Dynamics of gene silencing during X inactivation using allele-specific RNA-seq

Marks, Hendrik; Kerstens, Hindrik H. D.; Barakat, Tahsin Stefan; Splinter, Erik; Dirks, René A. M.; van Mierlo, Guido; Joshi, Onkar; Wang, Shuang-Yin; Babak, Tomas; Albers, Cornelis A.; Kalkan, Tüzer; Smith, Austin; Jouneau, Alice; de Laat, Wouter; Gribnau, Joost; Stunnenberg, Hendrik G.

doi:10.1186/s13059-015-0698-x

Dynamics of gene silencing during X inactivation using allele-specific RNA-seq

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Published: 03 August 2015

Volume 16, article number 149, (2015)
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Dynamics of gene silencing during X inactivation using allele-specific RNA-seq

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Hendrik Marks¹,
Hindrik H. D. Kerstens¹,
Tahsin Stefan Barakat³,
Erik Splinter⁴,
René A. M. Dirks¹,
Guido van Mierlo¹,
Onkar Joshi¹,
Shuang-Yin Wang¹,
Tomas Babak⁵,
Cornelis A. Albers²,
Tüzer Kalkan⁶,
Austin Smith⁶,
Alice Jouneau⁷,
Wouter de Laat⁴,
Joost Gribnau³ &
…
Hendrik G. Stunnenberg¹

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An Erratum to this article was published on 05 February 2016

Abstract

Background

During early embryonic development, one of the two X chromosomes in mammalian female cells is inactivated to compensate for a potential imbalance in transcript levels with male cells, which contain a single X chromosome. Here, we use mouse female embryonic stem cells (ESCs) with non-random X chromosome inactivation (XCI) and polymorphic X chromosomes to study the dynamics of gene silencing over the inactive X chromosome by high-resolution allele-specific RNA-seq.

Results

Induction of XCI by differentiation of female ESCs shows that genes proximal to the X-inactivation center are silenced earlier than distal genes, while lowly expressed genes show faster XCI dynamics than highly expressed genes. The active X chromosome shows a minor but significant increase in gene activity during differentiation, resulting in complete dosage compensation in differentiated cell types. Genes esca** XCI show little or no silencing during early propagation of XCI. Allele-specific RNA-seq of neural progenitor cells generated from the female ESCs identifies three regions distal to the X-inactivation center that escape XCI. These regions, which stably escape during propagation and maintenance of XCI, coincide with topologically associating domains (TADs) as present in the female ESCs. Also, the previously characterized gene clusters esca** XCI in human fibroblasts correlate with TADs.

Conclusions

The gene silencing observed during XCI provides further insight in the establishment of the repressive complex formed by the inactive X chromosome. The association of escape regions with TADs, in mouse and human, suggests that TADs are the primary targets during propagation of XCI over the X chromosome.

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Background

Gene dosage of X-chromosomal genes in mammals is equalized between sexes by inactivation of one of the two X chromosomes in female cells [1]. During early embryonic development of mice, two waves of X chromosome inactivation (XCI) occur. At the two- to four-cell embryonic stage [embryonic day (E)1.5] the paternally derived X chromosome is inactivated, referred to as imprinted XCI. At the early blastocyst stage (E4.5) the X chromosome is reactivated, after which random XCI takes place: during a stochastic process either the maternally or paternally derived X chromosome is silenced (see Heard and Disteche [2], Barakat and Gribnau [3] and Jeon et al. [17, 18]. Although the earliest regions containing enriched occupancies of **st are spread across the entire linear X chromosome, these regions have a high frequency of close contact to the XIC. The early-enriched **st localization sites are gene dense and enriched for silent genes [17, 18]. From these early ‘docking stations’, a second wave of **st spreading occurs by pulling the actively transcribed genes as well as the gene-poor regions in closer proximity to the XIC. **st recruits the Polycomb repressive complex 2 (PRC2) and other proteins involved in gene silencing and chromatin compaction, creating a repressive nuclear compartment present in differentiated cells displaying stable XCI [18–20]. In line with these observations, **st binding is proportional to the increase of PRC2 and the repressive trimethylation of lysine 27 on histone 3 (H3K27me3) on the ** [18, 21]. Similar to **st, the Polycomb proteins and H3K27me3 are first detected at ~150 canonical sites distributed over the **, after which spreading over active genes occurs [21, 22].

Despite recent advances in chromatin-associated changes of the ** during XCI, little is known on how this affects silencing of genes located on the ** at the transcript level. Lin et al. [23] investigated gene silencing during XCI by a comparative approach in which differentiating female and male ESCs were profiled in parallel. The female-specific changes were considered to be associated with XCI. However, female and male ESCs maintained in serum-containing media are distinct in their epigenetic make-up, with female ESCs being hypomethylated and male ESCs being hypermethylated [24–26]. Also, differences in activity of the MAPK, Gsk3 and Akt signaling pathways have been reported [27], complicating direct comparisons between ESCs of different sexes.

After establishment of XCI, silencing of the ** is stably maintained in somatic cells during replication [28]. Although most genes are silent on the ** at this stage, some genes escape XCI and remain active. In human, at least 15 % of the X-linked genes have been shown to escape XCI [29]. These escape genes are distributed in clusters over the X chromosome [29–31]. This suggests a common regulatory mechanism acting on chromatin domains, the nature of which remains elusive thus far. In mouse, around 15 escape genes have been identified [32–37]. Except for **st, these genes are generally lower expressed from the ** compared with the Xa. It has been shown that the escape of Kdm5c in mouse adult tissues is preceded by silencing during early embryonic development [38]. However, for most other escape genes it is currently unclear whether they are initially silenced and reactivated or whether they are never subject to XCI.

Here, we set out to study the dynamics of X-linked gene silencing during the early stages of XCI by differentiation of female ESCs to embryoid bodies (EBs). To avoid comparative analysis between sexes and enable direct quantitative profiling of gene silencing on the **, we used female mouse ESCs with non-random XCI and polymorphic X chromosomes [39] to specifically determine the changes occurring on the (future) ** by high-resolution allele-specific RNA-seq. To investigate later stages, these ESCs were differentiated in vitro to neural progenitor cells (NPCs) [35]. We used allele-specific RNA-seq on the NPCs, in which XCI is fully established and maintained, to correlate the silencing dynamics of genes observed during early XCI with escape from XCI in the NPCs. By associating the genes that escape XCI with topologically associating domains (TADs) as determined in the female ESCs by genome-wide chromosome conformation capture (Hi-C) profiling, we investigate the role of chromatin domains during XCI. By determining the kinetics of gene silencing and correlating this to epigenomic features, our data provide further insight into the formation of the repressive complex during XCI.

