ASO therapy rescues NOTCH2NLC GGC repeat expansion-induced genomic damage, 3D chromatin structural abnormalities, and senescence

Domain: neuroscience
Published: 2026-04-07
Editorial outcome: major_revision

Abstract
Neuronal intranuclear inclusion disease is caused by abnormal GGC repeat expansion in the
NOTCH2NLC gene, though its pathogenic mechanism remains incompletely understood. This study
shows that the abnormally expanded polyG-uN2C protein, encoded by the repeat sequence, contains
intrinsically disordered regions and forms aggregates, leading to mislocalization of nucleophosmin and
downregulation of fibrillarin. PolyG aggregates interact with nucleophosmin and rRNA, disrupting
ribosomal homeostasis. Furthermore, polyG facilitates the downregulation of chromatin structural
proteins CTCF and RAD21, thereby impairing chromatin organization. This pathological manifestation
can be mitigated by restoring CTCF/RAD21 expression. Furthermore, in brain organoids derived from
neuronal intranuclear inclusion disease patients, we observe nucleolar stress accompanied by genome-
wide chromatin structural alterations. These changes correlate with increased DNA damage and cellular
senescence phenotypes. Notably, antisense oligonucleotides targeting GGC effectively reduce polyG
aggregation and ameliorate related molecular defects, ultimately alleviating senescence-associated
phenotypes. These findings establish key mechanisms underlying neuronal intranuclear inclusion disease
pathogenesis and provide proof-of-concept for targeted therapy.

Introduction
Neuronal intranuclear inclusion disease (NIID) is a progressive neurodegenerative disorder
characterized by a wide spectrum of clinical manifestations, including cognitive impairment, dystonia,
and motor dysfunction1-3. Research has established that abnormal GGC repeat expansions (repeat
number >60) in the 5' untranslated region (UTR) of the NOTCH2NLC gene are causative for NIID4, 5.
These expanded GGC repeats can induce the translation of an upstream open reading frame (uORF),
producing a toxic polyglycine (polyG) protein, which is considered a key driver of NIID pathogenesis6-9.
Our previous studies demonstrated that polyG predominantly forms intranuclear inclusions and
triggers nucleolar stress by inducing the nucleocytoplasmic translocation of nucleophosmin (NPM1).
NPM1 plays critical roles in DNA damage repair10 and the maintenance of three-dimensional (3D)
genome architecture11. Under physiological conditions, NPM1 is recruited to DNA damage sites to
facilitate repair processes10; its dysfunction may lead to impaired DNA repair and increased genomic
instability, ultimately accelerating cellular senescence. Furthermore, as a nucleolar protein, NPM1
interacts with the chromatin structural protein CTCF12, anchoring chromatin around the nucleolus to
regulate gene expression. CTCF, in cooperation with RAD21, maintains 3D chromatin organization by
binding specific loci to facilitate the formation of chromatin loops and topologically associating domains
(TADs)13. These findings suggest that in NIID, polyG-induced NPM1 dysfunction may contribute to DNA
damage and aberrant 3D chromatin structure, though the precise mechanisms remain unclear.
In recent years, antisense oligonucleotides (ASOs) have emerged as promising therapeutic agents for
rare neurological disorders, including spinal muscular atrophy14, myotonic dystrophy15-17, and
amyotrophic lateral sclerosis (ALS) 18-21. Notably, in fragile X-associated tremor/ataxia syndrome
(FXTAS) another disorder caused by GGC repeat expansions steric-blocking ASOs targeting the repeat
tract effectively reduced the accumulation of toxic FMRpolyG and demonstrated significant therapeutic
efficacy. This raises the intriguing possibility that GGC-targeting ASOs may also hold therapeutic
potential for NIID.
In this study, we further elucidate how NPM1 dysfunction in NIID models leads to increased genomic
instability and disrupted 3D chromatin architecture. Moreover, we demonstrate that ASO-mediated
targeting of GGC repeat expansions rescues these pathological phenotypes, highlighting its potential as a
therapeutic strategy for NIID. Our findings provide insights into the molecular mechanisms underlying
NIID and establish a theoretical foundation for the future development of gene-targeted therapies.

Results
PolyG disrupts nucleolar structure and function
PONDR22 (Predictor of natural disordered regions) is a bioinformatics tool specifically designed for
predicting intrinsically disordered regions (IDRs) in protein sequences. By analyzing amino acid sequence
features such as composition and hydrophobicity, it assesses the propensity of protein regions to lack
stable three-dimensional structures. Analysis using this tool revealed that polyG contains segments with
scores exceeding the threshold (>0.5), indicating potential intrinsic disorder. This characteristic suggests
its possible involvement in biological functions such as phase separation or multivalent interactions (Fig.
1a). The IDR feature of polyG resembles that of pathogenic repeat polypeptides generated by trinucleotide
expansions in FMR1 and C9orf72, as previously reported23, 24. Although our prior studies demonstrated
that pathogenic polyG induces nucleolar stress marked by NPM1 nucleocytoplasmic mislocalization8, its
specific impact on nucleolar structure and function remained unclear. The nucleolus consists of fibrillar
centers (FCs) enriched with NPM1 and granular components (GCs) enriched with fibrillarin (FBL), where
the IDR of NPM1 plays a critical role in maintaining liquid-liquid phase separation (LLPS) homeostasis
and structural integrity. We thus hypothesized that pathological polyG, harboring IDR, may disrupt
nucleolar structure and function.
To test this hypothesis, we transfected HEK-293T cells with our previously constructed 20- and
90-polyG overexpression plasmids8. Specifically, we generated expression vectors containing the 5'-
UTR of NOTCH2NLC with 20 (normal repeat) or 90 (pathogenic repeat) GGC repeats, fused to mCherry
(polyG-mCherry). We observed that 90-polyG formed more abundant aggregates in HEK-293T cells
compared to the control groups (Supplementary Fig. 1a), while demonstrating significant spatial
colocalization with ubiquitin and p62 (Supplementary Fig. 1b) - a characteristic pathological hallmark
of NIID4, 6, 7, 9, 25. An mCherry-only plasmid served as a negative control. CCK8 assays revealed that 90-
polyG significantly reduced cellular metabolic activity compared to 20-polyG (Fig. 1b), suggesting its
cytotoxic potential.
NSC348884 can specifically induce nucleocytoplasmic translocation of NPM1. Our experiments
revealed that the 90-polyG group induced nucleocytoplasmic translocation of NPM1, a phenotype
consistent with that observed in the positive control group treated with the specific inducer NSC348884
(Fig. 1c). This effect was not observed in the negative control (transfection reagent only), mCherry, or
20-polyG groups (Fig. 1c). This conclusion was further supported by statistical analysis of the
occurrence frequency (Fig. 1d). Moreover, we found that 90-polyG upregulated the expression of
NPM1 (Fig. 1e&f). These results suggest that 90-polyG induces nucleolar stress. Concurrently, 90-
polyG disrupted the GC layer (Fig. 1g) and downregulated FBL expression (Fig. 1e, h) in our cellular
model. Furthermore, in HEK-293T cells, the 90-polyG-induced alterations in NPM1 and FBL were
consistent with changes observed at the mRNA level (Supplementary Fig. 1c, d), suggesting that polyG
exerts its effects through transcriptional regulation. To validate these findings, we confirmed the presence
of aggregates co-formed by 90-polyG, ubiquitin, and p62 in the neurogenic mouse hippocampal cell
line HT-22 (Supplementary Fig. 1e). Moreover, overexpression of 90-polyG in HT-22 cells similarly
resulted in increased NPM1 and decreased FBL at both transcriptional and translational levels
(Supplementary Fig. 1f-j), accompanied by loss of normal structural localization of both NPM1
(Supplementary Fig. 1k) and FBL (Supplementary Fig. 1l). In summary, these results demonstrate that
90-polyG disrupts nucleolar structure and function.
In our earlier work, we successfully established three iPSC lines from NIID patients carrying
NOTCH2NLC GGC repeat expansions (130, 109, and 139 repeats) and differentiated them into 3DCOs
(Supplementary Fig. 2aA-c) and neurons (Supplementary Fig. 2d) 8. Age- and sex-matched normal
control-derived neurons and 3DCOs were used as normal controls. To evaluate the developmental stability
of the 3DCOs, we analyzed the structural integrity and cellular composition of three normal control-
derived 3DCOs. The results demonstrated consistent thickness of SOX2+ pseudoventricles and stable
TBR2+/SOX2+ area ratios across samples (Supplementary Fig. 2e-g). Furthermore,
immunofluorescence analysis revealed maintained GFAP distribution area, as well as fluorescence
intensity of NeuN (MNs) and DCX (immature neurons) (Supplementary Fig. 2h-j, Supplementary Fig.
3a&b). Additionally, comparative analysis of three independent batches from one normal control
confirmed consistent SOX2+ thickness, TBR2+/SOX2+ area ratio, GFAP distribution area, and NeuN
fluorescence intensity across batches (Supplementary Fig. 3c-h). Likewise, we observed typical cortical
pseudoventricular structures in three patient-derived cerebral organoids, with no significant differences in
the thickness of SOX2+ or the TBR2+/SOX2+ area ratio (Supplementary Fig. 3i-k).
We previously demonstrated that these patient-derived 3DCOs retained NOTCH2NLC GGC repeat
mutations and exhibited characteristic NIID pathological features, including p62- and ubiquitin-positive
intranuclear inclusions (Supplementary Fig. 4a-d) 8. Furthermore, we observed that these inclusions
were present across multiple cell types in 3DCOs, specifically within DCX+ immature neurons, MAP2+
MNs and GFAP+ astrocytes (Supplementary Fig. 4e). Comparative analysis revealed significantly
elevated NPM1 expression (Fig. 1i, j; Supplementary Fig. 4f, g) and reduced FBL levels (Fig. 1i, k;
Supplementary Fig. 4f, h) in NIID patient-derived neurons and 3DCOs. These alterations at the protein
level were consistent with corresponding changes in their transcriptional levels (Supplementary Fig. 4i-
l). Collectively, these results demonstrate that pathogenic polyG disrupts nucleolar structure and function.
