Dynamic allele usage of X-linked genes ameliorates neurodevelopmental disease phenotypes in brain organoids

Domain: chemistry
Published: 2026-01-14
Editorial outcome: major_revision

Abstract
While random X-chromosome inactivation in female cells of placental mammals silences one allele of the majority of X-chromosomal genes, a considerable fraction is only incompletely and variably inactivated. Human model systems to study the dynamics of incomplete X-inactivation are limited mostly to post mortem tissue, thereby disregarding developmental trajectories. Here, we used clonal human female induced pluripotent stem cells to track allele-specific expression of X-chromosomal genes along neural differentiation. We discovered dynamic reactivation and late-silencing of gene expression from the inactive X-chromosome leading to differentiation-induced locus- and lineage-specific usage of the two X-chromosomal alleles. In brain organoids modeling Opitz BBB/G syndrome, an X-linked neurodevelopmental disorder, reactivation of alleles from the inactive X-chromosome rescued cellular phenotypes and led to intermediate manifestations in female tissue. Taken together, our data demonstrate that alleles on the inactive X-chromosome can serve as a critical reservoir dynamically used during differentiation, thereby enhancing resilience of female neural tissue.

Introduction
In somatic cells of female eutherian mammals, one of the two X-chromosomes is randomly inactivated early during embryonic development leading to a silent gene reservoir1–3. However, a significant proportion of genes on the inactive X-chromosome have been found to escape the inactivation process4–6. In addition to constitutive escapees, which escape X-chromosome inactivation (XCI) in all cells and tissues, a similar number of genes escape inactivation facultatively. Facultative escape entails biallelic expression that is observed only in selected tissues and is heterogenous between individuals7–9. Both XCI as well as escape show considerable differences in timing and underlying mechanisms between eutherian mammals2,10–12, pointing towards distinct regulatory principles and highlighting the importance of species-specific model systems. Escape from XCI results in biallelic expression and frequently in a female expression bias, i.e., higher expression levels of the same gene in female compared to the corresponding male tissue8,13. However, there is little experimental data interrogating the developmental timeline of XCI, escape, and the establishment of facultative escape in human cells. Taking advantage of the clonal nature and the retention of the X-chromosome activation status in human induced pluripotent stem cells (iPSCs)14, we here monitored XCI and escape throughout neural differentiation from a defined point-of-origin. We observed dynamic reactivation and late-silencing of alleles on the inactive X-chromosome induced by differentiation. Furthermore, we used publicly available single cell RNA sequencing data from spinal cord of human embryos at two different developmental stages to confirm a dynamic time-and cell-type specific usage of gene expression from the inactive X-chromosome in vivo. Strikingly, many of these reactivating genes are linked to neurodevelopmental disorders (NDDs).

NDDs exhibit an intriguing sex bias with females being less often and less severely affected than males15. While NDD associated genes are evenly distributed across all autosomes, they are overrepresented on the X-chromosome16, highlighting the particularly important role of the X-chromosome in the phenotype formation of NDDs. In males, most X-linked genes are hemizygous, i.e., males only carry the allele inherited from the mother. In contrast, random XCI in female cells results in a mosaic expression of alleles from the two X-chromosomes in female tissues, substantially influencing phenotype development of X-linked gene defects. However, hemizygosity of X-linked genes in males and random XCI in females only partially explain why females are disproportionately less affected by NDDs17–19. We hypothesized that dynamic X-chromosome reactivation provides a protective mechanism in female neural tissue, reducing the frequency and severity of NDDs in females. To test this hypothesis, we employed an iPSC-based brain organoid model of Opitz BBB/G syndrome (OS), a clinically variable NDD characterized by cerebellar vermis hypoplasia, microcephaly, ventral midline aberration, developmental delay, and intellectual disability20–23. OS is caused by mutations in the X-linked MID1 gene, which we found dynamically reactivated during neural differentiation. While brain organoids derived from iPSC of a male OS patient exhibited a dramatic decrease of differentiated neurons accompanied by a delayed cell cycle exit of neural stem and progenitor cells, we found that organoids derived from female cells with the same mutation on their active X-chromosome showed a markedly milder, intermediate phenotype. To interrogate the influence of the allele on the inactive X-chromosome, we employed CRISPR/Cas9 to introduce the same patient-specific loss-of-function mutation on both the active and inactive X-chromosome. Strikingly, brain organoids derived from these homozygous OS female iPSC lines exhibited a phenotype similar to the hemizygous male organoids. This demonstrates that the allele on the inactive X-chromosome substantially influences phenotype development.

Taken together, we here dissected the temporal usage of the two X-chromosomes along neural differentiation trajectories in brain organoids and human embryos and thereby uncloaked dynamic reactivation and late silencing of individual alleles on the inactive X-chromosome as a mechanistic basis for facultative escape. Furthermore, we showed that this fine-tuned symphony of dynamic biallelic X-chromosomal gene expression has substantial protective effects rescuing cellular phenotypes of OS and contributes to resilience of female neural tissue.

Results
Reactivation of X-linked genes during neural differentiation
To trace the X-activation status along differentiation trajectories, we took advantage of the clonal nature of human iPSCs which inactivate the same X-chromosome as the cell of origin14. Female fibroblasts were reprogrammed into iPSCs and upon clonal selection we determined which X-chromosome is active in which clone by using Quantification of Allele-Specific Expression by Pyrosequencing (QUASEP) assays (Fig. S1a–d, Supplementary Data 1). This technique measures allelic expression of selected genes based on heterozygous single nucleotide polymorphisms (SNPs) and enables distinguishing the two X-chromosomes. Establishing this defined point-of-origin in iPSCs allowed us to follow individual allele usage along the differentiation axis. Using bulk RNA-sequencing of iPSCs, neural stem and progenitor cells (NPCs), and neurons (Fig. 1a) of three distinct control (ctrl) cell lines from three different donors (M-ctrl, J-ctrl and A-ctrl, Fig. S1a and k, Supplementary Data 1), we performed a global characterization of the allele usage of X-linked genes leveraging the presence of expressed heterozygous sequence variants covered by at least 20 reads (see Methods). Principal component analysis of X-chromosomal allelic expression profiles showed a clear distinction between iPSCs, NPCs, and neurons (Fig. S1e). Surprisingly, we identified a set of genes that were monoallelically expressed in iPSCs but exhibited differentiation-dependent reactivation (reactivating genes) resulting in biallelic expression in either NPCs, neurons, or both cell types (Fig. 1b, Fig. S1f). Although this allele usage analysis depends on the presence of expressed heterozygous sequence variants that vary between donors and cell lines, still a striking number of 22 genes were reactivated in at least two of the cell lines used. Furthermore, we detected full escapees that were expressed biallelically in iPSCs, NPCs, and neurons with 35 genes found escaping in at least two cell lines (Fig. 1c, Fig. S1g). Finally, a group of genes (5 in at least two of the cell lines) switched from biallelic expression in iPSCs to monoallelic expression in NPCs and neurons (late-silenced genes, Fig. 1d, Fig. S1h). To ensure that detection of biallelic expression is independent of the overall expression level of a gene, we correlated mRNA-expression (transcripts per million) with allelic expression (Xi/total allelic expression) for all genes together (Fig. S1i) and for individual genes (Fig. S1j). Importantly, reactivated and late-silenced genes showed no significant correlation between overall gene expression levels and allelic expression, confirming that mono-allelic expression for reactivated and late-silencing genes was not spuriously detected due to lack of overall gene expression in the respective cell types (Fig. 1b, c, d heatmaps and Fig. S1i, j). To validate the dynamic allelic usage from the inactive X-chromosome, we employed QUASEP assays. While CA5B, a gene known to constitutively escape XCI24, was expressed biallelically in all tested cells, including iPSCs, ZNF185 showed purely monoallelic expression throughout neuronal differentiation (Fig. S2a, b). In contrast MID1 and GPM6B, two genes located in close vicinity on the short arm of the X-chromosome, were reactivated in NPCs and neurons indicating locus-specific reactivation (Fig. S2c–g). These data confirmed that, apart from purely monoallelic genes and full escapees, additional categories of X-chromosomal genes exist with dynamic expression from the inactive X-chromosome during neural differentiation thereby resulting in facultative escape in our model.

Loss of the lncRNA XIST and consequently erosion of X-inactivation is a known phenomenon in iPSCs occurring mainly in higher cell culture passages25–27. In the bulk RNA-sequencing analysis, the A-ctrl line retained high XIST levels, however the M-ctrl and J-ctrl lines were low XIST expressing (Fig. S3a). To unambiguously visualize differentiation-induced reactivation of X-chromosomal genes in XIST expressing NPCs at single cell resolution, we employed RNAscope, detecting unspliced pre-mRNA of selected target genes together with an XIST cloud surrounding the inactive X-chromosome. H3K27 tri-methylation and XIST enrichment on the inactive X-chromosome were continuously monitored in all iPSC and NPC samples used in the experiments (Fig. S3b–e), with XIST high expressing lines showing localized XIST expression in more than 70% of the cells. No signals were seen in negative controls where no probes were used, and in samples pre-treated with RNase before probe incubation (Fig. S3f). Visualizing the unspliced pre-mRNA allows to detect the origin of the nascent RNA and hence to detect whether a given transcript is expressed monoallelically (one spot) or biallelically (two spots, Fig. 1e). The inactive SPIN3 gene showed monoallelic expression with no reactivation of expression from the inactive X-chromosome in iPSCs or NPCs (Fig. 1f, g) while the full-escapee NLGN4X was biallelically expressed in 40–50% of both, iPSCs ad NPCs (Fig. 1h, i). The reactivated genes MID1 and GPM6B, however, showed only one pre-mRNA spot distant to the XIST positive territory in the vast majority of iPSC nuclei in all cell lines tested (Fig. 1j, k, Fig. S3g–l). In NPCs, however, we observed a pronounced increase of XIST positive nuclei showing two sites of nascent RNA transcription, with one site co-localizing with the XIST signal (Fig. 1j, k, Fig. S3g–l). These data show on a single cell level that the two genes MID1 and GPM6B switch from monoallelic expression in iPSCs to biallelic expression upon differentiation into NPCs in non-eroded, XIST expressing cells (findings summarized in Fig. 1l).

Re-activating genes cluster and are associated with active chromatin
We next overlapped the different types of biallelic genes we detected with constitutive and facultative escapees that were reported in previous studies8,13. This analysis revealed that reactivated, escape and late-silenced genes shared a significantly higher overlap than expected by chance with previously reported escapees in humans (Fig. S3m, Supplementary Data 2). Biallelic expression of X-chromosomal genes often leads to higher expression levels in female cells. Therefore, we next assessed whether biallelically expressed genes can show a female expression bias by looking at the female to male ratios of mRNA-expression across 44 human tissues from the GTEx project13. We found a marked increase in the proportion of genes with a female expression bias in multiple tissues not only for full-escapees but also for reactivated genes compared to inactive genes which were consistently monoallelic in our data (Fig. 2a). Recently, age-related reactivation of gene expression from the inactive X-chromosome has been described in the mouse28. As such ageing induced biallelic expression was concentrated at distal chromosomal regions, we investigated the distribution of the biallelic genes along the X-chromosome. Notably, reactivated genes did not accumulate at the distal ends of the X-chromosome. Yet, the biallelic genes were not randomly distributed but rather clustered together, indicated by significantly lower pairwise distances compared to a random background distribution (Fig. 2b, c). By contrast, such spatial organization along the X-chromosome was not observed for full-escapees and late-silenced genes.

Previous studies have reported distinct epigenetic signatures for genes that are subject to or escape XCI (reviewed in ref. 29). To gain insight into the epigenetic characteristics of the different categories of biallelic genes in our study, we therefore employed the Roadmap Epigenomics 15-state chromatin model30. Using this in-silico approach we analyzed the chromatin states at reactivated compared to inactive (monoallelic) genes in human female and male fetal brain tissue. This assessment revealed that reactivated genes were enriched for active transcription states (“Flanking active TSS”, “Strong transcription” and “Weak transcription”) (Fig. 2d) specifically in female brain tissue and not in male samples. An enrichment of the same epigenetic states was found for full escapees compared to inactive genes (Fig. 2d). While some states were also enriched in male fetal brain tissues, this effect was attributable to the pseudoautosomal-region (PAR) genes that have a homolog on the Y-chromosome. In line with their mono-allelic expression in NPCs and neurons, late-silenced genes were associated with repressed chromatin states in fetal brain tissue (Fig. 2d). To follow up on this analysis, we examined the pattern of key histone modifications in female fetal brain tissue obtained from ENCODE. We observed an increased signal for the active mark H3K36me3 over gene bodies for reactivated and full-escape genes with a concomitant decrease in the constitutive heterochromatin mark H3K9me3, which correlates with the enrichment of active chromatin states for these gene categories (Fig. 2e). Furthermore, late-silenced genes were associated with a higher H3K27me3 and H3K4me1 signal around their transcription start sites (TSS). These histone modifications are associated with bivalent/poised TSS. As late-silenced genes also showed decreased H3K9me3 signal compared to inactive genes, this pattern suggests that late-silenced genes are only facultatively silenced and not part of the constitutive heterochromatin as supported by their biallelic expression in undifferentiated iPSCs (Fig. 1d).

Dynamic escape is conserved in the mouse
Among eutherian mammals, the mechanisms of X-linked dosage compensation vary31. However, recent evidence has drawn a picture of dynamic escape from X-inactivation also in the mouse28,32,33. Notably, the here described reactivated, full-escape and late-silenced genes were significantly enriched for genes previously reported to escape X-inactivation in the mouse (Fig. 3a). In agreement with the diverse categories of biallelic genes that we observed in human differentiating cells, a recent study identified NPC-specific facultative escapees during mouse neural differentiation32. 50% of the genes reactivating during human neural differentiation in our study escape XCI either facultatively or NPC-specific in mice (Fig. 3b). Moreover, NPC-specific escapees in the mouse only overlapped with reactivated and late-silenced genes and not with full escapees. Together, these data suggest that differentiation-specific facultative escape is a broader feature of eutherian mammals rather than a trait specific to humans.

To follow up on this observation, we investigated allele-specific expression of the mouse homolog of the MID1 gene, which we found to be reactivated during neural differentiation in human cells (Fig. 1j, k, Fig. S2c–e, Fig. S3i–l). By using RNAscope, we visualized the Mid1 pre-mRNA in E13.5 mouse brain and skin tissues. It is important to mention that the Mid1 locus has rearranged substantially between mouse and human. While in humans MID1 is located on the short arm of the X-chromosome, distal to the PAR, in mice Mid1 spans the PAR boundary (Fig. 3c) and the gene is considered to constitutively escape X-chromosomal inactivation32. Remarkably, single-molecule pre-mRNA FISH for the co-detection of the murine Mid1 gene and Xist revealed that Mid1 is monoallelically expressed in the majority of cells in both brain and skin, suggesting a more complex escape pattern at the single-cell level in the mouse embryo. Biallelic expression of Mid1 was found in both tissues but significantly more cells exhibiting biallelic expression in the brain compared to skin (Fig. 3d, e), indicating that, similar to humans, Mid1 is a facultative escape gene with a dynamic cell-and tissue-specific allele usage in mouse embryos. The increased biallelic expression in the developing mouse brain correlates with the differentiation-induced reactivation we observed in the human model and implies that inter-species conserved regulatory elements orchestrate tissue-specific escape from X-inactivation of MID1.

Dynamic allele usage of X-linked genes in human developing tissue
To follow reactivation of gene expression from the inactive X-chromosome along neural differentiation on a single cell level, we generated brain organoids from two iPSC lines expressing high levels of XIST (hoik1, A-ctrl, see Supplementary Data 1, Fig. S4a) and performed single nucleus RNA-seq (snRNA-seq) from iPSCs and organoids (d30) (Fig. S4b). Force-directed graph embedding of the data allowed reconstructing differentiation trajectories from iPSCs via NPCs to multiple neuronal and glial fates (Fig. 4a, Fig. S4c–i). To follow the usage of the two X-chromosomes we pseudo-bulked the data and determined heterozygous X-chromosomal expressed SNPs for each cell line. We then constructed a variant-aware reference genome, to which were mapped the raw sequencing data to. We detected a total of 52 X-linked genes with a traceable SNP, i.e., an SNP located within 150bp upstream of the polyA-tail. Despite the different cellular sources of the samples and the employed techniques, several of these genes overlapped with the genes detected in the bulk RNA-seq data (Supplementary Data 2). Biallelic expression was detected to be low in iPSCs but substantially increased along differentiation of XIST expressing lines to NPCs and in distinct neuronal lineages (Fig. 4b, c). Interestingly, when we aggregated the data in clusters using a PAGA representation (Fig. 4d), we found a highly variable degree of biallelic expression between the different clusters and trajectories, with an overall lower extent of dual allele usage at the onset of the differentiation trajectories, i.e., the iPSCs (Fig. 4d). When plotting biallelic expression of X-chromosomal genes versus XIST levels we confirmed the previous RNA-FISH results detecting a considerable number of cells with high XIST levels that were actively transcribing genes from their inactive X-chromosome (Fig. 4e). To further address the relationship between biallelic expression and XIST expression, we analyzed the biallelic expression scores of the reactivating (monoallelic in iPSCs, biallelic in NPCs and more differentiated cells, Fig. 4f, left panel) and the full escape genes (biallelic in iPSCs and differentiated cells, Fig. 4f, right panel) along differentiation pseudotime in both XIST high expressing cells and those in which we could not detect XIST suggesting no or low (below detection threshold) XIST expression. While the data suggest that the absence of XIST impacts on the degree of biallelic expression of full escape genes, in particular along the differentiation trajectories, we did not observe such an influence for reactivating genes. Furthermore, it became apparent that reactivation is most prominent at the progenitor level (marked by high SOX2 and NES expression, Fig. 4f left panel). In sum, these data further corroborate the RNA-FISH results showing that biallelic expression induced upon neural differentiation is dynamic and lineage-specific across individual cells.

