Loss of UBE3A impacts both neuronal and non-neuronal cells in human cerebral organoids

Estridge, R. Chris; Yagci, Z. Begum; Sen, Dilara; Johnson, Tyler J.; Kelkar, Gautami R.; Ptacek, Travis S.; Simon, Jeremy M.; Keung, Albert J.

doi:10.1038/s42003-025-08262-x

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Published: 30 May 2025

Loss of UBE3A impacts both neuronal and non-neuronal cells in human cerebral organoids

Communications Biology volume 8, Article number: 838 (2025) Cite this article

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Abstract

Angelman syndrome is a neurodevelopmental disorder caused by (epi)genetic lesions of maternal UBE3A. Research has focused largely on the role of UBE3A in neurons due to its imprinting in that cell type. Yet, evidence suggests there may be broader neurodevelopmental impacts of UBE3A dysregulation. Human cerebral organoids might reveal these understudied aspects of UBE3A as they recapitulate diverse cell types of the developing human brain. In this study, scRNAseq on organoids reveals the effects of UBE3A disruption on cell type-specific compositions and transcriptomic alterations. In the absence of UBE3A, progenitor proliferation and structures are disrupted while organoid composition shifts away from proliferative cell types. We observe impacts on non-neuronal cells, including choroid plexus enrichment. Furthermore, EMX1+ cortical progenitors are negatively impacted; potentially disrupting corticogenesis and delaying excitatory neuron maturation. This work reveals impacts of UBE3A on understudied cell types and related neurodevelopmental processes and elucidates potential therapeutic targets.

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Introduction

Human UBE3A is an imprinted gene located on chromosome 15¹. While both alleles are expressed in most tissues, only the maternal allele is active in neurons due to epigenetic silencing of the paternal allele^2,3,4. Loss of functional maternal UBE3A therefore results in a lack of UBE3A protein in neurons; this is the common lesion driving Angelman Syndrome (AS), a neurodevelopmental disorder characterized by intellectual disability, speech impairment, ataxia, and seizures⁵. Furthermore, its overexpression has been linked to other neurodevelopmental disorders including Dup15q and Autism Spectrum Disorder⁶. Thus, the dosage of UBE3A in the brain is likely critical for proper brain development and function⁷.

Deletion, mutation, or aberrant silencing of UBE3A in neurons have been associated with defects in GABAergic circuit development^8,9, synaptic formation and plasticity^10,11, altered neuronal proteasome^{8,12,13,14,15,16}, and defects in dendrite^17,18,19 and axon²⁰ growth. This prior research focus on neurons is for three primary reasons: (1) UBE3A is imprinted in neurons, and thus an affected maternal allele results in almost complete loss of UBE3A protein in neurons; (2) while loss of one allele could still result in lower UBE3A dosage in non-neuronal cell types, with functional consequences, these cell types can be challenging to access and study, particularly those present in prenatal neurodevelopment such as neuroepithelial stem cells and progenitors; and (3) the spatial and temporal scales of neurodevelopment differ dramatically between rodent and human. Thus, while clearly important in neurons, it is important to also consider the role of UBE3A in other cell types, technology permitting.

Indeed, experimental and clinical evidence supports the hypothesis that there may be broader impacts of UBE3A dysregulation. In addition to its ubiquitin ligase activity, UBE3A plays a role in transcriptional regulation^21,22 and has been reported to have centrosomal function^23,24. Furthermore, studies showing partial paternal imprinting in neural progenitors²⁵ and aberrant nuclear UBE3A in neural precursor cells²⁶ suggest that the effects of altered UBE3A dosage may not be limited to neurons. Relatedly, conditional deletion and expression of UBE3A in mouse models at pre and postnatal time points indicate UBE3A dosage is important not only in adulthood but also during prenatal neurodevelopment^27,28. There are also intriguing potential impacts of UBE3A dosage on cerebrospinal fluid (CSF), which is known to regulate both developmental and homeostatic processes of the brain^29,30. Increased extra-axial CSF production has been observed in individuals with autism spectrum disorder²⁹, and UBE3A has been found in CSF³⁰ suggesting UBE3A may affect the choroid plexus (ChP), the brain region responsible for CSF production.

Human cerebral organoids could be a useful and timely model to investigate these understudied aspects of UBE3A biology as they contain the cell types that have been previously challenging to study, provide access to human molecular and cell biology, are genetically and pharmacologically perturbable, and mimic early prenatal neurodevelopmental processes and states^31,32,33,34 including the proliferation and differentiation of stem cells into diverse neural cell types^{26,31,32,34,35}. Our lab has previously shown these organoids can recapitulate the spatiotemporal localization and paternal silencing of UBE3A²⁶. Electrophysiological studies have also revealed a potential mechanism contributing to epileptic phenotypes in AS³⁵. Here we exploit organoids and single-cell RNA-sequencing (scRNAseq) to reveal the compositional and cell type-specific transcriptomic effects^32,36 of complete and maternal UBE3A loss, with potential implications for understudied impacts of UBE3A on neurodevelopment, non-neuronal cells, and CSF biology. In this study, we performed scRNAseq on 6 week and 11 week human cerebral organoids generated from H9_WT and H9_{UBE3A m−/p−} pluripotent stem cells to study effects of complete UBE3A loss at different neurodevelopmental time points. We make two primary observations. In the absence of UBE3A, stem cells exit multipotency earlier towards neuronal and ChP fates. In addition, organoids derived from cell lines containing partial or complete disruption of UBE3A might exhibit disrupted corticogenesis.

Results

Absence of UBE3A alters the cell type composition of human cerebral organoids

Our central hypothesis is that UBE3A may have impacts on neurodevelopment beyond neuronal function. We therefore began by performing scRNAseq on whole brain human cerebral organoids derived from isogenic H9_WT and H9_{UBE3A m−/p−} pluripotent stem cells to assess how the absence of UBE3A affects cell type compositions and gene expression (Fig. 1). The H9_{UBE3A m−/p−} cell line was generated by the Chamberlain group^37,38,39 and contains homozygous deletions from around the start site of isoform 1 to just upstream of the stop codon (from the end of exon 4 to the upstream of the stop codon on exon 13 of transcript UBE3A-226, GRCh38). We confirmed the absence of UBE3A in H9_{UBE3A m−/p−} organoids (Supplementary Fig. S1A). We chose a relatively early time point of 6 weeks to capture a variety of cell types, from stem cells to immature neurons²⁶. Using 7498 high quality cells (median UMI count: 8278, median gene count: 3558; H9_WT: 3376 cells, H9_{UBE3A m−/p−}: 4122 cells), we identified 34 discrete cell clusters comprising of 15 cell types consisting of mainly radial glia (RG), intermediate progenitors (IPs), mesenchymal cells (Mes), choroid plexus (ChP), RSPO+ cells, and neurons (Fig. 2A, B; Supplementary Fig. S1B, C). There were RG and IP cells that also expressed cell cycle marker genes and were assigned as proliferating RG or IP. Neurons were partitioned into excitatory-like (EN) and inhibitory-like (IN) neuron clusters depending on the expression of excitatory and inhibitory marker genes (Supplementary Fig. S1C).

**Fig. 1: Schematic diagram of the overall workflow.**

**Fig. 2: Absence of *UBE3A* alters the cell type composition of human cerebral organoids.**

Both H9_WT and H9_{UBE3A m−/p−} organoids contained each cell type. However, quantifying the proportions of cell types revealed differences in organoid compositions (Fig. 2C–E). H9_WT organoids exhibited greater proportions of less mature, proliferative cell types (proliferating RG and proliferating IP) compared to H9_{UBE3A m−/p−} organoids. Interestingly, H9_{UBE3A m−/p−} organoids contained proportionally more mature cells like ENs, but this increase was not statistically significant. These compositional differences suggest the absence of UBE3A (i) might limit progenitor pools and (ii) may cause progenitors to prematurely exit multipotency and differentiate towards an EN lineage.