Results

Experimental setup to study gene silencing on the ** using allele-specific RNA-seq

To determine the dynamics of gene silencing during XCI, we used female ESCs derived from an intercross of Mus musculus (M.m.) musculus 129/SV-Jae (129) and M.m. castaneus (Cast) as previously described [39, 40]. Due to the cross of genetically distant mouse strains, this ESC line contains two sets of chromosomes with many polymorphic sites, around 20.8 million genome-wide (~1 single-nucleotide polymorphism (SNP) per 130 bp) and around 0.6 million on chromosome X (~1 SNP per 300; see “Materials and methods”). These sites can be used to perform allele-specific quantification of X-linked and autosomal transcripts by RNA-seq [40]. The introduction of a transcriptional stop signal into the transcribed region of Tsix on the 129-derived X chromosome in the female ESC line results in complete skewing of **st expression toward the 129-targeted allele [39]. Therefore, the 129-derived X chromosome will always be inactivated during differentiation, allowing specific quantification of transcripts from the ** and the Xa, respectively (Fig. 1, “ES_Tsix-stop”, pink background). In undifferentiated female ESCs cultured in serum-containing ESC media, inhibition or blocking of Tsix transcription has been shown to be associated with aberrant **st upregulation and/or partial XCI [6, 23, 41]. Interestingly, we observed a fourfold reduction in **st expression and increased expression of X-linked genes during culturing of the ES_Tsix-stop ESCs in serum-free ESC culture media supplemented with two kinase inhibitors to maintain pluripotency (“2i” ESCs) [24, 27, 42–45] compared with culturing in serum-containing media (“serum” ESCs; Additional file 1: Figure S1). Therefore, we used 2i ES_Tsix-stop ESCs to initiate XCI by differentiation towards EBs and performed allele-specific RNA-seq of the undifferentiated 2i ESCs as well as after 2, 3, 4 and 8 days of EB formation. Validation of the EB time course is documented in Additional file 1: Figure S2, and Figure S3.

To investigate stable XCI, we included allele-specific RNA-seq of three NPC lines that were previously generated in vitro from the polymorphic ESCs [22] by applying a new procedure based on the GSNAP (Genomic Short-read Nucleotide Alignment Program) algorithm [46], in which the alternative alleles of polymorphic sites are included in the reference genome during map**. This results in an unbiased map** of the 129- and Cast-derived sequence tags and an equal contribution in expression from the Cast- and 129-derived genomes in undifferentiated ESCs (Additional file 1: Figure S4a). To enable reliable allele-specific quantification of the RNA-seq, we only included genes for further analysis that (i) showed consistent Cast versus 129 allelic ratios over the polymorphic sites that are present within the gene body (standard error of the mean < 0.1); (ii) contained a total of at least 80 tag counts over polymorphic sites for each allele over the EB formation time course (equivalent to a standard deviation of the allelic ratio of a gene of < 15 % over the time course; see Additional file 1: Figure S4b and “Materials and methods” for further details). Together, our stringent criteria resulted in accurate quantification of allele-specific expression as exemplified in Additional file 1: Figure S4c, d. In total, we obtained allele-specific quantification for 9666 out of a total of 13,909 unique RefSeq genes showing a mean expression of >0.5 RPKM (Reads Per Kilobase of exon per Million mapped reads) over the time course of EB formation (69 %). These include 259 genes on the X chromosome (out of a total of 590 genes with expression > 0.5 RPKM (49 %)). Further details on the samples profiled for this study are provided in Additional file 2: Table S1. Additional file 3: Table S2 contains the gene expression values and allelic counts for all RNA-seq samples.

XCI during EB formation of female ESCs and in NPCs

In order to evaluate the XCI occurring during the EB differentiation of the female 2i ESCs, we examined expression within the XIC. RNA-seq shows Tsix expression in the undifferentiated ESCs (ES_Tsix-stop T = 0 days), while **st is highly upregulated after two days of differentiation, specifically from the 129 allele (Fig. 2a, b). In line, **st clouds are robustly detected in more than half of the cells after two days of EB formation by RNA fluorescent in situ hybridization (FISH), and in 94 % of the cells after 8 days (Fig. 2a, right column). Activation of **st coincides with a global reduction in expression of X-linked genes of ~30 % after two days of EB formation (Fig. 2c). As the reduction of X-linked expression was not observed during EB differentiation of male cells, nor for autosomal genes, we conclude that this reflects the XCI occurring in the female cells. Within the NPCs, **st is highly expressed. As expected, **st is exclusively expressed from the 129 allele in *NPC_129-** and NPC_129-**, while in NPC_Cast-** **st is expressed from the Cast allele (Fig. 2b). Together, the data show that XCI is robustly initiated on the 129 allele during the EB differentiation time course of ES_Tsix-stop, and stably present in the NPCs.

Kinetics of gene silencing during XCI on the **

To investigate the transcriptional changes occurring on the ** and Xa specifically, we calculated the ratio of 129/Cast over the time course (Fig. 3a). At a global level, the allelic ratios for autosomal genes remain stable. In contrast, genes on chromosome X show an increasing bias towards expression from the Cast allele, the X chromosome that remains active. After 8 days, gene expression is, on average, approximately fourfold higher from the Xa than from the **. Absolute quantification of gene expression shows that expression from the 129 and Cast alleles remains similar on autosomes (Fig. 3b, left panel). For X-linked genes, expression from the 129 allele (**) is gradually downregulated, while expression of the Cast allele (Xa) shows a relatively minor but significant (p < 0.05 [47]) increase in expression (Fig. 3b, right panel). The increase in activity is not specific for female cells but rather associated with differentiation, as male ESCs also show a similar trend (albeit not significant) of increased X-linked expression during EB formation (Fig. 2c, blue boxplots). Notably, by comparison of the individual time points in female cells we observed a slight but significant difference (p < 0.05 [47]) in XCI dynamics between lowly (RPKM ≤2) and highly (RPKM >2) expressed genes, as the lowly expressed genes show faster XCI dynamics than the highly expressed genes (Fig. 3c; Additional file 1: Figure S5).