In a next step we took advantage of existing scRNA-seq data derived from the developing human spinal cord of two different embryonic stages to address allele usage of X-chromosomal genes in vivo during human CNS development. The Rayon dataset34 includes transcriptomic data of single cells from the developing spinal cord of 6 female embryos across two developmental stages, Carnegie Stage (CS) 14 and CS19, i.e., several weeks after completion of XCI in humans. Following clustering and embedding of the data, distinct lineages of the CNS, the PNS, and paraxial mesoderm (Fig. 4g, Fig. S4j) were discernible. High XIST expression was observed across cells of all embryos and an overall high but variable degree of biallelic expression of X-linked genes located outside of the pseudoautosomal region was detected (Fig. 4h, Fig. S4k). In order to delineate the extent of biallelic expression of genes in vivo, we grouped the cells according to their lineage and calculated the level of mean biallelic expression of the individual genes across different lineages (Fig. 4i, Supplementary Data 4). This analysis showed a striking lineage-and locus-specific dual allele usage. In line with the data derived from brain organoids, biallelic expression is highest at the progenitor stage and is diminishing along differentiation (Fig. 4j, Fig. S4l). Finally, these data also show an overall lower degree of biallelic expression in the cells from developmentally older embryos (CS19, Fig. 4k, Fig. S4m). Together with the observation that we again find biallelic expression in both, XIST high and XIST low expressing cells (Fig. 4k), this suggests that it is the differentiation stage rather than the expression of XIST that promote reactivation of gene expression from the inactive X-chromosome.

X-reactivation impacts on phenotype dimorphism of OS
The observation that some reactivating genes cluster in NDD hotspot regions on the X-chromosome (Xq11.235,36, Fig. 2c), raised the question whether X-reactivation is a more general component associated with NDDs. Indeed, amongst the reactivating genes, but not full escapees and late-silencing genes, we discovered an enrichment of NDD associated genes (Fig. 5a, Supplementary Data 2) beyond the expected overrepresentation on the X-chromosome17. Furthermore, disease enrichment analysis revealed that reactivating genes were associated with ‘developmental disorder of mental health’ and ‘intellectual disability’ (Fig. S5a). This suggests that in particular genes associated with neurodevelopment and NDDs were evolutionary endowed with dynamic X-reactivation as a regulatory mechanism. To investigate the molecular framework the reactivated genes are contributing to, we constructed a protein–protein interaction network based on reactivated genes expressed in NPCs and neurons (Fig. S5b). A gene ontology (GO) enrichment analysis revealed an overrepresentation of terms associated with RNA binding and epigenetic regulatory processes such as histone binding as well as protein ubiquitination (Fig. S5c, Supplementary Data 3) highlighting the potential global impact on gene and protein function by the reactivation of X-linked genes. Remarkably, MID1, which we already validated as a reactivating gene during human neural differentiation and a facultative escapee in mouse embryos, was one of the hub proteins in the protein–protein interaction network.

To study the potential effect of reactivation of gene expression from the inactive X-chromosome on NDD-associated phenotypes, we employed fibroblasts from a female heterozygous carrier of a 4-bp deletion in the MID1 gene, causative for Opitz BBB/G Syndrome (OS)21–23, as well as fibroblasts from her affected male fetus. The fetus had inherited the 4-bp deletion and had prenatally developed OS. iPSCs of the female carrier and the male fetus (M-OS/male) were generated. The clonal nature of human iPSCs maintaining the X-inactivation status of the cell of origin14 in conjunction with the possibility to trace the expression of the 4-bp deletion enabled us to establish patient-derived female iPSC lines carrying the 4-bp deletion variant either on the inactive X-chromosome (M-ctrl) or on the active X-chromosome (M-OS/het). Moreover, we employed CRISPR/Cas9 to additionally engineer independent female iPSC lines (J-ctrl, A-ctrl) introducing the 4-bp deletion on the active X-chromosome (J-OS/het, A-OS/het) or on both X-chromosomes (J-OS/hom, A-OS/hom) (Fig. S6). We further included the male patient lines before (M-OS/male) and after CRISPR/Cas9-mediated repair (M-OS/maleR) in our experimental setup (Fig. S5d–i, Supplementary Data 1). To analyze if X-chromosomal reactivation of the inactive wildtype allele has an influence on the phenotypic presentation of X-linked OS, we generated brain organoids from these iPSC lines carrying OS-causing MID1 mutations (Fig. 5b). Inspection of the cellular organization of the brain organoids at day 30 (d30) revealed an increase in the area covered by NPC-containing ventricular zone-like structures (VZLS) in MID1 mutant organoids (Fig. S7a, b) while the cell density remained unchanged (Fig. S7c). Quantification of the cellular composition of brain organoids showed a decrease in MAP2+ neurons within MID1 mutant organoids (M-OS/het, M-OS/male, J-OS/het, J-OS/hom, A-OS/het, A-OS/hom) compared to MID1 wildtype organoids (M-ctrl, J-ctrl, A-ctrl) (d30: Fig. 5b–d, Fig. S7d; d50: Fig. S7e–g), recapitulating microcephaly and hypoplasia of the cerebellum observed in OS patients. Importantly, CRISPR/Cas9-mediated repair of the MID1 mutation in the male cells (M-OS/maleR) led to a rescue of the phenotype (Fig. 5b, c, Fig. S7a, b, d) strongly supporting MID1 dependency of the phenotypic effects.

To directly assess whether altered cell cycle dynamics contribute to the development of the OS phenotype, BrdU pulse-chase experiments were conducted in d30 brain organoids (Fig. S7h). We determined the number of postmitotic cells (Ki67-) that had incorporated the thymidine analog BrdU (BrdU+, i.e., passed through S-phase) during the time of the pulse-chase experiment. Quantification of BrdU+ Ki67- cells within the organoid VZLS showed a reduced cell cycle exit of NPCs in OS organoids and a concomitant decrease of cells that differentiated into neurons (BrdU+ NeuN+) (Fig. S7h–m). In addition, more BrdU+ cells were retained within the VZLS (Fig. S7n) correlating with the observed increase of the VZLS area in OS organoids (Fig. S7a, b). These data indicate that MID1 controls the balance between proliferation and differentiation of NPCs.

Strikingly, the brain organoids derived from the heterozygous female iPSC lines, M-OS/het, J-OS/het and A-OS/het, carrying the mutant MID1 allele on the active X-chromosome, exhibited a less severe differentiation phenotype compared to M-OS/male (Fig. 5b-d, Fig. S7a, b, d). While this is reminiscent of the clinical manifestations of X-linked diseases, in our paradigm using clonal iPSC lines with a defined and stable X-(in)activation status random X-inactivation cannot contribute to the amelioration of the phenotype in female tissue. To assess if the difference in the phenotypes severity correlates with the reactivation of the wildtype allele on the inactive X-chromosome, we used allele-specific RT-PCR of the MID1 locus. Substantial reactivation of the MID1 allele on the inactive X-chromosome was found in NPCs, neurons (Figs. S2c-e, S8a, b) and brain organoids (Fig. 5e–g) derived from all heterozygous cell lines. The presence of H3K27me3 territories and XIST expression also upon CRISPR/Cas9-mediated genome editing was confirmed in the A-ctrl, A-OS/het and A-OS/hom lines (Figs. S3b, S8c). Allele-specific RT-PCR analysis of MID1 showed that it kept monoallelic expression in iPSCs up to high passages in low XIST expressing iPSC lines (Fig. S8d), indicating that it is not a target of erosion. Western blot analysis confirmed that reactivation of the wildtype allele from the inactive X-chromosome resulted in the expression of MID1 wildtype protein in the OS/het NPCs, while no wildtype MID1 protein expression was detected in the corresponding iPSCs (Fig. S8e, f). To test the impact of the reactivation of the wildtype MID1 allele on the inactive X-chromosome in heterozygous female OS cells on the phenotype manifestation, we took advantage of the female iPSCs homozygous for the 4-bp deletion (J-OS/hom, A-OS/hom) (Fig. S5d). Brain organoids derived from these lines showed a more severe phenotype than the OS/het lines (J-OS/het, A-OS/het), comparable to the male OS organoids (Fig. 5b–d, Fig. S7a, b, d). Interestingly, the presence of a wildtype allele on the inactive X-chromosome ameliorates the disease phenotypes in XIST high (A-line) and low (M- and J-lines) expressing cells. These data demonstrate that the presence of a wildtype allele on the inactive X-chromosome in heterozygous mutant female cells, curtails the severity of the disease phenotype through reactivation of the allele on the inactive X-chromosome.

To identify the molecular signatures underlying the gradual OS phenotypes, we performed scRNA-seq of cells derived from brain organoids (d30) generated from the M- (M-ctrl, M-OS/het) and the J-lines (J-ctrl, J-OS/het, J-OS/hom) and focused our attention on the neural lineages (Fig. S9a, b). Within the neural tissue we found cells with molecular signatures of neural crest, choroid plexus, midbrain, and hindbrain lineages (Fig. S9b, c). This hindbrain organoid model recapitulated the major lineages of the developing cerebellum, formation of which is disturbed in OS patients as well as in the mouse model37,38. NPC-to-neuron trajectories producing distinct neurotransmitter identities were discriminated: rhombic lip progenitors (ATOH1) giving rise to glutamatergic cells expressing SLC17A6, VZ progenitors (PTF1A) differentiating into GABAergic cells expressing GAD2 (Fig. S9c). To elucidate the molecular framework driving the early disruption of the balance between proliferation and differentiation observed in OS brain organoids (Fig. 5b–d), differentially expressed genes in NPCs (clusters 0, 1, 2, 3, 4, 5, Fig. S9d) between ctrl and OS lines were determined followed by GO analysis (Fig. S9e, f). Amongst the differentially expressed genes, we found ZIC1 and ZIC2 (Fig. S9d). Mutations in ZIC1 and ZIC2 lead to Dandy-Walker cysts39 which are characteristic malformations of the cerebellum. Interestingly, similar to MID1, these genes regulate neuronal differentiation40. Hence, our data suggest a partial overlap and a surprising molecular crosstalk linking OS and Dandy-Walker pathogenesis. Moreover, compared to wildtype, OS NPCs showed MID1 genotype dependent gradual deregulation of more general cell cycle regulators associated with proliferation and differentiation (e.g., CCND1, NIN, CDK6, KIF2A, CDKN1A, CDC42, Fig. S9g)41–45, providing a potential molecular framework underlying the altered cell cycle progression observed in OS organoids (Fig. 5b–d, Fig. S7b, d).

The severity of the phenotype in OS brain organoids (Fig. 5) varied depending on the presence (M-OS/het, J-OS/het, A-OS/het) or absence (J-OS/hom, A-OS/hom) of a wildtype MID1 allele on the inactive X-chromosome with heterozygous lines showing intermediate phenotypes. To disentangle these tissue level phenotypes with cellular resolution we further leveraged the scRNA-seq data and computed the transcriptional deviation of each cell from their respective controls. These analyses revealed that the transcriptomes of cells in the cerebellar clusters (0, 2, 3, 4, 5, 6, 8, 10) differed strongest while the transcriptomes of midbrain (14), choroid plexus (15, 16) and neural crest (17, 18, 19) derived cells varied less upon MID1 mutation (Fig. S9h) with slight variations between M- and J-lines. Importantly, lines with a wildtype MID1 allele on the inactive X-chromosome (J-OS/het) exhibited an intermediate transcriptional phenotype reminiscent of the characterization of tissue composition (Fig. 5b–d, Fig. S7a, b, d). To temporally resolve the impact of the MID1 mutation on the NPC-to-neuron differentiation trajectory of hindbrain lineages we plotted the degree of transcriptional deviation along the developmental pseudotime (Fig. S9i). The deviation from controls of both, heterozygous and homozygous lines was strongest early in the lineage at the NPC stage with high expression of the NPC marker SOX2 and low expression of the mature neuronal marker SNAP25 (Fig. S9i). These transcriptional differences occurred concomitant with an accumulation of cells at a transcriptional NPC stage in OS lines (Fig. S9j) in accordance with the increase of VZLS and SOX2 in OS organoids (Fig. S7a, b, d). The observation that cells with homozygous MID1 mutations (J-OS/hom) react stronger to the lack of functional MID1 and heterozygous lines (M-OS/het, J-OS/het) show intermediate transcriptional phenotypes reinforces the gradual manifestation of the phenotype on an individual cell level.

The MID1 target PAX6 correlates with OS phenotype attenuation
With the goal to identify a putative molecular target of the ubiquitin ligase MID1 underlying the reduced neuronal differentiation and progenitor cell cycle progression phenotype, we set out to further analyze the progenitor cell population within the VZLS of the J- (low XIST expression) and the A-line (high XIST expression) organoids with cellular resolution. It has been shown previously that PAX6 can be targeted by MID1 for proteasomal degradation46. We hypothesized that also in human neural cells, the NPC regulatory protein PAX6 would be affected by the absence of MID1. In fact, we found a marked increase in the PAX6 protein levels within the VZLS of both XIST low J-OS/het and XIST high A-OS/het organoids, which was further increased in J-OS/hom and A-OS/hom organoids (Fig. 6a–d). Modulation of the cell cycle regulator and PAX6 target p21 is a possibility by which PAX6 can control the proliferation rate of NPCs47. We found p21 protein levels increased in the VZLS resident progenitors (Fig. S10a–c), and with similar correlation between PAX6 and the p21 protein levels in all three genotypes (Fig. S10d), resulting in an increase of non-apical mitotic NPCs (Fig. S10e–g). Together these data underscore the notion that the presence and reactivation of a wildtype allele on the inactive X-chromosome ameliorates the disease phenotypes not only on a cellular but also on the molecular level.

In sum, our data show that the reactivation of the expression of alleles from the inactive X-chromosome leads to a dynamic increase in the diversity of the available allele pool. Furthermore, our data demonstrate that in X-linked NDDs, this mechanism can enhance resilience of female neural tissue beyond the general random X-inactivation in tissues by ameliorating the phenotype on an individual cell level (Fig. 6e).

Methods
Human fibroblast cell lines
Skin punch biopsies (4 mm) were taken in the Children’s Hospital of the University Medical Center in Mainz. Primary human fibroblasts were isolated from skin punch biopsies as previously described with small adaptations69. Briefly, biopsies were cut in small pieces containing all skin layers and plated on a 6-well plate coated with 0.1% gelatin with fibroblast extraction media (FEM; DMEM (Thermo Fisher), 20% fetal bovine serum (FBS, Thermo Fisher), 1% penicillin/streptomycin (P/S) (Thermo Fisher)). Cells were monitored daily, and fresh medium was added every other day. Fibroblasts started to migrate out of the skin biopsies after 7–10 days and were transferred to two T75 flasks after 3–4 weeks using TrypLE™ Express enzyme (Thermo Fisher). Fibroblasts were then cultured in IMDM (Thermo Fisher), 15% FBS, 1% P/S. When reaching 90% confluency, fibroblasts were replated into T175 flasks and expanded as needed or frozen in liquid nitrogen for long-term storage. M-lines: Patients’ fibroblasts (for M-lines) had been established at Bristol Genetics Laboratory following the same procedure as described above.

Reprogramming to pluripotent stem cells
Fibroblasts were reprogrammed into iPSCs with retroviruses and by using the feeder-dependent approach of the CytoTune(TM)-iPS 2.0 Sendai Reprogramming Kit (Thermo Fisher) according to the manufacturer’s protocol. HEK 293T cells used for retrovirus production were cultured in IMDM, 10% FBS and 1% Pen/Strep and replated every three to four days at a ratio of 1:10 or 1:20 by using TrypLE™ Express enzyme. To generate the retroviruses for reprogramming, HEK 293T cells were transfected by using Lipofectamine 3000 (Thermo Fisher) following the manufacturer’s instruction. Fibroblasts were transduced by retroviral spinfection. Briefly, 24 h prior to transduction 1×105 cells fibroblast were seeded on a 6-well plate coated with 0.1% gelatin and cultured in IMDM plus 15% FBS. On day 0, medium was replaced with 5 ml of retrovirus solution supplemented with a final concentration of 8 μg/ml Polybrene (Sigma-Aldrich). Cells were then centrifuged for 1 h at 800 × g and the retrovirus solution was replaced by fresh medium afterwards. Treatment was repeated after 10 hours and after 24 hours. On day 2 (24 h after the last treatment), cells were harvested and seeded in 10-cm dishes on mouse embryonic fibroblast (MEF) feeder cells, one well of the 6-well plate per dish. On day 3, medium was changed to iPSC medium adding 0.1% SB431542 (StemCell Technologies). Medium was changed every other day. For the feeder-dependent approach of the CytoTune(TM)-iPS 2.0 Sendai Reprogramming kit, on day −2 fibroblasts were seeded in increasing concentrations on two 6-well plates (10–35×104 cells/well) with reprogramming fibroblast medium (RFM; DMEM, 10% ESC-qualified FBS (Thermo Fisher), 1% NEAA (Thermo Fisher), 0.1% 2-mercaptoethanol (Thermo Fisher)). On day 0, viral transduction was performed on 30–60% confluent cells using the Sendai virus-based reprogramming vectors containing the four Yamanaka factors, Oct3/4, Sox2, Klf4, and c-Myc70. The required virus amount was calculated using the given formula from the manufacturer’s protocol and added to 1 ml of RFM replacing the old medium. 24 hours after transduction, medium was replaced by fresh RFM. Cells were then fed every other day. On day 5 after transduction, MEF feeder cells were thawed and seeded on a 6-well plate (2.5×105 cells/well) as described in the manufacturer’s protocol. On day 7 after transduction, fibroblasts were harvested, and seeded in different concentrations (5, 10, 20, 40, 80, 100×103 cells/well) onto the previously prepared 6-well plate with MEFs and cultured with RFM. On day 8, medium was replaced with iPSC medium (DMEM/F-12 (Thermo Fisher), 20% KOSR (Thermo Fisher), 1% NEAA, 0.1% 2-mercaptoethanol, 1% P/S, 0.04% bFGF (Thermo Fisher)) and then changed every day. About 21–28 days after transduction, iPSC colonies were emerging from the plate and approximately 50 colonies were picked and transferred to a single well of a 12-well plate coated with Matrigel (Corning) and cultured with mTeSR1 (StemCell Technologies). Medium was changed every other day; colonies were monitored regularly and expanded until ready for characterization. All iPSC lines were cultured in colonies in mTESR1 medium (StemCell Technologies) on Matrigel-coated dishes in 5% CO2 at 37°C until they reached a confluency of 80–90% and then replated at a ratio of 1:3 to 1:10 using a self-made enzyme-free splitting buffer containing PBS, NaCl and EDTA. Spontaneously differentiated cells were manually removed under a stereomicroscope. iPSC lines used were regularly karyotyped. All cells were regularly tested for the presence of mycoplasma using the PCR Mycoplasm Test Kit (PromoKine) or the LookOut Mycoplasma PCR detection kit (Sigma-Aldrich).