Further observation revealed that a subset of inclusions exhibited nuclear localization across these
models. We quantified the nuclear localization rates of inclusions in different models. Based on data from
a published genetically engineered mouse model (32-day-old) 9, the nuclear localization rates varied
across brain regions: 14% in hippocampus versus 24% in cortex. The nuclear localization rates in our
patient-derived neurons (30-day-old, 25%) and cortical organoids (90-day-old, 23%) closely matched that
observed in the cortical region of the mouse model (24%) (Supplementary Fig. 4l). Similarly, the nuclear
localization rate in HT-22 cells (14%) aligned with that in the hippocampal tissue of the mouse model
(14%) (Supplementary Fig. 4l). These data indicate that while overexpression models may exhibit
supraphysiological expression levels, both patient-derived models and HT-22 cells show remarkable
consistency with the genetically engineered animal model in key pathological features-particularly in
nuclear localization rates of inclusions-reflecting the dynamic progression of polyG-mediated
pathogenesis across different spatiotemporal contexts.
Pathogenic polyG activates the NPM1-centric protein quality control system.
Notably, we observed colocalization between 90-polyG and NPM1 in HEK-293T, HT-22 and
neurons cellular models (Supplementary Fig. 5a-c), suggesting a potential interaction. To investigate
this, we controlled plasmid transfection duration in HEK-293T cells and found that 90-polyG
(mCherry-tagged) upregulated NPM1 expression in a time-dependent manner (Supplementary Fig. 5d-
f). At 12 hours post-transfection, 90-polyG aggregates became detectable (Fig. 2a) and progressively
enlarged (Supplementary Fig. 5g). Intriguingly, 90-polyG initially colocalized with NPM1 at 12 hours
(Fig. 2a). We observed colocalization of 90-polyG with NPM1 in patient-derived neurons (Fig. 2b;
Supplementary Fig. 5c). Given NPM1’s role in the protein quality control system26, pathological polyG
may activate this pathway. Specifically, in the nervous system, misfolded proteins enter the nucleolus and
bind NPM1 to mitigate toxicity, with HSP70 partially restoring refolding capacity26. To test whether
polyG engages this mechanism, immunofluorescence confirmed robust colocalization between 90-
polyG and HSP70 in HEK-293T and HT-22 cells (Fig. 2c; Supplementary Fig. 5h), alongside
significantly elevated HSP70 levels (Fig. 2d; Supplementary Fig. 5i, j). Consistent with observations in
cellular models, we found that inclusions in NIID patient-derived neurons (Fig. 2e) and 3DCOs
(Supplementary Fig. 5k) also exhibited spatial colocalization with HSP70, along with a significant
upregulation of HSP70 expression (Fig. 2f; Supplementary Fig. 5l, m), when compared to normal
controls. These findings suggest that 90-polyG activates an NPM1-centric protein quality control
response.
Since NPM1 binds rRNA through its IDR to regulate protein biosynthesis, we discovered that rRNA
also participates in inclusion body formation (Supplementary Fig. 5n). However, whether 90-polyG
colocalizes with rRNA in an IDR-dependent manner remains unclear. To test this hypothesis, siRNA-
mediated NPM1 knockdown (>90% efficiency, Supplementary Fig. 5o) abolished 90-polyG-NPM1
colocalization but preserved polyG-rRNA binding (Supplementary Fig. 5p), indicating that polyG
independently associates with rRNA, yet preferentially interacts with NPM1. This behavior contrasts with
C9orf72-derived repeat dipeptides, which preferentially bind rRNA27. Together, our results demonstrate
that 90-polyG independently engages both NPM1 and rRNA, with binding priority for NPM1.
PolyG-induced nucleolar dysfunction disrupts 3D chromatin architecture
Nucleolar homeostasis plays a critical role in maintaining 3D genome organization. Previous studies
have shown that NPM1 interacts with the chromatin structural protein CTCF to regulate the formation of
3D chromatin architecture12. Additionally, RAD21 cooperates with CTCF to participate in the formation
of TADs and chromatin loops13. We therefore hypothesized that 90-polyG-mediated perturbation of
NPM1 might induce alterations in 3D genome organization. To test this hypothesis, we found that
compared to 20-polyG and empty vector controls, 90-polyG significantly reduced both the
fluorescence intensity of CTCF and RAD21 (Fig. 3a, b), as well as their protein expression levels (Fig.
3c-e) in HEK-293T models. We consistently observed these effects in 90-polyG-expressing HT-22
models (Supplementary Fig. 6a-e). Furthermore, 90-polyG reduce CTCF and RAD21 expression in
HEK-293T cells in a time-dependent manner (Supplementary Fig. 6f-h), suggesting sustained effects on
3D chromatin structure proteins. Consistent with these findings, NIID patient-derived neurons (Fig. 3f-h)
and 3DCOs (Supplementary Fig. 6i-k) also showed downregulation of CTCF and RAD21. Collectively,
these results providing a potential mechanistic basis for the subsequent alterations in 3D chromatin
structure.
Furthermore, patient-derived 3DCOs exhibited a more diffuse nuclear distribution pattern of CTCF
compared to controls (Supplementary Fig. 7a), suggesting potential functional impairment. Notably,
control 3DCOs demonstrated significant co-expression of RAD21 with the neural progenitor marker
PAX6 (Supplementary Fig. 7b), consistent with RAD21's role in maintaining stem cell identity. In
contrast, this co-expression pattern was altered in patient-derived 3DCOs (Supplementary Fig. 7c) and
associated with reduced signal intensity of the pan-neuronal marker TUJ1 (Supplementary Fig. 7d).
These findings indicate that pathogenic polyG-induced downregulation and mislocalization of
CTCF/RAD21 underlie the abnormalities in neural progenitor identity by dysregulating the expression of
genes involved in neural differentiation.
To further characterize polyG-mediated 3D chromatin structural changes, we performed Hi-C
sequencing on 90-day-old 3DCOs derived from three NIID patients and three normal controls. Using Hi-
C 2.0 technology, we obtained approximately 3 billion paired-end reads per sample, generating about 1
billion uniquely mapped valid interactions in total. Data quality was verified by cis/trans interaction ratios
and distance-dependent interaction frequency decay analysis (Supplementary Fig. 8a). The interaction
matrices showed high correlation between groups (Pearson correlation coefficient R=0.96)
(Supplementary Fig. 8b) at 5-kb resolution (Supplementary Fig. 8c), enabling multiscale analysis of
3D chromatin structure including compartments, TADs, and loops.
Chromatin is spatially segregated into generally active A compartments and repressive B
compartments, with A compartments located in the nuclear interior and B compartments anchored at the
nuclear periphery via Lamin B1. Compared to normal controls, disease 3DCOs showed significant
compartment changes (Fig. 4a). Detailed analysis revealed that 512 compartments (8.9%) underwent
switching, including 295 (5.1%) A-to-B and 217 (3.8%) B-to-A conversions, while 5,231 (91.1%)
compartments remained stable (Fig. 4b). Previous finding that 90-polyG induces Lamin B1 disruption7
may explain these compartment conversions.
Given that CTCF and RAD21 cooperatively regulate TAD and loop formation, we further
investigated the effects of their dysregulation. We identified 5,592 and 5,568 TADs in disease and control
groups, respectively. Compared to normal controls, the disease group showed loss of 2,026 TADs and
gain of 2,050 TADs. Control TADs exhibited sharp boundaries, whereas disease TAD boundaries
appeared blurred (Fig. 4c, middle), indicating more frequent intra-TAD interactions in controls.
Quantification of boundary insulation strength revealed higher insulation scores at TAD boundaries in
NIID 3DCOs (Fig. 4d), indicating reduced boundary strength. Moreover, intra-TAD insulation scores
were lower in NIID 3DCOs (Fig. 4d), suggesting decreased interaction frequencies within TADs.
Loop analysis identified 4,990 and 5,456 loops in disease and control groups, respectively. The
disease group showed loss of 2,308 loops and gain of 1,845 loops compared to controls. Among 3,153
conserved loops, 191 exhibited enhanced interaction strength (fold change ≥2) while 441 showed
weakened interactions (fold change ≤0.5). As shown in Fig. 4c (right), loop interactions were generally
reduced in NIID 3DCOs. Loop anchors are enriched with enhancer-promoter interactions. To further
clarify the potential functional impact of the differential loops between the two groups, we analyzed genes
located near loop anchors and those within loops. The results showed that there were 5,419 genes within
50 kb of loop anchors and 8,549 genes located inside loops. These gene sets overlapped with the DEGs
from scRNA-seq results by 587 and 506 genes, respectively (Supplementary Data 1). Enrichment
analysis revealed that the DEGs near anchors were associated with functions such as ribosome, translation,
DNA mismatch repair, DNA replication, and the ubiquitin-proteasomal system. In contrast, the DEGs
within loops were linked to 5q35 copy number variation and apoptosis (Supplementary Data 1). In
summary, these results indicate that the dysregulation of loop structures in patient-derived cerebral
organoids is consistent with pathological features, and genes near loop anchors appear to play a more
significant role.
To further elucidate the role of CTCF and RAD21 in maintaining 3D chromatin architecture in NIID
patient-derived 3DCOs, we performed lentivirus-mediated rescue of CTCF/RAD21 expression. We
transduced 60-day-old 3DCOs from three patients with lentiviral vectors. One week post-transduction,
robust expression of CTCF (EGFP-tagged) and RAD21 (mCherry-tagged) was confirmed (Fig. 4e) and
further validated by protein quantification (Supplementary Fig. 8d-f). Hi-C sequencing was
subsequently performed 30 days post-transduction (OE group).