To further stratify genes showing similar XCI dynamics, we performed K-means clustering on the **/Xa ratio over the time course (Fig. 4a). The clustering revealed four clusters containing genes that show similar dynamics. The genes in cluster 1 are mainly silenced on the ** within 2 days of EB formation, and therefore these genes are inactivated relatively fast (labeled as “early”). The genes in cluster 2 (labeled as “intermediate”) mainly show silencing between 4 and 8 days of EB formation. Genes in cluster 3 show some initial silencing of the ** over the time course, and only show a mild bias for higher expression from the Xa at the latest time point of 8 days EB formation. However, most of the cluster 3 genes are fully silenced during stable XCI, including in NPCs (as discussed later; Fig. 5). Therefore, we labeled this cluster as “late”. The relatively small number of genes present in cluster 4 did not show any sign of silencing (labeled “not silenced”), and include many known escape genes such as **st, Kdm6a (Utx), Utp14a and Chm. Figure 4b shows three examples of genes present in the “early”, “late” and “not silenced” cluster, respectively. Genes within the “late” cluster were significantly higher expressed than genes in the other clusters (Additional file 1: Figure S7) [47], reinforcing the observation that highly expressed genes generally show slower silencing kinetics during XCI (Fig. 3c; Additional file 1: Figure S5).

A comparison of the kinetic clusters with a previous study that used RNA FISH to determine X-linked silencing at the single gene level [37]. Only ** XCI in at least one NPC line. Only Shroom4 and Car5 are stably inactivated in the NPCs used for the current study, while they escape XCI in the Patski cells as reported by Yang et al. [37] (see Table 1 for detailed comparisons). Most genes that escape XCI in mouse brain tissue [48] also escape XCI in NPCs (Table 1). In line with their tissue-specificity, only one gene (Utp14a) of the 24 genes that specifically escape XCI in mouse spleen and/or ovary [48] escapes XCI in the NPCs. Furthermore, nearly all genes that escape in mouse trophoblast cells during imprinted XCI [49] (and for which there is sufficient allele-specific coverage in the NPCs profiled in the current study) escape XCI in at least one of the NPC lines (Table 1). However, we identify more escape genes compared with these previous studies (Table 1), as further discussed below.

Table 1 Genes esca** XCI in any of the three NPC lines in comparison with other studies

Full size table

Comparison of the kinetic clusters (Fig. 4) with the 38 genes that escape XCI in *NPC_129-** XCI in the three NPC lines (Fig. 5a, Table 1), we plotted the genes esca** XCI over the linear X chromosome (Fig. 5d). This shows that all three NPCs share escape genes over most of the X chromosome, except for three distal regions (regions 1–3) that were also pronounced in cluster 4 within the previous analysis (Fig. 4c, “not silenced”). Within these regions *NPC_129-** XCI. The escape genes identified in the three NPC lines showed a large overlap with the escape genes as determined at the start of the culturing (Additional file 1: Figure S14a), including the escape genes present in the three escape regions (Additional file 1: Figure S14b). Notably, most genes showing differential escape before and after one month of NPC culturing are expressed from the ** XCI in the NPCs are stably maintained over time.

Regions of genes that escape XCI in the NPCs are associated with TADs

The clustering of genes that escape XCI, as observed in the NPCs, might suggest regulatory control at the level of chromatin domains in which epigenetic domains on the ** XCI colocalize with TADs as identified in ES_Tsix-stop ESCs. a–c Overview of the TADs present at regions 1, 2 and 3 [indicated with a box in (a), (b) and (c), respectively] in the female ES_Tsix-stop ESCs. In red, the interaction matrix used for TAD calling with domains indicated by dashed lines. The second row shows the spearman correlation between the 40 kb-binned Hi-C interaction matrices of the female ES_Tsix-stop and male J1 ESCs [51] (see “Materials and methods” for further details). The legend for genes that escape XCI or genes that are silenced is indicated in (a). Coloring of genes indicates escape in one or two NPC lines, respectively, while genes in black are X-inactivated in all NPC lines. Wdr45 and Slc35a2 are included as escape gene for *NPC_129-** as the contribution in gene expression from the ** is >10 % (Table 1). Additional file 1: Figure S17 contains the same information as Fig. 6, but includes genes for which no allelic information was obtained (mainly due to low expression or the absence of polymorphic sites), as well as the interaction matrix in male J1 ESCs obtained from Dixon et al. [51] for comparison

Full size image

To determine the TADs over the three escape regions on the 129-derived X chromosome (that is being inactivated during differentiation of the ES_Tsix-stop ESCs), we performed allele-specific calling of TADs. In line with the non-discriminative 129/Cast Hi-C analysis, the allele-specific Hi-C shows the presence of the domains overlaying the regions of escape on the 129-derived X chromosome (Fig. 6a–c). For validation of the overlap between the escape regions and the TADs, we analyzed allele-specific RNA-seq data from very similar 129/Cast hybrid female NPCs generated by Gendrel et al. [52]. We observed a high number of escape genes within the three regions (Additional file 1: Figure S18), but not in neighboring regions/domains, showing that the three regions have a consistent tendency to escape XCI in NPCs. Together, these observations suggest that the three regions esca** XCI represent TADs that are affected during the onset of XCI.

To further investigate the spatial organization of the three escape regions within the NPCs, we overlaid these regions with the allele-specific chromosome conformation capture-on-chip (4C) profiles generated by Splinter et al. [1: Figure S19b). Therefore, the control of these clustered escape genes in human might well occur at the level of the TADs, in line with our observations in mouse NPCs.

Discussion

In this study, we determined the dynamics of gene silencing on the (future) ** by GSNAP [46] in an efficient and straightforward procedure, thereby obtaining unbiased high-resolution gene expression profiles from both alleles. The silencing kinetics for individual genes during XCI reveals a linear component in the propagation of inactivation over the ** XCI correspond to TADs suggests that genes within TADs are co-regulated to induce silencing in a domain-type fashion during XCI. This would imply that TADs are the functional compartments in the higher order chromatin structure that are targeted for inactivation during initiation of XCI. Once targeted, silencing might be propagated within the TAD such that the associated genes become inactivated. How this would work remains to be resolved, but the functional mechanisms might resemble those acting in long range epigenetic silencing (LRES) by which large regions (up to megabases) of chromosomes can be co-coordinately suppressed [60].

Together, the dynamics of XCI we observe fit with previously proposed biphasic models in which secondary spread of inactivation occurs via so-called relay elements, way stations or docking stations, the nature of which still remains elusive [63]. An interesting possibility to further investigate the role of TADs during inactivation of the (future) ** using GSNAP

FASTQ files were mapped using GSNAP version 2011-03-10 [46]. To avoid bias in the map** of either the Cast- or the 129-derived reads, the alternative alleles of polymorphic sites between the 129 and Cast genome (see above) are included in the reference during map** (GSNAP SNP-tolerant map**; flag –v). Only sequence tags aligning to a single position on the genome were considered for further analysis on the 32–42 bp aligned sequence reads. The output data were converted to Browser Extensible Data (BED) files for quantification, Wiggle (WIG) files for viewing and GSNAP output files for determining allelic bias per gene. To obtain RNA-seq gene expression values (RPKM), we used Genomatix [76] (ElDorado 12–2010) selecting RefSeq genes (NCBI m37 genome assembly; MM9; Additional file 3: Table S2).