Ethical approval
Patients’ fibroblasts71–73 to generate M-ctrl, M-OS/het, M-OS/male, M-OS/maleR iPSC lines were established in the Bristol Genetics Laboratory, following consent for further analysis and usage for research in an anonymized way was given by the family. Fibroblasts to generate J-ctrl, J2-ctrl, J-OS/het, J-OS/hom, J2-ctrl, the A-ctrl, A-OS/het, A-OS/hom, C1-male, and the KO-line iPSCs were taken at the University Medical Center in Mainz following approval by the local ethical committee (No. 4485). Consent for further analysis and usage for research in an anonymized way was given.
Animal experiments were conducted following German animal welfare legislation, and according to European (EU directive 2010/63/EU), national (TierSchG), and institutional guidelines.

iPSC lines used in this study
A summary of all iPSC lines including information about the origin and genotype as well as for which experiments they were used is provided in Supplementary Data 1.
The following female iPSC lines were used. A female patient heterozygously carrying a 4-bp MID1 deletion provided the M-fibroblasts, from which the M-ctrl and the M-OS/het iPSC lines were derived through clonal expansion. Two healthy female individuals provided fibroblasts termed A- and J- to derive the iPSC lines A-ctrl and J-ctrl (low XIST) and an additional clone, termed J2-ctrl (high XIST).
Using CRISPR/Cas9-mediated genome editing the 4-bp MID1 mutation was introduced either heterozygously (J-OS/het, A-OS/het) or homozygously (J-OS/hom, A-OS/hom) into the J-ctrl and A-ctrl lines, respectively.
As additional control of a healthy individual, the hoik1 line from the HipSci feeder free panel (ECACC 77659901) was used for generating the snRNA-seq data of iPSC and brain organoids and used for RNA-FISH experiments.
The following male iPSC lines were used. The MID1 KO-line74 was generated through CIRSPR/Cas9-mediated knockout of the MID1 gene in a male iPSC line derived from a healthy individual (C1-male). In addition, the patient-derived M-OS/male and the corresponding isogenic control M-OS/maleR, in which the 4-bp deletion in the MID1 gene was corrected by CRISPR/Cas9 mediated genome editing, were employed.

Clonal selection of iPSC lines
Following reprogramming into iPSC, individual clones derived from the M-, A- and J-fibroblasts, respectively, were picked and propagated for further analyses. Monoallelic expression of one X-chromosome in iPSCs was confirmed by using allele-specific PCR for the X-linked MID1 gene (M-line) and by using a QUASEP assay for a heterozygous SNP located in the 3´ untranslated region of the X-linked gene ZNF185 (M-, A-, and J-line).

Genome editing
For CRISPR/Cas9 genome editing iPSCs were electroporated using the Lonza 4D-NucleofectorTM X Unit. 800.000 single cells per transfection reaction were pelleted. Cells were resuspended in 100 μl electroporation buffer P3 (P3 Primary cell solution box, Lonza) plus 2.5 μg of the CAG-Cas9-Venus plasmid75 (pU6-(BbsI)sgRNA_CAG-Cas9-venus-bpA was a gift from Ralf Kuehn (Addgene plasmid #86986)) and 2.5 µg of gRNA-containing plasmid. The CAG-Cas9-Venus plasmid did not contain any gRNAs, but the gRNAs were provided by using the gRNA cloning vector76 (gRNA_CloningVector was a gift from George Church (Addgene plasmid #41824)). Cell-plasmid solution was transferred to the electroporation cuvette (AmaxaTM P3 primary cell 4D-NucleofectorTM X, Lonza) and electroporated using the program CB-150 of the Nucleofector. After electroporation 100 μl of RPMI (Thermo Fisher) was pipetted into the cuvette and incubated for 10 min at 37°C and then transferred to a fresh well of a Matrigel coated 6-well plate with 2 ml of mTeSR1 supplemented with ROCK-inhibitor (10 μM, StemCell Technologies). Single GFP-positive cells were sorted in a 96-well plate with mTeSR plus CloneR (StemCell Technologies). 14 days after plating, colonies were transferred to a 12-well plate with mTeSR plus CloneR. A small volume (~10 µl) of the dissociated cells was used for DNA isolation by using Quick-DNA Microprep Kit (Zymoresearch). The DNA was amplified by PCR and sequenced via Sanger Sequencing. Clones carrying the desired mutation were selected for further expansion.

2D iPSC differentiation into neural progenitor cells (NPCs)
iPSCs were differentiated into NPCs with the PSC Neural Induction Medium (NIM) (Gibco)77. On day −1, iPSCs were washed with DMEM/F-12 and incubated with Collagenase IV (Thermo Fisher) for 20 min, followed by three wash steps with DMEM/F-12. Cells were scraped and resuspended with mTeSR1, transferred to ultra-low attachment plates and incubated for 24 h. On day 0, medium was replaced with neuronal medium (NM: DMEM/F-12, 1% N2-supplement (Thermo Fisher), 2% B27-supplement (Thermo Fisher), 1% P/S) and replaced every other day. On day 7, EBs were seeded on one well of a 6-well plate coated with poly-Ornithine/Laminin (Thermo Scientific). Medium was changed every other day. On day 14, rosette-like structures were visible inside the attached EBs and were transferred to a new well of a poly-Ornithine/Laminin-coated 6-well plate. On day 2 after replating, medium was replaced and supplemented with 0.1% FGF2 (Thermo Fisher). NPCs were expanded as needed or frozen in liquid nitrogen for long-term storage. M-ctrl NPCs for bulk RNA seq were generated according to this protocol. All other iPSC-to-NPC differentiations followed a similar but commercial alternative: on day −1, iPSCs were seeded on a Matrigel-coated 6-well plate. After 24 h, when cells reached a confluency of 15–25%, medium was changed to Neural Induction Medium (NIM: Neurobasal (Thermo Fisher), 2% Neural Induction Supplement (Thermo Fisher), 1% P/S), renewed with increasing volumes every other day to compensate for cell growth. On day 7 of neural induction cells were replated following the manufacturer’s protocol in a Geltrex-coated (Thermo Scientific) 6-well plate (500×103 cells/well) in Neural Expansion Medium (NEM: 49% Neurobasal, 49% Advanced DMEM (Thermo Scientific), 2% Neural Induction Supplement, 1% P/S) supplemented with ROCK-inhibitor (5 μM, StemCell Technologies). After 24 h, medium was exchanged with NEM and cells were monitored daily with medium changes every other day. When reaching confluency, cells were replated using TrypLE™ Express and cultured on poly-Ornithine/Laminin-coated dishes with NM supplemented with FGF2 (20 ng/ml).

2D differentiation of NPCs into neurons
NPCs were seeded at low confluence (50–100×103 cells/well) onto poly-Ornithine/Laminin-coated cavities of a 6-well plate. 24 h after seeding, cells were washed twice with PBS and medium was replaced with NM+VitA (DMEM/F-12, 1% N2-supplement, 2% B27+VitA supplement (Thermo Fisher), 1% P/S). Cells were cultured in a humidified incubator at 37°C and 8% CO2. Every 3–4 days, fresh medium was added to the cells. When reaching a total volume of 10–12 ml per well, 20–50% of the medium was removed every time before adding fresh media. After 35 days, differentiated neurons were harvested as needed.

Brain organoid formation
All iPSC lines were cultured in colonies in mTeSR Plus medium (StemCell Technologies) on Matrigel (Corning)-coated dishes in 5% CO2 at 37°C until they reached a confluency of 80–90%. Brain organoids were generated with slight modifications following the Lancaster protocol78. Accutase (Gibco) was used to generate single cell suspensions of cells. Following centrifugation, cells were resuspended in organoid formation medium supplied with 4 ng/ml of low bFGF (Peprotech) and 5 µM ROCK-inhibitor (StemCell Technologies). Organoid formation medium consisted of DMEM/F12+GlutaMAX-I (Gibco), 20% KOSR (Gibco), 3% FBS (Gibco), 0.1 mM MEM-NEAA (Gibco), 0.1 mM 2-mercaptoethanol (Sigma-Aldrich). 12,000 cells in 150 µL organoid formation medium/well were reaggregated in low attachment 96-well plates (Corning) for at least 48 h. After 72 h half of the medium was replaced with 150 µL of new organoid formation medium without bFGF and ROCK-inhibitor. At day 5 neural induction medium consisting of DMEM/F12+GlutaMAX-I (Gibco), 1% N2 supplement (Gibco), 0.1 mM MEM-NEAA (Gibco), and 1 µg/ml Heparin (Sigma-Aldrich) was added to the EBs in the 96-well plate to promote their growth and neural differentiation. Neural induction medium was changed every two days until day 12/13, when aggregates were transferred to undiluted Matrigel (Corning) droplets. The embedded organoids were transferred to a petri dish containing organoid differentiation medium without vitamin A. Organoid differentiation medium consisted of a 1:1 mix of DMEM/F12+GlutaMAX-I (Gibco) and Neurobasal medium (Gibco), 0.5% N2 supplement (Gibco), 0.1 mM MEM-NEAA (Gibco), 100 U/ml penicillin and 100 µg/ml streptomycin (Gibco), 1% B27 ± vitamin A supplement (Gibco), 0.025% insulin (Sigma-Aldrich), 0.035% 2-mercaptoethanol (Sigma-Aldrich). Three to four days later the medium was exchanged with organoid differentiation medium containing vitamin A and the plates were transferred to an orbital shaker set to 30 rpm inside the incubator. Medium was changed twice per week. For fixation, organoids were transferred from petri dishes to 1.5 ml tubes at day 30 and day 50. Organoids were washed with PBS and then fixed with 4% paraformaldehyde (PFA, Carl Roth) for 30 min. Time of PFA fixation was extended up to one hour depending on the size of the organoids. Afterwards, organoids were washed three times for 10 min with PBS and incubated in 30% sucrose (Sigma-Aldrich) in PBS for cryoprotection. For cryosectioning, organoids were embedded in Neg-50™ Frozen Section Medium (Thermo Fisher) on dry ice. Frozen organoids were cryosectioned in 30 μm sections using the Thermo Fisher Cryostar NX70 cryostat. Sections were placed on SuperFrost Plus™ Object Slides (Thermo Fisher) and stored at −20°C until use.

RNA-FISH procedure and imaging
To trace the allelic expression of individual X-chromosomal genes with a single cell resolution we employed the RNAScope technology (Bio-Techne) by following the manufacturer´s protocol with small adjustments. Cells seeded on coverslips were fixed when reaching 80–90% of confluency with 1 ml of freshly prepared PFA (4% in PBS) for 30 minutes at room temperature. After three washes with PBS, cells were dehydrated by incubation with increasing volumes of ethanol, 50%, 70%, and 100% for 1 min. 100% ethanol was replaced with fresh 100% ethanol, and cells were stored at +4°C for up to 48 h or −20°C for up to 6 months. Cells were rehydrated (70% and 50% ethanol for 1 min) followed by incubation with PBS for 10 min. For the pre-treatment procedures, coverslips were covered for 10 min with RNAScope Hydrogen Peroxide solution and further incubated for 10 min with RNAScope Protease III diluted 1:5 with PBS (cat. no. PN322381). Hs-XIST-C2 probes (cat. no. 311231-C2) were mixed with Hs-MID1-O1-C3 (cat. no. 1224591-C3), Hs-GPM6B-intron-C3 (cat. no. 1201031-C3), Hs-NLGN4X-O1-C3 (cat. no. 1180201-C3), or Hs-SPIN3-Intron-C3 (cat. no. 1329651-C3) according to the manufacturer´s protocol. Coverslips were incubated with mixed probes for 2 h at 40°C. Negative control samples were incubated with Probe Diluent (cat. no. 300041). Additional negative control samples were treated with RNase A (100 μg/ul, cat. no. R1253, Thermo Scientific) for 1 h at +37°C prior probe incubation. The subsequent steps of amplification and signal detection were performed as described in the protocol. The Opal Dye 520 (Akoya) was used for Hs-XIST-C2 (1:750, excitation 494 nm, emission 525 nm), and Opal Dye 690 (Akoya) for Hs-MID1-O1-C3, Hs-GPM6B-intron-C3, Hs-NLGN4X-O1-C3 and Hs-SPIN3-Intron-C3 (1:750, excitation 676 nm, emission 694 nm) as fluorophores. Samples were mounted with ProLong Glass Antifade Mountant and ProLong Gold Antifade Mountant (Thermo Fisher). Fluorescence microscopy images were acquired using the VisiScope 5 Elements spinning disc confocal (Visitron Systems GmbH), built on a Ti-2E (Nikon) stand and equipped with a spinning disc unit (CSU-W1, 50 µm pinhole, Yokogawa), and controlled by the VisiView software (v7.0.0.7). Images were acquired with a 60x water-immersion (NA 1.2 Plan Apo lens) objective with an additional 2x magnification, and a CMOS camera (BSI, Photometrics). BC43 spinning disk confocal (Oxford instruments) with a 100x oil-immersion (NA 1.45 Plan Apo lens) objective was also used for image acquisition. 3D stacks of images (with a voxel size of x 0.0568, y 0.0568, 100 nm for VisiScope, and x 0.0609, y 0.0609, 100 nm for BC43) were acquired for each sample.

Quantification of RNA-FISH signals
At least 5 images for each experiment were acquired for the signal quantification. Images were analyzed using the Imaris Software (v10, Oxford instruments). Manual quantification was conducted after setting the same threshold values for all pictures. XIST-positive nuclei were annotated as nuclei with a clear XIST cloud representing the inactive X-chromosome territory. Nuclei having at least one signal from the specific probe used were considered as positive nuclei for the specific probe. For the colocalization experiments between XIST signal and MID1, GPM6B, NLGN4X, and SPIN3, nuclei having a single probe signal non co-localizing with the XIST signal were considered as monoallelic nuclei, while nuclei having two probe signals, with one of them colocalizing with the XIST signal, were considered as biallelic nuclei. Pre-mRNA signals that were overlapping, adjacent to, or in close proximity (distant 2.5 µm) of the XIST signal were considered as co-localizing with the XIST cloud.

Mouse embryonic tissue isolation and processing
Pregnant C57BL/6 mice at embryonic day E13.5 were anaesthetized with isoflurane and euthanized via cervical dislocation and embryos were harvested (E13.5). Subsequently, embryonic brains and skin tissue isolated from limb areas were dissected and fixed in freshly prepared 4% paraformaldehyde (PFA) overnight at 4°C. Small portions of the tails were used for genotyping. Dehydration was performed by incubating the tissues in 50% ethanol for 1 h on the orbital shaker at 4°C, followed by 70% and 100%. Tissues were stored at 4°C in 100% ethanol prior further use.

RNA-FISH on mouse embryonic tissue
The RNAScope technology (BioTechne) was employed by following the manufacturer´s protocol with small adjustments. Tissues were sliced in suspension, and a single thin slice was transferred into a well of a 96-well plate for the entire procedure. Tissues were rehydrated (70% and 50% ethanol for 1 min) followed by two incubation steps with PBS for 10 min. For the pre-treatment procedures, tissues were incubated for 10 min with RNAScope Hydrogen Peroxide solution and further incubated for 10 min with RNAScope Protease plus (322331) for 30 min. Mm-XIST-C1 probes (cat. no. 418281) were mixed with Mm-Mid1-O1-C3 (cat. no. 1802811-C3) according to the manufacturer´s protocol. Tissues were incubated with mixed probes for 2 h at 40°C. Subsequent steps of amplification and signal detection were performed as suggested in the protocol. The Opal Dye 520 (Akoya) was used for Mm-XIST-C1 (1:750, excitation 494 nm, emission 525 nm), and Opal Dye 690 (Akoya) for Mm-Mid1-O1-C3 (1:750, excitation 676 nm, emission 694 nm) as fluorophores. Samples were mounted with ProLong Gold Antifade Mountant. Fluorescence microscopy images were acquired using the VisiScope 5 Elements spinning disc confocal (Visitron Systems GmbH), built on a Ti-2E (Nikon) stand and equipped with a spinning disc unit (CSU-W1, 50 µm pinhole, Yokogawa), and controlled by the VisiView software (v7.0.0.7). Images were acquired with a 60x water-immersion (NA 1.2 Plan Apo lens) objective with an additional 2x magnification, and a CMOS camera (BSI, Photometrics). 3D stacks of images (with a voxel size of x 0.0568, y 0.0568, 100 nm) were acquired for each sample.

Quantification of RNA-FISH signals in mouse embryonic tissues
At least three images for each experiment were acquired for the signal quantification. Images were analyzed using the Imaris Software (v10, Oxford instruments). Manual quantification was conducted after setting the same threshold values for all pictures. For each image, a set of nuclei with clear and well-defined borders was selected for further analysis. Xist-positive nuclei were annotated as nuclei with a clear Xist cloud representing the inactive X-chromosome territory. Nuclei having at least one signal from the Mid1 probe used were considered as positive Mid1 nuclei. For the colocalization experiments between Xist and Mid1, nuclei having a single probe signal non co-localizing with the Xist signal were considered as monoallelic nuclei, while nuclei having two probe signals, with one of them colocalizing with the Xist signal, were considered as biallelic nuclei. Pre-mRNA signals that were overlapping, adjacent to, or in close proximity (distant 2.5 µm) of the Xist signal were considered as co-localizing with the Xist cloud.