H9_WT and H9_{UBE3A m−/p−} organoids exhibit cell type specific differences in gene expression

We performed differential gene expression analyses to inform how the absence of UBE3A might alter organoid compositions and how different cell types are affected. We first analyzed the sequencing reads in bulk (Fig. 3A, B; Supplementary Data 1). In the absence of UBE3A, we observed 4206 differentially expressed genes (DEGs; 1973 down, 2233 up). This motivated further analysis for specific cell types, with EN, RG, and proliferating RG exhibiting the highest number of DEGs (Fig. 3C). Qiagen Ingenuity Pathway Analysis (IPA) revealed that the absence of UBE3A affected a plethora of canonical pathways (Fig. 3D; Supplementary Data 2). RG and proliferating RG cells exhibited upregulation for some neuronal signaling pathways (CREB signaling in neurons and opioid signaling pathway) and neuronal activity pathways (synaptic long-term depression) while these pathways were downregulated for EN (Fig. 3D; Supplementary Data 2). Cell cycle and division-related pathways (e.g., cell cycle checkpoints, mitotic prophase, mitotic prometaphase, mitotic metaphase and anaphase, telomere maintenance and telomerase signaling) were downregulated for RG and ENs (Supplementary Data 2). Apoptosis, senescence, and DNA damage-related pathways were also affected. RG exhibited decreased DNA damage-related pathways (role of BRCA1 in DNA damage response), apoptosis-related pathways (intrinsic pathway for apoptosis, Granzyme B signaling), and senescence-related pathways (oxidative stress induced senescence), whereas Granzyme A signaling was upregulated. Decreased telomerase signaling and increased senescence and intrinsic apoptosis pathways were present in proliferating RG. Senescence and ferroptosis signaling pathways were downregulated for ENs. Furthermore, many corticogenesis- and neurogenesis-related pathways including RHO GTPase cycle⁴⁰, estrogen receptor signaling⁴¹, docosahexaenoic acid (DHA) signaling⁴², EPH-Ephrin signaling⁴³, mTOR signaling⁴⁴, some WNT-related pathways^34,45,46,47, reelin signaling in neurons⁴⁸, intra-golgi and retrograde golgi to ER traffic⁴⁹, and signaling by ROBO receptors⁵⁰ were affected by the absence of UBE3A (Fig. 3D; Supplementary Data 2). Many cancer- and cardiac-related pathways were also deregulated by the absence of UBE3A (Supplementary Data 2) which is not surprising due to UBE3A’s potential role in these pathways/mechanisms^51,52. However, within the scope of the cerebral organoid model, we focused mainly on pathways related to neurodevelopment. Altogether, the absence of UBE3A in organoids influenced an extensive variety of pathways implicated in broad cellular processes ranging from corticogenesis to cell cycle in multiple cell types.

Absence of UBE3A disrupts progenitor proliferation and rosette structure

Our pathway analysis suggests proliferation, apoptosis and senescence might be affected (Fig. 3D; Supplementary Data 2). Furthermore, the absence of UBE3A has been linked to changes in cell proliferation and apoptosis in non-neuronal cell types^23,53,54. In addition, based on our 6 week scRNAseq cell type composition analysis, we also observed a decrease in proliferative cell types in the absence of UBE3A (Fig. 2C, E). Thus, as alterations in cellular processes such as cellular apoptosis, senescence, and proliferation can contribute to changes in cell type compositions, we hypothesized that the absence of UBE3A might drive compositional differences in organoids through one or more of these processes. First, through immunostaining, we did not observe differences on apoptosis or senescence in the absence of UBE3A (Supplementary Fig. S2A–C). However, we did observe an impact on proliferation; we performed an EdU assay on embryoid bodies generated from H9_WT, H9_{UBE3A m−/p−} and H9_{UBE3A m−/p+} pluripotent stem cells (Fig. 4A, B; Supplementary Fig. S2D). The percentage of EdU-positive cells were significantly lower for H9_{UBE3A m−/p−} and H9_{UBE3A m−/p+} compared to H9_WT organoids indicating that not only the absence of UBE3A but a reduced dosage of UBE3A leads to reduced cell proliferation in the progenitors of embryoid bodies. To check whether this inhibition of proliferation capacity happens at the pluripotent stem cell level as well, we performed an EdU assay on pluripotent stem cells (Figs. 4C, D; Supplementary Fig. S2E). The percentage of EdU-positive pluripotent stem cells were comparable between the cell lines, suggesting that UBE3A impacts proliferation only of non-pluripotent stem cells and progenitors. However, interestingly, the percentage of cells in G2/M phase for H9_{UBE3A m−/p−} pluripotent stem cells was significantly lower compared to H9_WT and H9_{UBE3A m−/p+} pluripotent stem cells, consistent with the role of UBE3A in cell cycle progression^23,54.

To investigate how UBE3A might be affecting cell proliferation, we revisited our pathway analysis. It suggested TP53 related pathways (i.e., regulation of TP53 activity through phosphorylation) and telomere related pathways (i.e., telomerase signaling and telomere maintenance) were altered in the absence of UBE3A (Supplementary Data 2). UBE3A is also known to interact with p53⁵⁵ and telomerase reverse transcriptase (TERT)⁵⁶. Interestingly, TP53 expression was unaffected in the H9_{UBE3A m−/p−} organoids; however, TERT expression was reduced to non-detectable levels in the absence of UBE3A (Supplementary Fig. S2F). We also measured TERT expression levels in embryoid bodies generated from H9_WT, H9_{UBE3A m−/p−} and H9_{UBE3A m−/p+} pluripotent stem cells. There was no significant difference between TERT levels in these embryoid bodies suggesting transcriptional changes occurred at a later timepoint in organoid development (Supplementary Fig. S2G).

We next turned our attention to potential additional downstream impacts of altered progenitor proliferation. For example, reduction in progenitor pools could result in decreased organoid size. Microcephaly is a common phenotype of AS^20,23 and has also been observed in organoid models of other neurodevelopmental disorders^31,34,57,58. We therefore measured organoid size and indeed observed H9_{UBE3A m−/p−} organoids were significantly smaller than H9_WT organoids (Fig. 4E, F). We chose the 3 week timepoint for size measurements (Fig. 4F). However, this trend holds beyond 3 weeks in which H9_{UBE3A m−/p−} organoids are overall smaller than H9_WT organoids (Fig. 4E).

Neural progenitors also contribute to tissue and organ morphogenesis and structure through their architectural arrangement and generation of neurons. Our pathway analysis indicated alterations in pathways related to corticogenesis (Fig. 3D; Supplementary Data 2), and in particular we noticed that the cortical progenitor marker, EMX1^59,60, was reduced in H9_{UBE3A m−/p−} organoids although this difference was not statistically significant potentially due to the read depth limitations of scRNAseq (Fig. 4G). However, this dataset was generated from whole brain cerebral organoids which generate many other brain regions besides cortical tissue. To better assess the impact of EMX1 loss, which is particularly important in cortical development, we generated cortical organoids⁵⁹ from isogenic H9_WT, H9_{UBE3A m−/p−}, and H9_{UBE3A m−/p+} pluripotent stem cells and immunostained for EMX1 (Fig. 4H; Supplementary Fig. S2H). Interestingly, the dosage of UBE3A strongly impacted EMX1 levels and organization. With both UBE3A alleles present, EMX1+ progenitors organized into characteristic rosette structures (Fig. 4H). In the complete absence of UBE3A, organoids exhibited a near total absence of EMX1 (Fig. 4H). While EMX1 was almost completely lost in H9_{UBE3A m−/p−} organoids, SOX2 was not lost (Fig. 4H), indicating that the cortical progenitor population was specifically and severely affected in the absence of UBE3A.