Calling of allele-specific gene expression

Within the individual samples, we used the mapped tags to determine the sequence tag coverage per allele for each of the 20,785,351 polymorphic sites using GSNAP tally. A total of 4,888,065 polymorphic sites were covered at least once in any of the samples used for this study. Per single polymorphic nucleotide, the pile-ups were subsequently assigned to either the 129 or the Cast allele using custom Perl-based scripts (the allelic counts over individual polymorphic sites for each of the Tsix-stop profiles are present within GEO GSE60738). To avoid including counts from positions which were reported to be polymorphic in the Sanger sequencing project and/or the NIEHS/Perlegen resequencing projects, but which were not present in the genotypes used for the current study, we selected polymorphic sites that were covered at least twice from both the 129 and the Cast allele. This resulted in a total of 1,121,809 polymorphic sites used in further analysis. Counts over polymorphic sites within exons of individual RefSeq genes for either 129 or Cast were summed to obtain allele-specific gene expression counts for both species (Additional file 3: Table S2). The ratio between the 129 counts or the Cast counts versus the total counts (129 + Cast) represent the relative contribution of the 129 or Cast allele, respectively, to expression of a particular gene. To calculate absolute allele-specific expression values, we multiplied the relative contribution of either 129 or Cast with the total RPKM expression value of a gene. For the ESC differentiation time course, only genes that contained a count of >80 over the complete time course from both the 129 as well as from the Cast allele were included for further analysis as further explained in the main text and in Additional file 1: Figure S4b.

Consistency of allelic bias per polymorphic site over the full transcript

Genes containing a single polymorphic site and fulfilling the criteria as described above were included in the analysis for the EB differentiation time course. In case multiple polymorphic sites were included in the allele-specific gene expression calling for a given gene (see previous section), we evaluated the consistency in allelic ratio over the individual polymorphic sites. For genes containing at least two polymorphic sites showing a coverage of more than nine counts over the ESC differentiation time course from either the 129 or Cast allele, the relative contributions from 129 and Cast were calculated for these individual sites. Genes that showed a standard error of the mean (STDEM) of >0.1 over the individual polymorphic sites were excluded from further analysis.

Escape from XCI in NPC lines

Genes were considered escape genes if they fulfilled the following criteria: (i) at least two polymorphic sites showing signals from the ** notches are significantly different (p < 0.05 [47]).

Imprinting in undifferentiated ESCs

Genes were considered imprinted according to the following two criteria: (i) for at least two polymorphic sites, at least 80 % of these sites show the same allelic bias towards either 129 or Cast with binomial p < 0.01; (ii) the total relative contribution of either 129 or Cast to the total expression of a gene is >75 %.

Sanger sequencing of cDNA

cDNA was synthesized from 2 μg total RNA using 1 μg random hexamers by Superscript III Reverse Transcriptase (Invitrogen). PCR fragments for sequencing were obtained using the Phusion High Fidelity DNA Polymerase kit (NEB M0530C) on the synthesized cDNA, followed by purification of the PCR products using Agencourt AMPure (Beckman Coulter). Further details on the conditions of the PCR, as well as on the PCR primers and sequencing primers used, are listed in Additional file 7: Table S6. Sanger sequencing was performed on the 3730 Sequence Analyzer (Life Technologies) using Big Dye Terminator version 1.1 according to standard protocols.

Hi-C (data) analysis

Collection of the cells for Hi-C and the Hi-C sample preparation procedure was performed as previously described [79], with the slight modification that DpnII was used as restriction enzyme during initial digestion. Paired-end libraries were prepared according to Lieberman-Aiden et al. [79] and sequenced on the NextSeq 500 platform using 2 × 75 bp sequencing (Additional file 2: Table S1). Reads were mapped to the reference mouse genome (mm9) using BWA MEM [80] with default parameters. Reads were filtered based on map** quality score (mapQ ≥10) and PCR duplicates were removed. (Normalized) interaction matrices at a resolution of 40 kb and the corresponding two-dimensional heat maps were generated as previously described [79] using optimized LGF normalization (normLGF) from the R package HiTC [81]. The Hi-C Domain Caller package was used to calculate the directionality index from the normalized interaction matrix and to determine domains and boundaries using default parameters [51]. For allele-specific domain calling, we first filtered the 129-derived sequence tags using intersection and assignment based on the polymorphic sites between Cast and 129 (20,785,351 polymorphic sites as reported above). Allele-specific domain calling was identical to the procedure for the total data set.

Correlation between Hi-C experiments and Hi-C TAD boundaries

Correlations and overlaps for the Hi-C experiments were calculated according to Dixon et al. [51]. In short, the correlation between Hi-C experiments was calculated by the Spearman’s rank correlation coefficient for each 40 kb bin based on the number of interactions (signal) within the 25 bins upstream and 25 bins downstream. For overlap of boundaries, we considered a cutoff of ≤40 kb of boundaries between samples. The Spearman’s rank correlation coefficient for TAD boundaries was based on ten 40-kb bins upstream and downstream of the boundaries of two samples. For random correlation, we 2000 times randomly selected 20 bins from each of the two experiments and calculated correlations.

Other datasets used

RNA-seq of the ESC line E14 (male; 2i and serum) was obtained from Marks et al. [44]. RNA-seq of EpiSC lines was obtained from Veillard et al. [67] and includes one newly generated profile from a female EpiSC that was obtained by nuclear transfer (NT) [66]. Three-dimensional genome organization by Hi-C of male serum J1 ESCs grown on feeders was obtained from Dixon et al. [51]. For human, the Hi-C was generated from the female fibroblast line IMR90 [51]. Additional data of escape genes in NPCs was obtained from Gendrel et al. [52]. Escape genes from human were obtained from Carrel and Willard [29], and were profiled in hybrid lines generated from human female fibroblasts and mouse cells. Genes were considered as escapers in case of a ratio >5/9 in the hybrid lines.