Immunohistochemistry
Cells on coverslips were fixed with 1 ml of PFA (4% in PBS) for 20 minutes, washed three times with PBS, and incubated with blocking solution (PBS, 5% BSA, 0.3% Triton) for 1 h. Afterwards, cells were incubated with primary antibodies diluted in blocking solution at 4°C overnight. On the second day, cells were washed three times with PBS, 0.1% Triton for 10 min, followed by an incubation with secondary antibody diluted in blocking buffer for 1 h. After three wash steps with PBS, 0.3% Triton, coverslips were transferred to glass slides with 10 μl of mounting medium (Vectashield, 0.5% DAPI, Vector Laboratories). Organoid sections were post-fixed with 4% PFA for 15 minutes, washed three times for 5 min with 1x PBS, and briefly washed with blocking solution containing 4% normal donkey serum (NDS, Sigma-Aldrich) and 0.25% Triton-X (Sigma-Aldrich) in 1x PBS and subsequently incubated with blocking solution for at least one hour at RT. Tissue sections were incubated overnight at 4°C with primary antibodies diluted in antibody solution containing 4% NDS (Sigma-Aldrich) and 0.1% Triton-X (Sigma-Aldrich) in 1x PBS. Following 3 wash steps using PBS with 0.5% Triton-X (Sigma-Aldrich), secondary antibodies were diluted in antibody solution and incubated for one hour at RT. Finally, sections were washed three times for five minutes with PBS and PBS with 0.5% Triton-X was used for the last wash step. Slides were counterstained with DAPI 1:1000 in PBS for 5 min, washed with PBS and mounted using Aqua Poly Mount (Polysciences, Inc.). Slides were kept in a humidified chamber in the dark during the entire staining procedure.
Antibodies used were selected according to the antibody validation reported by the distributing companies (information is provided in the reporting summary). Mouse (IgG1) anti-MAP2 (Sigma-Aldrich; M4403; HM-2; 139117; 1:300), rabbit anti-PAX6 (Biolegend; 901301; B277104; 1:300), rat anti-BrdU (Abcam; ab6326; BU1/75 (ICR1); GR3365969-9; 1:300), rabbit anti-H3K27me3 (Cell Signaling Technology; 9733S; C36B11; 27; 1:300), rabbit anti-Ki67 (Invitrogen; MA-14520; SP6; SI2454941R; 1:300), rabbit anti-p21 Waf1/Cip1 (Cell Signaling Technology; 2947; 12D1; 12; 1:300), human anti-PAX6 (Miltenyi Biotec; 130-107-582; 5170301048); 1:300), mouse (IgG1) anti-Phospho-Histone H3 (Ser10) (Cell Signaling Technology; 9706S; 6G3; 10; 1:300), rabbit anti-SOX2 (Abcam; ab137385; GR3313268-18; 1:300), mouse (IgG2b) anti-TUBB3 (Sigma; T8660; SDL.3D10; 127270; 1:300). The following secondary antibodies were used (1:500 dilution): goat anti-mouse IgG1 Alexa 488 (Thermo Fisher; cat. no. A21121; 2083196), goat anti-mouse IgG Alexa 488 (Thermo Fisher; cat.no. A11001; 2140660), goat anti-rabbit Alexa 488 (Thermo Fisher; cat.no. A11008; 2521157), goat anti-rabbit Cy3 (Thermo Fisher; cat.no. A10520; 2160048), goat anti-rat Alexa 555 (Thermo Fisher; cat.no. A10522; 2153107), goat anti-mouse IgG1 Alexa 555 (Thermo Fisher; cat.no. A21127; 2110847)), goat anti-Human Alexa 555 (Thermo Fisher; cat.no. A21433; 2150293), goat anti-rat Alexa 633 (Thermo Fisher; cat.no. A21094; 2087716), goat anti-rabbit Alexa 633 (Thermo Fisher; cat.no. A21070; 2079350), goat anti-mouse IgG1 Alexa 633 (Thermo Fisher; cat.no. A21126; 2128996), Phalloidin-Atto 647 (Sigma; cat. no. 65906).

Microscopy and image analysis
Epifluorescence pictures were taken using Axio Observer (Zeiss), or the EVOS™ M7000 Imaging System (Thermo Fisher). Z-resolved pictures were acquired at a LSM710 Confocal Microscope (Zeiss) or with the Apotome 2 (Zeiss) equipped with the Colibri 5 light source (Zeiss). To quantify the VZLS area we measured for each organoid section, the total organoid area (excluding areas covered by cysts), and the area covered by VZLS using the selection tools in FIJI (v1.52-1.53). In R (v3.5.1-4.1.2) we then calculated the fraction of organoid area covered by VZLS area, averaged these values from different sections from the same organoids and normalized the resulting values to the average value of the respective control organoids (J-ctrl for J-ctrl, J-OS/het and J-OS/hom; M-ctrl for M-ctrl and M-OS/het; M-OS/maleR for M-OS/maleR and M-OS/male) in each batch. To quantify the area covered by SOX2 and MAP2 in organoid sections we used epifluorescence pictures acquired with the EVOS M7000 and employed OpenCV (v4.4.0−4.5.1) in python (v3.9.1-3.9.10) for automated thresholding and counting of thresholded pixels. The neural area was determined as the number of pixels thresholded for either SOX2 or MAP2 and the fraction of SOX2/neural area and the fraction of MAP2/neural area was calculated using NumPy (v1.21.5) and pandas (v1.3.4).
To quantify PAX6 protein levels we used sections stained for SOX2 and PAX6 and considered the central single optical sections of each Apotome z-stack. We segmented the SOX2 positive cells using the cyto3 model in CellPose (v2.2.3)79 and quantified the PAX6 protein levels in each segmented SOX2 positive cell employing measure.regionprops of the scikit-image package (v0.21.0) in python (v3.8.19). The correct for differences in staining intensity due to batch-to-batch variations we normalized each value to the mean PAX6 level in corresponding controls of each batch. Finally, we plotted the normalized mean intensity of PAX6 vs the distance to the apical side for each SOX2 positive cell within a VZLS.
In a very similar approach, we quantified p21 protein levels in PAX6 positive cells, however this time the segmentation was done on PAX6 and both p21 as well as PAX6 levels were measured in segmented cells. To segment pH3 positive cells we used cell detection (v0.4.9).

Western blot analysis
Protein lysates were generated from cell pellets using MagicMix (48% urea, 15 mM Tris pH 7.5, 8.7% Glycerin, 1% SDS, 143 mM 2-mercaptoethanol) containing protease and phosphatase inhibitors (cOmplete Tablets easypack, PhosSTOP easypack, Roche) and transferred to a QIAshredder column (Qiagen). After centrifugation at 13,500g for 2 min, the solution was transferred into a fresh tube and frozen at −80°C until needed. SDS gel-electrophoresis was used to separate proteins by their size. Proteins were transferred to a PVDF-membrane by using the Trans Blot Turbo Transfer Pack (Bio-Rad). Membranes were incubated for 24 h with blocking buffer (PBS, 0.1% Tween, 5% milk), followed by 24 h incubation with primary antibody diluted in blocking buffer. Membranes were washed 3 times for 10 min with PBS-T (PBS, 0.1% Tween), incubated for 1 h with secondary antibody diluted in blocking buffer, and washed 3 times for 10 min with PBS-T. Membranes were exposed using the Western Lightning Plus-ECL (Perkin Elmer) and imaging was performed by using ChemiDoc Imaging System (Bio-Rad). Images were prepared and analyzed using the Image Lab software (Bio-Rad). Mouse monoclonal anti-β-ACTIN (Sigma; A2066-200UL; 1:2000), rabbit polyclonal anti-MID1 C-terminal (Novus, NBP1-26612; 1:500), rabbit anti-MID1 N-terminal (courtesy of Dr. Sybille Krauss; 1:500)80.

qRT-PCR
Total RNA was extracted with the RNeasy Mini Kit (Qiagen) or the High Pure RNA Isolation Kit (Roche). Samples were stored at −80°C until use. The RNA concentration was measured using a Nanodrop (PeqLab) or Qubit 4 (Thermo Fisher). cDNA was generated starting from 125 ng up to 500 ng of total RNA with the Maxima First Strand cDNA Synthesis Kit (Thermo Fisher) or the PrimeScript RT Master Mix (Takara). In each individual experiment, equal amounts of RNA were used for the generation of cDNA. The primers used are listed in Supplementary Data 5. For qRT-PCR analysis, all samples were run in triplicates each with a reaction volume of 10 μL, using the QuantiFast SYBR® Green PCR Kit (Thermo Fisher) or TB Green Premix Ex Taq II (Tli RnaseH Plus) Kit (Takara), 1 μM primers and 1 μL of cDNA. The reaction was performed in a QuantStudio 6 Flex Real-Time PCR System (Thermo Fisher) or the StepOne Plus Real-Time PCR System (Thermo Fisher) using the following amplification parameters: 5 min at 95°C, 40 cycles of 10 s at 95°C and 1 min at 60°C. Data were analyzed using the ΔΔCT method as previously described81; expression levels were obtained normalizing each sample to the endogenous GAPDH control.

Allele-specific RT-PCR
Specific primers were generated to bind to the wildtype or to the mutant (4-bp deletion) MID1 allele (Supplementary Data 5). For each sample, two reactions were performed, one with the reverse wildtype primer, and one with the reverse mutant primer, both using the same forward primer. After an initial denaturation step (95°C for 2 min), 35 cycles of denaturation step (95°C for 30 s), primer annealing step (70°C for 30 s), and elongation step (72°C for 30 s) were followed by a final elongation step (72°C for 5 min). The PCR products were analyzed by separation on a 1.5% agarose gel with EtBr.

Quantification of allele-specific expression by pyrosequencing (QUASEP)
Amplification and sequencing primers (Supplementary Data 5) were designed by using the PyroMark Assay Design Software (v2.0, Qiagen) for the region of interest, with one of the amplification primers being biotinylated. PCR reactions were performed for each sample with the amplification primers using 60°C as annealing temperature. Each sample was measured in triplicates. The PCR product was then used for pyrosequencing following the standard protocol of the PyroMark Gold Q96 Reagents kit (Qiagen). A sequencing cartridge was prepared with all four nucleotides, the enzyme, and the substrate mix for sequencing, with the volumes calculated by the Pyro QC pG (Qiagen) software. Each sample was measured in triplicates. The results were analyzed using the same software.

Bulk RNA-sequencing
M-ctrl and J-ctrl lines. The library for RNA-seq experiments was prepared from 5 ng of total RNA using the Ovation® Solo RNA-Seq Library Preparation Kit (Tecan) following the manufacturer’s instructions. The library was denatured for sequencing by mixing 5 μl of the 4 nM library with 5 μl of NaOH (0.2 M). After incubating for 5 min, 5 μl of Tris buffer (200 nM Tris-HCl, pH 7) were added. Finally, 985 μl of prechilled HT1 were added. The sequencing run on a NextSeq 500/550 was performed as a paired-end run with 2 times 76 cycles and an expected output of 50 million reads per sample for allele-specific analysis.
A-ctrl line. NGS library prep was performed with Illumina’s Stranded Total RNA Prep Ligation with Ribo-Zero Plus Kit following Stranded Total RNA Prep Ligation with Ribo-Zero Plus Reference Guide (Document #1000000124514 v02 April 2021). Libraries were prepared with a starting amount of 613 ng and amplified in 10 PCR cycles. Two post PCR purification steps were performed to exclude residual primer and adapter dimers. Libraries were profiled in a DNA 1000 chip on a 2100 Bioanalyzer (Agilent technologies) and quantified using the Qubit dsDNA HS Assay Kit, in a Qubit 4.0 Fluorometer (Invitrogen by Thermo Fisher Scientific). All samples were pooled in equimolar ratio and sequenced on 1 NextSeq 2000 P3 flow cell, PE for 2×151 cycles plus 2×10 cycles for the dual index read and 1 dark cycle upfront R1 and Read 2.

Bulk RNA-seq data pre-processing
M-ctrl and J-ctrl lines. Sequencing reads were demultiplexed and base call (BCL) files were converted into Fastq files using bcl2fastq conversion software (v2.17.1.14, Illumina). Sequence adapters were trimmed and reads shorter than 6 bp were removed from further analyses using Cutadapt (v0.18). Quality control checks were performed on the trimmed data with FastQC (v0.11.7). Read mapping of the trimmed data against the human reference genome and transcriptome (hg19) was conducted using the STAR aligner (v2.5.3)82. PCR duplicates were removed from the mapped reads using the python script nudup.py (v2.3) provided from NuGen (https://tecangenomics.github.io/nudup/).
A-ctrl line. Adapter and quality trimming were performed with the BBDuk tool from BBMap (v38.86). Quality control was facilitated using FastQC (v0.11.9). Reads were then aligned against the human reference genome (hg19) using STAR (v2.7.10b). A count table was obtained with featureCounts from the Subread package (v2.0.1).

Allele-specific expression analysis (ASE) of bulk RNA-seq data
For ASE, all bulk RNA-seq data from the M-ctrl, J-ctrl and A-ctrl lines were processed with the NVIDIA Clara Parabricks Pipeline (v3.5) (https://www.nvidia.com/en-us/clara/genomics/). The built-in RNA pipeline rna_gatk was employed to align the fastq-files to the hg19 reference genome with the built-in STAR82 aligner. After coordinate sorting and marking duplicates of the resulting BAM file, a base recalibration step was performed before variant calling with built-in gatk HaplotypeCaller83,84. Analyses were restricted to the X-chromosome by defining -L chrX. For joint genotyping, a genomic database was built with built-in gatk GenomicsDBImport including all variants from all samples and subsequent genotyping of the X-chromosomal variants was performed with built-in gatk GenotypeGVCFs. Gene names were annotated by the Ensembl Variant Effect Predictor85 before transforming the resulting VCF file to a table format with gatk VariantsToTable for smaller file size and further downstream analysis in R. We used R base functions to remove all variants that did not intersect known genes, multi- and monoallelic variants, variants with a quality score <100 as well as intermediate and very long indels (>50 bp). Then we calculated the allelic ratio for each variant site by dividing the number of reads mapping to the reference allele (No. Ref) by the total number of reads covering the variant site (No. Total). For further processing, we set a threshold of at least 5 reads in all samples to consider a variant (SNP or indel) to be expressed. We then calculated average allelic ratios for each cell type (iPSCs, NPCs, and neurons) in the sequenced M-ctrl, J-ctrl and A-ctrl lines, respectively, by summing up all reads mapping to the reference allele across all replicate samples from the corresponding cell type and cell line and dividing this number by the total number of reads covering the variant site. This analysis was performed only including variants sites that were covered by at least 20 reads on the group level. To detect reactivated genes which are characterized by monoallelic expression in iPSCs but biallelic expression in NPCs and/or neurons, we first identified all variant sites with an allelic ratio <0.025 or >0.975 in iPSCs. The allelic ratio was converted to an estimate of the probability of expression p^ Xi from the inactive chromosome Xi using the following formula (1):
p^ Xi = No:Ref / No:total if No:Ref < No:Alt; No:Ref / No:total otherwise (1)
We then employed one-sided binomial tests to investigate if p^ Xi is significantly greater than 0.025, indicating statistically significant expression from the inactive X-chromosome. P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method implemented in the stats R package (v4.0.2). Results with an adjusted P<0.01 were considered statistically significant, indicative of a reactivated variant site. Variant sites were considered as escapees of XCI if p^ Xi is significantly greater than 0.1 in all cell types (iPSCs, NPCs, and neurons). To detect late-silenced ASE sites, we first identified all variants with p^ Xi <0.025 in NPCs and neurons and then used one-sided binomial tests to examine if Xi expression was significantly greater than 0.025 in iPSCs.

Gene-wide estimates of biallelic expression
For genes that intersected only one ASE variant site, we used the Xi expression for the respective variant as an overall estimate of biallelic expression for the whole gene. If a gene contained more than one type of biallelically expressed variants, the gene was assigned to a single category (reactivated, escapee or late-silenced) following a predefined set of rules: (1) the gene was assigned to the category with the highest number of biallelic variants (2) if a gene covered the same number of biallelic variants from different categories, then SNPs were considered as more reliable than indels, and variant of the same type with a higher coverage were preferred. After assigning genes to a unique category, we used the metaprop function from the meta R package (v4.18.1) to calculate gene-level estimates of biallelic expression from values for individual variants by using an inverse-variance method with logit-transformed proportions of Xi expression. 99% confidence intervals of biallelic expression based on a normal approximation were also obtained with the metaprop function.

Manual curation of biallelically expressed variant sites
All predicted biallelic variant sites were subjected to manual inspection. 22 variants from M-ctrl, 14 variants from J-ctrl and 59 variant sites from the A-ctrl line were flagged as false positives. In most cases the predicted biallelic expression for these sites was due to all but one replicate expressing the reference allele and only one the alternative (or vice versa). These positions were then excluded from the analysis.

X-chromosome ideograms
Ideograms showing the cytogenetic location of reactivated, escape and late-silenced genes were produced with the karyoploteR package (v1.4.1) based on the hg19 genome assembly for human and mm10 for the mouse. Gene coordinates were obtained from the Ensembl/Gencode annotation.

Overlap with known escape genes in humans
The overlap of reactivated, full escape and late-silenced genes in this study with known escape genes in humans was obtained using escape status assignment from two previous studies8,13. To calculate an expected value, we performed 1000 random sampling iterations by drawing the number of genes in each category (reactivated: n=95; full escapees: n=82; late-silenced: n=29) from all X-linked genes in the Ensembl annotation and determining the overlap with known escape genes. The enrichment was calculated by dividing the observed overlap by the mean value for the expected overlap from the 1000 sampling iterations. The significance of the observed overlap was obtained using the cumulative distribution function of the normal distribution.

Overlap with known escape genes in the mouse
To assess the escape status of orthologues of reactivated, full escape and late-silenced genes in the mouse, we utilized a recently published study by Hauth et al.32, which described constitutive, variable and NPC-specific escape genes. Enrichment of previously reported mouse escapees for our reactivated, full-escapee and late-silenced genes was calculated as described above for the overlap with previously reported escapees in humans.

Enrichment of NDD-associated genes
To obtain the overlap of reactivated, full-escape and late-silenced genes with NDD genes, we used the recently published GeneTrek database containing high-confidence NDD genes16. Then, we calculated the expected overlap of biallelically expressed genes with NDD genes by randomly sampling the 513 X-chromosomal genes present in the GeneTrek database that are also expressed in the NPCs and neurons in our study (>10 reads in each sample following DESeq2 (v.1.42.1) normalization of the data). In each one of 1000 iterations we randomly drew the number of genes present in the respective category (reactivated: n=95, late-silenced: n=29, escapee: n=82) and determined the number of NDD associated genes in each class. The mean value over all iterations was determined as the expected number of NDD genes and the enrichment was calculated as the ratio between observed/expected number of NDD genes in each class. The significance of the observed overlap in each class was determined by the cumulative distribution function of the normal distribution.

