- Research article
- Open Access
Human genes escaping X-inactivation revealed by single cell expression data
BMC Genomics volume 20, Article number: 201 (2019)
In mammals, sex chromosomes pose an inherent imbalance of gene expression between sexes. In each female somatic cell, random inactivation of one of the X-chromosomes restores this balance. While most genes from the inactivated X-chromosome are silenced, 15–25% are known to escape X-inactivation (termed escapees). The expression levels of these genes are attributed to sex-dependent phenotypic variability.
We used single-cell RNA-Seq to detect escapees in somatic cells. As only one X-chromosome is inactivated in each cell, the origin of expression from the active or inactive chromosome can be determined from the variation of sequenced RNAs. We analyzed primary, healthy fibroblasts (n = 104), and clonal lymphoblasts with sequenced parental genomes (n = 25) by measuring the degree of allelic-specific expression (ASE) from heterozygous sites. We identified 24 and 49 candidate escapees, at varying degree of confidence, from the fibroblast and lymphoblast transcriptomes, respectively. We critically test the validity of escapee annotations by comparing our findings with a large collection of independent studies. We find that most genes (66%) from the unified set were previously reported as escapees. Furthermore, out of the overlooked escapees, 11 are long noncoding RNA (lncRNAs).
X-chromosome inactivation and escaping from it are robust, permanent phenomena that are best studies at a single-cell resolution. The cumulative information from individual cells increases the potential of identifying escapees. Moreover, despite the use of a limited number of cells, clonal cells (i.e., same X- chromosomes are coordinately inhibited) with genomic phasing are valuable for detecting escapees at high confidence. Generalizing the method to uncharacterized genomic loci resulted in lncRNAs escapees which account for 20% of the listed candidates. By confirming genes as escapees and propose others as candidates from two different cell types, we contribute to the cumulative knowledge and reliability of human escapees.
Sex chromosomes pose an inherent genetic imbalance of gene expression between sexes. In order to ensure a balanced expression in mammalian somatic tissues, one of the female’s X-chromosomes (ChrX) is randomly selected to undergo inactivation . The random choice of an inactivated X-chromosome (Xi) (i.e., paternal or maternal) is completed at a very early phase of embryonic development . Importantly, once this decision is made the selected inactivated chromosome is deterministically defined for all descendant cells, and this choice is maintained throughout the organism’s life in every somatic tissue . This highly regulated process has been extensively studied [2,3,4,5].
The initial silencing of ChrX is governed mainly by XIST (X-inactive specific transcript) [3, 4], a non-coding RNA (ncRNA) unique to placental mammals. XIST is a master regulator located at the X-inactivation center (XIC) that together with neighboring ncRNAs (e.g., FTX and JPX) activate the process of X-inactivation . XIST is exclusively transcribed from Xi, and its RNA products act in cis by coating the chromosome within a restricted chromosomal territory . The activity of XIC genes in recruiting chromatin remodeling complexes [3, 7, 8], results in an irreversible heterochromatinization. The heterochromatin state underlies the steady, lifelong phenomenon of X-inactivation .
Ample studies have indicated that silencing does not apply to all genes in the inactivated X-chromosome. Specifically, genes that are located at the Pseudoautosomal regions (PARs) are expressed from both alleles, similar to the majority of genes from autosomal chromosomes . In addition, on the ChrX there are also genes that escape X-inactivation (coined escapees). Investigating these escapee genes is important to understand the basis of ChrX evolution  and X-inactivation mechanism . Moreover, numerous clinical and phenotypic outcomes are thought to be explained by the status of escapee genes .
Complementary methods have been adapted for identifying escapees [12, 13]. For example, the expression levels of mRNAs were compared between males and females in various tissues [14,15,16]. Additionally, extensive lists of escapee candidates were reported from mouse-human cell hybrids, and from allelic expression patterns in fibroblast lines carrying a fragmented X-chromosome . The correlation of chromatin structure and CpG methylation patterns with genes that escape X-inactivation was also used. For example, loci on Xi with low methylation levels were proposed as indicators for escapee genes and were thus used as an additional detection method [18, 19].
In recent studies, genomic information from individuals and isolated cells became useful for marking the status of X-inactivation. Specifically, RNA sequencing (RNA-Seq) was used to infer allelic-specific expression (ASE) from the two X-chromosomes, according to a statistical assumption for the minor and major expressed alleles . ASE analysis from B-lymphocytes derived from two ethnically remote populations identified 114 escapees based on heterologous SNPs (hSNPs) . By default, the low-expressing hSNP alleles were considered as evidence for Xi expression. Recently, a large-scale ASE-based analysis was completed based on a few individuals using single cells .
Numerous observations indicate conflicts and inconclusive labeling of a ChrX gene as inactivated or escapee. Such variability reflects the inherent properties of the phenomenon with respect to tissues, individuals and developmental stages. Several trends characterize X-inactivation and escaping from it: (i) Escapees are located at the p-arm, which comprises evolutionary young segments that diverged more recently from ChrY [17, 21, 22]. (ii) Human escapees account for 15–25% ) of all known ChrX genes. Notably, this fraction is only ~ 3% for mice [11, 23]. (iii) A low level of expression signifies Xi when compared to Xa (e.g., [10, 16, 24]). (iv) Most escapees show a substantial heterogeneity across cell types, individuals and experimental settings [13, 16, 24, 25]. (v) Only a few of the escapees exhibit consistent behavior across a wide range of cell types and conditions [16, 24]. (vi) Some clinical and phenotypic diversity results from the varying degree of X-inactivation and escapee’s expression (e.g., [10, 26]).
The X-inactivation is an event that occurs independently for each cell. Thus, collecting expression data from single cells allows monitoring explicitly a single Xi allele in each cell. In the present study, we use the ASE data extracted from RNA-Seq of single cells (scRNA-Seq) for identifying escapees. We present an analytical protocol using genomic data for two sets collected from scRNA-Seq experiments. One set is based on primary fibroblasts, and the other is based on GM12878 lymphoblast cell line with a fully sequenced diploid genome. We report on 24 genes from fibroblasts and 49 genes from lymphoblasts as candidate escapees. Finally, we demonstrate the potential of the method to identify a large number of escapees despite a modest number of single cells analyzed. We show that while most of our identified escapees strongly agree with the current knowledge, we also provide an extended list of escapes that were previously undetected.