Abbreviations

129:: M.m. musculus 129/SV-Jae
bp:: base pair
Cast:: M.m. castaneus
E:: embryonic day
EB:: embryoid body
EpiSC:: epiblast stem cell
ESC:: embryonic stem cell
FISH:: fluorescent in situ hybridization
GEO:: Gene Expression Omnibus
GSEA:: gene set enrichment analysis
GSNAP:: Genomic Short-read Nucleotide Alignment Program
H3K27me3:: trimethylated lysine 27 on histone 3
M.m. :: Mus musculus
MAPK:: mitogen-activated protein kinase
NPC:: neural progenitor cell
PBS:: phosphate-buffered saline
PFA:: paraformaldehyde
PRC:: Polycomb repressive complex
SNP:: single-nucleotide polymorphism
TAD:: topologically associating domain
Xa:: active X chromosome
XCI:: X chromosome inactivation
**:: inactivate X chromosome
XIC:: X-inactivation center

References

Wutz A. Gene silencing in X-chromosome inactivation: advances in understanding facultative heterochromatin formation. Nat Rev Genet. 2011;12:542–53.
Article CAS PubMed Google Scholar
Heard E, Disteche CM. Dosage compensation in mammals: fine-tuning the expression of the X chromosome. Genes Dev. 2006;20:1848–67.
Article CAS PubMed Google Scholar
Barakat TS, Gribnau J. X chromosome inactivation in the cycle of life. Development. 2012;139:2085–9.
Article CAS PubMed Google Scholar
Jeon Y, Sarma K, Lee JT. New and **sting regulatory mechanisms of X chromosome inactivation. Curr Opin Genet Dev. 2012;22:62–71.
Article PubMed Central CAS PubMed Google Scholar
Heard E, Avner P. Role play in X-inactivation. Hum Mol Genet. 1994;3:1481–5.
CAS PubMed Google Scholar
Lee JT, Lu N. Targeted mutagenesis of Tsix leads to nonrandom X inactivation. Cell. 1999;99:47–57.
Article CAS PubMed Google Scholar
Brockdorff N, Ashworth A, Kay GF, McCabe VM, Norris DP, Cooper PJ, et al. The product of the mouse **st gene is a 15 kb inactive X-specific transcript containing no conserved ORF and located in the nucleus. Cell. 1992;71:515–26.
Article CAS PubMed Google Scholar
Brown CJ, Hendrich BD, Rupert JL, Lafreniere RG, **ng Y, Lawrence J, et al. The human XIST gene: analysis of a 17 kb inactive X-specific RNA that contains conserved repeats and is highly localized within the nucleus. Cell. 1992;71:527–42.
Article CAS PubMed Google Scholar
Clemson CM, McNeil JA, Willard HF, Lawrence JB. XIST RNA paints the inactive X chromosome at interphase: evidence for a novel RNA involved in nuclear/chromosome structure. J Cell Biol. 1996;132:259–75.
Article CAS PubMed Google Scholar
Jonkers I, Barakat TS, Achame EM, Monkhorst K, Kenter A, Rentmeester E, et al. RNF12 is an X-encoded dose-dependent activator of X chromosome inactivation. Cell. 2009;139:999–1011.
Article CAS PubMed Google Scholar
Shin J, Bossenz M, Chung Y, Ma H, Byron M, Taniguchi-Ishigaki N, et al. Maternal Rnf12/RLIM is required for imprinted X-chromosome inactivation in mice. Nature. 2010;467:977–81.
Article PubMed Central CAS PubMed Google Scholar
Barakat TS, Gunhanlar N, Pardo CG, Achame EM, Ghazvini M, Boers R, et al. RNF12 activates **st and is essential for X chromosome inactivation. PLoS Genet. 2011;7:e1002001.
Article PubMed Central CAS PubMed Google Scholar
Gontan C, Achame EM, Demmers J, Barakat TS, Rentmeester E, van IW, et al. RNF12 initiates X-chromosome inactivation by targeting REX1 for degradation. Nature. 2012;485:386–90.
Article CAS PubMed Google Scholar
Navarro P, Oldfield A, Legoupi J, Festuccia N, Dubois A, Attia M, et al. Molecular coupling of Tsix regulation and pluripotency. Nature. 2010;468:457–60.
Article CAS PubMed Google Scholar
Sun S, Del Rosario BC, Szanto A, Ogawa Y, Jeon Y, Lee JT. Jpx RNA activates **st by evicting CTCF. Cell. 2013;153:1537–51.
Article PubMed Central CAS PubMed Google Scholar
Barakat TS, Loos F, van Staveren S, Myronova E, Ghazvini M, Grootegoed JA, et al. The trans-activator RNF12 and cis-acting elements effectuate X chromosome inactivation independent of X-pairing. Mol Cell. 2014;53:965–78.
Article CAS PubMed Google Scholar
Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, et al. The **st lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science. 2013;341:1237973.
Article PubMed Central PubMed Google Scholar
Simon MD, Pinter SF, Fang R, Sarma K, Rutenberg-Schoenberg M, Bowman SK, et al. High-resolution **st binding maps reveal two-step spreading during X-chromosome inactivation. Nature. 2013;504:465–9.
Article PubMed Central CAS PubMed Google Scholar
Chaumeil J, Le Baccon P, Wutz A, Heard E. A novel role for **st RNA in the formation of a repressive nuclear compartment into which genes are recruited when silenced. Genes Dev. 2006;20:2223–37.
Article PubMed Central CAS PubMed Google Scholar
Clemson CM, Hall LL, Byron M, McNeil J, Lawrence JB. The X chromosome is organized into a gene-rich outer rim and an internal core containing silenced nongenic sequences. Proc Natl Acad Sci U S A. 2006;103:7688–93.
Article PubMed Central CAS PubMed Google Scholar
Pinter SF, Sadreyev RI, Yildirim E, Jeon Y, Ohsumi TK, Borowsky M, et al. Spreading of X chromosome inactivation via a hierarchy of defined Polycomb stations. Genome Res. 2012;22:1864–76.
Article PubMed Central CAS PubMed Google Scholar
Marks H, Chow JC, Denissov S, Francoijs KJ, Brockdorff N, Heard E, et al. High-resolution analysis of epigenetic changes associated with X inactivation. Genome Res. 2009;19:1361–73.
Article PubMed Central CAS PubMed Google Scholar
Lin H, Gupta V, Vermilyea MD, Falciani F, Lee JT, O’Neill LP, et al. Dosage compensation in the mouse balances up-regulation and silencing of X-linked genes. PLoS Biol. 2007;5:e326.
Article PubMed Central PubMed Google Scholar
Habibi E, Brinkman AB, Arand J, Kroeze LI, Kerstens HH, Matarese F, et al. Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell. 2013;13:360–9.
Article CAS PubMed Google Scholar