Quality control assessments using cis/trans interaction ratios and distance-dependent interaction
decay confirmed high-quality Hi-C data (Supplementary Fig. 8g). The OE group exhibited higher
correlation with normal controls (NC group) (Pearson R=0.97) than with the disease group
(Supplementary Fig. 8h), with achieved resolution of 5 kb (Supplementary Fig. 8i). Notably, the OE
group partially reversed disease-associated compartment switching, as visualized by interaction frequency
heatmaps (Fig. 4f). Among previously switched compartments, 245 of 512 (47.9%) were restored (135
B-to-A and 110 A-to-B reversions), while 5,499 compartments remained conserved. We identified 5,577
TADs and 5,519 chromatin loops in the OE group. Specifically, 707 previously lost TADs were
reestablished and 889 aberrantly gained TADs were eliminated (Fig. 4g). Insulation score analysis
demonstrated enhanced TAD boundary strength and increased intra-TAD contact frequency in the OE
group compared to NIID group (Fig. 4h). Furthermore, 2,129 (51.3%) altered loops were reversed, with
717 loops regained and 1,412 loops eliminated. Using the aforementioned method, we identified 319
differentially DEGs located near the rescued loop anchors in the OE group (Supplementary Data 1),
which remain associated with functions such as ribosome and mRNA processing (Supplementary Data
1). In summary, CTCF/RAD21 overexpression partially rescues the 3D chromatin structural abnormalities
induced by NOTCH2NLC GGC repeat expansions. In summary, our results demonstrate that CTCF and
RAD21 significantly contribute to the abnormalities in 3D chromatin architecture observed in NIID
3DCOs models.
NPM1 dysfunction induces DNA damage and cellular senescence in NIID pathogenesis
As NPM1 is recruited to DNA damage sites and participates in DNA repair processes28, we
hypothesized that 90-polyG-induced nucleocytoplasmic mislocalization of NPM1 might lead to
genomic instability. Phosphorylated γH2AX (p-γH2AX), a well-established marker of DNA double-
strand breaks29, demonstrated significantly elevated fluorescence intensity and a higher percentage of
positive cells in both HEK-293Tand HT-22 cells transfected with 90-polyG, compared to both 20-
polyG and empty vector controls (Fig. 5a, b; Supplementary Fig. 9a-c). Among HEK-293T cells
successfully expressing 90-polyG, approximately 86.0% exhibited DNA damage, while this proportion
reached 92.3% in HT-22 cells. In contrast, only 36.7% of HEK-293T cells expressing mCherry alone or
20-polyG showed DNA damage, decreasing further to 14.0% in HT-22 cells. Protein quantification
further confirmed that 90-polyG significantly increased p-γH2AX levels in both HEK-293T and HT-22
cells (Fig. 5c, d; Supplementary Fig. 9d, e). Notably, p-γH2AX induction appeared independent of 90-
polyG aggregation, as enhanced p-γH2AX fluorescence was still observed in cells without visible
aggregates (Supplementary Fig. 9a, b).
We next conducted related experiments in neurons and 3DCOs derived from three NIID patients.
Consistent with our earlier findings, DEGs located near differential loop anchors between the two groups
in cerebral organoids were significantly enriched in pathways related to DNA damage, such as DNA
mismatch repair (Supplementary Data 1). Consistent with previous findings, NIID patient-derived
neurons showed a significantly higher percentage of p-γH2AX-positive events (approximately 18.0%)
along with elevated p-γH2AX expression compared to normal controls (Fig. 5e; Fig. 5c, f). Notably, we
observed that over 80% of neurons containing inclusions exhibited enhanced p-γH2AX fluorescence.
Similarly, patient-derived 3DCOs demonstrated a substantially increased proportion of p-γH2AX-positive
events (approximately 14%) (Supplementary Fig. 9f, g) accompanied by upregulated p-γH2AX
expression (Supplementary Fig. 9h, i). Furthermore, to comprehensively evaluate DNA damage levels
in patient-derived 3DCOs, we performed GSVA on our previously published scRNA-seq data8. The results
demonstrated significantly enhanced DNA damage responses across all major cell types, including NPCs,
neuroblasts (NBs), neurons, and glutamatergic neurons, radial glial cells (Fig. 5h； Supplementary Fig.
9j). Consistent with these findings, we observed significant activation of the TP53 pathway – a key
downstream signaling cascade of DNA damage – in patient-derived 3DCOs (Fig. 5i). These findings
collectively indicate widespread genomic instability in NIID disease models.
Since accumulated DNA damage is a known driver of cellular senescence30, we next investigated
whether NIID models exhibit accelerated aging phenotypes. Bulk RNA sequencing of HEK-293T cells
transfected with 20-polyG, 90-polyG, or empty vector revealed distinct transcriptional profiles.
Principal component analysis showed clear separation between groups while maintaining high intra-group
consistency (Supplementary Fig. 10a). Hierarchical clustering analysis further demonstrated that 20-
polyG-transfected cells clustered with empty vector controls, whereas 90-polyG-expressing cells
formed a distinct group (Supplementary Fig. 10b), suggesting that 90-polyG induces unique
transcriptional alterations. Venn diagram analysis illustrated the numbers of differentially expressed genes
among groups (Supplementary Fig. 10c), and heatmap visualization confirmed the specific gene
expression pattern in 90-polyG cells (Supplementary Fig. 10d). Notably, pathway enrichment analysis
of upregulated genes in 90-polyG cells revealed significant enrichment in senescence-associated
pathways (the SHAP pathway; Fig. 5j, k), while 20-polyG cells showed no such enrichment compared
to controls (Supplementary Fig. 10e), indicating that senescence phenotypes primarily occur in 90-
polyG cells.
Consistent with cellular senescence characteristics, we observed significantly increased β-
galactosidase activity in both HEK-293T (Fig. 5l, m) and HT-22 cells (Supplementary Fig. 10f, g)
expressing 90-polyG. Similarly, patient-derived neurons (Fig. 5n, o) and 3DCOs (Supplementary Fig.
10h, i) exhibited markedly elevated β-galactosidase activity compared to controls. Additionally, GSVA
analysis of scRNA-seq data from patient-derived 3DCOs confirmed elevated senescence signatures
associated with DNA damage responses (Fig. 5p). Interestingly, neurons in the scRNA-seq showed lower
senescence scores than controls (Supplementary Fig. 10j), potentially due to their heightened
vulnerability to damage and subsequent loss.
In conclusion, our results demonstrate that accumulation of DNA damage events in NIID disease
models promotes cellular senescence. These findings reveal that polyG disrupts nucleolar function and
NPM1 homeostasis, thereby inducing DNA damage and cellular senescence.
ASO reduces polyG biosynthesis in NIID models
ASOs have demonstrated therapeutic efficacy in treating FMR1 repeat expansion-associated
disorders29, 31. Given the similar repeat mutations and pathogenic mechanisms between FMR1 and
NOTCH2NLC, we investigated whether ASOs could be applied to treat NIID caused by NOTCH2NLC
GGC repeat expansions. Based on previous studies, we designed ASO-GGC, ASO-GGC-cy3, and control
ASO-ctr, which block the translation of repeat RNAs through a steric hindrance mechanism32. Our
experiments showed that ASO-GGC-cy3 (200 nM) could efficiently enter HEK-293T cells via lipofection
(Supplementary Fig. 11a). To evaluate the cytotoxicity of ASO-ctr and ASO-GGC, we measured cell
viability and found no significant effects compared to the transfection reagent-only control group
(Supplementary Fig. 11b). Additionally, apoptosis and necrosis assays revealed that neither ASO-ctr nor
ASO-GGC significantly increased cell death (Supplementary Fig. 11c, d). These results confirm the
minimal cytotoxicity of ASO-ctr and ASO-GGC, prompting us to further assess their therapeutic potential
in NIID models.
We co-transfected HEK-293T cells with 90-polyG plasmids and 200 nM ASO-GGC or ASO-ctr,
followed by analysis after 48 hours. ASO-GGC significantly reduced 90-polyG (mCherry) expression
(Fig. 6a, b), accompanied by a marked decrease in 90polyG inclusions (Fig. 6c, d). To determine
whether ASO-GGC suppresses translation by degrading 90-polyG-mCherry mRNA, we quantified
mRNA levels and observed a significant increase in mCherry mRNA (Fig. 6c, d). This suggests that the
steric hindrance imposed by ASO-GGC may stabilize the mRNA structure, reducing its degradation while
simultaneously preventing ribosomal binding and subsequent translation. These results indicate that ASO-
GGC reduces 90-polyG production primarily by inhibiting translation rather than degrading mRNA.
Further evaluation of endogenous NOTCH2NLC demonstrated that ASO-GGC did not significantly affect
its protein expression (Supplementary Fig. 11e, f) while notably increasing NOTCH2NLC transcript
levels (Supplementary Fig. 11g).
These findings suggest that ASO-GGC specifically binds to the GGC repeat expansion in the 5'UTR
of NOTCH2NLC mature mRNA, thereby impeding ribosomal progression after translation initiation. This
steric hindrance prevents efficient readthrough of the GGC repeat region, leading to substantially reduced
polyG production. Meanwhile, the ASO binding stabilizes the mRNA structure, slowing its degradation
and resulting in elevated mRNA levels32. Although ribosomes are blocked at the ASO-bound repeat region,
they can bypass this 5'UTR segment to translate full-length, normal NOTCH2NLC protein downstream.
To validate ASO efficacy in patient-derived 3DCOs, we first treated 3DCOs with 200 nM ASO-GGC-cy3
for two weeks, confirming successful ASO delivery (Supplementary Fig. 11a). We subsequently treated
patient-derived neurons and 3DCOs from three NIID cases with 200 nM ASO-GGC to evaluate
therapeutic effects, with ASO-ctrl-treated patient-derived models serving as controls. During the treatment
period, ASO-GGC or ASO-ctrl was dissolved in culture medium and replenished with each medium
change for two consecutive weeks. Immunofluorescence analysis demonstrated that ASO-GGC
significantly reduced the number of p62- and ubiquitin-positive intranuclear inclusions in these models
(Fig. 6f-i), indicating decreased polyG production. Consistent with observations in cellular models, ASO-
GGC increased endogenous NOTCH2NLC mRNA levels in both patient-derived neurons and 3DCOs
without affecting its protein expression (Supplementary Fig. 11h-k).
In conclusion, these findings demonstrate that ASO-GGC effectively reduces polyG biosynthesis and
aggregation in NIID models by inhibiting translation, while preserving endogenous NOTCH2NLC
protein expression.
ASO rescues 3D genomic disorganization and senescence in NIID models
Given that ASO-GGC significantly reduces polyG expression and aggregation, we investigated its
potential to ameliorate genomic instability and senescence in NIID models. In HEK-293T cells co-
transfected with 90-polyG/ASO-GGC and 90-polyG/ASO-ctr, ASO-GGC significantly decreased
NPM1 expression levels (Supplementary Fig. 11l, m), indicating alleviated nucleolar stress.