A framework for measuring the escape from X-inactivation in single cells
We identify escapees by analyzing gene expression from somatic single cells using scRNA-Seq methodology (see Methods). To evaluate the sensitivity of the method, we compare X-chromosome (ChrX) expression to other autosomal chromosomes. Specifically, we focused on the gene-rich chromosome 17 (Chr17) as a prototype of an autosomal chromosome. Chr17 was selected as it represents a chromosome with a minimal number of parent-specific imprinted genes . The quantitative properties of ChrX and Chr17 are listed in Fig. 1a.
This study is based on analysis of two female origin resources: (i) Primary UCF1014 fibroblasts (with 104 cells, see Methods). This set is specified by a higher coverage transcriptomic data, but lacks information on haplotype phasing (Fig. 1b); (ii) A smaller dataset of clonal lymphoblasts (n = 25) from the GM12878 cell line with fully phased and sequenced parental diploid genomes (Fig. 1c). In both datasets, transcription at heterozygous SNPs (hSNPs) is the source of information for determining monoallelic or biallelic expression. Each hSNP, in every cell, that is supported by expression evidence above a predetermined threshold is considered an informative SNP (iSNP) (see Methods, Additional file 1: Text). The sum of iSNPs per gene defines its unique label as inactivated or escapee gene (see Methods, Fig. 1b-c, Additional file 1: Text).
Quantifying biallelic expression from single cell primary fibroblasts
We analyzed the published scRNA-Seq data from female primary human fibroblasts . Before analysis, we took care of an experimental pitfall relevant to many single-cell studies. The pitfall concerns cell doubles in which more than one cell is sequenced per one library. In such a scenario, different active X-chromosomes (Xa) from two different cells that are included in the sample will produce a biallelic signal along the entire X-chromosome. While the fraction of doublets is expected to be small, it may lead to wrong interpretation . Therefore, before analyzing the data from the fibroblasts, we revisited all 104 fibroblasts and tested their biallelic ratio with respect to ChrX (see Methods). Three cells showed an exceptionally high degree of biallelic expression that might indicate a mixture of two parental X-chromosomes (Additional file 1: Text and Figure S3). We removed all three suspicious cells from all the analyses.
Next, for every single cell, we counted the number of reads that were uniquely mapped to hSNP alleles. The allelic ratio (AR) for each iSNP is defined as the fraction of the reads mapped to the alternative allele (Alt) out of the total reads (see Methods, Additional file 2: Table S1). Figure 2a-c summarizes the AR of ChrX, Chr17, and the entire autosomal chromosomes according to the primary fibroblasts collection (101 out of 104 cells). In addition, Fig. 2d shows the distribution of the AR of an annotated set of imprinted genes from skin tissues (according to ). As previously reported [30, 31], a bias in mapping towards the reference genome (AR = 0) is evident (Fig. 2a-d). Additionally, a substantial fraction of monoallelic expression was observed for all tested sets (Fig. 2a-d). This dominant appearance of monoallelic expression in single cells is caused by a combination of both under-sampling of transcripts, and a phenomenon that is known as “transcriptional bursting” [28, 32,33,34].
We focused only on iSNPs that show a non-monoallelic signature (i.e., excluding AR = 0 and AR = 1). We observed a marked difference in the AR distribution of ChrX and imprinted genes relative to Chr17 and all autosomal chromosomes (compare Fig. 2a to b and Fig. 2c to d). Accordingly, several observations from the results shown in Fig. 2 can be drawn: (i) Chr17 and all autosomes share a similar AR profile. (ii) A clear tendency towards balanced expression (AR = 0.5) is apparent for any autosomal chromosomes (Fig. 2a-b), but not ChrX or imprinted genes (Fig. 2c-d). (iii) The fraction of non-monoallelic expression in autosomal chromosomes is higher (~ 18%) relative to ChrX (~ 9%). (iv) The fraction of non-monoallelic expression in imprinted genes shows an intermediate level (13%). Such an intermediate level is probably a reflection of the inherent inconsistency in the identity of imprinted genes [35, 36]. Additional file 3: Table S2 lists the supportive iSNPs for all the analyzed chromosomes in fibroblasts including the imprinted gene set.
Identifying escapees in single cell primary fibroblasts
In the dataset of the primary fibroblasts, there are 232 and 485 genes that are supported by iSNPs evidence for ChrX and Chr17, respectively. As these cells lack information of genome phasing (Fig. 1b), information on escaping from X-chromosome is limited to the set of biallelic iSNPs (see Fig. 1b). We aggregated the iSNPs according to their corresponding genes (Fig. 1b). The aggregation is performed across different single cells and across multiple iSNPs within a specific cell-gene pair. A gene will be labeled escapee candidate when it is associated with multiple biallelic iSNPs. Altogether we identified 24 such genes (Table 1) which account for 10.3% of all expressed genes in ChrX. As expected, the fraction of genes on Chr17 showing biallelic expression is substantially higher (49.3%, Additional file 4: Table S3).
Table 1 lists the escapee candidates at varying degrees of support. For example, ZFX (Zinc finger X-chromosomal protein) and SMC1A (Structural maintenance of chromosomes protein 1A) genes are strongly supported with 103 and 19 biallelic iSNPs, respectively. A further increase in the reliability of identifying escapees is based on having at least 2 independent cells that contributed information on biallelic expression. We show that 21 out of 24 genes met this strict criterion (Table 1). Notably, among the identified escapees we detected only three PAR genes (SLC25A6, CD99, and DHRSX, Table 1). The assignment of these genes as escapees agrees with the expected PAR expression. From the number of biallelic PAR genes out of the expressed PAR genes, we estimated the false negative discovery rate for escapees to be as high as 70% (i.e., missed 7 of 10 expressed PAR genes). Additional file 4: Table S3 shows the support for Table 1.
Quantifying allelic expression from clonal phased lymphoblasts
A major limitation in the protocol described above concerns the lack of parental haplotype phasing. Under this setting, iSNPs cannot be assigned to Xa or Xi. Consequently, the random choice of Xi which characterizes primary cells limits the discovery rate for escapees. We expanded the analysis of scRNA-Seq to female-originated lymphoblasts from the clonal cell-line GM12878 . See Fig. 1c and Additional file 1: Figure S4.
