SETD1A regulates psychiatric gene networks involved in genomic stability and synaptic function in rare and sporadic schizophrenia

Domain: chemistry
Published: 2025-12-20
Editorial outcome: minor_revision

Abstract
Rare loss-of-function (LoF) mutations in SETD1A are associated with schizophrenia (SCZ). However, how SETD1A haploinsufficiency leads to SCZ-associated phenotypes and its relevance to patients without these rare mutations is unknown. Here, we identify SETD1A bound loci and regulated genes in human prenatal cortex and isogenic pluripotent stem cell-derived neuronal models engineered with SETD1A LoF variants, including the most common patient mutation. SETD1A preferentially binds the promoters of polygenic risk loci for psychiatric disorders that regulate chromatin remodeling, DNA repair, and synaptic function. Additionally, SETD1A binds to DNA damage-prone sites in neural progenitor cells and postmitotic neurons. SETD1A haploinsufficiency causes accelerated neurogenesis, reduced neuronal complexity, and DNA damage accumulation in postmitotic neurons that is rescued by inhibiting the H3K4me2/3 demethylase KDM5. In postmortem SCZ cortical tissue, individuals who lack SETD1A mutations exhibit reduced SETD1A expression, associated with downregulation of SETD1A-regulated genes, implicating SETD1A-H3K4me dysfunction in sporadic SCZ cases. We demonstrate that SETD1A functions as a convergent upstream modulator of a gene network that underlies polygenic risk for psychiatric disorders, which suggests that restoring the SETD1A-H3K4 methylation epigenetic imbalance may benefit a broad psychiatric population.

Introduction
Schizophrenia (SCZ) is a severe, chronic psychiatric disorder with neurodevelopmental origins, affecting ~1% of the global population. Its complex etiology and heterogeneous clinical presentation have limited progress in therapeutic development. Since therapies targeting genetically associated disease mechanisms have a higher success rate, understanding SCZ’s genetic architecture and pathogenic mechanisms is essential for new treatments.

SCZ has a strong genetic basis. Genome-wide association studies (GWAS) have identified 287 common variant loci associated with SCZ risk. Although each contributes modest risk, their cumulative burden is substantial. Among biological pathways enriched for SCZ-associated variants, the histone H3K4 methylation pathway is prominent. Genes encoding H3K4 methyltransferases, demethylases, and readers are implicated in several neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and intellectual disability (ID). The frequent implication of H3K4me3 regulatory proteins in neurodevelopmental disorders suggests a critical role for H3K4me3-associated pathways in human brain development, which may occur through both enzymatic and non-enzymatic mechanisms.

In addition to common variants, rare copy number variations (CNVs) and loss-of-function (LoF) mutations in individual genes also contribute to risk. Rare heterozygous LoF mutations in SETD1A are among the strongest single-gene risk factors for SCZ (OR = 20.1) and are also implicated in NDDs. SETD1A encodes a methyltransferase that catalyzes H3K4me2/3 as part of the COMPASS complex. A recurrent heterozygous patient mutation, SETD1A c.4582-2delAG, affects the exon 16 splice acceptor and has been identified in multiple SCZ and NDD cohorts. Despite this genetic evidence, SETD1A’s targets and function in the developing human brain remain poorly understood. Whether its regulatory actions converge on SCZ polygenic risk loci or contribute to sporadic SCZ in the broader population remains unclear.

In mouse models, Setd1a is required for neural progenitor proliferation and its deficiency leads to impaired neuronal morphology and synaptic function. These phenotypes are rescued by LSD1 (KDM1A) inhibition but not by KDM5 inhibition, implicating H3K4me3 dynamics in disease mechanisms.

SETD1A has also been implicated in genome stability, but the role in non-dividing neuronal cells is unknown. In yeast and non-neuronal human cells, SETD1A localizes DNA damage sites and mediates H3K4me3 to promote repair. During early brain development, neural progenitors undergo extensive proliferation, making accurate DNA repair critical. Defective repair can lead to somatic mutations, particularly at recurrent DNA break clusters (RDCs) that overlap with common fragile sites (CFSs), mosaic CNVs, and long neural genes linked to psychiatric disorders. Recent studies in human postmortem neurons revealed increased somatic single nucleotide variants (SNVs) in SCZ brains at transcription factor binding sites active during fetal development. Similarly, DNA repair hotspots (DRHs) have been identified in open chromatin of postmitotic neurons.

Here, we used CRISPR to engineer human iPSC-derived isogenic lines carrying the SETD1A c.4582-2delAG mutation. We characterized the effects of SETD1A haploinsufficiency on neurodevelopment, gene expression, chromatin occupancy, and DNA repair. Integrating GWAS, CUT&Tag profiling, and postmortem transcriptomic data, we show that SETD1A regulates genes involved in synaptic signaling and genome maintenance, converging with both rare and common SCZ genetic risk.

Results
SETD1A c.4582-2delAG mutation causes SETD1A haploinsufficiency and reduced H3K4me3

To investigate the consequences of SETD1A haploinsufficiency during early human neurodevelopment, we used CRISPR-Cas9 to introduce heterozygous LoF mutations (SETD1A+/−) into two neurotypical iPSC lines (LIBD2c1 and LIBD7c6). We generated 12 isogenic SETD1A+/− lines, including two clones carrying the recurrent patient-specific SETD1A c.4582-2delAG mutation at exon 16 splice acceptor site, causing intron retention and a premature stop codon in exon 16 (SETD1Ac.4582-2delAG/+), and six unedited wild-type (WT) clones. No off-target edits or CNVs were detected, and six SETD1A+/− and six WT lines were selected for downstream analysis.

Upon differentiation into 2D NPCs, 2D forebrain neurons, and 3D cerebral organoids (COs), SETD1A mRNA was significantly reduced in all heterozygous lines across all cell types. RNA-seq analysis confirmed allele-specific downregulation of the SETD1A c.4582-2delAG allele, consistent with nonsense-mediated decay in SETD1Ac.4582-2delAG/+ lines. Western blotting showed reduced full-length SETD1A protein with no detectable truncated protein products in SETD1Ac.4582-2delAG/+ NPCs and neurons.

We next examined the impact of SETD1A haploinsufficiency on H3K4 methylation. A specific and significant reduction in H3K4me3 was observed in NPCs from SETD1Ac.4582-2delAG/+ lines. H3K4me1/2 levels were also reduced, although these did not reach statistical significance.

SETD1A haploinsufficiency disrupts NPC proliferation and alters neuronal morphology and network activity

We next assessed whether SETD1A LoF affects neurodevelopmental cellular phenotypes. Neural progenitor cell (NPC) proliferation was significantly decreased in SETD1A+/− lines compared to WT, consistent with prior mouse studies. To evaluate neuronal maturation and function, we performed Sholl analysis and multi-electrode array (MEA) recordings on neurons co-cultured with astrocytes. Neurons from SETD1Ac.4582-2delAG/+ lines showed reduced neurite complexity and exhibited earlier and more robust increases in spontaneous activity and synchrony than WT neurons. This hyperexcitability phenotype was most pronounced between weeks 7–9 of culture and aligns with findings from CRISPR-engineered neurons with heterozygous SETD1A mutation not found in patients. Collectively, these data indicate that SETD1A haploinsufficiency leads to disrupted NPC proliferation and altered neuronal morphology and activity during early neurodevelopment.