Concurrently, ASO-GGC substantially reduced DNA damage and senescence phenotypes in 90-polyG-
expressing HEK-293T cells, as demonstrated by decreased p-γH2AX levels (Supplementary Fig. 11l, n)
and β-galactosidase activity (Supplementary Fig. 11o, p). Similarly, in NIID patient-derived neurons and
3DCOs, ASO-GGC significantly improved pathological conditions compared to the ASO-ctrl group.
Specifically, ASO-GGC ameliorated nucleolar stress by reducing NPM1 expression (Fig. 7a, b, d, e).
Furthermore, ASO-GGC attenuated DNA damage in patient-derived models, as evidenced by reduced p-
γH2AX levels (Fig. 7a, c, d, f) and fewer DNA damage foci (Fig. 7g-j), concomitant with elevated TUJ1
expression in 3DCOs (Supplementary Fig. 11q). Finally, ASO-GGC significantly reduced senescence in
both patient-derived neurons (Fig. 7k, l) and 3DCOs (Fig. 7 m, n).
To further elucidate the therapeutic effects of ASO-GGC on NIID, we performed scRNA-seq on
ASO-GGC- and ASO-ctr-treated NIID patient-derived 3DCOs. Specifically, 3DCOs from three patients
were randomly divided into two groups and treated with either ASO-GGC or ASO-ctr for two weeks.
After dissociation into single-cell suspensions and equal mixing, we obtained 26,423 and 14,284 high-
quality cells from ASO-GGC and ASO-ctr groups, respectively. ScType analysis identified six cell types:
NPCs, NBs, MNs, GABAergic neurons (GNs), radial glial cells (RGCs), and oligodendrocyte precursor
cells (OPCs) (Fig. 8a; Supplementary Fig. 12a). Bubble plots (Supplementary Fig. 12b) and heatmaps
(Supplementary Fig. 12c) of marker genes are provided. Cell proportion analysis revealed that following
ASO-GGC treatment, the proportions of OPCs, GNs, and NPCs increased, whereas the proportions of
RGCs, MNs, and NBs decreased (Supplementary Fig. 12d). We identified 325 differentially expressed
genes between groups (Fig. 8b), which were significantly enriched in pathways related to ribosome
biogenesis and polypeptide elongation (Fig. 8c), closely associated with translation and macromolecular
biosynthesis (Fig. 8d). Subsequently, we identified DEGs within each major cell subtype between ASO-
treated and control organoids, including NPCs (883 DEGs), NBs (476 DEGs), MNs (468 DEGs), GNs
(605 DEGs), RGCs (763 DEGs), and OPCs (218 DEGs) (Supplementary Data 2). Furthermore, we
performed functional enrichment analysis on DEGs, the results revealed significant enrichment in
pathways related to rRNA processing and translation (Supplementary Fig. 13a-j). Notably, our previous
study showed impaired ribosomal and translational functions in NIID models8, suggesting ASO-GGC
may exert therapeutic effects by restoring these functions. The proportion of neuronal populations showed
no significant change before and after ASO-GGC treatment (ASO-ctrl group: 44.3% vs. ASO-GGC group:
41.2%). GSVA further confirmed that ASO-GGC significantly attenuated DNA damage (Fig. 8e),
suppressed TP53 pathway activation (Fig. 8f), and reduced senescence-associated signatures (Fig. 8g).
These improvements were primarily observed in neural progenitor cells, glutamatergic neurons, and
oligodendrocyte precursor cells (Supplementary Fig. 14a, b). Interestingly, we noted a slight but
statistically significant increase in DNA damage scores in neuroblasts, indicating that the efficacy of
polyG-clearing therapy may exhibit cell-type-specific heterogeneity.
Additionally, hdWGCNA identified nine gene modules (Fig. 8h; Supplementary Data 3), among
which the pink (90 genes), blue (929 genes), red (231 genes), and turquoise (1913 genes) modules were
most significantly affected by ASO-GGC (Fig. 8i). Functional enrichment analysis of all genes within
these modules revealed strong associations with pathways involved in mRNA metabolic processes,
ribosome biogenesis, and translational regulation (Fig. 8j, k), further supporting ASO-mediated
restoration of translational function. Detailed enrichment results for these four individual modules are
provided in Supplementary Fig. 15 and Supplementary Data 3.
In summary, our results demonstrate that ASO-GGC can significantly improve DNA damage and
senescence in NIID models by restoring nucleolar function, reducing genomic instability, and rescuing
the dysregulated expression patterns of genes associated with neuronal development.
To determine whether ASO-GGC could rescue 3D chromatin structural abnormalities in NIID models,
we first evaluated changes in chromatin structural proteins. Results showed that compared to ASO-ctr,
ASO-GGC significantly upregulated CTCF and RAD21 expression in HEK-293T cell models
(Supplementary Fig. 16a-c). Similarly, in patient-derived models, ASO-GGC increased CTCF and
RAD21 expression (Fig. 8a-f). Concurrently, we observed that the intranuclear distribution of CTCF in
patient-derived 3DCOs reverted from a diffuse pattern to more distinct focal puncta following treatment
(Supplementary Fig. 16d), indicating restoration of normal CTCF localization. Collectively, these
findings demonstrate the therapeutic potential of ASO-GGC in ameliorating polyG-induced chromatin
architectural abnormalities.
To further assess the structural restoration by ASO-GGC, we performed Hi-C sequencing on treated
patient-derived 3DCOs, generating approximately 3 billion paired-end reads and ~1 billion uniquely
mapped valid interactions. Data quality was verified by cis/trans interaction ratios and distance-dependent
interaction frequency decay (Supplementary Fig. 16e). Compared to pre-treatment Hi-C results, ASO-
GGC-treated samples showed higher correlation with normal controls (Pearson's R=0.97)
(Supplementary Fig. 16f) at 5-kb resolution (Supplementary Fig. 16g). Heatmaps displayed
compartment changes (Fig. 9g). Detailed analysis revealed that ASO-GGC restored 407/512 (79.5%)
aberrant compartments (244 B to A and 163 A to B reversions) (Fig. 9h). We identified 5,973 TADs and
5,673 loops in treated samples, with ASO-GGC restoring TAD boundaries and loop structures on
chromosome 8 (Fig. 9i). Specifically, 1,182 lost TADs were regained while 427 aberrant TADs
disappeared. Insulation score analysis showed ASO-GGC enhanced TAD boundary insulation and
partially restored intra-TAD contact frequencies (Fig. 9j). Moreover, ASO-GGC reversed 2,082/4,159
(50.1%) altered loops (977 regained, 1,105 lost). Using the aforementioned method, we identified 329
DEGs located near the restored loop anchors after ASO-GGC treatment (Supplementary Data 1), which
remained associated with functions such as ribosome and mRNA processing (Supplementary Data 1).
To compare whether OE and ASO-GGC exert similar therapeutic effects, we found that among the DEGs
near the rescued loops, there were 227 overlapping genes, which were also enriched in pathways related
to ribosome and mRNA processing (Supplementary Data 1). These findings demonstrate that ASO-GGC
can partially rescue NOTCH2NLC GGC repeat expansion-induced 3D chromatin abnormalities, further
supporting its therapeutic potential for NIID and providing mechanistic insights.
Comparative analysis with healthy control-derived 3DCOs revealed that ASO-GGC prompted
switching in 212 compartments (3.7%), comprising 90 A-to-B and 122 B-to-A transitions. At the TAD
level, ASO-GGC triggered the formation of 1,321 and disappearance of 1,293 TADs. Regarding
chromatin loops, the treatment led to the emergence of 1,989 loops and loss of 2,210 existing ones. We
propose three potential explanations for these observations: (1) partial restoration, where treatment
incompletely reverts the genome to its precise healthy baseline; (2) potential off-target effects; or (3)
dynamic genomic recalibration representing a transitional state during recovery from pathological
conditions.

Methods
Ethics statement
The induced pluripotent stem cell (iPSC) lines used in this study were derived from skin biopsies
obtained in our previous work8, which was approved by the Ethics Committee of The First Affiliated
Hospital of Zhengzhou University. All participants provided written informed consent for the collection
and use of samples for research purposes. All experiments conducted in this study were performed in
accordance with relevant guidelines and regulations. Since this study involved only the use of previously
established iPSC lines and did not recruit new human subjects or collect new human tissues/data,
additional ethical approval was not required.
Generation of iPSC lines
Briefly, skin fibroblasts were obtained from three NIID patients harboring pathogenic NOTCH2NLC
GGC repeat expansions and three healthy controls with normal NOTCH2NLC alleles. Fibroblasts were
cultured in fibroblast medium containing 10% fetal bovine serum (FBS; Gibco). Reprogramming was
performed using the CytoTune™-iPCS 2.0 Sendai Reprogramming Kit (Invitrogen) according to the
manufacturer's protocol. After infection, cells were plated onto Matrigel-coated plates (Corning) and
cultured in fibroblast medium for 7 days, followed by replacement with Essential 8™ complete medium
(Gibco) changed daily. iPSC colonies emerged after 2–3 weeks and were manually picked and expanded.
Colonies were passaged using EDTA (Invitrogen) and treated with 10 μM Y-27632 (Rock inhibitor,
Sigma-Aldrich) for the first 24 hours after passaging. All iPSC lines used in this study (passages 20–30)
were confirmed to have normal karyotypes prior to differentiation.
Three-dimensional cerebral organoids (3DCOs) differentiation
3DCOs were generated using the STEMdiff™ Cerebral Organoid Kit (Stemcell, 08570) 39. Briefly,
iPSCs with optimal growth morphology were maintained on Matrigel-coated 6-well plates in mTeSR Plus
medium (Stemcell, 100-0276). At ~80% confluency, cells were dissociated into single-cell suspensions
using Gentle Cell Dissociation Reagent (Stemcell, 07174) and seeded in low-attachment 96-well U-
bottom plates with EB seeding medium to form embryoid bodies (EBs). On day 5, EBs were transferred
to low-attachment 24-well plates containing induction medium. At day 7, EBs were embedded in Matrigel
(Corning, 354277) and cultured in 6-well plates with expansion medium to initiate neuroepithelium
formation. From day 10 onward, 3DCOs were maintained in maturation medium on an orbital shaker,
with medium changes every 3 days until full maturation. The disease and control groups consisted of
3DCOs derived from three NIID patients and three healthy controls, respectively. Both cohorts were sex-
matched (each comprising two males and one female) and were exclusively of Han Chinese ethnicity.