GLI3, a radial glia marker⁶¹, has been reported to be upstream of EMX genes during dorsal telencephalon development⁶² and implicated in the sonic hedgehog (SHH) signaling pathway^62,63. Our pathway analysis suggested downregulated HH signaling in RG, proliferating RG, and EN cells, so we checked GLI3 expression levels across cell types for H9_WT and H9_{UBE3A m−/p−} organoids (Supplementary Fig. S2I). Indeed, nearly all cell types exhibited decreased GLI3 expression in the absence of UBE3A indicating that UBE3A might act upstream of EMX1 by affecting GLI3 expression. We next immunostained H9_WT, H9_{UBE3A m−/p−}, and H9_{UBE3A m−/p+} cortical organoids for GLI3 (Fig. 4I) to check whether GLI3 levels were affected at the protein level. We indeed observed a near complete loss of GLI3 in the H9_{UBE3A m−/p−} organoids. In addition, the dosage of UBE3A seems to be affecting GLI3 levels and organization. Based on the composite EMX1/GLI3 images for H9_WT organoids, GLI3 appears to localize to the inner edge of EMX1+ neural rosettes as well as on the rosettes. In comparison, SOX2 does not appear as prominent as GLI3 in the inner edge of the EMX1+ rosettes in H9_WT organoids. In H9_{UBE3A m−/p+} organoids, GLI3 localization in this inner edge looks to be thinner compared to H9_WT organoids. This suggests that UBE3A might be playing an important role in dorsal telencephalon development. However, whether UBE3A interacts with GLI3 directly remains unknown.

Lack of UBE3A may disrupt cortical layer-like EN development

The absence of UBE3A affected progenitor proliferation and organization as well as organoid size. However, we had also observed differentially expressed genes in other differentiated cell types, especially ENs, that might impact their development (Fig. 3D; Supplementary Data 1 and 2). To investigate these potential impacts of UBE3A loss, we performed scRNAseq on 11 week old H9_WT and H9_{UBE3A m−/p−} organoids (Fig. 1) as they exhibit greater proportions of differentiated and more mature cells than in the 6 week organoids we had analyzed. We chose the 11 week timepoint specifically as that is when we have previously observed a substantial increase in UBE3A-ATS expression²⁶, the long noncoding antisense transcript that is expressed in neurons⁶⁴ and which suppresses paternal UBE3A expression^65,66,67.

Using 6759 high quality cells (median UMI count: 12756, median gene count: 4558; H9_WT: 4811 cells, H9_{UBE3A m−/p−}: 1948 cells), we identified 45 discrete cell clusters comprising of 19 cell types including one unknown cell type which was excluded from further analysis (Fig. 5A; Supplementary Fig. S3A, B). Overall, there were greater compositional differences between H9_WT and H9_{UBE3A m−/p−} organoids at 11 weeks compared to 6 weeks. In the absence of UBE3A, organoids contained proportionally more EN, Neuron, ChP, RG/ChP, and Mes cells and fewer Neuron/IP, EN-CL, and proliferating RG cells. Strikingly, cortical layer-like neurons (EN-CL) were largely absent from H9_{UBE3A m−/p−} organoids (Fig. 5B–E). While we named this cluster as “cortical layer-like neurons” due to presence of genes like FRMD4B^59,68 and SOX5⁶⁹, it cannot be determined based upon prior work examining early stage neurons whether their fate will be cortical or hippocampal. We performed differential expression analysis to examine how the EN-CL transcriptomic profiles differed from other neurons (Supplementary Fig. S3C; Supplementary Data 3). EN-CL included many highly upregulated genes related to synapses (e.g., LRRC7⁷⁰, ANKS1B⁷¹, GRIA1⁷²), axon and dendrite development (e.g., NELL2⁷³, NFIB⁷⁴, NEO1⁷⁵), cell migration (e.g., AFF3⁷⁶, BCL11A⁷⁷, SOX5⁷⁸, DAB1^79,80,81), and highly downregulated genes related to cell differentiation (e.g., CHD7⁸², ZFHX4⁸³) consistent with previous literature reporting the effect of UBE3A on these cellular functions^10,12,17,18 (Supplementary Fig. S3C; Supplementary Data 3). H9_{UBE3A m−/p−} organoids contained proportionally less proliferating RG consistent with the 6 week data, with the additional observation that loss of proliferating RG might lead to an increase in neurons through early exit from multipotency or a reduction in the proportion of progenitors relative to neurons. In addition, the loss of cortical layer-like neurons at 11 weeks appears to be an observation consistent with the loss of EMX1+ cortical progenitors at 6 weeks. We also immunostained the 11-week organoids with SATB2 and CTIP2 (Supplementary Fig. S3D, E). However, we did not observe any significant differences between H9_WT, H9_UBE3Am−/p+ and H9_{UBE3Am−/p−} organoids.

**Fig. 5: Lack of *UBE3A* may disrupt cortical layer-like EN development.**

Cell type specific transcriptomic differences between 11 week H9_WT and H9_{UBE3A m−/p−} organoids

We performed differential gene expression and pathway analyses to investigate how the absence of UBE3A affected individual cell types at the 11 week timepoint. We identified 6538 DEGs across different cell types (Fig. 6A–C; Supplementary Data 4). Some cell types did not have sufficient cell counts of each genotype for the analysis or did not contain any DEGs. Overall, more genes were downregulated than upregulated, and RG, Neuron, and ChP cells exhibited the highest number of DEGs (Fig. 6B, C). The DEGs were used for pathway analysis (Fig. 6D; Supplementary Data 5). Many pathways including general signal transduction pathways (RHO GTPase cycle, EPH-Ephrin signaling, RAF/MAP kinase, ephrin receptor signaling), neuronal signaling (CREB signaling in neurons, opioid signaling), cell cycle and division (mitotic prometaphase, cell cycle checkpoints, mitotic metaphase and anaphase), circadian rhythm-related (orexin signaling pathway) and neuron-related pathways (synaptogenesis signaling pathway, myelination signaling pathway) were affected by the absence of UBE3A. IPA revealed that there were cell-type specific differences in these pathways (Fig. 6D; Supplementary Data 5). The most salient observation was that inhibitory-like neurons (IN) had several upregulated signaling pathways while most other cell types repressed these pathways. Although performed at different times and therefore subject to batch effects, there were qualitative differences between 6 week and 11 week organoids (Figs. 3D and 6D; Supplementary Data 2 and 5). This difference was more obvious in proliferating RG where general signal transduction pathways involving RHO GTPases, EPH-Ephrin, RAF/MAP kinase, and CREB were all downregulated in 11 week organoids but upregulated in 6 week organoids. Additionally, most of the cell cycle- and division- related pathways were also downregulated at 11 weeks similar to what we observed in 6 week organoids.

Excess ChP generation in the absence of UBE3A

In addition to observed impacts on progenitors and neurons, we also observed alterations in organoid composition and DEGs related to ChP cells, including an increased proportion of ChP cells in H9_{UBE3A m−/p−} organoids and a substantial number of DEGs compared to H9_WT ChP cells. The ChP comprises the brain tissue that produces CSF. Recent human clinical studies also suggest a link between UBE3A and CSF^29,30. UBE3A has been detected in CSF³⁰, and individuals with Autism Spectrum Disorder (which has high comorbidity with AS) exhibit increased extra-axial CSF production²⁹. Given this set of experimental and clinical observations, we performed additional experiments to better characterize the phenotypes associated with complete loss of UBE3A as well as identify what phenotypes might arise in organoids generated from a clinically relevant genotype, H9_{UBE3A m−/p+}.

We generated H9_WT, H9_{UBE3A m−/p−}, and H9_{UBE3A m−/p+} organoids and observed two discrete morphological phenotypes through brightfield microscopy: large fluid-filled cysts^84,85 and sinuous/invaginated tissue⁸⁶, both characteristic of organoids with ChP tissue (Fig. 7A, B). Immunostaining for TTR confirmed the ChP identity of the cysts and sinuous tissue (Fig. 7C). Interestingly, UBE3A dosage appeared to control the balance of the two morphologies, with H9_WT containing cysts, H9_{UBE3A m−/p−} containing sinuous tissue, and H9_{UBE3A m−/p+} containing both morphologies.