Ooi SK, Wolf D, Hartung O, Agarwal S, Daley GQ, Goff SP, et al. Dynamic instability of genomic methylation patterns in pluripotent stem cells. Epigenetics Chromatin. 2010;3:17.
Article PubMed Central PubMed Google Scholar
Zvetkova I, Apedaile A, Ramsahoye B, Mermoud JE, Crompton LA, John R, et al. Global hypomethylation of the genome in XX embryonic stem cells. Nat Genet. 2005;37:1274–9.
Article CAS PubMed Google Scholar
Schulz EG, Meisig J, Nakamura T, Okamoto I, Sieber A, Picard C, et al. The two active X chromosomes in female ESCs block exit from the pluripotent state by modulating the ESC signaling network. Cell Stem Cell. 2014;14:203–16.
Article CAS PubMed Google Scholar
Lyon MF. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature. 1961;190:372–3.
Article CAS PubMed Google Scholar
Carrel L, Willard HF. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature. 2005;434:400–4.
Article CAS PubMed Google Scholar
Tsuchiya KD, Greally JM, Yi Y, Noel KP, Truong JP, Disteche CM. Comparative sequence and x-inactivation analyses of a domain of escape in human xp11.2 and the conserved segment in mouse. Genome Res. 2004;14:1275–84.
Article PubMed Central CAS PubMed Google Scholar
Zhang Y, Castillo-Morales A, Jiang M, Zhu Y, Hu L, Urrutia AO, et al. Genes that escape X-inactivation in humans have high intraspecific variability in expression, are associated with mental impairment but are not slow evolving. Mol Biol Evol. 2013;30:2588–601.
Article PubMed Central CAS PubMed Google Scholar
Chureau C, Chantalat S, Romito A, Galvani A, Duret L, Avner P, et al. Ftx is a non-coding RNA which affects **st expression and chromatin structure within the X-inactivation center region. Hum Mol Genet. 2011;20:705–18.
Article CAS PubMed Google Scholar
Li SM, Valo Z, Wang J, Gao H, Bowers CW, Singer-Sam J. Transcriptome-wide survey of mouse CNS-derived cells reveals monoallelic expression within novel gene families. PLoS One. 2012;7:e31751.
Article PubMed Central CAS PubMed Google Scholar
Lopes AM, Arnold-Croop SE, Amorim A, Carrel L. Clustered transcripts that escape X inactivation at mouse XqD. Mamm Genome. 2011;22:572–82.
Article CAS PubMed Google Scholar
Splinter E, de Wit E, Nora EP, Klous P, van de Werken HJ, Zhu Y, et al. The inactive X chromosome adopts a unique three-dimensional conformation that is dependent on **st RNA. Genes Dev. 2011;25:1371–83.
Article PubMed Central CAS PubMed Google Scholar
Tian D, Sun S, Lee JT. The long noncoding RNA, Jpx, is a molecular switch for X chromosome inactivation. Cell. 2010;143:390–403.
Article PubMed Central CAS PubMed Google Scholar
Yang F, Babak T, Shendure J, Disteche CM. Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res. 2010;20:614–22.
Article PubMed Central CAS PubMed Google Scholar
Lingenfelter PA, Adler DA, Poslinski D, Thomas S, Elliott RW, Chapman VM, et al. Escape from X inactivation of Smcx is preceded by silencing during mouse development. Nat Genet. 1998;18:212–3.
Article CAS PubMed Google Scholar
Luikenhuis S, Wutz A, Jaenisch R. Antisense transcription through the **st locus mediates Tsix function in embryonic stem cells. Mol Cell Biol. 2001;21:8512–20.
Article PubMed Central CAS PubMed Google Scholar
Panning B, Dausman J, Jaenisch R. X chromosome inactivation is mediated by **st RNA stabilization. Cell. 1997;90:907–16.
Article CAS PubMed Google Scholar
Morey C, Arnaud D, Avner P, Clerc P. Tsix-mediated repression of **st accumulation is not sufficient for normal random X inactivation. Hum Mol Genet. 2001;10:1403–11.
Article CAS PubMed Google Scholar
Ficz G, Hore TA, Santos F, Lee HJ, Dean W, Arand J, et al. FGF Signaling inhibition in ESCs drives rapid genome-wide demethylation to the epigenetic ground state of pluripotency. Cell Stem Cell. 2013;13:351–9.
Article PubMed Central CAS PubMed Google Scholar
Leitch HG, McEwen KR, Turp A, Encheva V, Carroll T, Grabole N, et al. Naive pluripotency is associated with global DNA hypomethylation. Nat Struct Mol Biol. 2013;20:311–6.
Article PubMed Central CAS PubMed Google Scholar
Marks H, Kalkan T, Menafra R, Denissov S, Jones K, Hofemeister H, et al. The transcriptional and epigenomic foundations of ground state pluripotency. Cell. 2012;149:590–604.
Article PubMed Central CAS PubMed Google Scholar
Ying QL, Wray J, Nichols J, Batlle-Morera L, Doble B, Woodgett J, et al. The ground state of embryonic stem cell self-renewal. Nature. 2008;453:519–23.
Article CAS PubMed Google Scholar
Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.
Article PubMed Central CAS PubMed Google Scholar
McGill R, Tukey JW, Larsen WA. Variations of box plots. Am Stat. 1978;32:12–6.
Google Scholar
Berletch JB, Ma W, Yang F, Shendure J, Noble WS, Disteche CM, et al. Escape from X inactivation varies in mouse tissues. PLoS Genet. 2015;11:e1005079.
Article PubMed Central PubMed Google Scholar
Calabrese JM, Sun W, Song L, Mugford JW, Williams L, Yee D, et al. Site-specific silencing of regulatory elements as a mechanism of X inactivation. Cell. 2012;151:951–63.
Article PubMed Central CAS PubMed Google Scholar
Schoeftner S, Blanco R, Lopez de Silanes I, Munoz P, Gomez-Lopez G, Flores JM, et al. Telomere shortening relaxes X chromosome inactivation and forces global transcriptome alterations. Proc Natl Acad Sci U S A. 2009;106:19393–8.
Article PubMed Central CAS PubMed Google Scholar
Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485:376–80.
Article PubMed Central CAS PubMed Google Scholar
Gendrel AV, Attia M, Chen CJ, Diabangouaya P, Servant N, Barillot E, et al. Developmental dynamics and disease potential of random monoallelic gene expression. Dev Cell. 2014;28:366–80.
Article CAS PubMed Google Scholar
Wutz A, Jaenisch R. A shift from reversible to irreversible X inactivation is triggered during ES cell differentiation. Mol Cell. 2000;5:695–705.
Article CAS PubMed Google Scholar