Culture of iPSC-derived neurons
The differentiation of iPSCs into neurons was performed as previously described with modifications40.
Briefly, iPSCs at 80-90% confluence were dissociated into single cells using StemPro Accutase and
seeded onto Matrigel (Corning, 354277)-coated plates at a density of 2.5–3 × 10⁵ cells per well in mTeSR
Plus medium (Stemcell, 100-0276). On the following day (Day 0 of neural induction), the medium was
replaced with complete PSC Neural Induction Medium (Neurobasal Medium supplemented with 10%
GIBCO Neural Induction Supplement). A 50% medium change was performed on Day 2, followed by
complete medium changes every other day until Day 7. The resulting neural progenitor cells (NPCs) at
passage 0 (P0) were harvested using Accutase and passaged at a ratio between 1:3 and 1:6 onto new
Matrigel-coated plates for subsequent neuronal differentiation. For neuronal differentiation, NPCs were
seeded onto Matrigel-coated plates and maintained in complete BrainPhys Neuronal Medium (Stemcell,
05795). The culture was fed by replacing 50% of the medium every 2-3 days. Mature neurons (MNs)
were obtained after 30 days of differentiation and used for subsequent experiments. Neurons were derived
from three patients (two males and one female) as the disease group and three normal controls (two males
and one female) as the control group.
Genetic constructs
GGC repeat-containing plasmids were constructed as previously described6, 8. Briefly, the human
NOTCH2NLC exon 1 sequence (including 5'-UTR GGC repeats) was cloned into pcDNA3.1 fused to an
ATG-deleted mCherry reporter. The construct initiates translation from the uN2C ORF start codon and
terminates at the first stop codon. We generated two variants: 20×GGC-mCherry (control) and 90×GGC-
mCherry (pathogenic expansion), along with an mCherry-only empty vector. All plasmids were
transformed into DH5α competent cells to ensure repeat stability and validated by Sanger sequencing.
Synthesis of ASOs
ASOs were designed based on prior research32. The 11-nt ASO-GGC (5′-CCGCCGCCGCC-3′)
contained 10 locked nucleic acid (LNA) modifications and a 3′ 2′-O-methyl group, with phosphorothioate
linkages at all LNA positions. Control ASO-ctrl (5′-GCGUATTATAGCCGATTAAC-3′) and Cy3-labeled
ASO-GGC-cy3 (5′-CCGCCGCCG-3′) were synthesized by Genema Gene with HPLC purification.
Cell culture and transfection
The HEK-293T human embryonic kidney cell line used in this study was purchased from the
American Type Culture Collection (ATCC, catalog number: CRLS-3216). The HT22 mouse hippocampal
neuronal cell line was obtained from Ubigene Biosciences (Guangzhou, China; catalog number: YC-
A004). HEK-293T and HT-22 cells were cultured in high-glucose DMEM (Gibco) supplemented with 10%
FBS, 1% L-glutamine (Lonza), and 1% penicillin/streptomycin at 37°C with 5% CO₂. For transfection,
cells were seeded in culture plates and transfected at ~80% confluency using Lipofectamine ® 3000
transfection kit (Thermo Fisher Scientific) per manufacturer's protocol. ASOs were denatured at 95°C for
30s and chilled on ice before transfection. NSC348884 (7 μM) was administered for 24 hours to
specifically induce nucleocytoplasmic translocation of NPM1.
Cell viability assay
HEK-293T cells (5×10³/well in 96-well plates) were treated with CCK-8 reagent (10 μL/well)
(Dojindo, CK04) for 2 h at 37°C. Absorbance was measured at 450 nm (reference: 650 nm). Viability was
calculated as: (ODtreatment - ODblank)/(ODcontrol - ODblank) × 100.
Apoptosis and necrosis detection
Cells were stained with YO-PRO-1 (491 nm excitation) and propidium iodide (PI; 618 nm). YO-
PRO-1+ cells indicated apoptosis; PI+ cells indicated necrosis.
Tissue immunofluorescence
Well-developed 3DCOs were fixed in 4% paraformaldehyde (PFA) for 30 minutes at room
temperature, washed three times with PBS, then dehydrated in 30% sucrose solution at 4°C until
sedimentation. The dehydrated 3DCOs were embedded in OCT compound and sectioned at 14 μm
thickness using a Leica cryostat (Germany), with sections stored at -80°C until use. For
immunofluorescence, thawed sections were permeabilized with 0.3% Triton X-100 in PBS, blocked with
5% BSA for 30 minutes at room temperature, then incubated with primary antibodies overnight at 4°C.
After PBS washes, sections were incubated with fluorophore-conjugated secondary antibodies (1:100) for
1 hour at room temperature, followed by nuclear counterstaining with DAPI (Southern Biotech, 0100-20).
Fluorescence images were acquired using a Zeiss confocal microscope. Primary antibodies included:
TUJ1 (Proteintech, 66375-1-Ig, 1:100), CTIP2 (Proteintech, 55414-1-AP, 1:100), PAX6 (Abcam,
ab78545, 1:100; ab5790, 1:50), TBR2 (Abcam, ab216870, 1:400), GFAP (Proteintech, 16825-1-AP,
1:100), NeuN (Proteintech, 66836-1-Ig, 1:100), DCX (Proteintech,13925-1-AP, 1:100), NPM1
(Proteintech, 60096-1-Ig/10306-1-AP, 1:200), FBL (Proteintech, 16021-1-AP, 1:400), CTCF (Proteintech,
10915-1-AP, 1:200), RAD21 (Proteintech, 27071-1-AP, 1:50), p-γH2AX (Proteintech, 29380-1-AP,
1:100), HSP70 (Proteintech, 10995-1-AP, 1:200), rRNA (Santa Cruz, sc-33678, 1:50), p62 (Proteintech,
66184-1-Ig, 1:200), and ubiquitin (Proteintech, 10201-2-AP, 1:200). Secondary antibodies were: FITC-
conjugated goat anti-mouse/rat IgG (Proteintech, SA00003-1/11, 1:100), Cy3-conjugated goat anti-
mouse/rabbit IgG (Proteintech, SA00009-1/2, 1:100), and CoraLite Plus 647-conjugated goat anti-mouse
IgG (Proteintech, RGAM0A05, 1:100).
Cell immunofluorescence
Cells were fixed (4% PFA, 15 minutes), permeabilized (0.3% Triton X-100, 30 minutes), and stained
as above.
High-throughput/resolution chromosome conformation capture (Hi-C) and analysis
Hi-C libraries were constructed from pooled samples of each group (5 × 10⁴ cells per sample) using
an in situ Hi-C protocol. Briefly, cells were crosslinked with 1% formaldehyde for 10 minutes at room
temperature and quenched with 0.125 M glycine. After lysis, chromatin was digested with 100 U of MboI
(NEB), and the resulting fragments were labeled with biotin-14-dCTP (Invitrogen). Ligation was
performed with 50 U of T4 DNA ligase (NEB), followed by reversal of cross-linking and DNA
purification using the QIAamp DNA Mini Kit. The purified DNA was sheared to 300–500 bp, and a
sequencing library was prepared through end repair, A-tailing, adapter ligation, and streptavidin-based
pull-down of biotinylated fragments, followed by PCR amplification. The final libraries were quantified
and sequenced on the MGISEQ platform (BGI, China) to a depth of approximately 300 million reads per
library to enable robust detection of 3D chromatin structures.
We processed high-resolution Hi-C data using the HiC-Pro pipeline (v3.1.0) 41. Briefly, raw Hi-C
reads were aligned to the human reference genome (hg38) using Bowtie2 (v2.4.4), followed by filtering
of duplicates and low-quality reads to extract valid interaction pairs. The contact matrices were
normalized using iterative correction and eigenvector decomposition (ICE) to remove technical biases.
Hi-C map resolution was defined as described previously42, calculated as the smallest bin size where ≥80%
of bins contained ≥1,000 contact reads.
Compartments (A/B) were assigned following established methods43. Expected contact frequencies
were calculated based on genomic distance between loci, and Pearson correlation matrices were generated
from observed/expected matrices. Principal component analysis (PCA) was performed on the correlation
matrices, with the first principal component (PC1) used for compartment calling. Genomic regions with
positive PC1 values (higher gene density) were designated as compartment A, while negative values
indicated compartment B.
TADs were identified using the Insulation Score method44 with 20kb bins and 1Mb sliding windows.
Insulation scores were normalized by the mean score across all bins per chromosome, with TAD
boundaries called at local minima (FAN-C boundaries). Chromatin loops were detected using the
HiCCUPS algorithm in Juicer Tools (v1.9.9) at 5-kb resolution with a significance threshold of FDR<0.2.
Differential chromatin loops were defined as those with interaction strength fold changes >2 or <0.5. Hi-
C interaction matrices were visualized using Juicebox (v1.9.8).
Analysis of Spatial Association between Chromatin Loops and Genes
To ensure the biological relevance of subsequent analyses, we constructed a stringently filtered gene
set based on the hg38 genome annotation. Through systematic exclusion based on naming conventions,
we removed all non‑coding RNAs (e.g., LINC‑, MIR‑, etc.), pseudogenes, and low‑confidence predicted
genes (e.g., LOC‑, AC‑ prefixes). Simultaneously, gene‑length filtering (500 bp – 5 Mb) was applied to
remove annotation noise, while all ribosomal protein genes (RPL‑/RPS‑) were intentionally retained.
Ultimately, we obtained a set of 20,190 high‑confidence protein‑coding genes, which served as a reliable
basis for subsequent chromatin loop association analysis. To identify genes potentially regulated by
differential chromatin loops, we performed spatial proximity analysis between all differential loops and
the above gene set. Specifically, for each loop, we calculated the centroid coordinates of its two anchors.
A gene was considered spatially associated with the loop if its transcription start site fell within a 50‑kb
window upstream or downstream of the centroid of either anchor. To ensure specificity of associations,
only the three closest genes per anchor were retained, and each gene was uniquely assigned to its nearest
loop to avoid multiple counting. The resulting candidate gene set was subsequently used for downstream
functional enrichment analysis.