Figure 3a shows the expression profile for clonal lymphoblast single cells (n = 25) (Additional file 2: Table S1, Additional file 5: Table S4). In any single cell, the monoallelic expression reflects the combination of an under-sampling of transcripts and the phenomenon of transcriptional bursting [28, 32, 33, 38]. It is clear that the maternal expression from the Xa dominates (Fig. 3a, top). An observation that agrees with the reported maternal Xa origin of cell-line GM12878 . In most of the analyzed cells, a small but substantial fraction of the measured total expression is from the paternal, Xi chromosome (Fig. 3a, top). In contrast, Chr17 and the autosomal chromosomes show an equal expression from both alleles (Fig. 3a, middle and bottom panels).
From the figure, it is evident that the phenomenon of transcriptional burst affects all chromosomes including ChrX. For assessing the impact of this phenomenon on identifying genes as escapees, we compared single cells with respect a pool of cells (Pool100, Fig. 3a, right bar). While the majority of the iSNPs from Chr17 display biallelic profiles, ChrX remains dominated by a maternal monoallelic expression.
Figure 3b is an aggregated view of ChrX, Chr17 and the autosomal chromosomes. The data are based on 375, 808 and 20,212 expressed hSNPs, respectively. Figure 3b (middle and right panels) shows an equal partition of the parental alleles from Chr17 and autosomal chromosomes (Fig. 3b, top). Performing the same analysis on data collected from Pool100 (Fig. 3c) shows that the partition of the parental alleles remains practically unchanged (compare the fraction occupied by pink and blue colors, Fig. 3b-c). Additionally, we observed a shift from a monoallelic (Fig. 3b-c, filled color) to a biallelic expression (Fig. 3b-c, stripped color). The fraction of the biallelic expression for Ch17 increased from 19% in single cells to 80% in Pool100, and for autosomal chromosomes from 18 to 79% (Fig. 3c, middle and right panels). The results from Pool100 indicate that the monoallelic expression observed in single cells is practically abolished by averaging the signal.
The results from ChrX (Fig. 3b (left) are fundamentally different relative to Chr17 or the autosomal chromosomes (Fig. 3b-c). The most notable difference is that only 21% of the expressed iSNPs are associated with the paternal Xi allele in ChrX (Fig. 3b, top). Furthermore, in analyzing Pool100, the fraction of biallelic expression remains bounded (a shift from 9% in single cells to 34% in Pool100). The observed pattern of ChrX from Pool100 (Fig. 3c, left) is best explained by an averaging of the stochastic monoallelic signal (at the same degree as the other chromosomes) while maintaining a strong signal of the Xa monoallelic expression. See Additional file 5: Table S4 for lymphoblasts allelic ratio of all tested chromosomes and the Pool100.
Identifying escapees from single cell lymphoblasts
Figure 4a is a gene-centric view that shows the iSNP allelic partition from lymphoblasts (colored according to their origin as maternal, paternal or mixed expression, see Methods). Only the subset of genes that are supported by multiple iSNPs is listed according to their ordered along the chromosomes. Altogether we report on 93 annotated genes on ChrX (Fig. 4a, 30 escapees and 63 inactivated genes). Note that the X-inactivated genes account for genes which are expressed primarily from the maternal Xa. A cluster of genes with a paternal expression at the tip of ChrX p-arm represents the expected biallelic expression from the PAR genes (Fig. 4a). Additional evidence for paternal expression is localized to the XIC with genes such as XIST, JPX, and FTX. While most of the escapees are supported by a limited number of iSNPs, a few of them such as ZFX, CD99, and SLC25A6 are supported by a relatively large number of supporting iSNPs (48, 38 and 34, respectively).
An alternative method for assessing the extent of the phenomenon of X-inactivation is by quantifying the evidence directly from the sum of all sequenced reads (abbreviated as the read-based protocol). Figure 4b-c compare read counts from Chr17 (Figs. 4b) and ChrX (Fig. 4c) by the paternal versus maternal origin. We compare the expression data from the single cells and the Pool100. The linear regression for the gene expression from Chr17 shows a high correlation fit-line (r2 = 0.823, Fig. 4b). As expected, the correlation is stronger in the data originated from the Pool100 (r2 = 0.946, Fig. 4b). We concluded that despite the monoallelic expression due to the transcriptional bursting phenomenon, balanced allelic expression of all genes is strongly supported. For ChrX however, the resulting linear regression of the single cells is poor (r2 = 0.238, Fig. 4c), and was not improved by the data from the Pool100 (r2 = 0.222, Fig. 4d). Inspecting the expression data for ChrX shows that the regression lines actually lean toward the maternal Xa expression (x-axis). The expression data are consistent with two distinct regression lines for ChrX. One that matches the inactivated genes (parallel to the x-axis), and the other matches a trustfully biallelic expression.
Applying the conservative iSNP-based protocol leads to the identification of 30 genes as escapee candidates that are also supported by the read-based protocol (Table 2). The read-based (i.e., labeling a gene as escapee by having a minimal number of paternal reads, see Methods) protocol expanded the escapee candidate list to include overall 49 genes (Additional file 4: Table S3).
Testing the parental origin of alleles along a gene in the same cell is a strict test for the reliability of the iSNPs. This test is only valid for genes with multiple hSNPs. Such genes that are supported with two or more expressing hSNPs account for 44% of the genes. We consider a gene to be consistent if the expression along the gene in a specific cell is not monoallelic to both alleles. Altogether, we identified 3 inconsistent genes - TEX11, FTX, and ZCCHC16. For another 6 genes, the inconsistency is only partial as there are other observations of biallelic expression. The estimate from full inconsistency (3 out of the 29 genes that were eligible for this test) suggests that an upper limit for a faulty interpretation of 10%. Additionally, the iSNP-based protocol identified 9 out the 11 expressed PAR genes. Thus, we extrapolate the escapee detection rate to be 82%. Interestingly, analyzing Chr17, under the assumption that there is no systematic allelic bias , showed that 7.3 and 9.6% of the genes were associated with maternal and paternal monoallelic expression, respectively. These results provide an upper limit of 17.9% to the likelihood of false gene labeling in Chr17 and can be used for estimating the limitation of the method.