Pairwise distance analysis of biallelically expressed genes
To investigate if different types of biallelically expressed genes cluster together on the X-chromosome, we calculated the mean pairwise distance between transcription start sites for reactivated, escapee, and late-silenced genes, respectively. We tested the probability of observing a lower mean pairwise distance by chance by comparing the actual distance to a background distribution of pairwise distances for 1000 sets of randomly selected X-linked genes which were expressed in NPCs and neurons in our bulk RNA-seq data (>10 reads in each sample following DESeq2 (v1.42.1) normalization of the data). The P-value of observing a lower mean pairwise distance by chance was obtained with the cumulative distribution function of the normal distribution.

Protein–protein interaction network of reactivated genes
To construct a protein–protein interaction (PPI) network of reactivated genes, we employed the data provided by the human binary reference interactome (HuRi)86 available at http://www.interactome-atlas.org/. The 54 protein-coding reactivated genes that were included in the HuRi portal, were used to create the network. To construct a differentiation-specific PPI, we filtered for genes that were expressed in all NPC and neurons samples in our bulk RNA seq data (>10 reads in each sample following DESeq2 (v1.42.1) normalization). We employed the igraph package (v1.2.6) in R to visualize the network. GO term analysis of the PPI network of reactivated genes was performed with the clusterProfiler package (v3.16.1) using the default settings of clusterProfiler, that is an upper-tail hypergeometric test with Benjamini Hochberg correction for multiple comparisons. GO terms with an adjusted P-value below 0.05 were considered significant. Redundant GO terms were combined using the simplify function.

Chromatin state analysis of biallelic genes
To investigate the association between biallelic expression and chromatin state, we employed the 15-state chromatin model from the Roadmap Epigenomics Consortium30 using the female (sample E082) and male (sample E081) fetal brain samples. The annotation files for the epigenomes were downloaded from https://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html. We used the ChromDiff tool87 to estimate the percentage of each gene body corresponding to each of the 15 chromatin states in the two epigenome samples. We then compared the chromatin state profiles of the 83 reactivated genes, the 63 full-escapees and 20 late-silenced genes that were also included in the GENCODE annotation files distributed with the ChromDiff tool against the profiles of X-chromosomal genes expressed in NPCs and neurons (>10 normalized reads in all samples) but not showing biallelic expression in our assay and not being previously reported as escapee genes8,13 (inactive genes, n=128). We then performed one-sided Wilcoxon rank sum tests to investigate the enrichment of epigenomic states in reactivated, full-escapee and late-silenced compared to inactive genes separately in the female and male fetal brain samples. P-values were adjusted for multiple comparisons with the Benjamini Hochberg method and an epigenomic state was considered enriched in the respective gene category if the adjusted P-value was <0.05.

Metaplots of histone modification signal in female fetal brain tissue
Metaplots for H3K36me3, H3K9me3, H3K27me3 and H3K4me1 signal for reactivated, full-escape, late-silenced and inactive genes in female fetal brain tissue from 17-week-old embryos were produced using the computeMatrix and plotProfile functions from deepTools v3.5.4. bigWig files with fold change over control were obtained from ENCODE, reference epigenome ENCSR189GMC.

Sex-biased expression of reactivated, escapee, and late-silenced genes
Information about sex biases in the expression of the biallelic and inactive genes predicted here was extracted from the study by Oliva13 and colleagues which investigated systematic differences of gene expression between males and females across 44 human tissues from GTEx V8. We compared the distributions of the number of tissues with a female bias in each of our gene categories compared to inactive genes using Fisher’s exact test.

Disease-ontology enrichment analysis
Disease ontology enrichment analysis for reactivated genes was performed using the DOSE R package v3.28.2 with an adjusted p-value cut-off of 0.05. X-linked genes from the Ensembl annotation were used as the background universe for this analysis.

Single cell RNA-seq data generation
For the scRNA-seq experiment, organoids were dissociated using the Neural Tissue Dissociation Kit P (Miltenyi Biotec). Briefly, selected organoids were cut into smaller pieces, washed with medium, and three times for 5 min with 1x PBS. Organoid pieces were transferred to a tube containing the enzyme mix P (according to the manufacturer’s protocol) and incubated at 37°C for 10 min. Pieces were then triturated gently with a 1000p pipette tip and incubated for another 10 min at 37°C in the presence of enzyme mix A (according to the manufacturer’s protocol). Pieces were then triturated gently with a 1000p and a 200p pipette tips and incubated for 5 min at 37°C. Cell suspension was filtered with a 30 µm filter (Miltenyi Biotec) and centrifuged at 300 × g for 5 min. After a second filtration step with a 20 µm filter (Miltenyi Biotec) and subsequent centrifugation as described above cell pellet was resuspended in 100 µl 1x PBS (without Ca2+ and Mg2+). Cells were counted and tested for viability with Trypan Blue and the automated cell counter Countess (Thermo Fisher). Cells were diluted to an appropriate concentration to obtain approximately 5000 cells per lane on a 10X Next GEM chip G v3. Libraries were constructed according to the Chromium Single Cell 3´ Reagent Kit User Guide (v3 Chemistry Dual Index) of 10X genomics and sequenced on an Illumina NovaSeq 6000.

Single nucleus RNA-seq data generation
To generate single cell solutions, organoids were dissociated using the Neural Tissue Dissociation Kit P as described above, while iPSCs were detached and singularized using Accutase. Cells were counted and tested for viability with Trypan Blue and the automated cell counter Countess. Nuclei were isolated following 10X Genomics CG000124 Rev F–Nuclei Isolation from Cell Suspensions & Tissues for Single Cell ENA Sequencing with slight adaptations. Briefly, 2.5×106 cells were pelleted at 400g for 5 min at 4°C. Cells were lysed using 0.025% Nonidet P40 Substitute (Millipore-Sigma), 10 mM NaCl (Millipore-Sigma), 3 mM MgCl2 (Millipore-Sigma) in Ambion Nuclease-Free water (Invitrogen) for 4 min. Lysis was stopped using Nuclei wash (1x PBS (without Ca2+ and Mg2+), 1% BSA (Carl Roth), 0.2 U/µL RNase Inhibitor (Millipore-Sigma)) and centrifuged at 500 × g for 10 min at 4°C. Nuclei were washed twice with Nuclei wash. Nuclei were counted and tested for successful cell lysis with Trypan Blue and the automated cell counter Countess. Two cell lines were multiplexed leveraging SNPs for later demultiplexing. Nuclei were diluted to an appropriate concentration to obtain approximately 10,000 cells per lane on a 10X Next GEM chip G v3. Libraries were constructed according to the protocol of 10X Genomics and sequenced on an NovaSeq 6000.

Single nucleus and single cell RNA-seq data preprocessing, clustering, visualization
The functions count and aggr of the Cell Ranger software (10x Genomics, v4.0.0–6.0.2) were used for aligning to the GRCh38 reference genome and for sequencing depth normalization. The multiplexed single nucleus RNA-seq data was demultiplexed using souporcell88 (v2.5) using default settings. Scanpy89 (v1.8.1) was used for further preprocessing, clustering, embedding, and visualization. Cells were excluded in which we detected less than 500 genes, less than 3000 UMI counts, more than 50,000 counts, fraction of the transcriptome of less than 1% or more than 10% mitochondrial genes. Moreover, genes expressed in less than 5 cells were excluded from further analysis. Doublets were identified with Scrublet90 (v0.2.3) and filtered out. After normalization and log transformation, highly variable genes were determined using the default settings in Scanpy. Cell cycle scores, percentage of mitochondrial genes, and number of detected UMIs were regressed out to reduce their confounding effects. PCA analysis with the arpack wrapper in SciPy (v1.7.0) followed by determining the 15 closest neighbors in the top 20 PCs, batch integration using bbknn’s (v1.4.0)91 Euclidian metric and force-directed graph embedding utilizing ForceAtlas292 provided by the python package fa2 (v3.5) was used for embedding the transcriptomes. Clustering was performed with Scanpy’s python implementation of the Leiden93 algorithm (v0.8.7). For a coarse-grained mapping of clusters, their connectivity and lineages we applied the Scanpy implementation of the partition-based graph abstraction (PAGA)89.

Single nucleus and single cell RNA-seq RNA velocity and pseudotime estimation
To determine spliced and unspliced transcripts, the Cell ranger produced BAM files were sorted by the cell barcode with samtools (v1.10) and counted with velocyto (v0.17)94. RNA velocity estimation was then done with scVelo (v0.2.3)95. In detail, moments were calculated with the ‘connectivities’ mode on the top 50 PCs and 10 closest neighbors. After recovering the velocity dynamics, latent time was calculated, a measure for the developmental time (pseudotime) exclusively depending on transcriptional dynamics. RNA velocity was then computed, using latent time, differential kinetics and highly variable genes with the stochastic model.

Differential gene expression and GO term analysis in the single cell RNA-seq data
Differentially expressed genes in NPC were determined between J-ctrl and J-OS/hom in the clusters 0, 1, 2, 3, 4, 5, and 6 of the neural lineages embedding using the rank_genes_groups function of Scanpy and testing for significance with the Wilcoxon rank sum test. Genes with an adjusted P<0.01 and an absolute log2 fold change >0.25 were considered differentially expressed. GO analysis was done with the R (v4.1.2) package TopGO (v2.44.0) considering genes expressed in NPCs as background and excluding GO terms with less than 5 annotated genes from the analysis. The default weight01 algorithm was applied to determine the GO enrichment. Only GO terms in which more than 3 differentially expressed genes were found and with a P-value <0.05 as determined by Fisher’s exact test were considered.

Transcriptional deviation
To determine the transcriptional deviation from control we determined for each cluster the differentially expressed genes between J-ctrl and J-OS/hom as well as M-ctrl and M-OS/het with P<0.01 and an absolute log2 fold change >2 (excluding cluster 11 due to the lack of cells from the J-OS/hom condition). For each differentially expressed gene we calculated the average expression in both controls (M-ctrl and J-ctrl) in this cluster. For each differentially expressed gene in each cell of the cluster we determined the fold change over the average expression of this gene in the corresponding control. For upregulated genes: cellgene/(average ctrl gene); for downregulated genes: 1/(cellgene/(average ctrl gene)). In each cell of the cluster, we determined the mean fold change of all cluster-specific differentially expressed genes as a measure for the transcriptional deviation. To determine the cluster-specific transcriptional deviation, we calculated the mean transcriptional deviation of all cells of that cluster in a cell line-specific manner. To resolve the temporal pattern of transcriptional deviation, the single cell values of transcriptional deviation were plotted against the RNA velocity based pseudotime estimation (latent time). The MID1 expression levels as well as the ratio of SOX2/SNAP25 expression were calculated by binning the pseudotime in 50 equally sized bins and calculating the mean values in J-ctrl cells.

Processing and analysis of published data of developing human spinal cord
The raw fastq files from the human developing spinal cord single cell RNA-seq experiments in the Rayon dataset34 were downloaded using fasterq-dump (v3.0.0). Processing of the raw reads was similar to the organoid single cell RNA seq data described earlier. In brief, we used the 10x genomics cell ranger (v. 7.1.0-8.0.0) functions, count and aggr to map the raw reads to the human reference genome GRCh38 and for sequencing depth normalization and then employed Scanpy89 (v1.8.2) for preprocessing, clustering, embedding, and visualization. Cells were excluded in which we detected less than 500 genes, less than 1000 UMI counts, more than 20,000 counts, fraction of the transcriptome of less than 1% or more than 10% mitochondrial genes. Moreover, genes expressed in less than 5 cells were excluded from further analysis. Female samples were selected and further analyzed as described above for the organoids single cell RNA-seq data. The developmental pseudotime was estimated using the diffusion pseudotime96 implementation in Scanpy. The POU5F1, NANOG, SOX2, NES, DCX and SNAP25 expression levels were calculated by binning the pseudotime in 40 equally sized bins and calculating the mean expression value of all cells in each pseudotime bin.

SNP calling and variant counting in sc and snRNA-seq data
To detect expressed heterozygous SNPs on the X-chromosome we pseudo-bulked the sequencing data of each sample and used FreeBayes (v1.3.5) for detection. The bam files generated by cell ranger for each sample were used to detect intragenic heterozygous variants with a PHRED quality score of at least 20 for each allele. Variants with a sequencing depth normalized threshold of at least 50 reads per 5×108 total reads were considered for further analysis in the single cell RNA-seq data and 2 reads per 5×108 total reads for the single nucleus RNA-seq data. The information on X-chromosomal variants from each line were then used for allele-specific alignments with the WASP97 implementation in the STAR aligner (v2.7.8a)82. Using NumPy (v1.21.4) and pandas (v1.3.4) in python (v3.9.7), the number of UMIs for each variant in each cell was determined. We then divided the number of UMIs of the most abundant allele by the total number of UMIs for that site (monoallelic=1, biallelic with 50% transcripts from each allele=0.5) and calculate the natural logarithm of this ratio as a measure for the allelic expression of a given gene in a given cell (monoallelic=0, biallelic with 50% transcripts from each allele=0.69). To calculate the degree of general biallelic expression from the X-chromosome in a cell, the values of biallelic expression for all biallelically expressed X-chromosomal genes were summed up. Pseudoautosomal genes were assigned using the HUGO gene nomenclature annotation for PAR1 and PAR2.