Fig. 7: Excess ChP generation in the absence of *UBE3A.*

The difference in proportion of ChP cells as well as the morphological differences of the ChP tissue suggested that loss of UBE3A may have functional impacts. We therefore performed pathway analysis on the scRNAseq data from both the 6 and 11 week organoids for the ChP cells (Fig. 7D; Supplementary Data 2 and 5). The absence of UBE3A affected the expression of many WNT-related pathways (e.g., TCF dependent signaling in response to WNT, HOTAIR regulatory pathway), circadian clock, autophagy and mitochondria-related pathways (e.g., mitochondria dysfunction) consistent with literature¹⁵.

Some pathways showed differences between the 6 and 11 week time points, such as autophagy (Fig. 7D). Given the dynamic nature of UBE3A expression as well as localization²⁶, this motivated us to examine where and when UBE3A was present in the ChP cells. We generated 3, 5, and 7 week old H9_WT organoids and immunostained for both TTR and UBE3A (Fig. 7E). This revealed strong nuclear localization of UBE3A in TTR+ epithelial cells of ChP-like regions in 3 and 5 week old organoids. However, this pattern was lost in older 7 week old organoids with UBE3A becoming diffuse across subcellular compartments.

Targeting metabolic pathways may rescue progenitor pool defects

We next asked whether we could rescue some of the phenotypes we observed in the absence of UBE3A including organoid size and progenitor pool depletion (Supplementary Fig. S4A–C). Based upon pathway/DEG analysis of our scRNAseq datasets, particularly in 6 week RG and proliferating RG (Supplementary Fig. S5A, S5B; Supplementary Data 1, 2, 4, and 5), we selected inhibitors to target the pathways affected by the absence of UBE3A (Fig. 8A). We also included inhibitors targeting pathways that could be downstream/upstream of the affected pathways such as FAK inhibitors.

**Fig. 8: Mechanistic exploration of potential UBE3A-related pathways.**

Due to the large number of inhibitors selected, their effects were assessed in two serial experiments. H9_{UBE3A m−/p−} whole brain organoids were generated with the inhibitors added on day 2 and kept in the media until the sample collection day (Fig. 8B). We focused first on quantifying organoid size based on our prior observations (Fig. 4E; Supplementary Fig. S4A) and its association with progenitor content⁵⁸. Several inhibitors at the following concentrations were likely toxic resulting in organoid failure or significantly smaller organoid sizes compared to the vehicle control within eleven days of organoid generation: KG-501 (15 and 10 µM), PF-573228 (2.5, 5 and 10 µM), 666-15 (1 µM), ICG-001 (2.5 µM), IWP-2 (2.5 µM), stattic (10 and 20 µM) and H-89 (20 and 50 µM) (Fig. 8C, D). H-89 (10 µM) organoids were significantly smaller than the control beyond 11 days (Fig. 8D). The organoids treated with Baricitinib (10 µM), Pifithrin-α hydrobromide (Pifithrin-alpha, 5 µM), Rapamycin (20 nM), CHIR99021 (5 and 10 µM), and Tofacitinib (10 and 50 µM) were significantly larger than the control organoids (Fig. 8C, D).

We next immunostained the organoids treated with vehicle control, baricitinib (10 µM), pifithrin-alpha (5 µM), rapamycin (20 nM) and adezmapimod (10 µM) to assess whether the SOX2+ progenitor pool expanded and if rosette structures were rescued with the inclusion of these inhibitors (Fig. 8E). While adezmapimod (10 µM) was excluded from the statistical analysis (Fig. 8C) due to insufficient data points (n = 2), immunostaining images are still provided here as it may be larger than the vehicle control organoids (Fig. 8C, E). We observed that organoids treated with Rapamycin (20 nM) showed clearer rosette structure formation suggesting that it might be rescuing the progenitor pool and their organized proliferation compared to vehicle control (Fig. 8E). Pifithrin-alpha (5 µM) showed more organized progenitor structures compared to the vehicle control, but these were very thin rosette structures. We also developed a rosette scoring system to characterize this phenotype compared to the vehicle control (Supplementary Fig. S5C). These results suggest that some of the inhibitors tested may lead to more organized progenitor pools.

Discussion

The primary conclusion of this work is that UBE3A may have multivariate and wide-ranging impacts on the developing brain, well beyond the previously considered impacts on the synaptic functions of adult neurons. More specifically, we showed the absence of UBE3A affected the composition, morphologies, functions, and gene expression of both neuronal and non-neuronal cell types and tissues. Here we present several models or interpretations consistent with our experimental results and their potential downstream clinical impacts that might serve as the basis for future experimental and clinical work.

First, we posit that UBE3A may help regulate the overall compositional balance and size of the brain through direct impacts on neural stem and progenitor cell proliferation. This may connect to observations of microcephaly in AS (albeit a phenotype with incomplete penetrance), as well as to potential imbalances in neuronal subtypes that might affect relative excitatory and inhibitory signaling. Models of other neurodevelopmental disorders have also reported altered organoid compositions^58,87. Our results suggest UBE3A helps support neural stem and progenitor proliferation potentially through impacts on cell cycle progression. The absence of UBE3A resulted in a greater proportion of cells in G0/G1 phase indicating possible cell cycle arrest. Several studies have also reported a potential role for UBE3A in cell cycle progression in other non-neural cell types^23,54: UBE3A expression is cell cycle dependent and UBE3A knockdown leads to defects in chromosome segregation and spindle dynamics²³; UBE3A has also been shown to regulate the levels of cyclin-dependent kinase inhibitor p27, and reduction of UBE3A increases p27 levels and cell cycle arrest⁵⁴. It has also been reported that UBE3A may influence apoptosis and senescence through its protein interactions with p53 and TERT^55,56, and increased apoptosis has been observed in neural precursor cells derived from an AS mouse model and attributed to increased reactive oxygen species⁸⁸. In the absence of UBE3A, we observed a transcriptional reduction of TERT expression below the detectable threshold in 6 week organoids, while we did not detect alterations in TERT levels in embryoid bodies. We were not able to detect altered rates of apoptosis or senescence and did not observe a significant transcriptional difference in TP53 level between H9_WT and H9_{UBE3A m−/p−} organoids; however, we cannot discount the possibility these processes may be affected at transcriptional levels undetectable in our model system and assays or at protein level.

Second, we propose a model where UBE3A negatively impacts signaling through the HH-GLI3-EMX1 axis with a resultant disruption of corticogenesis that might be connected to altered cortical activity and structure in AS. As described above, we observed that neural stem and progenitor proliferation was impacted, but these neural precursor cells are not homogeneous. Therefore, pathway analysis led us to specifically investigate cortical precursor cells which are characterized by their expression of EMX1 and downstream corticogenesis. Indeed, the absence of UBE3A resulted in a significant decrease in the EMX1+ cell population as well as disruption of neural rosette. Providing additional confidence in this functional connection, these phenotypes were dependent on UBE3A dosage.

These observations may also be consistent with the disruption of neuronal maturation observed in other model systems, although the underlying mechanisms remain unclear in all cases^39,89. In addition, to the best of our knowledge, this is the first report of a potential association between UBE3A and EMX1. Therefore, we explored the DEGs and pathway analysis more deeply to identify what pathways might be affected upstream of EMX1. Outside of neural contexts, UBE3A has been associated with many pathways related to corticogenesis and neurogenesis such as mTOR⁹⁰, Ephrin¹¹, and WNT/β-catenin¹⁵. Our differential gene expression/pathway analysis also implicated HH (Hedgehog) signaling. There is some mechanistic precedent for this potential connection between a ubiquitin ligase and SHH signaling: RNF220, a ubiquitin E3 ligase has been reported to modulate the SHH/GLI signaling gradient by interacting with GLI proteins through ubiquitination⁶³. Interestingly, we observed that the expression levels for GLI3, which is upstream of EMX genes⁶², to be lower in the absence of UBE3A both at transcriptional and protein levels. This is just one possibility of how UBE3A participates in the regulation of corticogenesis, and future work might assess this and the other affected pathways identified in our and others work.

It will also be prudent to note that EMX1+ progenitors were not the only class of progenitors affected by the loss of UBE3A. Proliferating RG in general were not only negatively impacted, but interestingly in 6 week old organoids they also upregulated neuron-related pathway genes. This might indicate that these neural precursors are proceeding through asymmetric division and exiting multipotency earlier than normal.