Patient-specific SETD1A c.4582-2delAG mutation alters NPC fate, neuronal maturation, and subtype specification

To define the neurodevelopmental impact of SETD1A haploinsufficiency, we performed single-cell RNA-seq (scRNA-seq) on COs at days 30 and 48 derived from three WT and two SETD1Ac.4582-2delAG/+ lines. We identified 13 cell types based on established marker genes and cell-state identifiers. Both genotypes showed increased neuronal proportions over time, but SETD1Ac.4582-2delAG/+ COs had a persistently lower fraction of S/G2/M-phase NPCs by day 30, indicative of impaired developmental trajectory. G1/postmitotic NPCs were also significantly reduced in mutant lines, and cycling NPCs skewed toward G2/M-phase, suggesting mitotic delay.

Differential expression analysis of S/G2/M NPCs identified 624 DEGs (FDR<0.05) with downregulation of DNA replication/repair genes and upregulation of mitotic and synaptic programs. Analysis of G1/postmitotic NPCs revealed 788 DEGs (FDR<0.05), but gene set enrichment analysis (GSEA) did not detect significantly enriched pathways. To validate findings, we performed bulk RNA-seq on 2D NPCs from six WT and six SETD1A+/− lines, including SETD1Ac.4582-2delAG/+ and CRISPR-engineered mutations not found in patients. A total of 161 DEGs (FDR<0.1) were detected in NPCs, with GSEA revealing downregulation of DNA replication, chromatin remodeling, histone modification, and telomere maintenance pathways, and upregulation of neurogenesis-related genes in SETD1A+/− lines.

Given that cell cycle length influences the transition of NPCs from proliferation to neurogenesis, we next evaluated the effect of SETD1A LoF on neuronal differentiation. Flow cytometry of PSA-NCAM immunostaining in day 30 COs showed a higher proportion of cells with high PSA-NCAM expression in SETD1A+/−, suggesting increased immature neuron presence without a change in total PSA-NCAM+ cells. Neuronal pseudo-bulk analysis of the scRNA-seq revealed 309 DEGs (FDR<0.05), including downregulated glutamatergic synapse genes and upregulation of early synapse and metabolic genes. To validate this with more isogenic lines, we performed bulk RNA-seq of 2D neurons from six WT and six SETD1A+/− lines. While the heterogeneous cell types and states in the 2D neuronal cultures reduced the power of bulk RNA-seq to detect DEGs, the analysis confirmed this trend with 41 DEGs (FDR<0.1) and showing downregulation of chromatin regulation, DNA repair, and glutamatergic pathways in SETD1A+/− neurons compared to WT neurons. Neuron projection guidance pathway was also reduced in SETD1A+/− neurons, aligning with observed neurite deficits.

Additionally, consistent with the significant downregulation of GABAergic differentiation genes in 2D SETD1A+/− neurons, subtype classification of neurons in COs scRNA-seq revealed an increased glutamatergic-to-GABAergic neuron ratio in SETD1Ac.4582-2delAG/+, with no change in dopaminergic cells. This excitatory shift aligns with elevated spontaneous activity observed in neurons carrying SETD1Ac.4582-2delAG/+ and CRISPR-engineered mutations not found in patients, reinforcing the functional impact of subtype imbalance.

Patient-specific SETD1A c.4582-2delAG mutation increases double-strand DNA breaks and leads to a signature of senescence

Given the transcriptional downregulation of DNA repair pathways observed in SETD1Ac.4582-2delAG/+ COs, we next investigated whether SETD1A haploinsufficiency leads to the accumulation of DNA damage in developing neural cells. GSEA of bulk RNA-seq data from 2D SETD1A+/− haploinsufficient NPCs and neurons revealed significant downregulation of genes involved in double-strand break (DSB) repair, chromatin remodeling, histone modification, and telomere maintenance. A moderate but significant correlation between transcriptomic changes in SETD1A+/− NPCs and neurons furthers supports shared dysregulation of gene programs across developmental stages. Genes downregulated in both SETD1A+/− NPCs and neurons were enriched for DNA damage response and DSB repair pathways, suggesting that SETD1A haploinsufficiency disrupts these pathways throughout neural development.

To directly assess DNA damage accumulation, we examined the presence of γH2AX foci, a marker of DSBs. Immunostaining of day 30 COs revealed a significantly higher proportion of γH2AX-positive NPCs in SETD1A+/− COs compared to WT. Similarly, 2D neurons derived from SETD1Ac.4582-2delAG/+ lines showed a greater proportion of γH2AX-positive cells and an increased number of γH2AX foci per nucleus relative to WT neurons. These results indicate persistent DNA damage in both proliferative progenitor and postmitotic cell populations under conditions of SETD1A haploinsufficiency.

To assess broader biological consequences of this damage, we analyzed aging-related transcriptional signatures in 2D SETD1A+/− NPCs and neurons. These included downregulation of telomere maintenance genes and upregulation of genes involved in inflammatory response pathways, both of which are associated with cellular senescence.

Together, these data demonstrate that SETD1A haploinsufficiency is associated with impaired DNA repair and increased accumulation of DNA damage across developmental stages in human neural cells. The accompanying transcriptomic changes suggest premature activation of stress response and aging-like gene expression programs, potentially contributing to altered neurodevelopmental trajectories.

Identification of genome-wide SETD1A targets in the human developing cortex and isogenic SETD1Ac.4582-2delAG/+ neuronal cells

To understand the mechanisms underlying the neurodevelopmental perturbation and transcriptional alterations in SETD1A haploinsufficient neuronal cells, we next sought to identify genome-wide targets of SETD1A. We performed CUT&Tag assays on 2D NPCs and neurons derived from two WT and two SETD1Ac.4582-2delAG/+ lines and compared SETD1A binding and H3K4me3 binding in SETD1Ac.4582-2delAG/+ cells to those observed in WT cells. We also performed CUT&Tag on three human prenatal frontal cortical samples in the second trimester, given the higher expression levels of SETD1A during the prenatal stage in the cortex throughout the human lifespan. To investigate neuronal cells at different stages of developmental trajectory, including NPCs, intermediate progenitor cells (IPCs), and neurons, we dissected the developing cortical layers from the ventricular zone to the marginal zone.

We first validated the specificity of anti-SETD1A antibodies by western blot analysis on adult postmortem dorsolateral prefrontal cortex (DLPFC) tissue. We found that two antibodies (CST and Sigma Atlas, which we will refer to as SETD1A_CST and SETD1A_Atlas) showed higher specificity than the other three antibodies, including the antibody from Bethyl that were widely used in previous studies. Specificity of the two anti-SETD1A antibodies, SETD1A_CST and SETD1A_Atlas, were further confirmed by western blot of HEK293T cells with SETD1A knockdown by siRNA. We then performed CUT&Tag on the prenatal cortical tissue using five antibodies: H3K27ac, H3K4me1, H3K4me3, and SETD1A (SETD1A_CST and SETD1A_Atlas), and on 2D NPCs and neurons using three antibodies: H3K4me3 and SETD1A (SETD1A_CST and SETD1A_Atlas).

We confirmed that peaks of H3K27ac, a marker for open chromatin regions, accumulated on both promoters and introns, including enhancers; peaks of H3K4me1, a marker for enhancers, accumulated on introns; and peaks of H3K4me3, indicating active promoters, showed marked accumulation on promoters.