Co-expression of CTCF and RAD21 via Lentiviral Transduction
To co-express CTCF and RAD21 in 3DCOs, we transduced three patient-derived 3DCOs (60-day-
old) using lentiviral vectors. Equal titers of CTCF-GFP and RAD21-mCherry lentiviral preparations were
pooled and directly added to the organoid culture medium at a total MOI of 40. To enhance transduction
efficiency, a transduction enhancer was supplemented during the procedure. After 48-hour incubation in
virus-containing medium, the organoids were transferred to fresh maintenance medium. Fluorescence
detection and protein quantification were performed 7 days post-transduction, followed by Hi-C
sequencing at 30 days after infection.
Bulk RNA sequencing and bioinformatics
Bulk RNA sequencing was performed by BGI Genomics using the following workflow: Total RNA
was extracted from plasmid-transfected HEK-293T cells using TRIzol reagent, with concentration and
purity assessed by Nanodrop 2000 and RNA integrity verified using Agilent 2100 Bioanalyzer. Potential
genomic DNA contamination was excluded through agarose gel electrophoresis. For library preparation,
2 μg of total RNA per sample underwent poly(A) enrichment using oligo magnetic beads, followed by
strand-specific library construction. Sequencing was performed on the MGI platform with 150 bp paired-
end reads.
For data analysis, clean reads were aligned to the hg38 reference genome using HISAT2 (v2.2.1) 45.
Gene expression levels were quantified as FPKM values and subjected to PCA and hierarchical clustering.
Differential expression analysis was performed using DESeq2 (v3.19) 46, with significantly differentially
expressed genes defined as those with adjusted p-value <0.05 and absolute fold change >2. Functional
enrichment analysis of these genes was subsequently conducted using Enrichr47.
β-galactosidase staining
Cells/tissue sections were fixed, permeabilized, and incubated with β-gal (1 mg/mL, 37°C overnight).
Blue precipitate area was quantified using ImageJ (v1.53).
Capillary electrophoresis immunoassay (Jess)
Cells were lysed in ice-cold lysis buffer containing protease inhibitors and 1 mM PMSF using
ultrasonication, followed by centrifugation at 12,000 × g for 15 minutes at 4°C to collect the protein
supernatant, with concentrations determined by BCA assay. For automated capillary western blot analysis
using the ProteinSimple Jess system, protein samples were mixed with master mix and sample buffer,
denatured at 95°C for 5 minutes, then processed according to manufacturer's protocol (https://www.bio-
techne.com/resources/protocols/jess-abby-wes-protocols) with sequential loading of antibody diluent,
primary antibodies [TUJ1 (Proteintech, 66375-1-Ig, 1:100), NPM1 (Proteintech, 60096-1-Ig/10306-1-AP,
1:200), FBL (Proteintech, 16021-1-AP, 1:50), CTCF (Proteintech, 10915-1-AP, 1:100), RAD21
(Proteintech, 27071-1-AP, 1:100), p-γH2AX (Proteintech, 29380-1-AP, 1:30), HSP70 (Proteintech,
10995-1-AP, 1:200), rRNA (Santa Cruz, sc-33678, 1:50), NOTCH2NL (Santa Cruz, sc-100307),
GAPDH (Proteintech, 10494-1-AP, 1:400)], HRP-conjugated secondary antibodies (1:100), chemiluminescent
substrate and wash buffer, followed by automated electrophoresis, immunodetection and image
acquisition using default settings, with data analysis performed using Compass software and signal
intensity quantified by ImageJ (v1.53). Uncroped and unprocessed images of all immunoblots are
provided in Supplementary Fig. 17.
RT-PCR and qPCR
Total RNA was extracted from cells using TRIzol reagent (Invitrogen), followed by cDNA synthesis
with the HiScript IV All-in-One Ultra RT SuperMix kit (Vazyme, R433-01) according to the
manufacturer's protocol. Quantitative PCR was performed using SupRealQ Ultra Hunter SYBR qPCR
Master Mix (Vazyme, Q713-02) on a CFX96 Deep Well Real-Time System (Bio-Rad) under the following
cycling conditions: initial denaturation at 95°C for 30 sec, followed by 40 cycles of 95°C for 10 sec and
60°C for 30 sec. GAPDH served as the internal reference gene, and relative expression levels were
calculated using the 2-ΔΔCt method. The following primer pairs were used: NPM1 (forward: 5'-
GCTGTGGAGGAAGATGCAGAGTC-3', reverse: 5'-GGGCAGACCGTCTTCCAGATATAC-3'), FBL
(forward: 5'- GCCACACCTTCCTGCGTAATG-3', reverse: 5'- TGCGGCTTCATGTTCTCCTGTTG-3'),
mCherry (forward: 5'-ACGACGCCGAGGTGAAGAC-3', reverse: 5'-
CTCGTTGTGGCTGGTGATGTC-3'), GAPDH (forward: 5'-GAGTCAACGGATTTGGTCGT-3',
reverse: 5'-TTGATTTTGGAGGGATCTCG-3'), and NOTCH2NLC (forward: 5'-
CTACCAGTGCCAGTGCCTTCAG-3', reverse: 5'-CCGACAGGTGCCTCCATTGAC-3').
Single-cell RNA sequencing (scRNA-seq) analysis
For scRNA-seq, patient-derived 3DCOs were cultured for 90 days and then treated with either 200
nM control ASO (ASO-ctr) or target ASO (ASO-GGC) for two weeks. 3DCOs from each treatment group
were enzymatically dissociated into single-cell suspensions, counted, and pooled in equal numbers for
sequencing on the Novogene platform, generating approximately 100 GB of raw sequencing data per
10,000 cells. The raw FASTQ data were processed using Celescope (v1.5.2) for quality filtering and gene
alignment, generating standardized output files compatible with Seurat (v5.1.0) 48. Quality control metrics
were applied to retain cells expressing 200-4,500 genes while excluding those with >20% mitochondrial
RNA content. Following normalization, we identified 2,000 highly variable genes for downstream
analysis. Principal component analysis was performed using Seurat's "ScaleData" and "RunPCA"
functions, with Harmony (v1.2.1) 49 employed for batch effect correction. Unsupervised clustering was
conducted at a resolution of 0.3 using the "FindClusters" and "FindNeighbors" functions, with results
visualized via UMAP projection. Cell type annotation was performed using ScType50.
In the scRNA-seq, differentially expressed genes (DEGs) were identified using the FindMarkers
function from the Seurat package (v5.1.0), based on a non-parametric Wilcoxon rank-sum test. The
significance thresholds were set at an adjusted p-value < 0.05 and an absolute fold change > 0.25. For
network analysis, we implemented single-cell weighted gene co-expression network analysis (hdWGCNA
v0.4.00) 51 by first creating a metacell expression matrix through k-nearest neighbors aggregation
("MetacellsByGroups") of genes detected in ≥5% of cells. The optimal soft threshold power was
determined via the "TestSoftPowers" function, followed by Pearson correlation-based network
construction ("ConstructNetwork"). Module eigengenes were derived through linear regression
("ModuleEigengenes"), with differential module analysis performed using Wilcoxon tests ("FindDMEs").
Full methodological details are available at:
https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html. Functional enrichment analysis was
conducted using Enrichr47 (significance threshold: p<0.05), while pathway activity was assessed via Gene
Set Variation Analysis (GSVA v1.52.3) 52 using gene sets obtained from the Molecular Signatures
Database (https://www.gsea-msigdb.org/gsea/msigdb/human/search.jsp). GSVA scores were calculated
using single-sample gene set enrichment analysis (ssGSEA) based on gene sets associated with specific
pathways. The following gene sets were utilized in this study: DNA damage response (WP707,
https://www.wikipathways.org/pathways/WP707), DNA damage response - TP5353, and DNA
Damage/Telomere Stress Induced Senescence (R-HSA-2559586,
https://www.reactome.org/content/detail/R-HSA-2559586).
Statistical and reproducibility
To minimize bias, all image analyses were performed using either fully automated pipelines or
blinded manual assessment. Statistical analyses were conducted using GraphPad Prism (v9.0.0), with data
presented as mean ± standard deviation (SD). Data normality was tested. No covariates were included in
the statistical models. Data are shown as violin plots showing the distribution of all values. In violin plots,
the upper, middle, and lower horizontal lines represent the 75th percentile (Q3), median (Q2), and 25th
percentile (Q1), respectively. All experiments were independently repeated three times. For each replicate
in 2D culture models, quantitative data were obtained by averaging measurements from 3 randomly
selected fields of view. For 3DCOs, five random, non-overlapping fields of view were imaged per stained
section to account for organoid heterogeneity. Measurements from these five fields were averaged to
generate a single data point per organoid. Data from three organoids per condition were then used for
statistical analysis. For statistical comparisons between two groups, two-tailed paired Student's t-test was
employed; for comparisons among three or more groups, one-way ANOVA with Tukey's or Dunnett's
post-hoc test was applied. For all experiments, p-values < 0.05 were considered statistically significant.