Demanding paternal evidence from at least two cells reduces the number of escapees from 49 to 18 (including 5 PAR genes). Many single evidence genes resulting from a specific outlier cell (SRR764803) that contributed to 17 of the 49 reported genes (Table 2, Additional file 5: Table S4). This cell exhibits the highest expression levels on both ChrX and Chr17 (Additional file 1: Text and Figure S4). Table 2 lists the discovered escapee candidates along with their supporting evidence (Table 2).
Comparison of the identified escapees to current knowledge
We tested the correspondence between the identified escapees from our study and a literature-based catalog . This unified catalog was compiled from the integration of four independent studies that covers 1144 genes from ChrX. The genes in this catalog are manually partitioned into nine defined categories (see Methods). The largest one accounts for genes that lack information (45%) . About 15% of the genes (168/1144) are considered ‘escapee-associated’ (See Methods). We consider the compiled set of ‘escapee-associated’ genes as a gold standard to test escapee’s discovery rate in our study (total of 124 genes, excluded PAR genes, collectively called Balaton-Esc).
We applied a hypergeometric statistical test (see Methods) to assess the overlap of the different escapee gene lists (Fig. 5). Figure 5a shows the number of identified escapees from the fibroblasts and lymphoblasts (excluding PAR genes). Note that only genes that are included in the Balaton-Esc benchmark are included in this analysis (Fig. 5). Figure 5b shows the statistical significance of the overlap between the gene lists from Fig. 5a and the Balaton-Esc . As can be seen, there is a significant overlap between the escapees from lymphoblasts (Table 2 and Additional file 4: Table S3) and Balaton Esc list (Fig. 5b, p-value = 7.43E-8). Applying the same test for the primary fibroblasts (Table 1) resulted in a lower significance (p-value = 4.07E-2).
Figure 5c depicts the overlap genes between the escapees identified in our study and the Balaton-Esc catalog (168 genes including PAR genes) . We also included a complementary resource based on 940 transcriptomes from scRNA-Seq (25 escapee genes, Tukiainen-Esc) . The Venn diagram shows that each of the above studies contributes to the current knowledge on escapees. Escapees from the two external resources overlap by 18 out of 25 reported genes (72%). As shown in Fig. 5c, 62% of the escapees reported from the lymphoblasts overlap with the external escapee lists, while the fibroblasts are supported by only 38% overlap. Notably, most of our discovered candidate escapee genes from fibroblasts (62%) have no correspondence with the other tested lists (Additional file 6: Table S5).
LncRNAs extend the list of escapee candidates
We extended our analysis to long non-coding RNAs (lncRNAs) using the same escapee criteria as used from coding genes (Fig. 6). Altogether we identified 15 lncRNAs as escapee candidates, among them only a few were previously studied. The location of the lncRNAs and coding escapes along ChrX is shown (Fig. 6a). We tested the positions of escapees along the ChrX relative to all ChrX genes. While the positional distribution for lncRNA escapees is similar (Kolmogorov–Smirnov test, p-value = 0.57), it is different for coding escapees (Kolmogorov–Smirnov test, p-value = 0.004, Fig. 6a).
Figure 6b lists all 15 identified lncRNA escapee genes, among them, are ncRNA genes from the XIC that coordinate the activation and maintenance of X-inactivation. Many of the lncRNAs are localized at transcriptionally active segments (e.g., within the PAR or the XIC), while others are localized in non-conserved regions which are enriched with long and short ncRNAs. For additional lncRNAs, including inactivated genes see Additional file 7: Table S6.
Evidence-based partition of escapee genes
Figure 6c summarizes the partition according to the evidence for all genes that are reported with any levels of confidence as escapees (Additional file 6: Table S5). This list includes 75 candidates that are reported in this study, including evidence from Pool100, a collection of novel lncRNAs. Figure 6d provides a detailed list of the finding from this data in view of serval external resources. The number of cells providing evidence is also indicated for every gene.
Taking these external resources into account, we matched each gene according to the quality of the independent support associated with it (Fig. 6c and d). Specifically, we partitioned the 75 gene list to four groups: (i) Genes are labeled “confirmed” if they are reported as escapees by both previously discussed external resources [13, 16]. There are 31 such genes, for which this study provides further confirmation for their identity as escapees. (ii) Additional 7 genes are labeled “approved”. These genes are tagged as escapees by only one of the two external resources [13, 16]. For these genes, the independent evidence from this study approves their identity. (iii) Additional 12 genes are marked as “probable” escapees. These genes are assigned according to the agreement with an additional external report reporting on 114 escapees , which was not included in the literature-based catalog , and thus can be considered as an independent resource. (iv) Additional 25 genes are marked as “possible” escapees. These genes lack any literature evidence for supporting their identity, thus their assignment as escapees remains less supported. Among them 5 genes are supported only from the outlier cell from lymphoblast and are thus likely to be false. In this set, there are additional 10 genes that were not reported by any of the three discussed external resources [10, 13, 16], The majority of the overlooked lncRNAs belong to this group. Two of these overlooked escapee lncRNAs (TCONS_00017125, and TCONS_00017281, Fig. 6b) are located at the PAR region that is exceptionally active in biallelic transcription. The source data for Fig. 6d is in Additional file 6: Table S5.
The goal of this study was to identify escapees by analyzing gene expression from somatic single cells. Transcription at heterozygous SNPs (hSNPs) is the source of information for determining ASE and consequently identifying escapees. We analyzed two female origin resources, fibroblasts (104 cells) without haplotype phasing, and a smaller set of 25 lymphoblasts (GM12878 cell line) with fully phased and sequenced parental diploid genomes. For the lymphoblast cells, the sequence of the diploid genome is available with a reference for the paternal and maternal haplotypes . Using the diploid genome as a reference, we assigned the expression of sequenced reads covering hSNPs to either Xi or Xa. The availability of both parental haplotypes eliminates the biases that are associated with a single reference genome [40, 41]. In addition to the benefit of having two reference haplotypes, data from clonal cells drastically reduce the cell to cell gene expression variability.
The methodology for escapee discovery dependents on extracting the ASE signal in single cells. Within single cells, a monoallelic expression caused by under-sampling of transcripts, and “transcriptional bursting” [28, 32,33,34] is dominant in all chromosomes. Such transcription bursts reflect the stochastic nature of gene expression in single cells . Therefore, throughout this study, we compared the ASE signal from ChrX with the other autosomal chromosomes.