We then annotated the CUT&Tag peaks in genomic regulatory elements, based on the histone modification patterns. Our definitions of genomic regulatory elements were: active promoters, open chromatin regions enriched in H3K4me3 peaks annotated as promoter [between −1kb to 100bp of transcription start site (TSS)] by ChIPseeker; active enhancers, regions enriched in H3K27ac but depleted in H3K4me3 and not annotated as promoter; and other genomic regions consisting of five genomic regions, namely 5′ UTR, 3′ UTR, exon, intron, and distal intergenic regions as annotated by ChIPseeker.

SETD1A preferentially binds active promoter regions and regulates chromatin remodeling and DNA repair in the developing human cortex

SETD1A peaks from the human prenatal cortices accumulated more on active promoter regions. In all three brains, we found that SETD1A peaks from the two antibodies accumulated more at active promoter regions (~80% of SETD1A_CST peaks and ~40% of SETD1A_Atlas peaks) compared to active enhancers (~3.5% of SETD1A_CST peaks and ~12% of SETD1A_Atlas peaks). Although SETD1A_CST peaks exhibited more specific accumulation on promoters than SETD1A_Atlas peaks, there was a highly significant overlap of SETD1A peaks between the two antibodies around TSSs in each of the three brains.

Similarly, we observed that SETD1A predominantly binds active promoter regions (more than 60% of both SETD1A_CST and SETD1A_Atlas peaks) in both NPCs and neurons from both WT and SETD1Ac.4582-2delAG/+. SETD1A peaks overall and at active promoter regions in NPCs and neurons showed significant overlap with the peaks in the human prenatal cortex. These findings contrast with a previous study, which showed that 66% of Setd1a peaks overlap with enhancer marks in the PFC of 6-week-old mice, perhaps due to differences in methodology, antibodies and human vs. mouse differences.

Gene ontology (GO) enrichment analysis with GREAT revealed that both active promoters and active enhancers bound by SETD1A in the prenatal cortical tissue genome-wide were significantly enriched in pathways that regulate DNA repair, histone modification, chromatin remodeling, the mitotic cell cycle, and autophagy. The SETD1A-bound active promoters were also specifically enriched in pathways such as the ERAD pathway and telomere maintenance. In contrast, the SETD1A-bound active enhancers showed specific enrichment in pathways regulating neurogenesis and synaptic functions, consistent with the previous mouse study.

Similar to the results from prenatal brains, genome-wide active promoters bound by SETD1A exhibit significant enrichment for genes involved in chromatin remodeling, DNA repair, proteolysis, and telomere maintenance in both 2D NPCs and neurons from WT lines. These findings indicate that SETD1A regulates these biological pathways throughout early neurodevelopment, consistent with the downregulation of genes associated with these pathways in SETD1A+/− NPCs and neurons compared to WT cells. Additionally, we found specific enrichment of SETD1A-bound active promoters for genes regulating DNA replication and mitotic cell cycle in NPCs, and for genes involved in H3K4me pathways in neurons, aligning with the downregulation of these pathways in SETD1A+/− neuronal cells.

To identify gene modules associated with SETD1A expression in the human developing brain, we performed weighted gene co-expression network analysis (WGCNA) from human prenatal cortical transcriptomes (n=50). Among 63 co-expression modules, 11 modules showed strong eigengene correlation with SETD1A expression (|Pearson correlation coefficient (r)| > 0.65). The genes in these modules positively correlated with SETD1A were associated mainly with chromatin organization and DNA repair pathways. Moreover, overrepresentation analysis revealed a significant enrichment of genes harboring SETD1A-bound active promoters and/or active enhancers with genes in the positively correlated modules. Genes in the negatively correlated modules, regulating synaptic signaling, mitochondrial function, and translation pathways, were not enriched with genes whose active gene regulatory regions are bound by SETD1A.

Overall, these data suggest that SETD1A targets gene regulatory regions of actively transcribed genes, preferentially at promoter regions, and regulates chromatin remodeling and DNA repair pathways in the developing human cortex.

SETD1A targets common SCZ risk genes that regulate chromatin remodeling, DNA repair, and synaptic signaling pathways in neuronal cells during human cortical development

To investigate how SETD1A contributes to SCZ risk, we examined the overlap between SETD1A-bound genomic regions and SCZ-associated loci and genes. SETD1A binding sites in prenatal cortical tissue, as well as in 2D NPCs and neurons (WT and SETD1Ac.4582-2delAG/+), showed significant enrichment at 287 SCZ GWAS loci. This overlap was more pronounced in neurons than in NPCs (Fisher’s exact test, OR = 1.21; p = 2.44 × 10−5 in WT; OR = 1.24; p = 0.0003 in SETD1Ac.4582-2delAG/+), underscoring SETD1A’s prominent role in regulating SCZ risk loci specifically in neurons.

We also observed that active promoters and enhancers in the prenatal cortex globally overlapped with SCZ GWAS loci, consistent with the enrichment of SCZ risk variants in non-coding regulatory regions. Notably, SETD1A-bound active promoters and enhancers exhibited greater enrichment for GWAS loci than unbound regions (OR = 1.50; p = 1.65 × 10−5 for promoters; OR = 1.39; p = 2.54 × 10−3 for enhancers), suggesting that SETD1A preferentially targets regulatory elements harboring common SCZ risk variants.

To test whether SETD1A-bound regions contribute to the heritability of neuropsychiatric disorders, we performed linkage disequilibrium score regression (LDSC). SETD1A-bound loci in the prenatal cortex and WT neuronal cells were significantly enriched for heritability of SCZ, bipolar disorder (BD), major depressive disorder (MDD), ASD, and attention-deficit/hyperactivity disorder (ADHD), but not for neurodegenerative disorders [e.g., Alzheimer’s disease (AD), Parkinson’s disease (PD)], or non-brain traits such as stroke and type 2 diabetes (T2DM). Active promoters and enhancers in the prenatal cortex and 2D neuronal cells were likewise enriched for SCZ heritability, aligning with prior reports that SCZ risk genes are more active in fetal than postnatal life.

Among active promoters, SETD1A-bound regions in the prenatal cortex and WT neuronal cells were enriched for SCZ, BD, MDD, and ASD heritability, whereas unbound regions lacked such enrichment, for all in neuronal cells and particularly for BD in the prenatal cortex. Interestingly, only SETD1A-unbound enhancers showed enrichment for ADHD heritability, suggesting that SETD1A-bound enhancer regions are not major contributors to ADHD genetic risk.

SETD1A binding was also enriched at active promoters of eight out of ten genes carrying rare coding variants linked to high SCZ risk in the SCHEMA study (CACNA1G, CUL1, HERC1, RB1CC1, SETD1A, SP4, TRIO, and XPO7), emphasizing its relevance to both common and rare genetic risk.

In contrast, in SETD1Ac.4582-2delAG/+ NPCs and neurons, while active promoters remained enriched for SCZ, BD, and ASD heritability, SETD1A binding itself no longer specifically contributed to this enrichment, indicating that the patient-specific c.4582-2delAG mutation disrupts SETD1A’s binding to disease-associated loci.

To explore the biological processes involved, we examined SETD1A-bound genes among the 682 SCZ GWAS-significant genes. We identified 165 such genes in the prenatal cortex and 199 in both WT NPCs and neurons. These genes were enriched in pathways regulating DNA damage response and repair, chromatin remodeling, synaptic signaling, cell cycle, inflammation, and telomere maintenance. Examples include: KAT5, encoding TIP60, a histone acetyltransferase involved in chromatin remodeling and DNA repair; DOT1L, encoding a methyltransferase critical for H3K79 methylation and genome integrity; KCNB1, a voltage-gated potassium channel gene; LRRC4, a synaptic adhesion molecule regulating excitatory synapse development.