All newly generated materials are available from the corresponding author upon reasonable request.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General Comments\nThe authors present an intriguing study that explores potential mechanisms involved in neuronal intranuclear inclusion disease (NIID), a devastating neurodegenerative condition. While it has been suggested that DNA repeat expansions and the resulting toxic peptides contribute to genomic and cellular alterations (e.g. C9ORF72 hexanucleotide repeat expansions), this current work makes advances.\nSpecifically, the authors claim that polyG, a toxic polyglycine peptide synthesized in NIID, disrupts genomic architecture and function by interacting with NPM1 and rRNA. This interaction leads to genomic instability, cellular senescence, and changes in neural differentiation. They also investigated the reversibility of polyG-induced pathology using an antisense oligonucleotide (ASO)-based strategy, which has important translational implications.\nHowever, despite the exciting findings, I have concerns regarding the potential overinterpretation of their data, particularly in relation to the mechanistic aspects of the study and the claims about functional relevance. The evidence for the impact on genomic architecture is largely correlative, lacking direct demonstration of causation. Additionally, the proof of neurodevelopmental and cell differentiation changes is not sufficiently convincing.\nOverall, the methodologies and statistical details (e.g. exact sample sizes and types) are not fully described or are used inappropriately, leading me to question the robustness of the assays. Importantly, the reliability and suitability of their brain organoid platform are questionable, which diminishes my enthusiasm for the translational aspect of their findings.\nNevertheless, I support their manuscript for publication, subject to major revisions, as it has the potential to yield valuable results. I have listed my specific concerns for the authors to address.\nSpecific comments\n1. The authors should provide a detailed description of the methods in each section. In particular, they should expand the information on the following: What were the biological and technical sample sizes for the organoid-based experiments? For instance, how many batches of cell lines and organoids were used in each experiment/group? Did they use three organoids from the same batch or multiple organoids for each of the three batches? What were the wet lab protocols/methods used for the HiC data? How many cells (for each sample) did they submit for sequencing? Have they submitted the raw data to a repository? Was the ASO treatment introduced as a single dose and left for 2 weeks or it was administered daily?\n2. The researchers should provide histological data and images that demonstrate the consistency of the cerebral organoid cortical plate structure and the proportions of different cell types. Any inconsistencies in these features could affect the results of their experiments. Additionally, they should specify the age of the organoids used in each experiment.\n3. Has the loss of nucleolar structure presented in Figs. 1C, 2A been seen across many cells? In the images, most of the nuclei are without polyG foci, which raises some reservations about the robustness of the phenotype. They should include overview images representing the frequency of distribution changes (foci) of polyG/NPM1, which should also be quantified.\n4. Fig S1D does not show evidence of intranuclear inclusion. In fact, these images demonstrate the distribution of cytoplasmic ubiquitin/p62. This is in contrast to their conclusions in the text, and as such, it should be explained how this conceptually fits into their overall findings and interpretation. Does polyG also colocalize with HSP70 in 3DCOs?\n5. In Fig. S2A,B, the sample size seems to be n=2. Are these biological or technical replicates. Running statistical tests on 2 samples per group or on technical replicates is inappropriate, and as such, the statement about the reduction of RAD21/CTCF is not supported.\n6. The claims regarding the neurodevelopmental phenotype are practically ungrounded. The evidence provided (Fig. 2I, reduced TUJ1+ cells) does not support cell differentiation or migration since their study is confined to a snapshot (in time), is not quantified and critical analyses are missing to reach that conclusion. To support any statements on neural differentiation/development, the authors should explore differences in progenitor cells and their progeny in a quantified fashion using canonical markers. Regarding cell migration, they should set up a dynamic cell migration assay to support their conclusions.\n7. The conclusion in the text for Fig. 2 (“… these results provide direct evidence for polyG371 induced alterations in 3D chromatin structure”) is incorrect. The work in Fig. 2 only demonstrates the dysregulation of 3D genome regulators, and as such, it cannot be considered as direct evidence for altering 3D structure (which is part of the investigations presented in Fig. 3).\n8. The data presented in Fig. 3 is exciting. However, the conclusions are premature as the mechanistic suggestions are only based on correlations. For direct evidence, the authors should perform perturbation experiments to confidently demonstrate polyG-induced changes in 3D genome architecture and their subsequent impact on cellular processes. Moreover, it would be prudent to perform rescue experiments for CTCF/RAD21 using NIID 3DCOs if they would like to prove their involvement. Knockdown experiments in control lines/3DCOs could also help address these issues and provide a more detailed understanding of polyG-mediated mechanisms.\n9. The scRNA-seq analysis is rather superficial. Further analysis would provide an opportunity to infer chromatin loop-induced changes, which may indicate the relevant assay to perform (e.g., cell migration, differentiation) in functional experiments. Moreover, it would be more informative to express DEGs for each cell type cluster individually rather than in bulk. They should present their clustering principles, annotation rules (marker genes) and heatmaps in the supplementary material.\n10. How did they calculate the gene expression scores for DNA damage/senescence? It is not appropriate to say there was an ASO-induced improvement in gene expression scores for DNA damage/senescence pathways because, at least in one cell type, there was a statistically significant worsening (Fig S5D). The conclusion should be rephrased.\n11. The data on cell senescence (Fig. 4 and S3F) could also be more appropriately interpreted when cell type (e.g. neuronal) proportions are established in healthy versus disease 3DCOs, which has been addressed in my comment at point 2. The authors should also provide information on the senescence reference pathway used in their scRNA-seq analysis.\n12. For similar reason addressed in point 6, it is also inappropriate to state that ASO treatment improves neurodevelopment.\n13. How many loops, TADs and A/B compartment changes were induced through ASO-treatment when compared to healthy controls? The authors present some interesting data on the restoration of normal-3DCO genomic architecture, but they do not discuss the new changes introduced by the treatment (i.e., compared to normal).\nMinor comments\n14. In Fig. 1R: what immunostaining does the siNPM1 image represent in the panel?\n15. Fig. 4H,O, 7E and similar graphs: There is no y-axis, please provide axis and metrics.\n16. Fig. 7: please show the GO terms for each coloured module to clarify interpretations.\n17. Please indicate the exact samples sizes to the figure legends.\n18. Western blot images are missing the molecular mass (kDa). Please supply those.\n19. The numbers on the scale bars are difficult to read. These are already provided in the legends. Please, either delete or replace.\n20. GO term analyses: please provide FDR (significance).", "reviewer_2": "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.", "reviewer_3": "In this manuscript, Mengjie and colleagues investigate the involvement of GGC repeat expansions in the 5-/UTR of NOTCH2NLC causing NIID. This expanded microsatellite repeat can be translated into a polyG tract. PolyG polypeptides then trigger expression of NPM1 involved in DNA damage and maintenance of the 3D genome architecture through interaction with CTCF, components of the DNA damage machineries and impinging on TADs. The manuscript is interesting and relevant, and we have the following suggestions before publication.\nThe authors are encouraged to provide more detailed information regarding the specific cell types used in each experiment. Additionally, statistical analyses should be clearly described, including the type of ANOVA performed and the specific post hoc test applied, with this information included in the figure captions (for each panel).\nQuantification of protein levels is currently difficult to interpret due to the low clarity and blurriness of the protein signals. Validation of key findings by Western blotting is strongly recommended. Furthermore, measurement of relative transcript levels may be important for supporting the conclusions and improving data interpretation.\nThe authors should also consider the potential impact of post-translational modifications on protein migration in capillary electrophoresis. Certain phosphorylation sites can significantly influence band or isoform separation, which may help explain discrepancies between the quantification data and the representative images—this issue is especially apparent across several panels.\nWhile nuclear inclusions are observed in HEK cells, it remains unclear whether the same nuclear localization occurs in the organoid models. Clarifying this distinction would strengthen the interpretation of the results.\nFinally, although the rationale for examining TADs, DNA damage, and senescence is provided, the specificity of these pathways in the context of NIID remains insufficiently addressed. Further explanation is warranted to clarify how these mechanisms contribute to NIID-related neuropathology. This can be expanded in the Discussion section.\nWe have the following comments and suggestions to improve clarity, strength and impact of results\n1) Please provide details on the type of analysis performed to obtain data of Fig 1A.\n2) Panel 1B: The assay measures metabolic processing through enzymatic processing of the provided substrate, and a reduction in signal can result from altered enzymatic activity, reduced cell proliferation or cell death. Moreover, expanded repeats did not suppress viability, rather reduced metabolic activity by 20%(?), notice that the curve is still growing over time, as likely a result of increased cell proliferation.\n3) Quantification of NPM1 inclusion formation (number of cells with these inclusions?) should be provided.\n4) Panels 1D to H: before concluding that transfection of the cells with the expanded construct increases NPM1 while decreasing FBL, the authors should measure the transcript levels and compare them with protein levels to dissect where the effect comes from. It is not clear how the quantification was normalized. Is the relative expression a ratio of the target gene/GAPDH? Are the three samples shown in panel G derived from each independent IPSC line obtained from NIID patients?\n5) Please add cell type in each subpanel in the figure captures. I suggest to move patient-derived data in the main figure and move HEK data to suppl. The number of experiments in IPSC derived organoids should be at least 3 independent experiments, each using all three independent cell lines and controls. If the data are obtained from the same cell line, then you need to have the isogenic control. There is no fig legend for panel Suppl 1l. It is difficult to establish if the images shown are representative of 3 or more cell lines/independent experiments.\n6) Why did the authors use capillary electrophoresis and not traditional Western blot, and proximity ligation assay to assay colocalization/interaction?\n7) In panel 2A, there are several cells with normal NPM1 localiztion and 1 cell with a NII, quantification is advised, is this cell for some reasons dying? In how many cells do the author see this phenomenon? Why is NPM1 subcellular localization different in panel 2A and 2B?\n8) In Fig 4D, the two nuclei positive for gH2AX do have nuclear inclusions?\n9) How do the authors explain the increase in transcript levels of NOTCH2NLC without differences in protein levels? Can they conclude that there is no change in translation by simply analyzing protein levels as in panel 5F? Quantification is not clear, what do they mean for relative protein levels? Clarify “relative to” and adding the loading control and the name of the protein in panel 5F may make the reading easier.\n10) Please revise the manuscript and figures to correct typos (like panel 5D leve should be level…)", "reviewer_4": "This manuscript is a timely and potentially impactful study examining the pathogenic mechanisms of neuronal intranuclear inclusion disease (NIID), focusing on the consequences of GGC repeat expansions in the NOTCH2NLC gene. The authors explore the hypothesis that polyglycine (polyG) proteins resulting from these expansions disrupt nucleolar function, chromatin organization, and cellular homeostasis. They combine data from HEK293T overexpression models and patient-derived cerebral organoids, further proposing antisense oligonucleotides (ASO-GGC) as a therapeutic approach to mitigate disease-associated molecular phenotypes.