The accuracy of the ASE signal is critical for a reliable escapee annotation. One possible source of misleading ASE signal is the presence of cell doublets in scRNA-Seq. Even a small number of doublets might lead to a faulty assignment of genes as escapees. We identified ~ 3% of fibroblasts as suspicious cells and removed them from the analysis. Another source of faulty assignment might originate from genomic DNA or cell-free RNA contaminations, that occurred during library preparation. We observed many genes that are both highly expressed and entirely monoallelic, indicating a strong X-inactivation signal. Notably, even a minute contamination of genomic DNA or cell-free RNA would result in a biallelic expression that could be detected throughout ChrX. Based on our observations, we disregard this possibility for both datasets.
Despite the limited number of cells analyzed in this study, we report on a relatively large number of escapee candidates. Importantly, we observed that one of the lymphoblasts (SRR764803) exhibits an exceptionally high expression from the ChrX and Chr17 and high proportion of paternal evidence (See Additional file 1: Text and Figure S4). This specific cell contributed to ~ 60% of total reported escapee candidate from lymphoblasts (29 out of 49 genes). While several of these genes have no additional support (marked in Table 2), the majority of these genes are supported by at least one external resource of escapes (Additional file 6: Table S5). We postulate that this outlier represents a cell that underwent partial relaxation from X-inactivation, as reported for cells in culture .
Our methodology included mapping of the scRNA-Seq to the entire genome, thus enabling the discovery of both coding and non-coding genes. We report on 15 lncRNA genes, many of them were overlooked by previous escapee discovery protocols. The overall unified gene list from our study extends the observations derived from a larger set of single cells  as well as from escapee catalog based on a literature compilation from four large-scale studies .
By considering the literature based catelog as a benchmark for escapees  enables us to roughly estimate the upper bound for false positives rates of both datasets. Solely based on these comparisons, we estimate that the fibroblasts false positive rate is bounded to 52%, while for the lymphoblasts, it is bounded to 30%. This is, of course, an upper bound limit since it is possible that not all escapee genes are included in the Balaton catalog . Notably, most studies have used lymphoblasts and not fibroblasts for the compilation of the Balaton-Esc catalog (See Methods).
To assess the annotation of genes as escapees, we compared our results to three independent resources [10, 13, 16]. The largest group from our identified set (31 ‘confirm’ escapees) fully agrees on the annotation provided in [13, 16] (Fig. 6). Among the 31 genes, 9 are PAR genes and the rest are repeatedly established escapees (Fig. 6). We will not further discuss this validated gene set.
The gene groups that are marked as ‘approved’ (7 genes) and ‘probable’ escapees (12 genes, Fig. 6) are reported by some external resources but not others, indicating inconsistency in their assignment. Our findings help to resolve some of these conflicts (total 19 genes). Two of the genes (XIST and FTX) belong to XIC and are critical to the X-inactivation process [3, 42] and have conflicting annotations. The strongest expressed gene from the Xi in lymphoblasts is XIST as expected from its X-inactivation role (Additional file 4: Table S3). XIST was annotated by Balaton-Esc catalog as an inactivated gene . We caution that this annotation is potentially misleading as Xist is inactivated in antiphase to all other X-inactivated genes, and thus requires its own separate category. We also have monitored FTX expression from Xi from both our datasets, in agreement with evidence from . We proposed to revise the annotations for these genes.
The two external resources [13, 16] indicated XIAP as inactivated. In contrast, we have identified it in both fibroblasts and lymphoblasts as a true escapee (Fig. 6d), which was further supported in . Interestingly, inconsistency in the annotation of XIAP was noted in individuals from different ethnic groups . The following ‘approved’ escapees (TBL1X, TRAPPC2, ZFX, DIAPH2) were listed in the Balaton-Esc and were also supported by . Based on our supportive evidence, such partial inconsistency is resolved.
The 12 ‘probable’ escapees corroborate findings from  in which lymphoblasts from different ethnic origin were tested by analyzing their expressed hSNPs. Our results support the findings for all 12 ‘probable’ escapees (TMSB4X, DMD, RBM3, MAGED2, FAM104B, FAAH2, KIF4A, BRWD3, RBM41, LAMP2, XIAP, and MAP 7D3). While most of these genes have a substantial support BRWD3, PRKX and XIAP are marked as ‘evidence poor’ (Tables 1 and 2). Nevertheless, a comparison to a study of Klinefelter syndrome (47, XXY) individuals from the Danish population reveals some interesting compatibilities. The individuals in that study were tested for sex biases from ChrX. It was found that 16 genes were overexpressed. Among them, XIST, numerous PAR genes as well as a few others that were confirmed in this study as escapees (EIF2S3, PRKX) . Specifically, TMSB4X (a ‘probable’ escapee) was associated with ChrX dosage effect in Klinefelter syndrome, presumably due to its escapee characteristics.
Multiple studies estimated that escapees occupy 15–25% of all ChrX genes in humans . Only 4.5% of all known ChrX genes are unquestionable escapees that are consistently detected by complementary methods and are considered ‘constitutive escapees’ according to . Within our two cell types there are 5 escapee genes (DDX3X, ZFX, SMC1A, JPX, and XIAP) in common, which confirm their strong tendency to escape X-inactivation (Tables 1 and 2). Four of these genes (ZFX, JPX, SMC1A, and DDX3X) were also reported by others as constitutive escapees. These constitutive escapees act within the nuclei in transcriptional regulation (DDX3X and ZFX) and in chromosomal dynamics (SMC1A and JPX). We propose that XIAP belongs to this small set of escapees showing a robust characteristic across different cells. In a study on a large population of Klinefelter syndrome individuals, XIAP was identified among the few ChrX overexpressed genes .
Having a robust and reliable list of escapees is critical in understanding sex-dependent phenotypic variability. In addition, the profile of escapees may explain the clinical severity of individuals with X-chromosome aneuploidy (e.g., Turner (XO) and Klinefelter (XXY) syndrome) [43, 44]. The expression level of certain escapee genes was also attributed to a number of sexually dimorphic diseases (e.g., cancer) , autoimmunity  and more. Therefore, our results refine the list of the relevant escapees and expand it.