Third, we suggest that UBE3A is involved in ChP generation and function with potential relevance to differences in CSF volume observed in individuals with autism spectrum disorder²⁹. This connection is perhaps the most tenuous to make, yet perhaps also the most intriguing. We observed increased ChP generation in the absence of UBE3A, as well as DEGs associated with IPA pathways previously linked to ChP including WNT-related pathways, mitochondrial dysfunction^91,92, circadian clock⁹³, and autophagy⁹⁴. We also observed an interesting transient nuclear localization of UBE3A within ChP in organoids. Although the subcellular localization of UBE3A is likely related to its function²⁶, it is unclear if this transient spatiotemporal localization pattern is functionally relevant. It is important to consider if the ChP phenotype is observed in true AS backgrounds as well. While underpowered in organoid number and donor numbers, a preliminary study indicates greater ChP may be observed in at least organoids generated from a patient with a UBE3A point mutation (AS_mut) and also potentially from a patient with a large maternal deletion (AS_del) (Supplementary Fig. S6A, B).

UBE3A expression has been detected in developing and adult human ChP in various databases^95,96 (Supplementary Table S1). It remains unclear how UBE3A might be mechanistically linked to ChP generation and function as well as what the functional consequences are for AS individuals; however, the potential implications could be important as the ChP regulates clearance and secretion of important regulatory factors both in adults and during brain development. This might also include UBE3A protein that has been detected in the CSF³¹ and that could be distributed throughout the brain via the CSF and enter other cells⁹⁷. It might also impact the generation, processing, and clearance of amyloid beta related to Alzheimer’s Disease pathology; previously, a study showed that UBE3A-deficient Alzheimer’s disease model mice exhibited decreased amyloid beta levels⁹⁸. Potentially consistent with this observation, the amyloid processing pathway was upregulated in 11 week ChP cells in organoids deficient for UBE3A (Supplementary Data 5).

Fourth, we performed a preliminary analysis on the pathways that might have been affected by the absence of UBE3A directly or indirectly based particularly on the 6 week scRNAseq data. We screened a set of 26 inhibitors at several concentrations. The H9_{UBE3A m−/p−} organoids treated with the small molecules targeting mTORC, JAK and WNT pathways were bigger than the organoids treated with vehicle control suggesting that targeting these pathways might rescue the small organoid size phenotype we observed (Fig. 4E, F). Previous studies reported UBE3A’s potential role in mTORC1^90,99 and WNT¹⁵ pathways. Among the organoids we immunostained, those treated with Rapamycin seemed to be the most promising in terms of rescuing progenitor organization and generating the clearer rosette structures. Another interesting observation we made was that the organoids treated with Rapamycin did not gain their size phenotype until later time points. Mammalian target of rapamycin complex 1 (mTORC1) has a role in various cellular processes including neural stem cell proliferation and differentiation^{100,101,102,103}. While its inactivation has been reported to lead to microcephaly¹⁰¹, its hyperactivation has been suggested to lead to apoptosis in neuronal progenitor cells and cortical abnormalities in embryonic cortex^100,104. UBE3A has been suggested to regulate mTORC1^90,99 and may normally keep mTORC1 levels at a ‘Goldilocks’ level. Rapamycin may be mimicking this effect. Furthermore, based on our observations of organoid size, a UBE3A-mTORC1 association might be established several weeks into organoid development.

Our observations suggested that several inhibitors reported here could rescue some of the phenotypes we associated with the absence of UBE3A. Further analysis with greater number of organoids per inhibitor condition, full titration series per inhibitor, and studies with neural progenitor cells in isolation should be performed to follow up on these results. Another follow-up question is whether these rescued progenitor populations/structures have cortical fate. It is important to note that the pathways we targeted in this study that failed to rescue the observed phenotypes should not be fully eliminated as potential UBE3A targets since (i) targeting a different reaction in the pathway, (ii) testing different concentrations for the inhibitors selected, and/or (iii) adding inhibitors at different time points could rescue the phenotypes we were focusing on.

We hope the scRNAseq datasets will be used beyond the scope of this study. For example, ErbB inhibition has been suggested to ameliorate some of the AS symptoms observed in AS model mice¹⁰⁵. Based on our scRNAseq data, ERBB4 was one of the significantly upregulated genes in 6 week H9_{UBE3A m−/p−} organoids compared to H9_WT organoids (Supplementary Data 1). As with any omics dataset, the altered gene expression and downstream effects of this gene need to be validated in isolated experiments. Furthermore, additional and/or different genes of interest may be identified through different types of bioinformatics analysis methods.

We end our discussion with two forward-looking technological notes. First, we discuss the specific utilities and limitations of the organoid models presented here. It is important to realize that the genetic perturbations and organoids used in this work are not directly reflecting physiological conditions. They cannot capture behavioral outcome measures as animal models can nor complex phenotypes arising from a complete organismal physiology. Instead, the strengths of organoids are in providing direct and clean perturbations while maintaining a high level of complexity in cell type, tissue structures, and human specific (epi)genetics. This has additional important implications. For example, the use of conditional deletions in rodent development would be subject to many potential compensatory mechanisms present in a full organism but not in an organoid. A corollary is that while a double knockout of UBE3A is non-physiological, it can uncover important biology that would otherwise be hidden from view or below detectable thresholds using existing analytical technologies, or in most animal and tissue culture models of disease. In other words, with the inherent lack of compensatory mechanisms present in cerebral organoids compared to a full organism, molecular and cellular “burdens” induced by pathological lesions may be exacerbated, uncovering novel biology that could be difficult if not impossible to observe in other model systems.

A second forward-looking note is that despite these scientifically useful aspects of organoids, it will remain important to explore how these findings might translate to animal models as well as patient derived samples. For example, here we focused on isogenic pluripotent cell lines with UBE3A completely absent. This provided the cleanest and most direct experimental approach to assess UBE3A biology in organoids. However, an important next step could be to investigate the findings from this work in the context of diverse AS induced pluripotent cell lines reflecting the multiple different AS etiologies (large deletion, mutation, uniparental disomy, imprinting center defects, mosaicism). This will be important in comparing phenotypes arising in a heterozygous background where paternal UBE3A is still present, as well as in parsing the contributions of other genes deleted or silenced in the different (epi)genotypes of AS.

Materials and methods

hESC lines

Feeder-independent H9 human embryonic stem cells (hESCs) (WA09) were originally obtained from WiCell. H9_{UBE3A m−/p−} and H9_{UBE3A m−/p+} cells with a 66 kb deletion (GRCh38/hg38 chr15: 25,339,159(160) − 25,405,460(459)) were generated by Dr. Stormy Chamberlain’s group^37,38,39 and provided by Dr. Stormy Chamberlain (UCONN)^37,38,39. Our group also confirmed the deletion in these cells in an earlier study²⁶. Angelman Syndrome (AS) hiPSC line with a Class II deletion (GRCh37/hg19 chr15: 23,406,313 − 28,897,752) (AS_del) was developed by the laboratories of Stormy J. Chamberlain and Marc Lalande at the University of Connecticut¹⁰⁶ and obtained from Kerafast. AS point mutation hiPSC line (F583S) (AS_mut) was provided by Dr. Stormy Chamberlain (UCONN). Cells were maintained in tissue culture dishes (Fisher Scientific) coated with 0.5 mg/cm² Vitronectin (rhVTN-N) (Gibco Thermo Fisher Scientific) or 8.7 µg/cm² reduced growth factor Matrigel solution (Corning) in TeSR-E8 medium (Stemcell Technologies) or mTeSR Plus medium (Stemcell Technologies). Cells were passaged using standard protocols. Cells were maintained in a humid incubator at 37 °C with 5% CO₂.