Together, these findings demonstrate that SETD1A targets a subset of SCZ-associated genes involved in key neurodevelopmental and epigenetic processes. Loss of SETD1A binding in SETD1Ac.4582-2delAG/+ neurons disrupts its association with these loci, suggesting that SETD1A LoF and common SCZ variants converge on a shared molecular network crucial for early brain development and psychiatric disease risk.

Patient-specific SETD1A c.4582-2delAG mutation affects SETD1A-mediated regulation of common SCZ risk genes involved in DNA damage response, chromatin remodeling, and synaptic signaling

To investigate how the SETD1A c.4582-2delAG/+ LoF mutation disrupts the regulation of SCZ-associated pathways, we compared SETD1A binding and H3K4me3 levels at active promoter regions between mutants and WT in 2D NPCs and neurons. SETD1Ac.4582-2delAG/+ lines exhibited a global reduction in SETD1A peaks, including at active promoters. In NPCs, both SETD1A and H3K4me3 binding densities were significantly reduced genome-wide in SETD1Ac.4582-2delAG/+ versus WT, consistent with lower H3K4me3 levels in SETD1Ac.4582-2delAG/+ NPCs. In contrast, SETD1A and H3K4me3 binding densities in neurons were not diminished but instead showed a small and significant increase in SETD1Ac.4582-2delAG/+ cells compared to WT, suggesting a cell-type-specific compensatory response.

We confirmed this by observing cell-type-specific alterations in SETD1A binding in active promoter regions where gained or lost SETD1A peaks in SETD1Ac.4582-2delAG/+ cells compared to WT cells. We classified SETD1A binding sites at active promoters into three groups: those lost or gained in cells from SETD1Ac.4582-2delAG/+ compared to WT, or shared between two genotypes. Even though some low-signal peaks might individually fall below detection thresholds, analyzing these loci as a group revealed a significant alteration of SETD1A binding overall. As expected, regions lost in SETD1Ac.4582-2delAG/+ cells showed significantly less SETD1A binding, while gained regions showed significantly more binding compared to WT. In NPCs, loss of SETD1A binding at active promoters in SETD1Ac.4582-2delAG/+ was associated with reduced H3K4me3, while gain of SETD1A binding also correlated with reduced H3K4me3. Unexpectedly, the opposite pattern was observed in neurons: both loss and gain of SETD1A binding at active promoters in SETD1Ac.4582-2delAG/+ neurons were associated with increased H3K4me3. This underscores the distinct epigenetic consequences of the SETD1A mutation in NPCs versus neurons.

GO analysis of genes with promoter regions that lost SETD1A binding in SETD1Ac.4582-2delAG/+ (3,771 genes in NPCs; 4357 in neurons) revealed strong enrichment for pathways regulating DNA repair, cell cycle, chromatin remodeling, and synaptic signaling. Many of these pathways were downregulated in SETD1A+/– neuronal cells, consistent with their dependence on SETD1A function. In neurons, regions that lost SETD1A binding in SETD1Ac.4582-2delAG/+ were also enriched for telomere maintenance. Active promoter regions bound by SETD1A in both SETD1Ac.4582-2delAG/+ and WT cells (shared regions) also mapped to similar pathways, yet SETD1A and H3K4me3 levels at these sites were significantly reduced in SETD1Ac.4582-2delAG/+ NPCs compared to WT NPCs (Wilcoxon signed-rank test, p < 0.0001), indicating reduced regulatory activity despite peak retention. No specific biological pathways were enriched in either NPCs or neurons for genes whose promoters gained SETD1A binding in SETD1Ac.4582-2delAG/+.

While SETD1A’s catalytic function regulates gene expression via H3K4 methylation, non-catalytic roles have also been described. Previous study showed that SETD1A interacts with cyclin K to regulate DNA repair gene expression during S-phase independently of H3K4me3. We tested this by assessing the correlation between H3K4me3 binding changes and gene expression for DNA repair genes with active promoter regions, where SETD1A binding was lost in SETD1Ac.4582-2delAG/+ neurons. No significant correlation was found, suggesting that SETD1A may regulate these genes via non-catalytic mechanisms.

We identified 116 SCZ GWAS-significant genes with active promoter regions where SETD1A binding was lost in SETD1Ac.4582-2delAG/+ neurons. These genes showed significantly reduced expression in patient mutant neurons, and were enriched for biological processes including histone modification, chromatin remodeling, DNA damage response, and synaptic signaling and vesicle cycling. These data support the idea that SCZ risk—whether from common variants or SETD1A haploinsufficiency—converges on shared pathways involving chromatin and synaptic regulation.

To assess the persistence of these regulatory relationships in the mature brain, we analyzed adult human DLPFC transcriptomes. Genes whose expression positively correlated with SETD1A in adult DLPFC were also enriched for chromatin remodeling, histone modification, DNA repair, and synaptic pathways, including cognition, memory, and social behavior. This suggests that SETD1A continues to regulate these core neurobiological functions into adulthood, and that disruption of these processes underlies broader SCZ pathophysiology.

To test whether the loss of H3K4me3 contributes to impaired NPC proliferation, we treated mutant NPCs with KDM5 inhibitors (KDOAM-25 and JQKD82), which block demethylation of H3K4me3. Following 48-hour treatment, we observed a significant rescue of proliferation in mutant NPCs to levels comparable with WT cells. These findings suggest that failure to maintain H3K4me3 at promoters of SETD1A target genes underlies the NPC proliferation deficit in SETD1A+/− lines.

SETD1A functions at DNA damage-prone sites and DNA repair hotspots in neuronal cells

Our findings thus far support a role for SETD1A in safeguarding genomic stability in NPCs and neurons. DNA damage can lead to somatic mutations, especially in RDCs that overlap with CFSs and mosaic CNVs. In mature neurons, thousands of DRHs occur in open chromatin. We first assessed whether active gene regulatory regions in the prenatal cortex co-localize with these DNA damage-prone regions. Active promoters and enhancers in prenatal cortex significantly overlapped with human CFSs and DRHs identified by SAR-seq and Repair-seq, as well as with their shared intersection. In agreement with previous reports, SAR-seq DRHs showed stronger enrichment at enhancers than at promoters. These regions also overlapped with somatic SNVs found in the DLPFC of individuals with SCZ, but not with SNVs from neurotypical controls, suggesting that gene regulatory elements active during fetal development are vulnerable to DNA damage in SCZ and may require tightly regulated repair mechanisms.

A recent study reported that SCZ-associated somatic SNVs are increased in frequency and enriched at fetal transcription factor binding sites, suggesting early progenitor vulnerability. We therefore examined whether SETD1A binds at these DNA damage-prone regions. Indeed, SETD1A-bound sites in the prenatal cortex and WT NPCs significantly overlapped with hCFSs and somatic SNVs from SCZ cases, indicating a potential protective role for SETD1A at fragile sites during early cortical development. This overlap was markedly reduced in SETD1Ac.4582-2delAG/+ NPCs, suggesting that the patient mutation impairs SETD1A’s ability to associate with and possibly stabilize these fragile genomic regions.

We next tested whether SETD1A binds neuronal DRHs in postmitotic neurons. SETD1A-bound regions in prenatal cortex and 2D neurons significantly overlapped with DRHs identified via SAR-seq and Repair-seq, both individually and in combination, independent of genotype. The magnitude of overlap was reduced in SETD1Ac.4582-2delAG/+ neurons compared to WT, indicating that SETD1A LoF may compromise its recruitment to neuronal repair hotspots, potentially impairing DNA repair efficiency.