Deng Q, Ramskold D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343:193–6.
Article CAS PubMed Google Scholar
Plath K, Fang J, Mlynarczyk-Evans SK, Cao R, Worringer KA, Wang H, et al. Role of histone H3 lysine 27 methylation in X inactivation. Science. 2003;300:131–5.
Article CAS PubMed Google Scholar
Silva J, Mak W, Zvetkova I, Appanah R, Nesterova TB, Webster Z, et al. Establishment of histone h3 methylation on the inactive X chromosome requires transient recruitment of Eed-Enx1 polycomb group complexes. Dev Cell. 2003;4:481–95.
Article CAS PubMed Google Scholar
Clark MB, Johnston RL, Inostroza-Ponta M, Fox AH, Fortini E, Moscato P, et al. Genome-wide analysis of long noncoding RNA stability. Genome Res. 2012;22:885–98.
Article PubMed Central CAS PubMed Google Scholar
Deng X, Berletch JB, Ma W, Nguyen DK, Hiatt JB, Noble WS, et al. Mammalian X upregulation is associated with enhanced transcription initiation, RNA half-life, and MOF-mediated H4K16 acetylation. Dev Cell. 2013;25:55–68.
Article PubMed Central CAS PubMed Google Scholar
Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature. 2012;485:381–5.
Article PubMed Central CAS PubMed Google Scholar
Clark SJ. Action at a distance: epigenetic silencing of large chromosomal regions in carcinogenesis. Hum Mol Genet. 2007;16 Spec No 1:R88–95.
Article PubMed Google Scholar
Gartler SM, Riggs AD. Mammalian X-chromosome inactivation. Annu Rev Genet. 1983;17:155–90.
Article CAS PubMed Google Scholar
Ng K, Pullirsch D, Leeb M, Wutz A. **st and the order of silencing. EMBO Rep. 2007;8:34–9.
Article PubMed Central CAS PubMed Google Scholar
Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin loo**. Cell. 2014;159:1665–80.
Article CAS PubMed Google Scholar
Csankovszki G, Panning B, Bates B, Pehrson JR, Jaenisch R. Conditional deletion of **st disrupts histone macroH2A localization but not maintenance of X inactivation. Nat Genet. 1999;22:323–4.
Article CAS PubMed Google Scholar
Wray J, Kalkan T, Gomez-Lopez S, Eckardt D, Cook A, Kemler R, et al. Inhibition of glycogen synthase kinase-3 alleviates Tcf3 repression of the pluripotency network and increases embryonic stem cell resistance to differentiation. Nat Cell Biol. 2011;13:838–45.
Article PubMed Central CAS PubMed Google Scholar
Maruotti J, Dai XP, Brochard V, Jouneau L, Liu J, Bonnet-Garnier A, et al. Nuclear transfer-derived epiblast stem cells are transcriptionally and epigenetically distinguishable from their fertilized-derived counterparts. Stem Cells. 2010;28:743–52.
Article CAS PubMed Google Scholar
Veillard AC, Marks H, Bernardo AS, Jouneau L, Laloe D, Boulanger L, et al. Stable methylation at promoters distinguishes epiblast stem cells from embryonic stem cells and the in vivo epiblasts. Stem Cells Dev. 2014;23:2014–29.
Article PubMed Central CAS PubMed Google Scholar
Barakat TS, Gribnau J. Combined DNA-RNA fluorescent in situ hybridization (FISH) to study X chromosome inactivation in differentiated female mouse embryonic stem cells. J Vis Exp. 2014;88:e51628.
Google Scholar
Gribnau J, Luikenhuis S, Hochedlinger K, Monkhorst K, Jaenisch R. X chromosome choice occurs independently of asynchronous replication timing. J Cell Biol. 2005;168:365–73.
Article PubMed Central CAS PubMed Google Scholar
Keane TM, Goodstadt L, Danecek P, White MA, Wong K, Yalcin B, et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature. 2011;477:289–94.
Article PubMed Central CAS PubMed Google Scholar
Yalcin B, Wong K, Agam A, Goodson M, Keane TM, Gan X, et al. Sequence-based characterization of structural variation in the mouse genome. Nature. 2011;477:326–9.
Article PubMed Central CAS PubMed Google Scholar
Mouse Genomes Project. ftp-mouse.sanger.ac.uk/REL-1003/20100301-high-confidence-snps.tab
Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, et al. A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature. 2007;448:1050–3.
Article CAS PubMed Google Scholar
Mouse Phenome Database at The Jackson Laboratory, Data set/project: Perlegen2 (2005). http://phenome.jax.org/db/q?rtn=projects/projdet&reqprojid=198.
Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, et al. Initial sequencing and comparative analysis of the mouse genome. Nature. 2002;420:520–62.
Article CAS PubMed Google Scholar
Genomatix. http://www.genomatix.de.
Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–73.
Article CAS PubMed Google Scholar
GeneTrail. http://genetrail.bioinf.uni-sb.de/.
Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive map** of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93.
Article PubMed Central CAS PubMed Google Scholar
Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014;30:2843–51.
Article PubMed Central PubMed Google Scholar
Servant N, Lajoie BR, Nora EP, Giorgetti L, Chen CJ, Heard E, et al. HiTC: exploration of high-throughput ‘C’ experiments. Bioinformatics. 2012;28:2843–4.
Article PubMed Central CAS PubMed Google Scholar
Guo G, Yang J, Nichols J, Hall JS, Eyres I, Mansfield W, et al. Klf4 reverts developmentally programmed restriction of ground state pluripotency. Development. 2009;136:1063–9.
Article PubMed Central CAS PubMed Google Scholar
Brons IG, Smithers LE, Trotter MW, Rugg-Gunn P, Sun B, de Sousa Lopes SM C, et al. Derivation of pluripotent epiblast stem cells from mammalian embryos. Nature. 2007;448:191–5.
Article CAS PubMed Google Scholar
Tesar PJ, Chenoweth JG, Brook FA, Davies TJ, Evans EP, Mack DL, et al. New cell lines from mouse epiblast share defining features with human embryonic stem cells. Nature. 2007;448:196–9.
Article CAS PubMed Google Scholar
Liu X, Wu H, Loring J, Hormuzdi S, Disteche CM, Bornstein P, et al. Trisomy eight in ES cells is a common potential problem in gene targeting and interferes with germ line transmission. Dev Dyn. 1997;209:85–91.
Article CAS PubMed Google Scholar