\nThe topic is of interest to the field and the authors employ an array of complementary techniques including Hi-C, immunostaining, senescence assays, and RNA-seq. However, while the findings are intriguing, several concerns regarding experimental design, model validity, data interpretation, and figure clarity weaken the current manuscript’s conclusions.\nIssues:\n- Overexpression Model Limitations: The study relies heavily on transient overexpression in HEK293T cells without evidence that expression levels reflect those in patient-derived tissue. This raises concerns about physiological relevance, particularly since HEK cells are prone to supraphysiologic expression. The absence of proper negative controls, such as mCherry-only constructs, further limits interpretability.\n- Cerebral Organoid Controls: The cerebral organoid work is promising but undermined by the lack of isogenic, mutation-corrected controls. Furthermore, there are notable discrepancies between findings in organoids and HEK293T cells—particularly regarding polyG localization and phenotypic effects—that are not sufficiently addressed.\n- CTCF and Chromatin Architecture: The conclusion that GGC expansions disrupt chromatin structure via CTCF is based on Hi-C alone. Without complementary ChIP-seq or methylation data, these conclusions remain tenuous. The authors should either calibrate their language or expand experimental support.\n- DNA Damage and Nuclear Localization: Figure 4A claims altered nuclear localization of DNA damage markers, but the immunofluorescence images are not convincing. Furthermore, it is unclear whether the γH2AX-positive cells are also expressing polyG? This co-localization should be clarified and quantified to buttress the conclusion. (Especially since in the figure, one of the two nuclei with a prodigious amount of yH2AX staining was absent polyG.) Similarly for Figures 4E and 4G, it would strengthen rigor to include the percentage of polyG-positive cells that also show DNA damage or senescence markers. This would help determine whether the phenotypes are specific or generalizable. Lastly, the polyG puncta in HEK293T cells vs. 3D organoids appear substantially different phenotypically, raising concerns about the extent to which the HEK model accurately recapitulates disease features.\n- Senescence Characterization: The authors rely on β-galactosidase as an indicator of DNA damage-induced senescence. However, this assay is not specific to DNA damage–induced senescence, and alternative pathways (e.g., oxidative stress) could yield similar results. This alternative should be addressed, especially in light of the authors' claim that senescence is downstream of DNA damage. Interestingly, senescence markers are detected in RNA-seq, but there is no accompanying evidence of p53 pathway activation or cell cycle arrest (e.g., p21, p16, p53) in bulk RNA-seq ‘pathway overrepresentation’ analysis. Similarly, the ASO rescue of b-gal surrogate for senescence does not appear to be reflected in the transcriptome as the single cell RNA-seq data in figure 7 is absent of senescence associated pathway enrichment. It would be helpful to address these peculiarities. More broadly, the reversal phenotype following ASO treatment should be contextualized by comparing it to the unexpanded \"DCO-norm.\" This would help determine whether ASO truly restores the system to baseline.\n- Figure Quality and Inconsistencies: Several figures lack essential quantification or display inconsistencies. For example, polyG signals in Figure 5B do not appear to overlap with DAPI, conflicting with earlier characterizations. Key control details (e.g., organoid genotype in Figure 7) are unclear or omitted. Line 441–442: The text refers to results that do not appear to be supported by corresponding data. Please clarify or provide additional evidence. Line 477: Please specify whether the DCOs analyzed in this context are derived from the normal or expanded genotype.\nThus, while the manuscript addresses an important and timely topic in neurodegeneration, the current version contains methodological limitations and interpretive overreach that are addressable in various ways and would lead to a markedly more impactful study."}, "author_response": "Dear Editor,\nWe sincerely thank you for arranging the peer review of our manuscript entitled\n\"ASO Therapy Rescues NOTCH2NLC GGC Repeat Expansion-Induced Genomic\nDamage, 3D Chromatin Structural Abnormalities, and Senescence\" (Manuscript ID:\nNCOMMS-25-33043) and for granting us the opportunity for \"major revision.\" We are\nequally grateful to the reviewers for their insightful, rigorous, and constructive\ncomments.\nWe have carefully read and thoroughly considered every comment from the Editor\nand all four reviewers. We agree that these comments are highly valuable, and\naddressing them will significantly enhance the rigor, breadth, and impact of our study.\nWe are pleased to submit herewith a substantially revised manuscript and a detailed\npoint-by-point response.\nAs you suggested, the core of this revision lies in addressing key issues through\nsubstantial additional experiments and analyses. We placed particular emphasis on the\nfollowing aspects:\nWe strengthened the model systems by going beyond the HEK-293T\noverexpression model. We systematically supplemented experimental data in NIID\npatient-derived neurons and the mouse hippocampal neuronal cell line (HT-22). These\nnew data validate the robustness of our core hypotheses across models of varying\nphysiological relevance, directly addressing the concerns raised by you and the\nreviewers regarding model validity.\nWe provided key causal evidence, rather than merely correlative evidence, for\npolyG-induced alterations in 3D chromatin structure. Following the reviewers'\nsuggestion, we performed rescue experiments restoring CTCF/RAD21 expression in\npatient-derived 3DCOs. The results clearly demonstrate that this is sufficient to partially\nreverse the disruption of the 3D genome, providing strong causal support for our core\nmechanistic hypothesis.\nWe have also supplemented the entire manuscript with all necessary experimental\ndetails, statistical methods (including specific post-hoc tests), and controls (such as\nempty vector controls). All figures and tables have been optimized according to the\nreviewers' comments, with necessary quantitative analyses added.\nFinally, we compared the biological significance of phenotypic differences\nobserved across different models and highlighted the consistency of the core findings,\nthereby enhancing the interpretative depth of the results.\nWe firmly believe that through these extensive and substantive revisions, the\nrevised manuscript has successfully addressed the major issues raised by the reviewers\nand yourself. The scientific rigor, depth of mechanistic insight, and persuasiveness of\nthe conclusions in the revised manuscript have been significantly enhanced.\nWe once again thank you and the reviewers for your valuable time and exceptional\nguidance. We believe that this revised manuscript now better meets the high standards\nof Nature Communications and we look forward to your further consideration."}, {"round": 2, "reviewer_comments": {"reviewer_1": "General comments:\nThe reviewer appreciates the attempt to improve the manuscript. From a mechanistic perspective, CTCF overexpression is an exciting initial finding for rescuing polyG-induced architectural changes. However, this analysis remains superficial, without any inference of biological significance or identification of other phenotypic rescue beyond reversing some genomic architecture changes. Methodological details are much clearer; however, several points remain unaddressed.\nMajor specific comments:\n1. How many fields of view were taken for the 3DCO immunofluorescence images? This is an important point, as organoid heterogeneity compromises the accuracy of quantification.\n2. In the Supplementary material, the authors should have also included images showing the typical layer architecture across the three organoid batches harbouring the GGC organoids. It is yet unclear if GGC organoids uniformly recapitulate cortical tissue organisation.\n3. The authors claim that the density of NeuN+ neurons remained constant across samples. NeuN is a marker of mature neurons, and several immature neuronal subpopulations are present at day 90, whose proportions may vary.\n4. Quantification of PolyG frequency across all cells per field of view has not been demonstrated. I am also unconvinced by their internal control argument, unless they have cultured a mix of non-transfected and transfected cells together (in which case this should be made clear in the methods and/or results section). The analyses in the paper would be further enhanced by identifying in the 3DCO which cell types exhibit the highest frequency of PolyG aggregates, using co-localisation with cell-type markers.\n5. The CTCF-overexpression (CTCF-OE) experiments help to tie CTCF dysregulation to some of the polyG-induced genomic architecture changes. However, its biological significance has not been demonstrated. No analysis has been made to reflect the significance of the regions showing genomic architecture changes (e.g., which genes are contained in the chromatin loops, and whether these genes are dysregulated).\n6. How were the DEGs determined in the scRNA-seq studies? How many DEGs were identified in each cluster? How did they distinguish radial glial cell, neural progenitor cell and neuroblast assignment? Were they not able to identify distinct layer-forming neuronal populations amongst mature neurons? Quantification of cell-type abundances in 3DCOs between ASO-ctrl and ASO-GGC treatments could help interpret cell-type- or gene-expression changes.\n7. The data are still not robust enough to claim that ASO treatment promotes neuronal development (lines 723 and 788) - at most, it affects gene expression patterns implicated in neurodevelopment, but this is not validated. Please amend this accordingly.\nMinor points:\n8. Figure 2G and 2H are still missing.\n9. This statement still appears overly confident—we recommend a slight change to: \"...providing a potential mechanistic basis for the subsequent alterations in 3D chromatin structure.\"\n10. In some places, the scale bar numerical size is still in the image, whereas in others it is described in the text. Please keep it consistent.\n11. Please provide FDR values in Figure 8J-K, or S15?", "reviewer_2": "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.", "reviewer_3": "The authors have addressed all my concerns. The manuscript, modified to respond to all reviewers' comments, is remarkably improved in the quality of results, details provided,and models used to validate the experiments. Statistical analysis is properly applied and clarified.", "reviewer_4": "The authors have substantially addressed my original concerns. I appreciate their time, commitment and additional work in strengthening the overall manuscript and its relevance to the field."}, "author_response": "Dear Editor,\nThank you for organizing another round of review and giving us this valuable opportunity for revision. We have carefully studied the final comments from all reviewers and fully understand the key issues that remain to be addressed.\nWe particularly appreciate Reviewers #3 and #4 for their positive assessments of the revised manuscript. Regarding the remaining important points raised by Reviewer #1—including essential methodological details, the necessity of additional controls, and the need for deeper interpretation of biological significance. We have conducted a thorough, point-by-point analysis and have formulated a detailed revision plan.\nWe will strictly adhere to all requests from Reviewer #1 and your editorial team, and will comprehensively revise the manuscript as follows:\n1. Supplement all key methodological details.\n2. Provide complete evidence for the quantification of polyG frequency across all fields of view, and improve the relevant control experiments.\n3. Deepen the discussion and analysis of the biological significance of findings such as CTCF overexpression, ensuring that all conclusions are firmly and precisely supported by the data.\n4. Address and implement all other specific comments to fully resolve the reviewer’s concerns.\nAdditionally, per the request of author A.R.L.S. and to comply with updated institutional policies following recent guidance on NIH funding attribution, we have removed the acknowledgment of NIH grants R35NS122140 and R01AG033082 (to A.R.L.S.) from the “Funding Acknowledgments” section of the manuscript. All other funding and competing interest declarations remain unchanged. This correction constitutes a necessary compliance update.\nWe are confident that these additions and refinements will further enhance the manuscript's scientific rigor, completeness, and clarity. We are committed to completing a high-quality revision and resubmitting it at your earliest convenience.\nOnce again, we sincerely thank you and the reviewer team for your diligent efforts and invaluable guidance in improving this work."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have substantially revised this manuscript and satisfied by concerns. I congratulate to the authors.", "reviewer_2": "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": ""}]

Decision letter