We show that X-inactivation and escape from it can be successfully studied from single cells, including primary, untransformed cells. The cumulative information from individual cells increases the potential of identifying escapees and inactivated genes. Moreover, clonal cells with genomic phasing are valuable for detecting escapees at high confidence. Generalizing the ASE based method to uncharacterized genomic loci resulted in a complete report on lncRNAs escapees. We affirm that even with a modest number of analyzed cells, the cumulative knowledge on escapees was reproduced, extended and numerous conflicting findings were resolved.
The pipeline for escapee identification using allele quantification was performed over two independent single cell datasets: (i) A collection of 104 cells of primary fibroblasts from a female newborn (UCF_1014). Additional file 2: Table S1 shows the quantitative information associated with the analyzed 104 primary fibroblasts; (ii) A collection of 25 single cells of clonal GM12878 lymphoblast from female (NA12878, Additional file 2: Table S1). For GM12878 cells, the pipeline was also applied to a pool of 100 cells (Pool100).
The analysis of both datasets was performed for ChrX, Chr17 and all autosomal chromosomes. For each of the datasets, we used scRNA-Seq or RNA-Seq for Pool100 (see Additional file 1: Text). From additional genomics data, extensive lists of all available heterozygous SNPs (hSNPs) for the two datasets were compiled. These lists are candidate sites for informative allelic expression. A detailed description of the pipeline protocols is available in Additional file 1: Text and Figure S1 that provides a scheme of the workflow for identifying escapees from scRNA-Seq data.
A dataset for scRNA-Seq of fibroblast UCF1014 was downloaded from the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home) using accession number EGAD00001001083. The data was produced by Borel et al. . DNA-seq of UCF1014 was also downloaded from EGAD00001001084. The quality of the NGS data from scRNA-Seq in term of sequencing errors, PCR biases, and mapping errors was thoroughly discussed .
A dataset for scRNA-Seq of GM12878 cell-line from female lymphoblast NA12878 along with Pool100 was downloaded from the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo) using the accession number GSE44618 . The NA12878 phased genome was downloaded from http://sv.gersteinlab.org/NA12878_diploid/NA12878_diploid_2012_dec16/NA12878_diploid_genome_2012_dec16.zip. The SNP locations were downloaded from: http://sv.gersteinlab.org/NA12878_diploid/NA12878_diploid_2012_dec16/CEUTrio.HiSeq.WGS.b37.bestPractices.phased.hg19.vcf.gz.
For additional information and detailed protocols, see Additional file 1: Text.
RNA-Seq from single cells (scRNA-Seq) and pooled cells were aligned to a relevant reference genome. Datasets of scRNA-Seq from fibroblasts were aligned against UCSC hg19 reference genome after removal of the Y chromosome. The lymphoid scRNA-Seq and RNA-seq are aligned against the Paternal and Maternal haplotypes of the phased diploid NA12878 genome at the hg19 format. Reads aligned to hSNPs were counted such that each of the reads is resigned to one of the two alleles. Each hSNP, expressed in every single cell with a minimal number of 7 aligned reads was considered an informative SNP (iSNP). For details see Additional file 1: Text. Quantitative values of the number of iSNPs for each cell of the 104 primary fibroblasts are shown in Additional file 1: Figure S2 and Additional file 2: Table S1. Quantitative values of the number of iSNPs for each cell of 25 single cell clonal lymphoblasts are in Additional file 1: Figure S4 and Additional file 2: Table S1.
Quantification and annotation of alleles by expression
For the primary fibroblasts without phased haplotype data, the Allelic Ratio (AR) of each iSNP is determined as follows:
Where c indicates a cell and i indicates an iSNP. #Alt and #Ref refer to the number of reads aligned to the alternative and the reference allele, respectively.
For the lymphoblasts that are associated with a phased genome, the Allelic Ratio (AR) of each iSNP is determined as follows:
Where c indicates a cell and i indicates an iSNP. #Pat and #Mat refer to the number of reads aligned to the paternal and maternal allele, respectively.
An iSNP with AR of <=0.1 or AR > 0.9 is considered monoallelic and an iSNP associated with 0.1 < AR ≤ 0.9 is considered biallelic.
For the fibroblasts data, tagging a specific iSNP as escapee relies on annotating multiple iSNPs of a gene as biallelic. For the clonal lymphoblasts data, the paternal ChrX is associated with Xi. Therefore, iSNPs with paternal or biallelic AR (expression from the paternal ChrX) are indicative of an escapee gene. An additional protocol for identifying escapees from the clonal single cell lymphoblasts is based on accumulating reads across cells with an escapee criterion of a minimal number of 7 paternal reads per gene. Lymphoblast Pool100 cells were quantified such that in each gene, the sum of hSNP reads with a paternal expression ratio of AR > 0.1 indicates an escapee gene. For further details, the quantitative values of the resources, and the NGS tools that were used see Additional file 1: Text (Sections 1–1.4).
Cell outliers according to extreme biallelic expression
A possible cause of faulty interpretation for biallelic expression of a gene can originate from a single cell experiment that accidentally included two (or more) cells instead of one cell. Therefore, if for a specific cell, the proportion of biallelic expression shows an extreme value, the data associated from that cell could potentially be explained by having a mixture of two cells expressing two different Xi (coined a doublet). We have calculated for each cell the biallelic ratio as biallelic ratio = biallelic iSNPs/total iSNPs. Following this investigation, we have excluded three single cells which had a biallelic ratio of over 3 standard deviations from the average of the entire cells. After filtering these cells outliers, 101 single cells remained for further analyses.
As the lymphoblasts were handpicked. No such procedure was needed.
A comparison to annotated ChrX gene catalog
There are 1144 known genes in ChrX that were annotated and compiled into a comprehensive catalog . All genes were annotated and partitioned into 9 categories: (i) PAR, (ii) escapee, (iii) mostly-escapee, (iv) variable-escapee, (v) mostly-variable-escapee, (vi) discordant, (vii) inactivated genes, (viii) mostly-inactivated, and (ix) genes having no data. The set of criteria for assigning a gene to any of these categories was described in . Annotation for the 9 categories is based on a careful analysis according to major publications combining numerous measurements for genes expression characteristics of ChrX . The reports used for compiling the catalog were from four major resources extracted from [13, 17, 20, 46].
From the genes on ChrX, an “escapee associated” list was compiled from the 6 first categories (i-vi) associated with escapees, including PAR and discordant genes. This unified set corresponds to 15% of the genes, the rest are divided between inactivated-related categories (40%) and genes that are are annotated as “no data” (45%).