Given this, we hypothesized that impaired SETD1A binding at DRHs may result in reduced local H3K4me3 deposition. Coupled with decreased expression of DNA repair genes in SETD1A+/− neurons, this could lead to increased DNA damage accumulation. To test whether stabilizing H3K4me3 could rescue this phenotype, we treated neurons with KDM5 inhibitors (KDOAM-25 or JQKD82) for 48h. Both compounds significantly increased H3K4me3 levels in WT and SETD1Ac.4582-2delAG/+ neurons, and no genotype-specific differences in H3K4me3 stabilization were observed. Moreover, consistent with our findings, baseline H3K4me3 levels in neurons were not significantly different between WT and SETD1Ac.4582-2delAG/+ cells, underscoring the importance of locus-specific regulation rather than global changes.

Importantly, H3K4me3 stabilization significantly reduced DNA damage in SETD1Ac.4582-2delAG/+ neurons. Following treatment, γH2AX-positive neurons in SETD1Ac.4582-2delAG/+ were significantly reduced to levels comparable to WT, suggesting that pharmacological stabilization of H3K4me3 can ameliorate DNA damage accumulation in the context of SETD1A LoF.

In summary, our results indicate that SETD1A binds to DNA damage-prone regions in NPCs and postmitotic neurons, including CFSs and DRHs, and plays a role in promoting DNA repair through both transcriptional regulation and H3K4me3 deposition. SETD1A haploinsufficiency likely leads to increased genomic instability across development, with implications for neuronal function and disease risk in SCZ.

Patient-specific SETD1A c.4582-2delAG mutation disrupts H3K4me3 regulation at long SCZ risk-associated genes

We next examined whether SETD1A c.4582-2delAG mutation affects H3K4me3 levels at common SCZ risk loci. Although overall H3K4me3 levels at the 287 SCZ GWAS loci were not significantly different between WT and mutant neurons, we identified a subset of 52 loci showing pronounced changes in H3K4me3 density (|log2FC| > 0.415) in patient mutants—hereafter referred to as the “most affected loci”. Strikingly, genes located at these most affected loci were significantly longer than those at minimally affected loci, implicating gene length as a potential vulnerability factor. These long genes included several SCZ-associated loci such as AKAP6, DCC, GRIN2A, SGCD, and PTPRD. Many of these genes exceed 100kb in length (20/37), aligning with prior reports that long neural genes are particularly susceptible to DSBs.

GO analysis of the most affected loci revealed significant enrichment for presynaptic functions, including regulation of synaptic transmission and membrane potential. In contrast, genes from both affected and unaffected loci were enriched for postsynaptic processes, suggesting a selective disruption of presynaptic regulatory programs in neuronal cells carrying SETD1A LoF mutations.

Together, these findings indicate that SETD1A-mediated H3K4me3 is especially critical for epigenetic stability at long, fragile genes involved in synaptic function. The disruption of H3K4me3 at these loci in SETD1Ac.4582-2delAG/+ may render these genes more susceptible to transcriptional dysregulation and genomic instability, further contributing to the convergence between SETD1A haploinsufficiency and SCZ genetic risk.

SETD1A downregulation is associated with SCZ pathogenesis in the broader patient population without rare LoF mutations

Finally, we asked whether the altered SETD1A activity might contribute to SCZ pathogenesis beyond rare LoF mutations. Using large-scale postmortem cortical transcriptomic datasets from PsychENCODE (PEC) and BrainSeq, we evaluated SETD1A expression and its downstream targets in adult individuals with SCZ who do not carry SETD1A LoF variants.

In the PEC dataset, SETD1A was modestly but significantly downregulated in SCZ cortex compared to neurotypical controls, implicating dysregulation of SETD1A in the broader adult patient population. Among DEGs in SCZ cortex, only downregulated—but not upregulated—genes were significantly enriched for SCZ GWAS loci. Notably, SETD1A target genes were significantly overrepresented among these downregulated SCZ GWAS genes (37/68), compared to the upregulated ones (19/53), indicating the preservation of SETD1A-targets between prenatal and adult patient brains. These SETD1A target genes were also specifically enriched for synaptic function, a pattern not observed among the non-SETD1A targets, suggesting that SETD1A downregulation in SCZ may impact synaptic gene networks associated with genetic risk.

To test whether low SETD1A expression drives this effect, we stratified both control and SCZ samples from PEC and BrainSeq into high, middle, and low SETD1A expression groups. We then compared gene expression between high-SETD1A controls (CTRL-high) and low-SETD1A SCZ (SCZ-low) samples. Although the smaller sample size in BrainSeq yielded fewer significant DEGs overall (245 vs. 4,821 in PEC), similar patterns emerged. In both datasets, downregulated genes in the SCZ-low group were significantly enriched for SETD1A targets and SCZ GWAS genes, whereas upregulated genes were not. Among SCZ GWAS-significant DEGs, SETD1A target genes were consistently overrepresented among downregulated genes (PEC: 96/197; BrainSeq: 91/146) compared to upregulated ones (PEC: 52/136; BrainSeq: 31/81).

GO analysis revealed that SETD1A target genes downregulated in the SCZ-low group were significantly enriched for DNA damage response, chromatin remodeling, and telomere maintenance—processes not enriched among non-SETD1A targets. Both gene sets were enriched for synaptic functions, but only the SETD1A targets showed consistent overlap in synaptic terms across datasets and stratification conditions. Shared synaptic SETD1A target genes included ASIC1, DGKZ, IGSF9B, LRRC4, and ZDHHC5, which were consistently downregulated in the SCZ cortex in association with low SETD1A expression.

Taken together, these findings demonstrate that reduced SETD1A activity contributes to SCZ pathogenesis in a broader subset of patients. Even in the absence of rare LoF variants, lower SETD1A expression—potentially driven by common genetic variation or environmental factors—results in downregulation of SETD1A target genes at SCZ risk loci. These genes are enriched for synaptic and genomic stability functions, suggesting that disruption of SETD1A-dependent regulation may represent a convergent mechanism in SCZ pathogenesis.

Methods
Cell lines and tissue samples
Human iPSC lines from two neurotypical individuals (LIBD2c1 and LIBD7c6) were established by reprogramming postmortem dura-derived fibroblasts. Fibroblast collection and use for research were approved by the Western Institutional Review Board (#WIRB 20111080).

Postmortem adult human DLPFC was obtained by autopsy from a 27-year-old Hispanic male with informed consent from the legal next of kin (under Protocol No. 12-24 from the State of Maryland Department of Health and Mental Hygiene). Clinical characterization, diagnoses, and macro- and microscopic neuropathological examinations were performed on all samples using a standardized paradigm, and this subject was free of any evidence of macro- or microscopic neuropathology, as well as any neurological or psychiatric diagnoses. Fetal brain tissue samples from three European males in the second trimester were obtained in conjunction with the Medical University of Sofia, with informed consent of the mother, under a protocol approved by the Research Ethics Committee of the university. Karyotyping or Aneufast QF-PCR (Genomed-Biotech) was performed on all fetal samples to rule out any major chromosomal abnormalities. For the fetal samples, full-thickness cortical samples from the developing frontal neocortex were dissected using a hand-held dental drill, over the dorsal convexity halfway between the frontal and temporal poles.