Cerebral organoid generation

Whole brain and cortical human cerebral organoids were generated and maintained using the protocols developed by Lancaster et al. ^31,107, and Velasco et al.^59,108, respectively. Briefly, for the whole brain human cerebral organoids^31,107, 70–80% confluent human pluripotent stem cells (hPSCs) were lifted from the plate with 0.5 mM EDTA (Invitrogen) solution in DPBS without calcium or magnesium (Gibco) and dissociated into single cells using Accutase (BioLegend). After removing accutase, the single cells were resuspended with hESC medium (DMEM/F12 (Gibco), KnockOut serum replacement (KOSR, Gibco), premium grade fetal bovine serum (FBS, Avantor Seradigm), GlutaMAX Supplement (Gibco), MEM non-essential amino acids (NEAA, Cytiva HyClone) and 2-mercaptoethanol (Amresco)) containing bFGF (Gibco, final concentration 4 ng/ml) and ROCK inhibitor (Y-27632, LC Laboratories, Stemcell Technologies and MedChemExpress, final concentration 50 µM). Approximately 9000 cells were seeded in each well of a low-attachment 96-well round bottom plate (Corning) except for the two AS_del whole brain organoid batches used in Supplementary Fig. S6B which had a seeding density of ~12,000–15,000 cells/well. The embryoid bodies (EBs) were fed every other day with hESC medium. bFGF and ROCK inhibitor were included in the medium until the EBs began to brighten. Around day 6, the EBs werere transferred to a low attachment 24-well plate (Corning) containing neural induction medium (DMEM/F12 (Gibco), N-2 supplement (Gibco), GlutaMAX Supplement (Gibco), NEAA (Cytiva HyClone) and Heparin (Sigma-Aldrich)). The media was changed after 48 h. Around day 11, the tissues were embedded in Matrigel (Corning) (or Geltrex (Gibco) for the organoid sets in Fig. 8 and the two AS_del whole brain organoid batches used in Supplementary Fig. S6B) and cultured in 100-mm or 60-mm dishes containing cerebral organoid differentiation medium without vitamin A (DMEM/F12 (Gibco), Neurobasal medium (Gibco), N-2 supplement (Gibco), Insulin (Gibco), GlutaMAX Supplement (Gibco), NEAA (Cytiva HyClone), Penicillin/Streptomycin (Lonza and GenClone), 2-mercaptoethanol (Amresco) and B-27 supplement without vitamin A (Gibco)). The media was changed after 48 h. After 4 days, the culture medium for the organoids were changed to cerebral organoid differentiation medium with vitamin A ((DMEM/F12 (Gibco), Neurobasal medium (Gibco), N-2 supplement (Gibco), Insulin (Gibco), GlutaMAX Supplement (Gibco), NEAA (Cytiva HyClone), Penicillin/Streptomycin (Lonza and GenClone), 2-mercaptoethanol (Amresco) and B-27 supplement with vitamin A (Gibco)) and the dishes were transferred to an orbital shaker in a tissue culture incubator (70 rpm for 100-mm dishes and 75 rpm for 60-mm dishes). The media changes with cerebral organoid differentiation medium with vitamin A were performed every 3–4 days until the sample collection. The recipes for each medium type as outlined by Lancaster et al.^31,107 were followed unless otherwise stated. DMSO (Fisher Chemical) was included in the final media change for 11 week organoids used for scRNAseq (day 74) with a final concentration of 0.1%. Please refer to “Inhibitor Treatment” section for the organoid experiments presented in Fig. 8. EBs and organoids were maintained in a humid incubator at 37 °C with 5% CO₂.

The human cortical organoids were generated following the protocol developed by Velasco and colleagues^59,108. Briefly, about 80% confluent cells were dissociated into single cells using Accutase (BioLegend). After removing Accutase, the cells were resuspended in cortical differentiation medium I (CDM I containing G-MEM (Gibco), KnockOut Serum Replacement (Gibco), NEAA (Cytiva HyClone), Sodium Pyruvate (Gibco), 2-mercaptoethanol (Amresco) and Penicillin/Streptomycin (Lonza and GenClone)). Approximately 9000–10,000 cells were seeded into each well of a 96-well V bottom plate (S-BIO) containing CDM I, ROCK inhibitor (LC Laboratories, Stemcell Technologies, MedChemExpress), IWR1 (Calbiochem/Millipore) and SB 431542 (Stemcell Technologies). The media was changed every 3 days until day 18. ROCK inhibitor was only included in the first 6 days of the organoid generation. The tissues were transferred to 100-mm dishes containing CDM II (DMEM/F12 (Gibco and GenClone), GlutaMAX Supplement (Gibco), N-2 supplement (Gibco), Chemically Defined Lipid Concentrate (Gibco), Fungizone/Amphotericin B (Gibco) and Penicillin/Streptomycin (Lonza and GenClone)) on day 18. The plates were moved to an orbital shaker in a tissue culture incubator (70 rpm). The media change was performed every 3 days. The organoids were cultured in 100-mm dishes containing CDM III (DMEM/F12 (Gibco and GenClone), FBS (Avantor Seradigm), chemically defined lipid concentrate (Gibco), GlutaMAX supplement (Gibco), N-2 supplement (Gibco), heparin (Sigma-Aldrich), fungizone/amphotericin B (Gibco), penicillin/streptomycin (Lonza and GenClone) and geltrex (Gibco)) starting day 35. No other media change with CDM III was performed since the organoids were collected at 4-week and 6-week time points. The recipes for each medium type as outlined by Velasco et al.^59,108 were followed unless otherwise stated. For CDM III, 1 mg/ml heparin (final concentration 0.5 µg/ml) was used. Geltrex was used instead of Matrigel for CDM III. EBs and organoids were maintained in a humid incubator at 37 °C with 5% CO₂.

Cryosectioning and immunohistochemistry

Tissues were fixed in 4% paraformaldehyde for 15–30 min at 4 °C followed by 3 × 10 min PBS washes. Tissues were allowed to sink in 30% sucrose at least overnight and then embedded in 10% gelatin/7.5% sucrose. Embedded tissues were frozen in an isopentane bath between −50 and −30 °C and stored at −80 °C. Frozen blocks were cryosectioned to 20 or 30 μm. For immunohistochemistry, sections were blocked and permeabilized in 0.3% Triton X-100 and 5% normal donkey serum in PBS. Sections were incubated with primary antibodies (Supplementary Table S2) in 0.3% Triton X-100, 5% normal donkey serum in PBS overnight at 4 °C in a humidity chamber. Sections were then incubated with secondary antibodies in 0.3% Triton X-100, 5% normal donkey serum in PBS for 2 h at RT, and nuclei were stained with DAPI (Invitrogen). Slides were mounted using ProLong Antifade Diamond (Thermo Fisher Scientific). Secondary antibodies used were donkey Alexa Fluor 488, 546 and 647 conjugates (Invitrogen, 1:250-1:500 and Jackson ImmunoResearch, 1:125).

Images were taken using a Nikon A1R confocal laser scanning microscope (Nikon Instruments). All samples within quantification experiments were imaged using the same laser intensity settings using the 10× objective. Quantifications were performed manually in FIJI¹⁰⁹. A region of interest was generated around the organoid DAPI outline. The area containing both marker of interest and MAP2 within the organoid was then calculated. At least three organoids per condition were collected and measured.

