female PGC in this case). Hierarchical clustering analysis The hierarchical clustering analysis based on the interaction frequency at various stages was conducted using an R package (ape) (Paradis et al., 2004) based on the Pearson correlation as indicated between each pair of datasets. The distance between two datasets was calculated by (1- correlation). The interaction frequencies within 5Mb were used in this anslysis.

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The P(s) analysis The P(s) was calculated with normalized interaction matrices in 100kb resolution as previously described (Naumova et al., 2013). We first divided distances into logarithmically spaced bins with increasing factor 1.15: (100kb, 100kb*1.15, 100kb*1.152.). For each bin, we then counted the number of interactions at corresponding distances. To obtain the probability P(s), we divided the number of interactions in each bin by the total number of possible region pairs. The P(s) was further normalized so that the sum over the range of the distances is 1. Identification of topologically associating domains (TADs) TAD boundaries were identified by calculating the insulation score of each bin using the 40kb resolution Hi-C data as previously described with minor modifications (Crane et al., 2015). Briefly, the insulation score was calculated by sliding a 1Mb X 1Mb square along the diagonal of the interaction matrix for every chromosome. A 200kb window was used for calculation of the delta vector and all boundaries with a ‘‘boundary strength’’ < 0.25 were removed. Identification of conventional chromatin compartments and Polycomb Associating Domains (PADs) Conventional chromatin compartments A and B were identified using a method described previously (Lieberman-Aiden et al., 2009) with some modifications. The normalized 100kb interaction matrices for each stage were used in this analysis. First, the bins that have no interactions with any other bins were removed before expected interaction matrices were calculated. Observed/Expected matrices were generated using a sliding window approach (Dixon et al., 2015) with the bin size of 400kb and the step size of 100kb. Principal component analysis was performed on the correlation matrices generated from the observed/expected matrices. The first principal component of the correlation matrices together with gene density were used to identify A/B compartments. To identify PADs, we called local PC1 values by only using the local interactions. Briefly, we called local PC1 values using the local interactions (contact matrices of 10Mb bins, with step size of 5Mb, with bin size 20kb) instead of the whole chromosomes. Principal component analysis was performed on the correlation matrices generated from the observed/expected matrices. The directions of local PC1 values for each 10Mb bin were adjusted using nearby bins. Two classed of domains are identified for each chromosome based on local PC1 values and the class with higher average enrichment of H3K27me3 were identified as PADs. Analysis of cis-interactions among PADs/iPADs For each analysis, same amount of random picked valid pairs is used for all samples to avoid potential bias caused by sequencing depth. 40-kb bin or 100-kb bin pairs on the same chromosome and with the genomic distance from 400kb to 5Mb are used in this analysis, if not specified. All filtered bin pairs are clustered to three groups (between different PADs, PAD-PAD; between PADs and iPADs, PAD-iPAD; and between different iPADs, iPAD-iPAD) according to their positions. Interaction frequencies of every bin pairs (bin pairs with the interaction frequency of 0 are included) are then used to plot box for each group. For the analysis of cis-interactions among PADs/iPADs of Scc1fl/fl and Scc1
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FGOs, and in different ranges of Ring1/Rnf2 dKO and control FGOs, the observed/expected matrices were used to strictly normalize the distance effects. Comparison of insulation scores between different stages For each analysis, same amount of random picked valid pairs is used for all samples to avoid potential bias caused by sequencing depth. As the length of Polycomb Associating Domains are much shorter than TADs, the insulation scores used in this analysis were calculated by sliding a 200kb X 200kb square along the diagonal of the interaction matrix for every chromosome. A 160kb window was used for calculation of the delta vector. The average insulation scores were plotted around the TAD-specific boundaries (inside PAD or iPAD), the TAD/PAD shared boundaries and PAD specific boundaries. The backgrounds of lines plotted within each individual panel are normalized to similar levels for better comparison. Analysis of compartmental interactions To compute the inter-compartment interactions between the same classes or different classes, we first removed local interactions that are shorter than 2Mb which mainly reflect interactions within TADs rather than long-distance interactions between compartments. The remaining interactions were assigned to two categories: interactions between two bins located in the same classes of compartments (including A-A or iPAD-iPAD interactions and B-B or PAD-PAD interactions) and interactions between two bins located in different classes of compartments (A-B or PAD-iPAD interactions). Compartments defined in mESCs were considered as the conventional compartments. PAD/iPAD and the conventional compartments’ positions were used for all stages in this analysis. For each stage, the average interaction frequency between a pair of bins was calculated for each of the two categories for each chromosome. The ratios between the average interaction frequency per pairs of A-A or B-B, compared to that per pair of A-B were calculated for each chromosome. Boxplots were used to show the ranges of such ratios for all chromosomes and to measure the degree of compartment segregation for each stage. Similar analyses were performed for PAD/iPAD compartmentalization.

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Analysis of gene density, GC content in PAD/iPAD and compartments A/B To examine the distribution of gene density and GC content in PAD/iPAD and compartments A/B, average numbers of genes per 100kb and the GC content (percentage of Gs and Cs) were computed for each PAD/iPAD or compartments A/B. The global distributions are shown in boxplots. Analysis of gene expression in PADs For PADs with at least one gene, we calculated average FPKM for each PAD and those with average FPKM < 0.5 (all three replicates) in control group and average FPKM > 0.5 in Ring1/Rnf2 dKO FGOs are defined as PADs with gene de-repression. On the other hand, PADs with average FPKM < 0.5 in both control and Ring1/Rnf2 dKO FGOs are defined as PADs without gene de-repression. QUANTIFICATION AND STATISTICAL ANALYSIS The number of n biological replicates is provided within each figure legend. Statistical p values were calculated using two-tailed Wilcoxon rank-sum test in interaction frequency results. Statistical p values were calculated using two-tailed t test in gene expression level results. The error bars denote SEM, and the center values denote mean. DATA AND CODE AVAILABILITY The raw sequence data reported in this paper have been deposited in the NCBI Gene Expression Omnibus (GEO) with the accession number GEO: GSE118263, GEO: GSE136093 and GEO: GSE132156. Public data analyses for mouse oocyte DNA methylation data were downloaded from DDBJ/GenBank/EMBL with the accession number DRA000570. Public data analyses for mouse oocyte and embryo histone modification datasets, mouse embryo sisHiC datasets and mouse oocyte snHiC datasets were downloaded from NCBI Gene Expression Omnibus with the accession numbers GEO: GSE76687, GEO: GSE71434, GEO: GSE82185 and GEO: GSE80006 respectively. Sequencing information summary in this study can be found in Table S1. A summary of locations of PADs and iPADs identified in mouse FGOs can be found in Table S2, while the summary of locations of TAD boundaries identified in mESC and PAD boundaries identified in mouse FGOs can be found in Table S3. All other data supporting the findings of this study are available from the Lead Contact on reasonable request.

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