Generation of isogenic SETD1A LoF mutant iPSCs
iPSC lines were maintained on a tissue culture plate coated with Cultrex Reduced Growth Factor Basement Membrane Matrix (R&D Systems) in StemFlex medium (Thermo Fisher Scientific). To generate isogenic SETD1A mutant lines, an oligonucleotide (sgRNA) targeting around the splice acceptor of intron 15 in SETD1A was designed and cloned into U6-gRNA:CMV-CAS9-2A-tGFP vector by MilliporeSigma. Single-strand oligonucleotide (ssODN) carrying SETD1A c.4582-2delAG mutation and PAM blocking mutation was also designed by MilliporeSigma. By following a recent study, iPSCs were treated with 1× RevitaCell Supplement (Thermo Fisher Scientific) for 2 h at 37 °C and dissociated with 0.75× TrypLE Select Enzyme (Thermo Fisher Scientific) diluted in 0.5 mM EDTA/PBS for 3 min at 37 °C into single cells. Single cell suspension (1×106 iPSC cells per reaction) was electroporated with 5 μg of U6-gRNA:CMV-CAS9-2A-tGFP plasmid and 100 pmol of ssODN with 4D Nucleofector Unit X (Lonza) using P3 Primary Cell 4D-Nucleofector X Kit (Lonza) and programs CA-137 and CM-130. Cells were plated and cultivated on a tissue culture plate coated with recombinant human Laminin-521 (Thermo Fisher Scientific) in StemFlex medium for 4 days. For the first 24 h, the medium was supplemented with 1× RevitaCell. Four days post-electroporation, the cells were dissociated into single cells with 0.75× TrypLE Select Enzyme and plated on a 96-well plate coated with Cultrex by targeting <0.5 cells per well by limiting dilution. Cells were maintained in StemFlex medium supplemented with 1× RevitaCell for the first 24 h. The single iPSC colonies were manually picked and expanded on a tissue culture plate coated with Cultrex in StemFlex medium. DNA was extracted from 148 subclones for LIBD2c1 and 229 subclones for LIBD7c6 and screened for edited genome by SURVEYOR assay with a Surveyor Mutation Detection Kit (IDT) and HinfI (NEB) enzyme digestion.

We validated SETD1A mutation in the selected candidate iPSC subcloned lines by TA cloning with TOPO TA Cloning Kit for Sequencing (Thermo Fisher Scientific) and Sanger sequencing using Cel-1 primers. Potential off-target mutations at NR4A1 and PTPRN2 loci were validated by Sanger sequencing. Primers used for PCR and Sanger sequencing were listed in Supplementary Data 17. All 18 lines were karyotyped by genome-wide genotyping array using an Infinium Omni2.5-8 Kit (Illumina) by Psomagen. iPSC lines used in each assay are listed in Supplementary Data 3.

Neuronal differentiation
To generate NPCs, iPSCs were treated with 10 μM Y-27632 (BioGems) at 37 °C overnight when they reached 50–60% confluency. Cells were then incubated with StemPro Accutase (Thermo Fisher Scientific) at 37 °C for 7 min and dissociated into single cells. Dissociated iPSCs were plated on 96-well V-bottom plates (S-bio) at 5000 cells/150 μL per well in StemFlex medium to make embryoid body (EB). The medium was supplemented with 30 μM Y-27632 for the first 24 h and was gradually switched to DFN2, consisting of DMEM/F12+GlutaMAX (Thermo Fisher Scientific), 1× N-2 supplement (Thermo Fisher Scientific), 1× non-essential amino acid (NEAA, Thermo Fisher Scientific), 100 μM 2-mercaptoethanol (2-ME, Thermo Fisher Scientific) and 1× Antibiotic-Antimycotic (Anti-Anti, Thermo Fisher Scientific), supplemented with 10 μM SB431542 (BioGems) and 100 nM LDN-193189 (BioGems) from day 2. The EBs were fed every other day till day 8. The EBs were transferred onto a 6-well plate (48 EBs/well) coated with Matrigel Basement Membrane Matrix Growth Factor Reduced (Matrigel GFR, Corning) in DFN2 B27-VA media consisting of DMEM/F12+GlutaMAX, 1× NeuroCult SM1 (StemCell Technologies), 1× N-2 supplement and 1× Anti-Anti, supplemented with 2 μg Heparin (MilliporeSigma) and 2 μM Cyclopamine (BioGems) on day 10 and were fed every other day. On day 16, neuronal rosettes were manually picked up, dissociated in StemPro Accutase at 37 °C for 15 min, and plated on a 6-well plate coated with 0.001% Poly-L-Ornithine (MilliporeSigma) and 5 μg/mL mouse Laminin (Thermo Fisher Scientific) in NPC medium: DFN2 B27-VA supplemented with 20 ng/mL human basic FGF (PeproTech) and 1 μg/mL mouse Laminin. The medium was supplemented with 10 μM Y-27632 for the first 24 h. The cells were fed every other day and passaged using StemPro Accutase on a Matrigel GFR-coated plate. At passage 2–3, NPCs were characterized by immunostaining with antibodies against SOX2, PAX6, and NESTIN. To measure cell proliferation, NPCs (passage 6–10) were plated on a 12-well plate at 200,000 cells/well in the NPC medium. Seventy-two hours later, the cells were dissociated with StemPro Accutase and counted on Countess II (Thermo Fisher Scientific) using a Trypan Blue solution (Thermo Fisher Scientific). Cell proliferation was also measured by CyQUANT Cell Proliferation Assay (Thermo Fisher Scientific) according to the manufacturer’s instructions. NPCs were plated at 10,000 cells/well in a 96-well plate. The cells were maintained for 72 h and subjected to assay. Cell viability was measured at 24 h post-plating. For KDM5 inhibition, SETD1A hetero mutant NPCs were treated with KDOAM-25 (MilliporeSigma) or JQKD82 (MedKoo) at 24 h post-plating for 48 h.

For differentiation of NPCs into neurons, NPCs were plated on a cell culture plate coated with 0.001% Poly-L-Ornithine and 5 μg/mL mouse Laminin at 20,000 cells/cm2 in the NPC medium. Two days post-plating, medium was switched to neuronal medium consisting of Neurobasal (Thermo Fisher Scientific), 1× GlutaMAX-I supplement (Thermo Fisher Scientific), NeuroCult SM1 (StemCell Technologies) and 1× Anti-Anti, supplemented with 20 ng/mL human BDNF (PeproTech), 20 ng/mL human GDNF (PeproTech), 100 μM dibutyryl-cAMP (BioGems) and 200 μM ascorbic acid (MilliporeSigma). Cells were fed every other day. For KDM5 inhibition, 6-week-old SETD1A patient mutant neurons were treated with 50 μM KDOAM-25 or 0.4 μM JQKD82 for 48 h.