Single cell dissociation of organoids for scRNAseq

Organoids were dissociated using a previously described protocol^59,110 which is a modification of the Worthington Papain Dissociation Kit manufacturer’s protocol (Worthington, #LK003153). Briefly, reagents were resuspended according to manufacturer’s instructions. Organoids were placed into papain and DNAse mix and minced with a sterile razor. Minced organoids were incubated for 30 min at 37 °C. Following this, minced pieces were broken up by pipetting up and down with p1000 pipette and incubated for 10 min at 37 °C. Next, the dissociated organoid mix was placed into inhibitor solution and passed through a 40 μm filter, centrifuged at 300 × g for 7 min, and resuspended with 1× ice cold PBS and kept on ice. Cells were counted with an automated hemocytometer (Invitrogen) and single cell formation was confirmed with an inverted tissue culture microscope (Nikon) before proceeding to the fixation.

scRNAseq library preparation, sequencing, and analysis

Fixation, cell barcoding, and library preparation was done using a previously described split-pool approach¹¹¹ with a commercial kit (Parse Biosciences). Briefly, 3,500,000 cells were fixed per sample using the manufacturer’s instructions. Approximately 13,500 cells/well were seeded in first barcoding plate and cDNA was generated with an in-cell reverse transcription reaction using well-specific barcodes. Cells were then pooled and split again twice more for the ligation of two more barcodes. After the second ligation, cells were pooled for a third time and split into eight sub-libraries with 12,500 cells/sub-library. Cell counting at this step was done with a manual hemocytometer for accuracy. A fourth barcode was added to each sub-library as the last step of barcoding protocol. Next, the cells were lysed, and barcoded cDNA was amplified, cleaned up, and fragmented. Illumina adapters were added to each sub-library as indexes. A fragment analyzer (Agilent) was used to confirm the correct size distribution in the libraries after cDNA amplification and fragmentation steps. 48,000 cells were barcoded for each genotype (H9_WT and H9_{UBE3A m−/p−}) for the 6 week scRNAseq experiment. 12,000 cells (2000 cells/biological replicate in each genotype, n = 3) were sequenced at 50,000 reads/cell on an Illumina HiSeq 4000 instrument at 10× configuration (600 M reads, 150 PE) for the 6 week experiment (Genewiz). 20,000 cells (2000 cells/biological replicate in each genotype, n ≥ 3) from the 11 week experiment were submitted to be sequenced at 50,000 reads/cell on a Novaseq 6000 instrument (5B reads, 150 PE) (Genomic Sciences Laboratory, NC State University).

Data processing for scRNA-seq

Data was processed using an established scRNAseq pipeline (https://combine-lab.github.io/alevin-fry-tutorials/2022/split-seq/). Briefly, raw sequencing data was preprocessed with splitp to pair oligo-dT primer and random hexamer barcodes used in the initial round of barcoding. A splici (spliced + intron) index was created using salmon¹¹² (v1.7.0) using human GENCODE v39 and hg38 human reference genome. Sequences were mapped to the splici index using salmon and quantified using alevin-fry¹¹³ (v0.5.0). Seurat¹¹⁴ (v4.0.4) was used to filter, process, and analyze gene expression matrices. The eight sequenced sub-libraries for the 11 week experiment were merged into a single Seurat object and reads not matching sample barcodes within a hamming distance of 1 were filtered out. QC filtering was performed removing cells that did not meet the following criteria: Count_RNA > 2000, nFeature_RNA > 1000, mitochondrial percentage <10%, resulting in 7498 high-quality cells for the 6 week experiment and 6759 high-quality cells for the 11 week experiment for analysis. SCTransform¹¹⁵ v2 was then used to normalize for read depth across cells, scale the data, and find variable features. The mitochondrial mapping percentage was regressed during normalization to remove this confounding source of variation. Principle component analysis (PCA) was performed and an ElbowPlot was generated to determine the number of informative principal components (50 PCs). FindNeighbors, FindClusters, and UMAP dimensionality reduction visualization were performed. Clusters were annotated based on differentially expressed genes in each cluster as well as known marker genes. Cell types for each cluster were determined using previously identified marker genes. Cell type proportions were calculated for each replicate. Speckle¹¹⁶ (v1.2.0) was used to compare cell type proportions (n = 3 replicates of 4–5 pooled organoids per genotype, unequal variance) between H9_WT and H9_{UBE3A m−/p−} organoids using the arcsin square root variance stabilizing transformation propeller test. Please refer to Supplementary Table S3 for software package information.

UBE3A fractional reads in sequencing data

Raw sequencing data was aligned to hg38 human reference genome using zUMIs¹¹⁷ (v2.9.4 d) processing pipeline. Samtools¹¹⁸ (v1.10) was then used to calculate the fraction of UBE3A reads mapping to exons within the UBE3A knockout region (GRCh38/hg38 chr15:25,339,159(160)-25,405,460(459)) relative to all UBE3A exons (GRCh38/hg38 chr15:25,331,728-25,441,024; n = 3 replicates of 4–5 pooled organoids per genotype). Please refer to Supplementary Table S3 for software package information.

Inhibitor treatment

The inhibitors used in the experiment for Fig. 8 are 666-15 (MedChemExpress, HY-101120), adezmapimod hydrochloride (MedChemExpress, HY-10256A), baricitinib (MedChemExpress, HY-15315), cyclopamine (MedChemExpress, HY-17024), gallein (MedChemExpress, HY-D0254), glycyrrhizic acid (MedChemExpress, HY-N0184), H-89 dihydrochloride (MedChemExpress, HY-15979A), KG-501 (MedChemExpress, HY-103299), CHIR-99021 monohydrochloride (MedChemExpress, HY-10182A), PF-573228 (MedChemExpress, HY-10461), pifithrin-α hydrobromide (MedChemExpress, HY-15484), Rapamycin (MedChemExpress, HY-10219), SH-42 (MedChemExpress, HY-143228), stattic (MedChemExpress, HY-13818), tofacitinib (MedChemExpress, HY-40354), defactinib hydrochloride (MedChemExpress, HY-12289A), YM 254890 (Tocris Bioscience, 7352), AS-1842856 (Cayman Chemical Company, 16761), GSK2256098 (Cayman Chemical Company, 22955), ICG-001 (Cayman Chemical Company, 16257), SB-334867 (Cayman Chemical Company, 19145), Rhosin hydrochloride (Cayman Chemical Company, 36415), NSC 23766 hydrochloride (Cayman Chemical Company, 13196), Ku-55933 (Cayman Chemical Company, 16336), IWP-2 (Cayman Chemical Company, 13951) and UK-5099 (Tocris Bioscience, 4186). All inhibitors were reconstituted in dimethyl sulfoxide (DMSO, Fisher Chemical). Due to the large number of inhibitors, the treatment experiment was performed in two serial experiments. The inhibitor treatment started on day 2 of the whole brain organoid generation. The inhibitors were added with every fresh media change and kept until the day of sample collection (week 4.5). The final concentration of DMSO was 0.1%. Some organoids were lost either due to toxic effects of the inhibitors or accidental aspiration during media changes resulting in different numbers of replicate organoids per condition.

The scoring for the neural rosette structures for the selected inhibitors (Fig. 8E; Supplementary Fig. S5C) was based on the following criteria: no rosette or incomplete/not well-defined rosette structures (score: 1), fully enclosed thin rosette structures (score: 2), well-defined rosette structures (score: 3) and well-defined and big rosette structures (score: 4). n = 3 organoids for vehicle control, rapamycin and baricitinib, n = 4 organoids for pifithrin-α hydrobromide and n = 2 organoids for adezmapimod were used for the neural rosette scoring.

Organoid size quantification

Images for size experiments were taken using an epifluorescence microscope with a documentation camera with a 4× objective (Nikon Instruments). Quantifications were performed manually in FIJI¹⁰⁹. The sizes of the organoids treated with the selected inhibitors were normalized to their corresponding average sizes on day 2 of generation, the day when the inhibitor treatment started. The numbers of samples used for measurement for each time point were as follows: 8 ≥ n ≥ 5 for day 11 of the first set (except for adezmapimod 10 µM being n = 2), 11 ≥ n ≥ 3 of day 32 of the first set, 11 ≥ n ≥ 5 of day 11 of the second set and 9 ≥ n ≥ 3 of day 15 of the second set (please refer to Supplementary Data 6 for exact values).