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Acknowledgements

We thank Eva Janssen-Megens and Yan Tan for technical support with sequencing, and Kees-Jan François for bioinformatic support. Allele-specific map**s were carried out on the Dutch national e-infrastructure with the support of SURF Foundation. The research leading to these results has received funding from the European Union grant PluriSys (FP7/2009: 223485) and ERC-2013-ADG-339431 “SysStemCell”. Research in the group of HM is supported by a grant from the Netherlands Organization for Scientific Research (NWO-VIDI 864.12.007).

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Radboud University, Faculty of Science, Department of Molecular Biology, Radboud Institute for Molecular Life Sciences (RIMLS), 6500HB, Nijmegen, The Netherlands
Hendrik Marks, Hindrik H. D. Kerstens, René A. M. Dirks, Guido van Mierlo, Onkar Joshi, Shuang-Yin Wang & Hendrik G. Stunnenberg
Radboud University, Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences (RIMLS), 6500HB, Nijmegen, The Netherlands
Cornelis A. Albers
Department of Reproduction and Development, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
Tahsin Stefan Barakat & Joost Gribnau
Hubrecht Institute, University Medical Center Utrecht, Uppsalalaan 8, 3584CT, Utrecht, The Netherlands
Erik Splinter & Wouter de Laat
Biology Department, Queen’s University, Kingston, ON, Canada
Tomas Babak
Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
Tüzer Kalkan & Austin Smith
INRA, UMR1198 Biologie du Développement et Reproduction, F-78350, Jouy-en-Josas, France
Alice Jouneau

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Hendrik Marks
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Tahsin Stefan Barakat
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Erik Splinter
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Onkar Joshi
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Shuang-Yin Wang
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Tomas Babak
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Cornelis A. Albers
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Tüzer Kalkan
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Austin Smith
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Joost Gribnau
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Hendrik G. Stunnenberg
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Correspondence to Hendrik Marks or Hendrik G. Stunnenberg.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

HM coordinated and conceived of the study, performed RNA-seq and computational analysis, interpreted the data and drafted the manuscript. HHDK performed GSNAP map**s and allele-specific quantification of the data, and contributed to interpretation of the data. TSB and ES contributed cells and/or cell pellets, and contributed to interpretation of the data. RAM and GvM assisted in cDNA Sanger sequencing. OJ and S-YW performed the wet-lab and computational part of the Hi-C, respectively. TB helped setting up the allele-specific map**s. CAA assisted in computational and statistical analysis. TK, AS, AJ and WdL contributed cells and/or cell pellets. JG contributed cells and cell pellets, participated in design of the study, and contributed to interpretation of the data. HGS conceived of the study, participated in its design, contributed to data interpretation and helped to draft the manuscript. All authors were involved in discussions, and read and approved the final manuscript.

Additional files

Additional file 1: Figure S1.

Decreased **. Figure S5. Dynamics of XCI for genes having high (RPKM >2) or low (RPKM ≤2) mean expression over the time course, showing that lowly expressed genes show faster XCI dynamics compared with highly expressed genes. Figure S6. Genome browser views (not allele‐specific) of the genes as shown in Fig. 4b. Figure S7. Expression dynamics of genes within the four clusters as characterized in Fig. 4. At all time points, genes within cluster 3 (the “late” cluster) are significantly higher expressed compared with genes in the other clusters. Figure S8. Overlap of the genes within the clusters identified in this study (on the x‐axis) with the genes within the clusters identified by Lin et al. [23] (colored), showing poor overlap between both studies for corresponding clusters. Figure S9. Running‐sum statistics for the genes present within each of the four clusters versus all 259 genes included in the analysis based on the distance to the XIC. Figure S10 Spread of gene silencing during XCI over the ** XCI in the three escape regions using cDNA Sanger sequencing. Figure S14. The three escape regions are stably maintained during propagation of NPCs. Figure S15. Distribution of ES_Tsix‐stop Hi‐C sequence tags over the genome shows that the ES_Tsix‐ stop ESCs contains no major genomic abnormalities besides trisomy 12. Figure S16. Domains and boundaries on autosomes as well as on chromosome X are largely stable between female ES_Tsix‐stop and male J1 ESCs [51]. Figure S17. The three regions esca** XCI colocalize with TADs as identified in ES_Tsix‐stop ESCs. Figure S18. Validation of the three regions esca** XCI and their associaPon with TADs. Figure S19. In human, clusters of genes esca** XCI on the short arm of chromosome X [29] colocalize with topologically associated domains (TADs) [51]. (PDF 204 kb)

Additional file 2: Table S1.

RNA-seq and Hi-C profiles generated for this study. (XLSX 11 kb)

Additional file 3: Table S2.

Genome-wide gene expression values and allelic counts for the profiles generated from the ES_Tsix-stop cells (tab 1, undifferentiated ESCs, EB time course and NPCs) and the ES_**st-del cells (tab 2, undifferentiated ESCs). (XLSX 8327 kb)

Additional file 4: Table S3.

Detailed overview of the overlap between genes in the clusters identified in this study and genes in similar clusters as identified by Lin et al. [23]. (XLSX 61 kb)

Additional file 5: Table S4.

Imprinting status of all X-linked genes in NPCs. (XLSX 421 kb)

Additional file 6: Table S5.

Coordinates of TADs as identified in the female ES_Tsix-stop ESCs. (XLSX 95 kb)

Additional file 7: Table S6.

Primers used for PCR on cDNA and for Sanger sequencing. (XLSX 47 kb)

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Marks, H., Kerstens, H.H.D., Barakat, T.S. et al. Dynamics of gene silencing during X inactivation using allele-specific RNA-seq. Genome Biol 16, 149 (2015). https://doi.org/10.1186/s13059-015-0698-x

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Received: 14 April 2015
Accepted: 18 June 2015
Published: 03 August 2015
DOI: https://doi.org/10.1186/s13059-015-0698-x

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Dynamics of gene silencing during X inactivation using allele-specific RNA-seq

Abstract

Background

Results

Conclusions

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Background

Results

Experimental setup to study gene silencing on the ** using allele-specific RNA-seq

XCI during EB formation of female ESCs and in NPCs

Kinetics of gene silencing during XCI on the **

Regions of genes that escape XCI in the NPCs are associated with TADs

Discussion

Calling of allele-specific gene expression

Consistency of allelic bias per polymorphic site over the full transcript

Escape from XCI in NPC lines

Imprinting in undifferentiated ESCs

Sanger sequencing of cDNA

Hi-C (data) analysis

Correlation between Hi-C experiments and Hi-C TAD boundaries

Other datasets used

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Acknowledgements

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