Cerebral organoid (CO) differentiation
CO differentiation was performed as previously described with small modifications. In brief, iPSCs were treated with 10 μM Y-27632 at 37 °C overnight when they reached 50–60% confluency. Cells were then incubated with StemPro Accutase at 37 °C for 7 min and dissociated into single cells. Dissociated iPSCs were plated on 96-well V-bottom plates at 9000 cells/150 μL per well in StemFlex medium to make EB. The medium was supplemented with 30 μM Y-27632 for the first 24 h. Forty-eight hours later, the medium was switched to DFN2 supplemented with 10 μM SB431542 and 100 nM LDN-193189 (day 0). Organoids were fed every three days. On day 12, each EB was embedded in a droplet of Matrigel GFR and transferred to a 60-mm culture dish in DFN2 supplemented with 100 nM LDN-193189. Organoids were fed on day 14. On day 16, the medium was switched to differentiation medium consisting of 1:1 mixture of DMEM/F12+GlutaMAX and Neurobasal supplemented with 0.5× N-2 Supplement, 0.5× NeuroCult SM1 without vitamin A, 0.5× NEAA, 100 μM 2-ME, and 2.65 μg/ml Insulin (Thermo Fisher Scientific). Organoids were fed every 3 days and maintained with agitation in an incubator shaker (Eppendorf, New Brunswick S41i) at 80 r.p.m. On day 30, the medium was switched to neuronal medium as described above. From day 60, dibutyryl-cAMP and ascorbic acid were withdrawn from the medium and the organoids were fed every 4 days. CO was treated with 0.25 μM aphidicolin (Cayman Chemical) for 24 h before fixation.

Immunostaining
Cells were fixed with 4% PFA in 1× PBS at room temperature for 15 min and permeabilized and blocked with 10% normal donkey serum in PBS containing 0.3% Triton X-100 for 30 min at room temperature. COs were fixed with 4% PFA in PBS overnight at 4 °C. After washing with PBS, organoids were placed in serial dilutions of PBS-buffered sucrose (10, 20, and 30%, in sequence) at 4 °C. Each solution was replaced every day. The dehydrated organoids were maintained in 30% sucrose solution at 4 °C until embedding with OCT compound (Sakura Finetek). One day before cryosectioning, fixed organoids were placed in a 1:2 mixture of 30% sucrose solution and OCT compound and left overnight at 4 °C. Then, organoids were embedded in a 1:2 mixture of 30% sucrose solution and OCT compound, frozen immediately in dry ice/acetone, and cryosectioned at 10 μm. Tissue sections were subjected to additional fixation with 4% PFA in 1× PBS for 3 min at room temperature and permeabilized and blocked with 10% normal donkey serum in 1× PBS containing 0.3% Triton X-100 for 30 min at room temperature. After washing with 5% serum in PBS containing 0.01% Tween-20, cells or sections were incubated with primary antibodies at 4 °C overnight and with secondary antibodies (Jackson ImmunoResearch) at room temperature for 90 min. Detailed information regarding primary antibodies is available in Supplementary Data 18. Image analyses were carried out with CellProfiler for COs and ImageJ (ver 1.54) for 2D neurons.

Real-time qPCR
Total cellular RNA was extracted from COs using TRIzol Reagent (Thermo Fisher Scientific) and a Direct-zol RNA MiniPrep kit (Zymo Research), following the manufacturer’s instructions. cDNA was prepared by reverse transcription using the SuperScript IV VILO Master Mix (Thermo Fisher Scientific). Real-time qPCR was carried out using QuantiTect SYBR Green PCR Kit (Qiagen) on QuantStudio 3 Real-Time PCR System (Applied Biosystems). The primer sequences are shown in Supplementary Data 17.

Single-cell RNA sequencing (scRNA-seq)
COs were differentiated from three WT and two SETD1Ac.4582-2delAG/+ lines from the LIBD7c6 prenatal iPSC line. Five organoids from each iPSC line on days 30 and 48 were dissociated into single cells with the Papain dissociation system (Worthington Biochemical). Cells from all five iPSC lines were pooled into one library per time point by cell hashing with TotalSeq-A Hashtag antibodies (BioLegend). The libraries were generated with Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (10× Genomics). We prepared three libraries (three technical replicates) from pooled cells for each time point. Libraries were sequenced on an Illumina NovaSeq 6000 (SP, 2×150 bp).

scRNA-seq data analysis
Raw sequencing data were preprocessed with Cell Ranger 7.2 (10× Genomics), in which reads were aligned to the hg38 human reference genome. Expression data were processed with Scanpy (ver. 1.9.3) in Python (ver. 3.11.3). Initially, we removed cells that contained fewer than 2000 total UMI, fewer than 1000 genes detected, greater than 20% of reads that corresponded to mitochondrial genes, or fewer than 0.8 log10(Genes per UMI), a measure of cell complexity. Read counts were then normalized by counts per 10,000 in each cell and subsequently log-transformed. We used Scanpy’s function sc.pp.highly_variable_genes to identify the top 4000 highly variable genes using the parameter “flavor=’seurat_v3”. We performed dimensionality reduction with principal components analysis on the highly variable genes, followed by nearest neighbor embedding in two dimensions using uniform manifold approximation and projection (UMAP). We then pooled data across multiple libraries and batches and performed dataset integration using Harmonypy (ver. 0.0.9) workflow. Cells were grouped into different clusters by using the Leiden algorithm and then annotated manually into 13 different cell types based on the expression of marker genes (Supplementary Data 4). Cell cycle phase scoring was performed using Seurat (4.3.0.1) in R (4.3.1). Celltype-specific pseudo-bulk differential gene expression analysis was performed to compare SETD1Ac.4582-2delAG/+ and WT cells using edgeR (3.42.4). Pseudo-bulk gene expression was calculated for each iPSC line at each time point by pooling data from three libraries. We used timepoint (“day”) as a covariate for differential expression analysis. GSEA was performed using the sign of the fold change multiplied by the inverse of the p-value.

Bulk RNA-seq
Total RNA was extracted from NPCs and four-week-old neurons derived from iPSCs using TRIzol Reagent and a Direct-zol RNA MiniPrep kit, following the manufacturer’s instructions. Libraries were prepared using a TruSeq Stranded Total RNA with RiboZero Human/Mouse/Rat Library Prep Kit (Illumina) and sequenced on NovaSeq 6000 with a read length of 150-bp paired-end by Psomagen. Fastq files were quality-checked using FastQC (ver. 0.11.3) and MultiQC (ver. 1.12). The reads were aligned to the human genome version hg38.p12 using HISAT2 (ver. 2.2.1). For count quantification, we used featureCounts algorithm (ver. 2.0.1), using the options of the paired end (option -P), ignoring chimeric reads (option -C), stranded alignment on the reverse strand (option -s 2) and considering only primary alignments (option --primary). For differential expression analysis between WT and SETD1+/− cells, we used DESeq2 (ver. 1.40.2). Sequencing batches were considered as a covariate. Differentially expressed genes were defined with a threshold of Benjamini and Hochberg corrected p-value (false discovery rate, FDR) < 0.1 or nominal p-value < 0.05. T-statistic was calculated by logFoldChange/logFoldChange S.E.M. GSEA was performed using the sign of the fold change multiplied by the inverse of the p-value. No technical replicates were analyzed.

Flow cytometry
Five COs per iPSC line were washed once with 1× PBS (Thermo Fisher Scientific) and incubated in 1 mL Cell Recovery Solution (Corning) at 4 °C for 1 h with agitation at 300 r.p.m. The organoids were washed once with ice-cold 1× PBS and incubated in 0.5 mL StemPro Accutase at 37 °C for 35 min to dissociate into single cells. Dissociated cells were resuspended in 200 μL of 1% BSA (Cell Signaling Technology) in 1× PBS. One million cells per iPSC line were fixed 100 μL Click-iT Fixative (Thermo Fisher Scientific) at room temperature for 15 min. The cells were then washed twice with 0.5% BSA in 1× PBS, resuspended in 100 μL of 0.5% BSA in 1× PBS and incubated with 2 μL anti-PSA-NCAM-PE antibody (Miltenyi Biotech) at 4 °C for 15 min. We washed the cells twice with 0.5% BSA in 1× PBS and incubated them in 2.5 μL 7-AAD (BD) in 100 μL of 0.5% BSA in 1× PBS at room temperature for 10 min. Stained cells were analyzed on LSRII flow cytometer (BD). Data were analyzed using FlowJo (ver. 10.7.1) (BD).