EdU cell proliferation assay

Assay was performed on embryoid bodies (EBs) and human pluripotent cells (hPSCs) using the Click-iT EdU Alexa Fluor 488 Flow Cytometry Assay Kit (Invitrogen Thermo Fisher Scientific). EBs used for the EdU cell proliferation assay were generated and maintained for 5 days using the protocol developed by Lancaster et al. ^31,107. The manufacturer’s guidelines for the EdU cell proliferation assay were followed. Briefly, EdU was added to the culture media of day 5 EBs and hPSCs at a final concentration of 10 μM. After 2 h incubation with EdU, EBs and hPSCs were washed with 1× PBS. Fresh media was then added. After incubating the EBs and hPSCs for 2 h, they were harvested using Accutase (BioLegend)-mediated dissociation. The cells were washed with 3 mL 1% BSA (Thermo Scientific) in PBS and centrifuged at 300 × g for 5 min. The cell pellets were resuspended using 100 μL 4% paraformaldehyde in PBS and incubated for 15 min at room temperature, protected from light, followed by 3 mL 1% BSA in PBS wash. The cell pellets were then resuspended in 100 μL 1× Click-iT saponin-based permeabilization and wash reagent and incubated for 15 min at room temperature protected from light. The Click-iT reaction cocktail was prepared per manufacturer guidelines and 500 μL of this cocktail was added to the cells. The reaction mixture was incubated for 30 min at room temperature, protected from light. The cells were washed with 3 mL 1× Click-iT saponin-based permeabilization and wash reagent. The cell pellets were resuspended in 500 μL Click-iT saponin-based permeabilization and wash reagent containing DAPI (Invitrogen). After DAPI staining, samples were loaded into a MACSQuant ® VYB flow cytometer (Miltenyi Biotec). FlowJo Software¹¹⁹ (v10.9.0, Becton Dickenson Life Sciences) was used to analyze flow cytometry data. Please refer to Supplementary Table S3 for software package information. Intact cells were gated based on FSC/SSC. The gates for the cell cycle stages were shown in Supplementary Fig. S2D, E. n = 3 replicates of 15 pooled embryoid bodies per genotype and n = 3 replicates of pluripotent stem cells from independent culture dishes were used in this experiment.

RNA extraction and RT-qPCR

EBs used for measuring TERT expression were generated and maintained for 5 days using the protocol developed by Lancaster et al. ^31,107. Day 5 EBs harvested by Accutase (BioLegend)-mediated dissociation were washed at least 2 times in cold PBS. Total RNA was isolated using Direct-zol RNA MicroPrep Kit (Zymo Research) according to the manufacturer’s protocol. RNA samples were collected in 2 mL RNAse-free tubes and chilled on ice throughout the procedure. cDNA synthesis was performed using 1000 ng of total RNA for EBs and the iScript Reverse Transcription Kit (BIO-RAD) according to the manufacturer’s protocol. qPCR reactions were performed using SsoAdvanced™ Universal SYBR® Green Supermix (BIO-RAD) for EBs on a BIO-RAD 384-well machine (CXF384) with primers for TERT (Forward primer: GAAGCCACCTCTTTGGAGGG, Reverse primer: GAGGAAGTGCTTGGTCTCGG)¹²⁰ and for TBP (Forward primer: GGGCACCACTCCACTGTATC, Reverse primer: CGAAGTGCAATGGTCTTTAGG)²⁶. Analysis of TERT expression along with the reference gene TBP was performed using Excel by calculating the ΔΔCt value. Data are presented as expression level (2^−ΔΔCt) relative to TBP. n = 3 replicates of ~110 pooled embryoid bodies per genotype were used.

Differential gene expression

A Wilcoxon rank sum test was used to identify differential gene expression of H9_{UBE3A m−/p−} cells relative to H9_WT on either all cells or a specific cell subtype based on the analysis. DEG was defined as log2FC ≥ |1|, and false discovery rate q < 0.05.

Canonical pathway analysis

Ingenuity pathway analysis¹²¹ (IPA, version 60467501, QIAGEN digital insights, https://digitalinsights.qiagen.com/IPA) was used to predict activated or inhibited biological functions. Differentially expressed genes with log2(fold change) ≥ |1|, false discovery rate q < 0.05 were used in IPA. Cancer related biological functions were excluded from the analyses. The categories of the pathways were selected based on literature and Qiagen GeneGlobe (https://geneglobe.qiagen.com/us/knowledge/pathways).

Statistics and reproducibility

Sample sizes and number of replicates are provided in the figure captions and methods. All error bars presented represent a 95% confidence interval assuming a normal distribution. p-values for Figs. 4B, D and 7B; Supplementary Figs. S2G, S3E were calculated using one-way Anova followed by Tukey-Kramer post hoc analysis in MATLAB (version: 23.2.0 (R2023b), The MathWorks Inc., Natick, Massachusetts, https://www.mathworks.com). p-values for the inhibitor treatment experiment (Fig. 8C, D) were calculated using one-way Anova followed by Dunnett’s Test in GraphPad Prism (version 10.4.2 for Windows, GraphPad Software, Boston, Massachusetts, USA, www.graphpad.com). q-values for organoid composition were calculated via t-test using the arcsin square root variance stabilizing transformation propeller test (Speckle¹¹⁶ v1.2.0) (Figs. 2C, E and 5C, E). p-values for Fig. 4F organoid size and Supplementary Fig. S1A were calculated using one tailed t-test. Supplementary Fig. S2I p-values were obtained from DEG analysis (Supplementary Data 1). False discovery rates for single cell sequencing were calculated using a Wilcoxon rank sum test. The exact values of number of replicates, data points, and p-values can be found in Supplementary Data 6.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw data (FASTQ files) and processed data (count matrices) were deposited in NIH GEO (RRID:SCR_005012) under accession no. GSE253230. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Source data behind the graphs in the manuscript can be found in Supplementary Data 6 file. All other data are available from the corresponding author on reasonable request.

Code availability

The code used to perform the analysis described in this study is available at https://github.com/keung-lab/Estridge-Yagci-CommBio¹²².

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Acknowledgements

We thank Dr. Stormy Chamberlain (University of Connecticut Genetics and Genome Sciences, Farmington, CT) for graciously gifting the H9_{UBE3A m−/p−}, H9_{UBE3A m−/p+} and AS point mutation (AS_mut, F583S) cell lines. We thank Dr. Maria Theresa M. Fadri, Dr. Heather Patisaul and Rachel Polak for their thoughtful insights. Figs. 1 and 8B, and Supplementary Fig. S4A were created in https://BioRender.com. This work was supported by the Foundation for Angelman Syndrome Therapeutics FT2020-003 (AJK), Foundation for Angelman Syndrome Therapeutics FT2022-008 (AJK), North Carolina State University Goodnight Early Career Innovator Award (AJK) and NC State Chemistry of Life Training Program NIH T32 GM141887 (TJJ).

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These authors contributed equally: R. Chris Estridge, Z. Begum Yagci, Dilara Sen.
These authors jointly supervised this work: Jeremy M. Simon, Albert J. Keung.

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Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
R. Chris Estridge, Z. Begum Yagci, Dilara Sen, Tyler J. Johnson, Gautami R. Kelkar & Albert J. Keung
UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Travis S. Ptacek & Jeremy M. Simon
Department of Genetics, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
Jeremy M. Simon
Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
Jeremy M. Simon
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Jeremy M. Simon

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R. Chris Estridge
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R.C.E., Z.B.Y. and D.S. contributed equally to this work. R.C.E., Z.B.Y. and D.S. jointly decided with agreement from all authors they all have the permission to list their name first on professional documents. Conceptualization: R.C.E., Z.B.Y., D.S., A.J.K. Methodology: R.C.E., Z.B.Y., D.S., T.J.J., G.R.K., T.S.P., J.M.S. Investigation: R.C.E., Z.B.Y., D.S., T.J.J., G.R.K., A.J.K. Visualization: R.C.E., Z.B.Y., D.S., T.J.J. Supervision: J.M.S., A.J.K. Writing—original draft: R.C.E., Z.B.Y., A.J.K. Writing—review and editing: R.C.E., Z.B.Y., D.S., T.J.J., G.R.K., T.S.P., J.M.S., and A.J.K.

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Estridge, R.C., Yagci, Z.B., Sen, D. et al. Loss of UBE3A impacts both neuronal and non-neuronal cells in human cerebral organoids. Commun Biol 8, 838 (2025). https://doi.org/10.1038/s42003-025-08262-x

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Received: 09 April 2024
Accepted: 20 May 2025
Published: 30 May 2025
DOI: https://doi.org/10.1038/s42003-025-08262-x

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- Xiaoshan Liu
- Wu Liu
Molecular Neurobiology (2025)