Statistics and reproducibility
Data were statistically analyzed with GraphPad Prism, R or Microsoft Excel using statistical tests indicated throughout the manuscript. At least 3 biological replicas were used for every experiment, unless otherwise stated. No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation and outcome analysis. The experiments were not randomized.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study addresses a complex and highly relevant question, offering novel insights into the dynamic reactivation of X chromosomes during normal brain development and in neurodevelopmental disorders, as exemplified by Opitz syndrome (OS). This is an important and timely area of research. Overall, this is a very well executed study but we have a few comments that may improve the presentation:\nMajor comments:\n1. The bioinformatics approach appears generally good. However, we find it difficult to assess the robustness of allele identification using SNPs derived from bulk RNA-seq data. Could the authors specify the read length used? It would be very helpful to see the distribution of chrX gene expression before and after applying the >20 reads per allele filter. Why was this particular threshold chosen? I understand that such cutoffs are somewhat arbitrary, but without seeing the distribution, it's challenging to evaluate the validity of the choice.\n2. Related to the above comment. How do the authors ensure that the number of reactivated genes identified in A-ctrl is not an artifact of this thresholding strategy? (seems like an outlier in comparison to the other two cell lines) What is the divergence in SNP capture across samples? Could these findings be influenced by technical variables such as sequencing depth? Are the same thresholds applied uniformly across all three cell lines? If so, we believe a relative, or normalized measure (e.g., adjusted for sequencing depth) would be a more robust approach.\n3. The observed transcriptional changes in the organoids are very interesting! Did the authors also observe any differences in organoid size during differentiation?\n4. A critical assumption underlying the paper is the stability of X-inactivation in iPSCs derived from fibroblasts. This point is mentioned but not directly supported with data. Given that the authors established these cell lines, providing a validation of chrX innactivation stability would strengthen the manuscript.\n5. Regarding the mutant clones: I’m missing the sanger sequencing of the mutant clones. The authors demonstrate reduced protein levels and size, but the manuscript is missing to document the specific sequence changes.\n6. Could the authors provide a theory or comment on possible explanations on why the discrepancy between their results and prior reports? (suggesting that gene reactivation is dependent on XIST dosage).\nMinor comment:\nThe abbreviation “pm” in the Fig. 4I heatmap is not defined in the figure legend.", "reviewer_2": "Neurodevelopmental disorders (NDDs) often present differently between sexes. While males may exhibit more severe symptoms during childhood, females frequently experience more pronounced challenges during adolescence and adulthood. In this manuscript, the authors test the hypothesis that dynamic X-chromosome reactivation provides a protective mechanism in female neural tissue, thereby reducing the frequency and severity of NDDs in females.\nThey explore this hypothesis using a range of experimental models and approaches, addressing a significant knowledge gap regarding the developmental timeline of X-chromosome inactivation (XCI), escape, and the emergence of facultative escape in human cells. By monitoring XCI and escape throughout neural differentiation from a defined point of origin, the authors uncover that many of the reactivating genes are linked to NDDs. Importantly, they demonstrate that differentiation-specific facultative escape is a general phenomenon, not limited to humans.\nA particularly novel aspect of the study is the mechanistic insight into the X-linked MID1 gene and its role in Opitz BBB/G syndrome (OS). The experiments are well-designed, and the manuscript is clearly written. I support its publication and offer the following suggestions to further strengthen the conclusions:\nMajor Points:\n1. Could the authors provide a list of all X-linked genes identified in their study that are associated with NDDs and intellectual disability, in addition to MID1? It would also be helpful if they could validate the expression of these genes at the protein level using publicly available protein databases.\n2. Regarding Figure 1D, the color coding is unclear. Could the authors clarify which genes are activated in female subjects and which are classified as facultative escapees?\n3. The time point chosen for brain organoid analysis (day 30) appears to be relatively early in development. This stage may be too immature to fully assess the impact of MID1 on neuronal identity and maturation. Could the authors justify this choice or consider extending the analysis to later stages?\n4. The current data on MID1 are not sufficient to explain the microcephaly and cerebellar hypoplasia observed in OS patients. Additional morphological analyses are needed to determine whether MID1 dysregulation leads to enrichment of microcephaly-associated genes or causes premature death of neural progenitor cells (NPCs).\n5. Is MID1 expressed differentially in adult male and female brains? The organoid data suggest that biallelic expression peaks at the progenitor stage and diminishes during differentiation. Does this imply that MID1 is primarily required during the NPC stage rather than for later neuronal specification?\n6. It is unclear is MAP2+ neurons have lower- or upper-layer neuronal identity.\n7. It remains unclear whether the observed decrease in MAP2 expression affects the physiological or functional properties of neurons. Could the authors provide data on synaptic dynamics. These data would support the genotype–phenotype correlations in human Opitz syndrome.\n8. In addition to the observed increase in p21 expression across all three genotypes, it would be interesting to assess whether Protein Phosphatase 2A (PP2A) levels are also elevated, as PP2A has been implicated in the phenotype associated with Opitz syndrome.", "reviewer_3": "Major comments:\n• This is a timely study about a little understood topic, X-chromosome inactivation. This study tries to elucidate the effects of XCI during neural differentiation, while also unravelling the effects of facultative X-reactivation.\n• Unfortunately, the findings in this manuscript are not well put together. In an effort to explain as many of the X-linked differentiation phenotypes as possible, the authors have taken a very diffuse approach that is not hypothesis-driven.\n• The authors have tried to integrate publicly available data and undertake mouse studies to validate their findings in human iPSCs. Neither approach is well executed, and leaving them out would be recommended.\n• The authors seemed to be struggling with how to present their data. The figures are a mess. There are too many figure panels with too much information. A number of figure panels do not actually contribute to the conclusions in the manuscript and can be moved to the supplementary figures. Some figure panels in both main figures and supplementary figures have not been labelled correctly or figure legends do not explain the figure panels accurately.\n• The section in organoids is commendable. The breadth of information that can be extracted from this single cell experiment can become the centre-stage of the manuscript. However if the authors feel that the other assays such as QUASEP provide more relevant information, then they should be more judicious about which single cell graphs to add to the main figures. There is a problem with too much information in the main figures that spans the entire manuscript.\n• The number of techniques and methods used in this manuscript is commendable, but their implementation does not always add to the central theme. Related to this matter, the titles for the sub-sections in the ‘Results’ sections should reflect the result, instead of the methods used. For example, “X-reactivation during neural differentiation of human iPSCs” is a good title, but “Analysis of gene expression from the inactive X-chromosome with single cell resolution” is just a description of methods. It may appear that the manuscript was written by multiple authors and hasn’t been integrated properly.\n• The evidence in support of Opitz BBB/G syndrome (OS) is not well integrated into the manuscript. The clinical symptoms of OS have been mentioned multiple times in the manuscript which is unnecessary. This can be limited to the introduction. The organoid study with OS iPSCs and MID1 iPSCs is very exciting and can be emphasised in the manuscript. Surprisingly, there is no mention of OS in the abstract.\nSpecific comments:\n1. Page 5: “Importantly, reactivated and late-silenced genes showed no significant correlation between overall gene expression levels and allelic expression, confirming that reactivation and late-silencing were not spuriously detected due to lower overall expression in the respective cell types”\n• This statement needs further explanation, or validation. Although it is clear there is silencing and reactivation going on, it is not clear how to interpret Fig1(H).\n2. Figures S1 and S2 labelling is confusing. For example, Fig S1B, C, D have figure legends saying ‘C’ and ‘G’. This is repeated in Fig S2. It is not clear what C and G mean in this context.\n3. Figure 1B, D, F: Can you point out differentiation stages (iPSC, NPCs, neurons) here please?\n4. Page 5: “Further characterization of the different types of biallelically expressed genes revealed that genes of all three classes shared a significantly higher overlap than expected by chance with previously reported biallelically expressed X-chromosomal genes including facultative escapees”\n• The method and data source used to undertake this further analysis is not clear. Although there is mention of “44 human tissues” in the next sentence, it is unclear where the tissue are coming from or what they are.\n• Authors should add the source of data in the text and reason for using it.\n5. Figure S3E: Is this M-ctrl?\n• Figure labelling is inconsistent. For example, in Fig S3B, the cell line name is on top and antibodies on the right side. This is switched in Figures S3C onwards.\n6. Page 7: “Conservation of dynamic escape in the mouse”\n• It is unclear why the authors have added X-inactivation escape study in mouse in an otherwise human study.\n• Conservation of interspecies regulatory elements between mouse and human would not be unexpected, so the mouse data does not add anything to the paper other than just validating the human data.\n• The authors could try a re-interpretation of this section.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-point response to reviewers from manuscript NCOMMS-25-40254\nWe thank the reviewers for thoroughly reviewing our manuscript and providing critical insights\nand feedback. Based on the recommendations, we have now substantially revised the manuscript\nand added new data and experiments to support and strengthen our main findings.\nWe have restructured the manuscript, revised, and edited the text accordingly. Relocated or new\nparagraphs are highlighted in light grey throughout the manuscript.\nBelow we provide a summary of the major revisions and changes based on the main and\nsupplementary figures.\nDetailed changes:\nFigure 1 - addition of data showing an increase in biallelic expression of target\ngenes upon neural differentiation on single cell level. New Figures 1F-K\nrelocated from previous Figure 3.\n- addition of a graphical scheme exemplifying mono- and biallelic\nexpression in the presence of XIST; new Figure 1E\n- relocation of previous Figures B, D, F to new Figure S1\n- relocation of previous Figure 1H to new Figure S1\nFigure 2 - relocation of previous Figure 2A to new Figure S3\n- addition of legend in Figure 2C\nFigure 3 - new Figure 3 now dedicated to show biallelic expression in the mouse\n- previous Figures 3A-H were relocated to the new Figure 1 and the new\nFigure S3\nFigure 4 - unchanged\nFigure 5 - unchanged\nFigure 6 - unchanged\nFigure S1 - Figures S1F-H were relocated from previous Figure 1\n- Figure S1I was relocated from previous Figure 1\n- new Figure S1J was added\nFigure S2 - unchanged\nFigure S3 - Figures S3G, H were relocated from previous Figure 3\n- Figure S3M was relocated from previous Figure 2\nFigure S4 - unchanged\nFigure S5 - Figures S5A, B were relocated from previous Figure 6\nFigure S6 - new Figure S6 showing the Sanger sequencing results of the genome-\nedited iPSC lines used in this study.\nFigure S7 - Figure S7 derived from previous Figure S6 which was split into two new\nFigures, now Figures S7 and S8 to increase clarity of data representation\nFigure S8 - Figure S8 derived from previous Figure S6 which was split into two new\nFigures, now Figures S7 and S8 to increase clarity of data representation\nFigure S9 - new Figure S9 with newly incorporated data set showing the molecular\nbasis of the gradual brain organoid phenotype depending on the\npresence or absence of a wildtype MID1 allele on the inactive X-\nchromosome in the iPSC lines. These data further support the\ncharacterization of the phenotype shown in Figures 5 and 6.\nFigure S10 - Figure S10 was relabeled from previous Figure S7\nReviewer #1 (Remarks to the Author):\nThis study addresses a complex and highly relevant question, offering novel insights into the\ndynamic reactivation of X chromosomes during normal brain development and in\nneurodevelopmental disorders, as exemplified by Opitz syndrome (OS). This is an important and\ntimely area of research. Overall, this is a very well executed study but we have a few comments\nthat may improve the presentation:\nWe thank the reviewer for the time spent in reviewing our manuscript and for recognizing the\nimportance of this study. Moreover, we thank the reviewer for the constructive criticism which we\naddress in detail below.\nMajor comments:\n1. The bioinformatics approach appears generally good. However, we find it difficult to assess the\nrobustness of allele identification using SNPs derived from bulk RNA-seq data. Could the authors\nspecify the read length used? It would be very helpful to see the distribution of chrX gene\nexpression before and after applying the >20 reads per allele filter. Why was this particular\nthreshold chosen? I understand that such cutoffs are somewhat arbitrary, but without seeing the\ndistribution, it's challenging to evaluate the validity of the choice.\nWe fully agree with the reviewer’s comment that the robustness of the analysis of allele-specific\nexpression using heterozygous variant sites from the bulk RNA-seq data is essential for the\nvalidity of results. Therefore, we paid special attention to ensure the reliability of our methodology.\nAs outlined in the “Allele-specific expression analysis (ASE) of bulk RNA-seq data” method\nsection of our manuscript, we performed multiple stringent quality filtering steps including\nremoving variants with a quality score <100, filtering out multi-allelic variants and long indels (>50\nbp), performing statistical analyses only for variant sites with >20 reads with an adjusted p-value\nthreshold of 0.01, and ultimately also performing a manual curation step to remove ambiguous\npredictions from the analysis. As specified in the “Bulk RNA-sequencing” section, the read length\nwas 76 bp for the M-ctr and J-ctrl lines and 151 bp for the A-ctrl line, for which sequencing was\nperformed at a later time point. We understand that our selected thresholds for the number of\nreads per variant site might appear arbitrary, however they were chosen after careful\nconsideration and literature research to balance the trade-off between specificity and sensitivity\nfor the analysis. For instance, previous studies have used a cut-off of 7 (Wainer Katsir and Linial,\n2019) or 10 (Tukiainen et al., 2017) reads to select informative SNPs and the prediction of biallelic\nexpression was performed using individual samples. In our analysis, we have 4 replicates for\nNPCs and neurons and 6-8 replicates for iPSCs in the M-ctrl and J-ctrl line as well as 3 replicates\nin the A-ctrl cell line. A group level analysis with at least 20 reads per variant site (and > 40 reads\nin case of 8 replicates) in our opinion increases the statistical rigor while also accounting for\nbiological variability in contrast to previous studies. As the reviewer requested, we also looked at\nthe number of candidate variant sites that showed indication of biallelic expression before and\nafter applying our filter for the number of reads as illustrated in Point-by-point letter Figure 1 (PP\nFig.1) below. Importantly, filtering reduced the candidate list by 82% to 93% showing that our\nfiltering is very stringent and conservative (PP Fig. 1a). While we did a hard filtering for variant\nquality scores (>100), the filter for the number of reads supporting a variant site led to a further\nsignificant increase in the distribution of the quality scores of the variants included in the final\nstatistical analysis (PP Fig. 1b). Interestingly, while the number of individual candidate variant\nsites varied between cell lines, on the gene level we observed a comparable number of genes\nincluding informative variant sites (PP Fig. 1c). Furthermore, even after applying our filtering for\nthe number of reads, we still observed that the candidate variant sites were distributed along the\nwhole X-chromosome (PP Fig. 1d), therefore our analysis was not biased towards a particular\nregion on the X-chromosome.\nPoint-by-point letter Figure 1. Quality control of candidate variant sites for prediction of biallelic\nexpression. a, Bar plot with the number of candidate variant sites before filtering for the number\nof reads (unfiltered) and after applying the >=20 reads per group filter (filtered) in each cell line.\nb, Box plots with variant quality scores before and after applying the >= 20 reads filter. c, Bar plot\nwith the number of genes containing candidate variant sites before filtering for the number of\nreads (unfiltered) and after applying the >=20 reads per group filter (filtered) in each cell line. d,\nDistribution of candidate variant sites along the X-chromosome before and after applying the >=\n20 reads per group filter. ****, p<0.0001.\n2. Related to the above comment. How do the authors ensure that the number of reactivated\ngenes identified in A-ctrl is not an artifact of this thresholding strategy? (seems like an outlier in\ncomparison to the other two cell lines) What is the divergence in SNP capture across samples?\nCould these findings be influenced by technical variables such as sequencing depth? Are the\nsame thresholds applied uniformly across all three cell lines? If so, we believe a relative, or\nnormalized measure (e.g., adjusted for sequencing depth) would be a more robust approach.\nAs outlined above, we took multiple precautions to limit the effect of technical variables on our\npredictions of biallelically expressed genes. However, we acknowledge that the impact of such\nfactors can never be completely excluded from any large-scale statistical analysis. We indeed\ndetected a higher number of biallelic genes in the A-ctrl line. Sequencing of this line was\nperformed at a later time point and while sequencing depth was comparable with sequencing for\nthe M-ctrl and J-ctrl lines, we increased the read length from 76 to 151 bp with the aim of\nincreasing the sensitivity of SNP calling. Subsequently, the quality filtered table used as input in\nthe statistical analysis for A-ctrl indeed included a higher number of candidate variant sites but a\ncomparable number of genes containing informative variants (see PP Fig. 1). Moreover, we\napplied the same stringent filtering criteria for all cell lines and the higher number of starting\npotential candidate sites in A-ctrl is mitigated by the correction for multiple comparisons which\nbecomes more conservative when the data set is bigger. Therefore, we are convinced that the\nhigher number of reactivated genes in the A-ctrl line is not an artifact but reflects the increased\nsensitivity of the analysis in this cell line. Furthermore, we would like to draw the reviewer’s\nattention to the overlap of biallelic genes detected in multiple cell lines which we highlight in Figure\nS1 in the manuscript. For instance, MID1 which takes center stage in the second part of our\nanalysis, is confidently detected as reactivated in all three cell lines. Furthermore, our genes are\nsignificantly enriched for previously reported constitutive and facultative escape genes in humans\n(Figure S3M, Table S2) which strengthens the validity and specificity of our methodological\npipeline.\n3. The observed transcriptional changes in the organoids are very interesting! Did the authors\nalso observe any differences in organoid size during differentiation?\nWe thank the reviewer for appreciating the organoid phenotyping. We have not observed any\ndifference in the size of the organoids on day 30. While we are seeing a reduction of MAP2\npositive neurons, PAX6 expressing progenitors are enriched in the OS organoids on day 30. At\nthis time point progenitor cells are still an important cellular fraction in the developing organoid. In\nOS patients, microcephaly is a variable symptom and is diagnosed relatively late during\npregnancy (around week 31). Due to the disproportion between MAP2 positive neurons and PAX6\npositive progenitor cells we found in the organoids, similar to the OS patients, we expect to see a\nreduction of organoid size later, when neurons are getting the predominant cell type.\n4. A critical assumption underlying the paper is the stability of X-inactivation in iPSCs derived from\nfibroblasts. This point is mentioned but not directly supported with data. Given that the authors\nestablished these cell lines, providing a validation of chrX inactivation stability would strengthen\nthe manuscript.