Hypergeometric probability between our results and the external annotated catalog was calculated by comparing the correspondence of any two lists of escapees. We used standard notations of N, k, n and x: N symbolizes all genes on ChrX from  with a label other than “No data”; k is the number of escapees by  which are associated with any escapee annotations (i.e. the escapee-associated); n is the number of escapees we identified by any of the settings from our protocols; x the number of genes in our list that match the literature-based escapee list in k. P(x) is the probability that an n-trial will result in a value that is > x. See Additional file 1: Text (Section 1.5).
Additional datasets for annotated escapees
Lists of the annotated escapees according to Tukiainen et al.  and Zhang et al.  were downloaded from the publication supplemental materials. Accordingly, the status of escapee genes was analyzed across the external resources reported [10, 13, 16]. See Additional file 1: Text (Section 1.6).
Allele specific expression
Long non-coding RNA
Next generation sequencing
NGS for RNA sequencing
Single nucleotide polymorphism
X inactivation center
Lee JT. Gracefully ageing at 50, X-chromosome inactivation becomes a paradigm for RNA and chromatin control. Nat Rev Mol Cell Biol. 2011;12(12):815–26.
Petropoulos S, Edsgard D, Reinius B, Deng Q, Panula SP, Codeluppi S, Plaza Reyes A, Linnarsson S, Sandberg R, Lanner F. Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell. 2016;165(4):1012–26.
Lee JT, Bartolomei MS. X-inactivation, imprinting, and long noncoding RNAs in health and disease. Cell. 2013;152(6):1308–23.
Galupa R, Heard E. X-chromosome inactivation: new insights into cis and trans regulation. Curr Opin Genet Dev. 2015;31:57–66.
Heard E. Recent advances in X-chromosome inactivation. Curr Opin Cell Biol. 2004;16(3):247–55.
Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, Kadri S, Xing J, Goren A, Lander ES, et al. The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science. 2013;341(6147):1237973.
Balaton BP, Dixon-McDougall T, Peeters SB, Brown CJ. The eXceptional nature of the X chromosome. Hum Mol Genet. 2018;27(R2):R242–9.
Moindrot B, Brockdorff N. RNA binding proteins implicated in Xist-mediated chromosome silencing. Seminars in Cell & Developmental Biology. 2016;56:58–70.
Raudsepp T, Chowdhary BP. The eutherian Pseudoautosomal region. Cytogenet Genome Res. 2015;147(2–3):81–94.
Zhang Y, Castillo-Morales A, Jiang M, Zhu Y, Hu L, Urrutia AO, Kong X, Hurst LD. 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(12):2588–601.
Berletch JB, Yang F, Disteche CM. Escape from X inactivation in mice and humans. Genome Biol. 2010;11(6):213.
Peeters SB, Cotton AM, Brown CJ. Variable escape from X-chromosome inactivation: identifying factors that tip the scales towards expression. Bioessays. 2014;36(8):746–56.
Balaton BP, Cotton AM, Brown CJ. Derivation of consensus inactivation status for X-linked genes from genome-wide studies. Biol Sex Differ. 2015;6:35.
Talebizadeh Z, Simon SD, Butler MG. X chromosome gene expression in human tissues: male and female comparisons. Genomics. 2006;88(6):675–81.
Yasukochi Y, Maruyama O, Mahajan MC, Padden C, Euskirchen GM, Schulz V, Hirakawa H, Kuhara S, Pan XH, Newburger PE, et al. X chromosome-wide analyses of genomic DNA methylation states and gene expression in male and female neutrophils. Proc Natl Acad Sci U S A. 2010;107(8):3704–9.
Tukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, Gauthier L, Fleharty M, Kirby A, et al. Landscape of X chromosome inactivation across human tissues. Nature. 2017;550(7675):244–8.
Carrel L, Willard HF. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature. 2005;434(7031):400–4.
Cotton AM, Lam L, Affleck JG, Wilson IM, Penaherrera MS, McFadden DE, Kobor MS, Lam WL, Robinson WP, Brown CJ. Chromosome-wide DNA methylation analysis predicts human tissue-specific X inactivation. Hum Genet. 2011;130(2):187–201.
Sharp AJ, Stathaki E, Migliavacca E, Brahmachary M, Montgomery SB, Dupre Y, Antonarakis SE. DNA methylation profiles of human active and inactive X chromosomes. Genome Res. 2011;21(10):1592–600.
Cotton AM, Ge B, Light N, Adoue V, Pastinen T, Brown CJ. Analysis of expressed SNPs identifies variable extents of expression from the human inactive X chromosome. Genome Biol. 2013;14(11):R122.
Ross MT, Grafham DV, Coffey AJ, Scherer S, McLay K, Muzny D, Platzer M, Howell GR, Burrows C, Bird CP, et al. The DNA sequence of the human X chromosome. Nature. 2005;434(7031):325–37.
Reik W, Lewis A. Co-evolution of X-chromosome inactivation and imprinting in mammals. Nat Rev Genet. 2005;6(5):403–10.
Peeters SB, Korecki AJ, Simpson EM, Brown CJ. Human cis-acting elements regulating escape from X-chromosome inactivation function in mouse. Hum Mol Genet. 2018;27(7):1252–62.
Balaton BP, Brown CJ. Escape artists of the X chromosome. Trends Genet. 2016;32(6):348–59.
Berletch JB, Ma W, Yang F, Shendure J, Noble WS, Disteche CM, Deng X. Escape from X inactivation varies in mouse tissues. PLoS Genet. 2015;11(3):e1005079.
Dunford A, Weinstock DM, Savova V, Schumacher SE, Cleary JP, Yoda A, Sullivan TJ, Hess JM, Gimelbrant AA, Beroukhim R, et al. Tumor-suppressor genes that escape from X-inactivation contribute to cancer sex bias. Nat Genet. 2017;49(1):10–6.
Baran Y, Subramaniam M, Biton A, Tukiainen T, Tsang EK, Rivas MA, Pirinen M, Gutierrez-Arcelus M, Smith KS, Kukurba KR, et al. The landscape of genomic imprinting across diverse adult human tissues. Genome Res. 2015;25(7):927–36.