\nThe reviewer raises a very important point. The long noncoding RNA XIST is the master regulator\nof X-inactivation. It is expressed from the inactive X-chromosome and influences its epigenetic\nlandscape so that gene expression is repressed. Another hallmark of X-inactivation is a territory\nof H3K27me3 modification found on the inactive X-chromosome. XIST expression and detection\nof H3K27me3 territories indicate stable X-inactivation, while loss of the two marks can mean that\nX-inactivation is unstable and eroded. We have monitored the X-(in)activation status throughout\nour experiments by visualizing XIST expression and H3K27me3 modification using RNA-FISH\n(Figures 1F-K, Figures S3B, S3D-L), immunofluorescence (Figures S3B, S3C, S3E) and single\ncell / single nucleus RNA sequencing (Figure 4). Hence, we show on single cell resolution that\nXIST and H3K27me3 modification is present in the lines used throughout the study. However, in\nthe M-lines (patient lines) and some of the J-lines XIST expression was very low. In order to make\nsure that the observed reactivation was not a result of unstable X-inactivation, we compared\nreactivation patterns in XIST high and low expressing cells. Similar levels of reactivation were\nseen in XIST high- and low expressing cells throughout the entire manuscript (Figure 4F). In order\nto show stable inactivation of selected loci in iPSCs we furthermore used allele-specific RT-PCR\nover many different passages and found reliable inactivation (Figure S8D), which gives additional\nsupport that differentiation induced reactivation of gene expression from the inactive X-\nchromosome is independent of erosion phenomena.\nWe adapted the paragraphs addressing stability of X-inactivation found across the manuscript\n(line 162 ff, 377 ff, 514 ff) to clarify and strengthen this point raised by the reviewer.\n5. Regarding the mutant clones: I’m missing the sanger sequencing of the mutant clones. The\nauthors demonstrate reduced protein levels and size, but the manuscript is missing to document\nthe specific sequence changes.\nWe thank the reviewer for pointing this out. We have added the sanger sequencing profiles as a\nnew supplementary figure (Figure S6).\n6. Could the authors provide a theory or comment on possible explanations on why the\ndiscrepancy between their results and prior reports? (suggesting that gene reactivation is\ndependent on XIST dosage).\nWe thank the reviewer for this suggestion. It is true, our data suggest that full escape on one hand\nand reactivation of gene expression from the inactive X-chromosome on the other hand follow a\ndifferent molecular logic. While for full escapees the expression of alleles from the inactive X-\nchromosome correlates with XIST levels, reactivated genes do not show such a dependency\n(Figure 4F). The XIST dependency of full escapees is in line with previous observations showing\nthat the expression of escapees depends on XIST dosage. Similarly, earlier studies showed that\nerosion of the XCI due to the loss of XIST expression results in expression of alleles from the\ninactive X-chromosome in the iPSCs. The reactivated genes described in our study are\nmonoallelically expressed in iPSCs over many passages (Figure 1B, S8D) and only become\nbiallelically expressed after differentiation and hence describe a different entity than eroding\ngenes. Genes biallelically expressed throughout the whole lineage from iPSC to differentiated\ncells (full escapees as well as eroded genes) are determined by the establishment of X-\ninactivation. Reactivation occurs later, when X inactivation is already stably established.\nMoreover, it has been described that X-inactivation maintenance can be independent of XIST.\nWe think that reactivation of expression from alleles from the X-chromosome follows a distinct\nmechanism that is induced by specific stimuli, in our case differentiation and likely entails the\nsubstantial chromatin reorganization occurring during differentiation. This is similar to recent\nstudies showing reactivation from the inactive X-chromosome due to aging, a process also\ncorrelating with reorganization of the chromatin.\nWe discuss these aspects in the present manuscript on line 514-530.\nMinor comment:\nThe abbreviation “pm” in the Fig. 4I heatmap is not defined in the figure legend.\nThe reviewer is right. We apologize for this mistake and added the abbreviation (pm = paraxial\nmesoderm).\n—----------------------------------------------------------------------------------------------------------------------------\nReviewer #2 (Remarks to the Author):\nNeurodevelopmental disorders (NDDs) often present differently between sexes. While males may\nexhibit more severe symptoms during childhood, females frequently experience more pronounced\nchallenges during adolescence and adulthood. In this manuscript, the authors test the hypothesis\nthat dynamic X-chromosome reactivation provides a protective mechanism in female neural\ntissue, thereby reducing the frequency and severity of NDDs in females.\nThey explore this hypothesis using a range of experimental models and approaches, addressing\na significant knowledge gap regarding the developmental timeline of X-chromosome inactivation\n(XCI), escape, and the emergence of facultative escape in human cells. By monitoring XCI and\nescape throughout neural differentiation from a defined point of origin, the authors uncover that\nmany of the reactivating genes are linked to NDDs. Importantly, they demonstrate that\ndifferentiation-specific facultative escape is a general phenomenon, not limited to humans.\nA particularly novel aspect of the study is the mechanistic insight into the X-linked MID1 gene and\nits role in Opitz BBB/G syndrome (OS). The experiments are well-designed, and the manuscript\nis clearly written. I support its publication and offer the following suggestions to further strengthen\nthe conclusions:\nWe thank the reviewer for valuable comments and acknowledging the novelty of our data as well\nas the breadth of our question.\nMajor Points:\n1. Could the authors provide a list of all X-linked genes identified in their study that are associated\nwith NDDs and intellectual disability, in addition to MID1? It would also be helpful if they could\nvalidate the expression of these genes at the protein level using publicly available protein\ndatabases.\nWe have reported the association between biallelic genes detected in our study and\nneurodevelopmental disorders in supplementary Table S2. However, we apologize that we had\nnot explicitly mention this point in the main text of the manuscript. In the revised version the\nreference to the list asked by the reviewer is mentioned in lines 313-317.\n34 out of 95 reactivated genes intersected with high confidence NDD genes as reported in Figure\n5A. This overlap was significantly higher than expected by chance. Furthermore, 15 out of the 82\nfull-escapees and 5 out of the 29 late-silenced genes are also associated with NDDs, however\nthe overlap was not statistically significant (Figure 5A, Table S2).\nAs suggested by the reviewer, we also examined the protein expression of NDD-associated\nreactivated genes by employing the Human Protein Atlas (Uhlén et al., 2015). This\ncomprehensive resource includes semi-quantitative data from immunohistochemistry\nexperiments that classify protein levels as not detected, low, medium and high across different\ntissues. Looking at the brain regions included in the atlas - cerebral cortex, cerebellum,\nhippocampus and caudate - we confirmed that NDD-associated reactivated genes are expressed\nin brain tissue at the protein level mostly at high and medium levels (PP Fig. 2).\nPoint-by-point letter Figure 2. Protein expression of NDD-associated reactivated genes in\ndifferent brain regions. Expression values were obtained from the Human Protein Atlas.\n2. Regarding Figure 1D, the color coding is unclear. Could the authors clarify which genes are\nactivated in female subjects and which are classified as facultative escapees?\nWe apologize if the color code was misleading in the figure and not described in the figure legend.\nSince the previous Figure 1D has only one color, we assume that the reviewer potentially refers\nto Figure 2D? Here, the colors are indicating the reactivated, full escapees (left side of the\nideogram), and late silenced genes. We have added a color code legend inside the relocated\nfigure (now Figure 2C) next to the ideogram and apologize for the inconvenience.\n3. The time point chosen for brain organoid analysis (day 30) appears to be relatively early in\ndevelopment. This stage may be too immature to fully assess the impact of MID1 on neuronal\nidentity and maturation. Could the authors justify this choice or consider extending the analysis to\nlater stages?\nWe thank the reviewer for this very important comment. It is true that the d30 timepoint is rather\nearly, yet we do see a considerable fraction of both progenitors and neurons at this stage. In the\nbulk sequencing approach we saw that a significant proportion of reactivation takes place\nbetween iPSCs and NPCs (Figure 1B), so rather early during differentiation. This is supported by\nthe single nucleus data that showed that dual allele usage is more frequent at the progenitor level\n(SOX2 positive, Figure 4F) and by the re-analysis of the scRNA-seq data of human spinal cord\n(Figures 4J, S4L). In this analysis we found substantially more reactivation in the early (CS14)\nembryos compared to the older (CS19) embryos (see Figure 4K). In a pseudotime analysis we\nperformed with the newly generated scRNA-seq data (new Figure S9), we further see that the\nimpact on the overall transcriptional deviation in the MID1 mutant samples is highest at the\nprogenitor level (new Figure S9J).\nIn order to study the role of reactivation of gene expression from the inactive X-chromosome on\ncellular phenotypes caused by NDD genes, we have therefore focused on the early organoids in\nthis study. In the newly incorporated scRNA-seq data (Figure S9) iPSC lines of all genotypes\ncontribute similarly to the different neuronal lineages present (PP Fig. 3)\nWe fully agree that in a follow-up study it will be interesting to see how organoids behave when\ngrowing older. In this context, very recently, gene reactivation has been described in the aging\nprocess (Gadek et al., 2025; Hoelzl et al., 2025).\nPoint-by-point letter Figure 3. Force-directed graph embedding shows similar contribution of\nthe experimental groups to all lineages.\n4. The current data on MID1 are not sufficient to explain the microcephaly and cerebellar\nhypoplasia observed in OS patients. Additional morphological analyses are needed to determine\nwhether MID1 dysregulation leads to enrichment of microcephaly-associated genes or causes\npremature death of neural progenitor cells (NPCs).\nWe thank the reviewer for the comment. To address this question, we generated scRNA-seq data\nfrom brain organoids of patient and genome-edited lines carrying the C-terminal 4-bp deletion in\nthe X-chromosomal MID1 gene either hetero- or homozygously. Analysis of this scRNA-seq data\nset revealed ZIC1 and ZIC2 as deregulated genes (Figure S9E) when comparing ctrl and mutant\nprogenitors. Mutations in ZIC1 and ZIC2 lead to Dandy-Walker cysts (Grinberg et al., 2004)\ncharacterized by malformation and hypoplasia of the cerebellum. Interestingly, similar to MID1\nthese genes regulate neuronal differentiation (Aruga et al., 2002). Hence, our data suggest a\npartial overlap and a surprising molecular crosstalk linking OS and Dandy-Walker pathogenesis.\nMoreover, compared to wildtype, OS NPCs showed MID1 genotype dependent (Figure S9H)\ngradual deregulation of more general cell cycle regulators associated with proliferation and\ndifferentiation (e.g. CDK6, CCND1, KIF2A, NIN, CDC42, Figure S9H) (Broix et al., 2018; Cappello\net al., 2006; Grison et al., 2018; Lange et al., 2009; Shinohara et al., 2013), suggesting that altered\ncell cycle progression accounts for the accumulation of NPCs at the expense of neurons in OS\nbrain organoids (Figures 5C, D, Figures S7A-D), an observation in line with the BrdU data\ndescribed in the manuscript (Figure SH-N) and that suggest that MID1 controls the balance\nbetween proliferation and differentiation of NPCs.\nThese data were added to the revised manuscript.\n5. Is MID1 expressed differentially in adult male and female brains? The organoid data suggest\nthat biallelic expression peaks at the progenitor stage and diminishes during differentiation. Does\nthis imply that MID1 is primarily required during the NPC stage rather than for later neuronal\nspecification?\nPrevious prominent studies that have conducted comprehensive investigations on sex biased\ngene expression in humans based on the GTEx project, have not reported differential expression\nof MID1 in adult female and male brains (Lopes-Ramos et al., 2020; Oliva et al., 2020).\nThe reviewer is right that MID1 expression is more pronounced in progenitors than neurons (see\nFigure S9J) where it controls the balance between proliferation and differentiation. The newly\nincorporated scRNA-seq data reveal a gradual deregulation of more general cell cycle regulators\nassociated with proliferation and differentiation (e.g. CDK6, CCND1, KIF2A, NIN, CDC42, Figure\nS9H) (Broix et al., 2018; Cappello et al., 2006; Grison et al., 2018; Lange et al., 2009; Shinohara\net al., 2013), suggesting that altered cell cycle progression accounts for the accumulation of NPCs\nat the expense of neurons in OS brain organoids (Figures 5C, D, Figures S7A-D). Moreover,\nmutation of MID1 alters the transcriptional landscape strongest at the progenitor levels (Figure\nS9J) highlighting the importance of MID1 early during neuronal differentiation, as suggested by\nthe reviewer.\nTo follow up on this observation, we examined MID1 expression in the developing human brain\nby employing data from the BrainSpan Atlas (https://www.brainspan.org/). In agreement with our\norganoid data, MID1 expression peaked at earlier developmental stages (post conception week\n8-9, 12) and decreased at later stages in both female and male subjects (PP Fig. 4).\nPoint-by-point letter Figure 4. Heatmap showing MID1 expression at different stages of\nembryonic brain development in female and male subjects. Values correspond to log2 RPKM.\nData were obtained from the BrainSpan Atlas (https://www.brainspan.org/).\n6. It is unclear is MAP2+ neurons have lower- or upper-layer neuronal identity.\nWe thank the reviewer for raising this important aspect. In fact, the newly incorporated scRNA-\nseq data of organoids generated from the M-, and J-lines revealed that these lines produce\norganoids with predominant hindbrain identity (new Figure S9). Therefore, the MAP2+ neurons\nare not cortical and thus neither of lower nor upper-layer identity. In the new Figure S9 we now\nshow the different lineages present in the organoids, i.e. rhombic lip progenitors (ATOH1) giving\nrise to glutamatergic cells expressing SLC17A6, VZ progenitors (PTF1A) differentiating into\nGABAergic cells expressing GAD2 (Figure S9C).\n7. It remains unclear whether the observed decrease in MAP2 expression affects the physiological or functional properties of\nneurons. Could the authors provide data on synaptic dynamics. These data would support the genotype–phenotype\ncorrelations in human Opitz syndrome.\nOur organoid tissue phenotyping is based on an image analysis pipeline in which SOX2 and\nMAP2 pixels are thresholded and counted. We have observed significantly less area covered by\nMAP2 with a concomitant increase in SOX2 positive areas in OS organoids indicative of less\nneurons and more progenitors. However, we did not perform a quantification of the protein levels\nof MAP2 expression within organoid-resident neurons.\nTo address the question asked by the reviewer we leveraged the newly incorporated scRNA-seq\ndata (Figure S9) to determine scores of gene sets related to neuron maturation (GO:0042551),\nsynapse organization (GO:0050807), and stem cell proliferation (GO:0072089) across samples\nand along pseudotime (PP Fig. 5). While stem cell proliferation associated gene set showed a\ngenotype dependent difference early in the lineage as expected by the cellular data described in\nour manuscript (Figures 5, 6 S7, S10), neither the gene sets associated with neuronal maturation\nnor synapse organization showed such a genotype dependent deregulation.\nPoint-by-point letter Figure 5. The scRNAseq dataset from hindbrain organoids was used to\ncalculate expression scores for genes associated with the GO terms neuron maturation\n(GO:0042551), regulation of synapse organization (GO:0050807), and stem cell proliferation\n(GO:0072089). The resulting scores were plotted along pseudotime (0=NPC, 1=neurons).\n8. In addition to the observed increase in p21 expression across all three genotypes, it would be\ninteresting to assess whether Protein Phosphatase 2A (PP2A) levels are also elevated, as PP2A\nhas been implicated in the phenotype associated with Opitz syndrome.\nWe have not looked at PP2A levels in the organoids yet. PP2A is highly abundant in all cells of\nthe brain. We had shown that only the microtubule-associated subunit PP2Ac is regulated in a\nubiquitin and MID1 dependent manner (Trockenbacher et al., 2001). Quantification of a\nsubcellular fraction of such a highly expressed protein in a selected group of cells on the protein\nlevel is very difficult. We will pick this suggestion of the reviewer up in a follow-up study.\nHowever, the radial glia cell marker PAX6, has also been described to be a target of MID1-\ndependent ubiquitination and degradation (Pfirrmann et al., 2016). Quantification of PAX6 protein\nlevels indeed revealed a substantial increase within the VZLS (ventricular zone like structure) in\nOS organoids carrying a homozygous loss-of-function mutation in MID1 (J-OS/hom, A-OS/hom)\ncompared to wildtype control. OS organoids carrying such a mutation heterozygous (M-OS/het,\nJ-OS/het, A.OS/het) showed intermediate PAX6 protein levels supporting a gradual rescue by\nreactivation of MID1 (Figures 6A-D).\n—----------------------------------------------------------------------------------------------------------------------------\nReviewer #3 (Remarks to the Author):\nMajor comments:\n• This is a timely study about a little understood topic, X-chromosome inactivation. This study tries\nto elucidate the effects of XCI during neural differentiation, while also unravelling the effects of\nfacultative X-reactivation.\nWe thank the reviewer for the time spent in reviewing our work, considering our work timely, and\nproviding constructive feedback and suggestions.\n• Unfortunately, the findings in this manuscript are not well put together. In an effort to explain as\nmany of the X-linked differentiation phenotypes as possible, the authors have taken a very diffuse\napproach that is not hypothesis-driven.\nWe appreciate the comment by the reviewer and considered it when revising the manuscript. We\nhave involved a professional editor (https://www.lifescienceeditors.com) to assist in restructuring\nthe manuscript so that the reader can more easily understand what has been done.\n(1) We have moved the RNA-FISH experiments from Figure 3 to Figure1 since this is a\nvalidation of the bulk sequencing results, we are describing in Figure 1 on a single cell\nlevel.\n(2) This led to Figure 3 solely describing conservation of dynamic allele usage of X-linked\ngenes in the mouse.\n(3) We have added further scRNA-seq data describing the OS cellular phenotype in organoids\nand showing phenotype rescue through reactivation on a transcriptional level (Figure S9).\n(4) We have divided the result section and have added headings describing the findings rather\nthan technical approaches.\n(5) We have moved parts of Figure 1 (previous Figures 1B, D, F, H), Figure 2 (previous Figure\n2A), Figure 3 (previous Figures 3G, H) into a supplementary figure to increase\ncomprehensibility of the main figures.\n(6) We are now mentioning the OS observations in the abstract.\n(7) We have added a description of the OS patient phenotype to the introduction and removed\nit from the result section.\nWe think that with these changes the manuscript has improved substantially. We agree with the\nreviewer that the organoid data and the rescue seen by MID1 reactivation are the core of the\nmanuscript and think that with the new structure this comes across.\nIn the first part of our study, we describe our observations on the dynamic differentiation stage-,\nlineage- and locus-specific allele-usage of X-chromosomal genes, supported by a variety of\ndifferent techniques. Based on the results from this broader explorative analysis, we then\nformulated the concrete hypothesis that the reactivation, hence biallelic expression of specific X-\nchromosomal genes, may have a direct impact on disease development. Subsequently, in the\nsecond hypothesis-driven part of our work, we then tested our hypothesis and showed that\nreactivation of genes from the previously inactive allele can directly result in the amelioration of a\ndisease-phenotype in the context of a neurodevelopmental disorder (Opitz BBB/G syndrome).\nWe apologize for the inconvenience and hope that the changes we have added to the manuscript\nnow better unravels the logic of our study.\n• The authors have tried to integrate publicly available data and undertake mouse studies to\nvalidate their findings in human iPSCs. Neither approach is well executed, and leaving them out\nwould be recommended.\nThe main observations in our study were made in in vitro systems, raising the question whether\nthe observed developmental stage-, lineage and locus-specific reactivation of gene expression\nfrom the inactive X-chromosome also occurs in vivo. We therefore consider the mouse data that\ndemonstrate a similar phenomenon in vivo in mouse embryos relevant. Likewise, we think that\nthe reanalysis of in vivo data from the developing spinal cord of human embryos is very important.\nThese data show that, as in the in vitro system, in human embryos allele usage of X-chromosomal\ngenes is highly dynamic. Both systems provide essential and independent evidence for the fact\nthat in vivo, besides the previously known escape genes, a substantial additional fraction of genes\nis dynamically accessed during neural development.\n• The authors seemed to be struggling with how to present their data. The figures are a mess. There are too many figure panels with too much information. A number of figure panels do not actually contribute to the conclusions in the manuscript and can be moved to the supplementary figures. Some figure panels in both main figures and supplementary figures have not been labelled correctly or figure legends do not explain the figure panels accurately.\nWe apologize for potential mistakes in the figure legends which we of course corrected.\nOn the initiative of the reviewer, we have substantially restructured and revised the manuscript\n(description see above). We have also involved a professional editor (see above) and think that\nthe manuscript has benefitted substantially.\n• The section in organoids is commendable. The breadth of information that can be extracted from\nthis single cell experiment can become the centre-stage of the manuscript. However, if the authors\nfeel that the other assays such as QUASEP provide more relevant information, then they should\nbe more judicious about which single cell graphs to add to the main figures. There is a problem\nwith too much information in the main figures that spans the entire manuscript.\nWe thank the reviewer for the comment on our organoid data and for appreciating the extent of\nknowledge gain. In fact, as described above we included a new supplementary Figure (Figure\nS9) further corroborating the organoid phenotyping data. We have restructured the manuscript\nand have streamlined the figures based on the advice of the reviewer. The organoid data and the\nobservations we made with the OS organoids are now at the core of the manuscript (see above).\nWe think that the manuscript has improved substantially and thank the reviewer for the valuable\nadvice.\n• The number of techniques and methods used in this manuscript is commendable, but their\nimplementation does not always add to the central theme. Related to this matter, the titles for the\n‘Results’ sections should reflect the result, instead of the methods used. For example, “X-reactivation during neural differentiation of human iPSCs” is a good title, but “Analysis of gene expression from the inactive X-chromosome with single cell resolution” is just a description of methods. It may appear that the manuscript was written by multiple authors and hasn’t been integrated properly.\nWe are grateful to the reviewer to point out this inconsistency in the titles of the result sections\nand we changed them accordingly. Now all the titles describe the relevant findings of the\nrespective paragraphs.\n• The evidence in support of Opitz BBB/G syndrome (OS) is not well integrated into the manuscript. The clinical symptoms of OS have been mentioned multiple times in the manuscript which is unnecessary. This can be limited to the introduction. The organoid study with OS iPSCs and MID1 iPSCs is very exciting and can be emphasised in the manuscript. Surprisingly, there is no mention of OS in the abstract.\nWe thank the reviewer for the recognition of the OS data in the manuscript. We fully agree. We\nhave included it in the abstract and streamlined the description of OS throughout the manuscript\nto avoid redundancy (see above).\nSpecific comments:\n1. Page 5: “Importantly, reactivated and late-silenced genes showed no significant correlation\nbetween overall gene expression levels and allelic expression, confirming that reactivation and\nlate-silencing were not spuriously detected due to lower overall expression in the respective cell types”\n• This statement needs further explanation, or validation. Although it is clear there is silencing and\nreactivation going on, it is not clear how to interpret Fig1(H).\nWe appreciate that the reviewer recognizes the occurrence of reactivation and late-silencing in\nour experimental set-up. However, one of our main priorities in the analysis was to ensure that\nour predictions are robust and not due to technical artifacts. Therefore, it is crucial to demonstrate\nthat the lack of biallelic expression in iPSCs for reactivated genes, for example, is not attributable\nto an overall absence of gene expression in this cell type. The correlation plot in (now relocated\nto Figure S1H; previous Figure 1H) shows that our estimates for biallelic expression are in fact\nnot dependent on overall gene expression for reactivated and late-silenced genes. To further\nclarify this point, we here provide an example plot with overall expression levels and biallelic\nexpression across differentiation stages for MID1 and have added this to the manuscript (Figure\nS1J and PP Fig. 6) below). This representation clearly demonstrates that MID1 is stably\nexpressed in all cell types, however, biallelic expression is detectable only in NPCs and/or\nneurons. As it is not feasible to show individual plots for all genes, the correlation plot in Figure\nS1H is a compact representation summarizing these results. We have updated the manuscript\ntext to convey this point more clearly.\nPoint-by-point letter Figure 6. Overall gene expression (left) and biallelic expression (right) for\nMID1 at different differentiation stages in the three cell lines A, M and J. Overall expression is\nshown as log2 transcripts per million (TPM) counts and biallelic expression corresponds to the\nallelic ratio of reads mapping to Xi to the sum of reads mapping to Xi and Xa. Xi: inactive X-\nchromosome, Xa: active X-chromosome.\n2. Figures S1 and S2 labelling is confusing. For example, Fig S1B, C, D have figure legends\nsaying ‘C’ and ‘G’. This is repeated in Fig S2. It is not clear what C and G mean in this context.\nWe apologize that this labelling was not intuitive. The QUASEP methodology relies on\nheterozygous variant sites (e.g. single nucleotide polymorphisms) to quantify allele specific\nexpression. The small boxes with the letter therefore indicate the DNA base at each of the two\nalleles for the respective heterozygous variant site. The plot then shows the relative distribution\nof the expression of either one or the other allele as revealed via QUASEP. We added this\ninformation to the respective figure legend.\n3. Figure 1B, D, F: Can you point out differentiation stages (iPSC, NPCs, neurons) here please?\nThe UpSet plots shown in previous Figures 1B, D and F have been moved to Figure S1 in\naccordance with the reviewer’s suggestion to optimize the main figures. In the plots we show the\nquantification of the number of biallelically expressed genes in each category and respective cell\nline as well as overlaps between cell lines. Here each type of biallelically expressed gene is\nassociated with a particular differentiation stage: reactivated genes are monoallelic in iPSCs and\nbecome biallelic in NPCs/neurons, full escapees are biallelic at all differentiation stages (iPCSc,\nNPCs and neurons) and late-silenced genes are biallelic in iPSCs and become monoallelic in\nNPCs and neurons. We hope this description clarifies the results.\n4. Page 5: “Further characterization of the different types of biallelically expressed genes revealed\nthat genes of all three classes shared a significantly higher overlap than expected by chance with\npreviously reported biallelically expressed X-chromosomal genes including facultative escapees”\n• The method and data source used to undertake this further analysis is not clear. Although there\nis mention of “44 human tissues” in the next sentence, it is unclear where the tissue are coming from or what they are.\nIn an effort to not overcrowd the results, we included a very detailed description of how each\nanalysis was performed and which data set was used only in the Methods section. The\nmethodology for the analysis of the overlap with previously reported escape genes with our\nresults, for example, is described in the section “Overlap with known escape genes in humans” in\nthe Methods. However, we acknowledge that this might make it difficult for the reader to follow\nour motivation for conducting certain analyses while reading the main text. We have therefore\nupdated the main text to include more details on source data and methodology and we hope that\nour results are now clearer and more understandable.\n• Authors should add the source of data in the text and reason for using it.\nWe have provided detailed information on the source data for each analysis in the Methods\nsection of the manuscript. To improve the clarity of the manuscript, we have now followed the\nreviewer’s suggestion and have included further details on the source data and our motivation for\nintegrating different data sets in the main text of the result section.\n5. Figure S3E: Is this M-ctrl?\nFigure S3E shows results for the hoik1 cell line which is another XIST high expressing control\nline. The information of all lines we used in our study is summarized in Table S1. We apologize if\nthis was not clearly pointed out.\n• Figure labelling is inconsistent. For example, in Fig S3B, the cell line name is on top and\nantibodies on the right side. This is switched in Figures S3C onwards.\nWe thank the reviewer for their attention to detail. We adjusted the labeling to be consistent within\neach figure and to increase clarity.\n6. Page 7: “Conservation of dynamic escape in the mouse”\n• It is unclear why the authors have added X-inactivation escape study in mouse in an otherwise\nhuman study.\nThe main observations in our study were made in in vitro systems, which raises the question if\nthe observed developmental-stage-, lineage and locus-specific reactivation of gene expression\nfrom the inactive X-chromosome can be observed in vivo. We therefore consider the mouse data\nthat demonstrate a similar phenomenon in vivo in mouse embryos relevant. We changed the\nfigure layout (solely mouse data in the new Figure 3 now) and adapted the result text to better\nconvey this message.\n• Conservation of interspecies regulatory elements between mouse and human would not be\nunexpected, so the mouse data does not add anything to the paper other than just validating the\nhuman data.\n• The authors could try a re-interpretation of this section.\nFacultative escape is less well described in mouse compared to human. Some substantial\ndifferences in the dosage compensation and the inactivation of one X-chromosome between the\ntwo species have been described. We think that with the reactivation of X-chromosomal genes\nwe describe an important phenomenon during neural differentiation. We are using the mouse data\nto show that, even if they display some differences in XCI compared to humans, dynamic escape\nfrom X-inactivation exists in the murine nervous system. As suggested by the reviewer we have\nre-phrased and re-interpreted parts of the respective section.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank reviewer 4 for the time spent to review our work and for providing suggestions to further\nimprove our manuscript.\nReferences:\nAruga, J., Inoue, T., Hoshino, J. and Mikoshiba, K. (2002). Zic2 controls cerebellar\ndevelopment in cooperation with Zic1. J Neurosci 22, 218–25.\nBroix, L., Asselin, L., Silva, C. G., Ivanova, E. L., Tilly, P., Gilet, J. G., Lebrun, N., Jagline,\nH., Muraca, G., Saillour, Y., et al. (2018). Ciliogenesis and cell cycle alterations contribute\nto KIF2A-related malformations of cortical development. Hum Mol Genet 27, 224–238.\nCappello, S., Attardo, A., Wu, X., Iwasato, T., Itohara, S., Wilsch-Bräuninger, M., Eilken, H.\nM., Rieger, M. A., Schroeder, T. T., Huttner, W. B., et al. (2006). The Rho-GTPase cdc42\nregulates neural progenitor fate at the apical surface. Nat Neurosci 9, 1099–107.\nGadek, M., Shaw, C. K., Abdulai-Saiku, S., Saloner, R., Marino, F., Wang, D., Bonham, L.\nW., Yokoyama, J. S., Panning, B., Benayoun, B. A., et al. (2025). Aging activates\nescape of the silent X chromosome in the female mouse hippocampus. Sci Adv 11,.\nGrinberg, I., Northrup, H., Ardinger, H., Prasad, C., Dobyns, W. B. and Millen, K. J. (2004).\nHeterozygous deletion of the linked genes ZIC1 and ZIC4 is involved in Dandy-Walker\nmalformation. Nat Genet 36, 1053–5.\nGrison, A., Gaiser, C., Bieder, A., Baranek, C. and Atanasoski, S. (2018). Ablation of cdk4\nand cdk6 affects proliferation of basal progenitor cells in the developing dorsal and ventral\nforebrain. Dev Neurobiol 78, 660–670.\nHoelzl, S., Hasenbein, T. P., Engelhardt, S. and Andergassen, D. (2025). Aging promotes\nreactivation of the Barr body at distal chromosome regions. Nat Aging 5, 984–996.\nLange, C., Huttner, W. B. and Calegari, F. (2009). Cdk4/cyclinD1 overexpression in neural\nstem cells shortens G1, delays neurogenesis, and promotes the generation and expansion\nof basal progenitors. Cell Stem Cell 5, 320–31.\nLopes-Ramos, C. M., Chen, C.-Y., Kuijjer, M. L., Paulson, J. N., Sonawane, A. R., Fagny,\nM., Platig, J., Glass, K., Quackenbush, J. and DeMeo, D. L. (2020). Sex Differences in\nGene Expression and Regulatory Networks across 29 Human Tissues. Cell Rep 31,\n107795.\nOliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A. D. H., Cotter, D. J.,\nParsana, P., Kasela, S., Balliu, B., Viñuela, A., et al. (2020). The impact of sex on gene\nexpression across human tissues. Science 369,.\nPfirrmann, T., Jandt, E., Ranft, S., Lokapally, A., Neuhaus, H., Perron, M. and Hollemann,\nT. (2016). Hedgehog-dependent E3-ligase Midline1 regulates ubiquitin-mediated\nproteasomal degradation of Pax6 during visual system development. Proc Natl Acad Sci U\nS A 113, 10103–10108.\nShinohara, H., Sakayori, N., Takahashi, M. and Osumi, N. (2013). Ninein is essential for the\nmaintenance of the cortical progenitor character by anchoring the centrosome to\nmicrotubules. Biol Open 2, 739–49.\nTrockenbacher, A., Suckow, V., Foerster, J., Winter, J., Krauss, S., Ropers, H. H.,\nSchneider, R. and Schweiger, S. (2001). MID1, mutated in Opitz syndrome, encodes an\nubiquitin ligase that targets phosphatase 2A for degradation. Nat Genet 29, 287–94.\nTukiainen, T., Villani, A.-C., Yen, A., Rivas, M. A., Marshall, J. L., Satija, R., Aguirre, M.,\nGauthier, L., Fleharty, M., Kirby, A., et al. (2017). Landscape of X chromosome\ninactivation across human tissues. Nature 550, 244–248.\nUhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A.,\nSivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015). Tissue-based map of\nthe human proteome. Science (1979) 347,.\nWainer Katsir, K. and Linial, M. (2019). Human genes escaping X-inactivation revealed by\nsingle cell expression data. BMC Genomics 20, 201.\nPoint-by-point response to reviewers from manuscript NCOMMS-25-40254\nWe thank the reviewers for thoroughly reviewing our manuscript and providing critical insights\nand feedback. Based on the recommendations, we have revised the manuscript and\nhighlighted changes in the manuscript in grey."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all our comments.", "reviewer_2": "The authors have thoroughly addressed my concerns and have resubmitted a meticulously revised manuscript. The revisions significantly improve the clarity and scientific rigor of the work. I have no further comments and recommend acceptance.", "reviewer_3": "Generally, the manuscript reads much better and several of the major and specific comments have been satisfactorily addressed, but some of the structural problems still remain. I recommend the authors to address these.\n1. The issue with the findings not well put together, still remains. For example, the use of 2D neurons in the first results section “Reactivation of X-linked genes during neural differentiation” is confusing, especially since there is a big section on brain organoids later. X-inactivation status during differentiation appears to have been measured in brain organoids, so what is the merit of undertaking this in 2D neurons too? 2D neurons are an older model system with no obvious biological advantages compared to brain organoids.\n2. The structure of the manuscript still feels confusing. Shouldn’t the brain organoid work be presented up front, and mouse work used as validation?\n3. From my previous comments about figures, I found fig 4 to be especially busy. For example, can the authors explain figure 4(I)? It is not immediately clear why it has been included in this figure. The caveat of this heatmap is the very small font size for the individual genes which is impossible to read. If the purpose is to highlight certain gene clusters within the heatmap that should be explicitly highlighted on the panel itself and the figure legend should reflect that. If this heatmap is merely informative as suggested by the figure legend it should be in the supplementary figures.\n4. Could you add a schematic to explain you findings visually?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewers for thoroughly reviewing our manuscript and providing critical insights\nand feedback. Based on the recommendations, we have revised the manuscript and\nhighlighted changes in the manuscript in grey.\n---------------------------------------------------------------------------------------------------------------------------\nReviewer #1\nThe authors have addressed all our comments.\nWe thank the reviewer for the effort reviewing our manuscript.\n---------------------------------------------------------------------------------------------------------------------------\nReviewer #2\nThe authors have thoroughly addressed my concerns and have resubmitted a meticulously\nrevised manuscript. The revisions significantly improve the clarity and scientific rigor of the\nwork. I have no further comments and recommend acceptance.\nWe thank the reviewer for the time and effort devoted to evaluating our manuscript, and for\nexpressing appreciation for the improvements incorporated in the revised version.\n---------------------------------------------------------------------------------------------------------------------------\nReviewer #3\nGenerally, the manuscript reads much better and several of the major and specific comments\nhave been satisfactorily addressed, but some of the structural problems still remain. I\nrecommend the authors to address these.\nWe thank the reviewer for the time and effort in evaluating our manuscript, and for\nacknowledging the improvements made in the revised version.\n1. The issue with the findings not well put together, still remains. For example, the use of 2D\nneurons in the first results section “Reactivation of X-linked genes during neural\ndifferentiation” is confusing, especially since there is a big section on brain organoids later.\nX-inactivation status during differentiation appears to have been measured in brain\norganoids, so what is the merit of undertaking this in 2D neurons too? 2D neurons are an\nolder model system with no obvious biological advantages compared to brain organoids.\nWe appreciate the reviewer’s suggestion, and we agree that every model system has its\nadvantages and disadvantages. While brain organoids are a more elaborate system providing\na tissue-like context to model early developmental process they are composed of a plethora\nof different cell types (neural stem cells, neurons, astrocytes, choroid plexus cells, neural crest\ncells, melanocytes). The 2D differentiation to NPCs and neurons provides a much more\nhomogenous system in which the vast majority of the cells are either NPCs or neurons. The\nhuge technical advantage of such a homogenous system is that we can use bulk approaches\nto measure biallelic expression, such as bulk RNA-seq and QUASEP. In particular the bulk\nRNA-seq allows to describe the transcriptome of a system much deeper which is particularly\nimportant for the detection of expression from the inactive X-chromosome because those\ntranscripts are significantly less abundant than the ones from the active X-chromosome. A\nfurther big advantage of the 2D differentiation approach is that we can use pre-mRNA FISH to\ndetect biallelic expression and corelated it with XIST expression on a single cell level.\nImportantly, this is a completely independent method to validate the findings from bulk RNA-\nseq and single cell RNA-seq in organoids and human in vivo tissue. In sum, while providing a\nless elaborate biological model the 2D NPC and neuron differentiation system is more\namenable to different techniques (bulk RNA-seq, QUASEP, pre-mRNA FISH) rendering the 2D\nsystem crucial for the description and validation of our findings. To make this argumentation\nmore accessible to the reader we adapted the manuscript text accordingly.\n2. The structure of the manuscript still feels confusing. Shouldn’t the brain organoid work be\npresented up front, and mouse work used as validation?\nWe appreciate the reviewer’s suggestion. The logic in the manuscript is to first describe X-\nreactivation as a new concept in a more simple human model system (2D). In a second step\nwe show that this new concept is evolutionary conserved between humans and mouse. We\nthen dissect in more detail how this new concept of reactivation of X-chromosomal genes\ndiffers between cell types and differentiation lineages in organoids and in vivo in the developing\nhuman spinal cord. In the last section we show how the reactivation of X-chromosomal genes\ncontributes to the development of disease phenotypes. Thus, the mouse data do not serve\nsolely as a validation of the organoid experiments. Rather, they show that, despite fundamental\ndifferences in X-inactivation mechanisms between mouse and human, reactivation of gene\nexpression from the inactive X-chromosome is a conserved phenomenon across species. We\nadapted the manuscript to highlight this more.\n3. From my previous comments about figures, I found fig 4 to be especially busy. For\nexample, can the authors explain figure 4(I)? It is not immediately clear why it has been\nincluded in this figure. The caveat of this heatmap is the very small font size for the individual\ngenes which is impossible to read. If the purpose is to highlight certain gene clusters within\nthe heatmap that should be explicitly highlighted on the panel itself and the figure legend\nshould reflect that. If this heatmap is merely informative as suggested by the figure legend it\nshould be in the supplementary figures.\nWe appreciate the reviewer’s feedback regarding the complexity of the data in Figure 4, and\nacknowledge that the font size of the gene names in the heatmap (Fig. 4l) is quite small. The\nheatmap demonstrates lineage-specific biallelic expression of X-chromosomal genes, with\nsignificant variation across different lineages. This differentiation lineage-specific reactivation\nof X-chromosomal genes is a central finding of our work. To improve readability, we increased\nthe font size indicating the lineages. Furthermore, we have included a new supplementary\ntable (Table S4) listing all gene names (with the quantitative data showing the biallelic\nexpression) in addition to the figure. Moreover, we added a hierarchical clustering to the figure\nto highlight gene groups that behave similar across different lineages as suggested by the\nreviewer.\nThe upper section of Figure 4 (panels A-F) illustrates the snRNA-seq data generated from\nbrain organoids, while the lower section (panels G-K) displays data from the published human\nin vivo spinal cord scRNA-seq dataset. This creates a natural division between the two\ndatasets. Nevertheless, we believe it is important to present both datasets within a single\nfigure, as they collectively reveal the extent of biallelic expression in human tissue-like samples\n(organoids) and in vivo tissues. We have also refined the figure legend to further improve clarity\nand comprehensibility.\n4. Could you add a schematic to explain you findings visually?\nWe thank the reviewer for this excellent suggestion. In response, we have added two new\npanels to our figures to improve clarity in presenting our findings. Figure 1L now provides a\nsummary of the results obtained from our 2D system. Additionally, we have replaced the\nprevious Figure 6E with an updated version that summarizes the key conclusions drawn from\nthe patient-specific organoids, as well as the broader implications of our observations\n---------------------------------------------------------------------------------------------------------------------------\nReviewer #4\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank the reviewer for the time spent in reviewing our manuscript."}]

Decision letter