Borel C, Ferreira PG, Santoni F, Delaneau O, Fort A, Popadin KY, Garieri M, Falconnet E, Ribaux P, Guipponi M, et al. Biased allelic expression in human primary fibroblast single cells. Am J Hum Genet. 2015;96(1):70–80.
Macaulay IC, Ponting CP, Voet T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 2017;33(2):155–68.
Panousis NI, Gutierrez-Arcelus M, Dermitzakis ET, Lappalainen T. Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies. Genome Biol. 2014;15(9):467.
Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009;25(24):3207–12.
Kim JK, Marioni JC. Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol. 2013;14(1):R7.
Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, Simpson ML, Weinberger LS. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proc Natl Acad Sci U S A. 2012;109(43):17454–9.
Deng Q, Ramskold D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343(6167):193–6.
Babak T, DeVeale B, Tsang EK, Zhou Y, Li X, Smith KS, Kukurba KR, Zhang R, Li JB, van der Kooy D, et al. Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse. Nat Genet. 2015;47(5):544–9.
Barlow DP, Bartolomei MS. Genomic imprinting in mammals. Cold Spring Harb Perspect Biol. 2014;6:8382.
Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, Wold BJ. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24(3):496–510.
Reinius B, Sandberg R. Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation. Nat Rev Genet. 2015;16(11):653–64.
Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011;7:522.
Satya RV, Zavaljevski N, Reifman J. A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res. 2012;40(16):e127.
Chen J, Rozowsky J, Galeev TR, Harmanci A, Kitchen R, Bedford J, Abyzov A, Kong Y, Regan L, Gerstein M. A uniform survey of allele-specific binding and expression over 1000-genomes-project individuals. Nat Commun. 2016;7:11101.
Wang J, Syrett CM, Kramer MC, Basu A, Atchison ML, Anguera MC. Unusual maintenance of X chromosome inactivation predisposes female lymphocytes for increased expression from the inactive X. Proc Natl Acad Sci U S A. 2016;113(14):E2029–38.
Belling K, Russo F, Jensen AB, Dalgaard MD, Westergaard D, Rajpert-De Meyts E, Skakkebaek NE, Juul A, Brunak S. Klinefelter syndrome comorbidities linked to increased X chromosome gene dosage and altered protein interactome activity. Hum Mol Genet. 2017;26(7):1219–29.
Fisher EM, Beer-Romero P, Brown LG, Ridley A, McNeil JA, Lawrence JB, Willard HF, Bieber FR, Page DC. Homologous ribosomal protein genes on the human X and Y chromosomes: escape from X inactivation and possible implications for turner syndrome. Cell. 1990;63(6):1205–18.
Selmi C, Brunetta E, Raimondo MG, Meroni PL. The X chromosome and the sex ratio of autoimmunity. Autoimmun Rev. 2012;11(6–7):A531–7.
Cotton AM, Price EM, Jones MJ, Balaton BP, Kobor MS, Brown CJ. Landscape of DNA methylation on the X chromosome reflects CpG density, functional chromatin state and X-chromosome inactivation. Hum Mol Genet. 2015;24(6):1528–39.
We thank Nati Linial for useful discussions and critical comments. We thank Liran Carmel, Shahar Shohat and Michal Linial’s lab members for critical reading of the manuscript. We thank Yuval Nevo and the CSE system for technical assistance. KWK is a recipient of the Ariane de Rothschild Scholarship. This study used best practice protocols that are compiled in EU Elixir collection.
KWK is a recipient of the Ariane de Rothschild Fellowship.
Availability of data and materials
All data generated or analyzed during this study are included in this published article as supplementary material. The datasets are available according to the following sources:
Fibroblasts UCF_1014 DNA-seq from European Genome-phenome Archive respiratory dataset EGAD00001001083 (https://www.ebi.ac.uk/ega/datasets/EGAD00001001083) Fibroblasts Single cells RNA-Seq from European Genome-phenome Archive respiratory dataset EGAD00001001084 (https://www.ebi.ac.uk/ega/datasets/EGAD00001001084).
Lymphoid genome of NA12878 from Gerstein Lab, Yale University, http://sv.gersteinlab.org/NA12878_diploid/NA12878_diploid_2012_dec16/NA12878_diploid_genome_2012_dec16.zip.
Lymphoid SNPs from Gerstein Lab, Yale University, http://sv.gersteinlab.org/NA12878_diploid/NA12878_diploid_2012_dec16/CEUTrio.HiSeq.WGS.b37.bestPractices.phased.hg19.vcf.gz.
Lymphoid GM12878 single and pooled cells RNA-Seq from Gene Expression Omnibus, at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44618.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
1.1–1.6 Detailed methods and protocols. Figure S1. A workflow for identifying escapee genes from single cells RNA-Seq data. Figure S2. Informative SNPs of 104 single cell primary fibroblasts. Figure S3. The Biallelic ratio of 104 fibroblasts. Figure S4. Informative SNPs on 25 clonal female lymphoblasts (DOCX 1447 kb)
Table S1. Quantitative data of primary fibroblast UCF_1014 cells and Identifiers and quantitative data of RNA-Seq for 25 lymphoid single cells and Pool100 (XLSX 18 kb)
Table S2. A list of informative SNPs (iSNPs) along with their labeling on ChrX, Chr17, all autosomal chromosomes and imprinted genes from 101 fibroblast single cells. (XLSX 60374 kb)
Table S3. Gene-centric tagging determined by iSNPs and a read-based method for the Fibroblast, Lymphoblast and Pool100 datasets on ChrX, Chr17 and all autosomal chromosomes. (XLSX 2939 kb)
Table S4. A list of informative SNPs (iSNPs) along with their labeling on ChrX, Chr17 and autosomes from single cell lymphoblasts and Pool100 lymphoblasts. (XLSX 5482 kb)
Table S5. Table comparing annotation of all 75 ChrX escape candidates. (XLSX 17 kb)
Table S6. LncRNAs informative SNPs (iSNPs) along with their labeling on ChrX on both fibroblasts, and Lymphoblasts. (XLSX 70 kb)
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Cite this article
Wainer Katsir, K., Linial, M. Human genes escaping X-inactivation revealed by single cell expression data. BMC Genomics 20, 201 (2019) doi:10.1186/s12864-019-5507-6
- Allelic bias
- Single cell
- Allele specific expression