Multi-electrode array (MEA) recording
Four-week-old neurons were dissociated with 1:1 mixture of StemPro Accutase and Accumax (Thermo Fisher Scientific) at 37 °C for 30 min, triturated and resuspended in wash buffer-1 consisting of 1× PBS, 2.5 mM EDTA pH 8.0 (Thermo Fisher Scientific), 25 mM HEPES-KOH pH 7.5 and 1% FBS (Thermo Fisher Scientific). The cell suspension was passed through 70 μm cell strainer (Falcon) and the cells were resuspended in wash buffer-2 consisting of DMEM (Thermo Fisher Scientific), 1% FBS and 20 U/mL DNase I (NEB). After centrifugation, the cells were suspended in neuronal medium and passed through a 35 μm cell strainer (Falcon). Dissociated neurons were plated on a 48-well MEA plate (Axion Biosystems) coated with 0.1% Polyethylenimine solution (MilliporeSigma) and 10 μg/mL mouse Laminin with human astrocytes (ScienCell #1800) at 4:1 by following the Elixirgen Scientific’s instruction. The cells were fed with the neuron medium supplemented with 20 μg/ml mouse laminin twice a week. The medium was switched to BrainPhys (StemCell Technologies) supplemented with 1× NeuroCult SM1, 10 mM Glucose (Thermo Fisher Scientific), 1× Anti-Anti, 20 ng/mL human BDNF and 20 ng/mL human GDNF from week 2 (week 5 of neuronal differentiation) and the cells were fed twice a week. Recordings were performed once a week from week 3 till week 10 (from week 6 till week 13 of neuronal differentiation) using a Maestro MEA system and AxIS Software Spontaneous Neural Configuration (Axion Biosystems). Spikes were detected with AxIS software using an adaptive threshold crossing set to 5.5 times the standard deviation of the estimated noise for each electrode. The plate was first allowed to rest for 3 min in the Maestro device, and then data was recorded for 5 min. For recording, electrodes exhibiting a minimum firing rate of 5 spikes per minute were designated as active using Axion Biosystems’ Neural Metrics Tool. Burst detection on individual electrodes was performed by applying an inter-spike interval (ISI) criterion that required at least 5 spikes occurring within a maximum ISI of 100 ms. Network burst identification required a minimum of 10 spikes meeting the same ISI parameters, with at least 25% of electrodes in the well showing activity. Cross-correlogram analysis with a 20 ms synchrony window was used to determine the synchrony index.

Sholl analysis
Three-week-old neurons were dissociated and co-cultured with human astrocytes as described above. Ten days after starting co-culture, neurons were transduced with Incucyte Neurolight Orange Lentivirus (Sartorius) and incubated for ten days. Neuronal morphology at six weeks of differentiation were analyzed using Neurolucida software (MBF Bioscience). Sholl profile was obtained by applying a series of concentric circles at 10 μm intervals from the soma center, subsequently, the number of intersections was measured for each distance interval.

SETD1A knockdown by siRNA
HEK293T cells (ATCC #CRL-3216) were transfected with a pool of three SETD1A siRNAs or a scrambled siRNA (Silencer® Select (Thermo Fisher Scientific)) at 10 nM, following the manufacturer’s protocol. Cells were harvested at 72 h after transfection.

Western blotting
For postmortem DLPFC whole tissue lysate, frozen tissue was homogenized and lysed in RIPA buffer, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris-HCl (pH 7.5) and 1× protease inhibitor (MilliporeSigma) and centrifuged at 20,000×g for 30 min at 4 °C. For postmortem DLPFC nuclei lysate, frozen tissue was homogenized in 5× tissue volume of nuclei extraction buffer (NEB) (320 mM sucrose, 5 mM CaCl2, 3 mM Mg(Ac), 0.1 mM EDTA, 0.1% NP-40, 10 mM Tris-HCl (pH 7.5) and 1× protease inhibitor) with a dounce homogenizer on ice. Brain homogenate was centrifuged at 94×g for 8 min at 4 °C. The pellet was resuspended gently in 500 mL NEB and debris was strained out with a 35 mm filter. Tissue suspension was mixed with iodixanol (Optiprep, MilliporeSigma) to make the final iodixanol concentration at 25%. The suspension was layered over 29% iodixanol and centrifuged at 13,500×g for 20 min at 4 °C. The pellet was lysed in RIPA buffer and centrifuged at 20,000×g for 30 min at 4 °C. Protein concentration was measured with BCA protein assay kit (Pierce). The whole tissue lysate and nuclei lysate (26 mg of protein for each) were loaded onto SDS-PAGE gel (Thermo Fisher Scientific). For NPCs, neurons, and HEK293T, cells were lysed in RIPA buffer and centrifuged at 20,000×g for 30 min at 4 °C. Cell lysates were sonicated on Bioruptor pico (Diagenode) for detecting histone modifications. The lysates were loaded on SDS-PAGE gel. Signals were detected on Odyssey DLx (LI-COR Biosciences). ImageJ (ver 1.54) was used to quantify signals. Primary antibodies used for WB were listed in Supplementary Data 18. SETD1A_CST antibody was used in Fig. 1g.

CUT&Tag
CUT&Tag was carried out following the step-by-step protocol from the Henikoff lab (dx.doi.org/10.17504/protocols.io.wnufdew). Tn5-adapter complex was purchased from EpiCypher. Nuclei were isolated from human prenatal frontal lobes (110-170 mg tissues) as described above. The nuclear pellets were resuspended gently in nuclei storage buffer (NSB) (167 mM sucrose, 5 mM CaCl2, 3 mM Mg(Ac), Tris-HCl (pH 7.5) and 1× protease inhibitor) and incubated on ice until the pellet is loosened. NPCs were dissociated with Accutase at 37 °C for 2 min. Four-week-old neurons were dissociated with Papain (Worthington Biochemical) dissolved in 1:1 solution of Accutase and 1× PBS at 37 °C for 30 min. Dissociated neurons were then washed with Neurobasal medium supplemented with 1× GlutaMax and 2,000 U/mL DNase I (Worthington Biochemical). 1× 105 nuclei or cells for histone marks and 1× 106 nuclei or cells for SETD1A were used. Antibodies used for CUT&Tag were listed in Supplementary Data 18. The control IgG and antibodies against H3K4me marks were used at 1:100 and the antibodies against H3K27ac and SETD1A were used at 1:50. Index primer mix (Nextera DNA UD Indexes Set A) was purchased from Illumina. Libraries were sequenced on HiSeq 2500 (Illumina) with a read length of 50-bp paired-end by Psomagen.

CUT&Tag data analysis
CUT&Tag data were processed and analyzed following a tutorial (dx.doi.org/10.17504/protocols.io.bjk2kkye) with modifications. Fastq files were pre-processed using FastQC (ver. 0.11.3) and MultiQC (ver. 1.12). Paired-end reads were aligned to the human genome version hg38.p12 using bowtie2 (ver. 2.4.5) with the following parameters: “--end-to-end --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700”. The reads were not trimmed, and we did not perform spike-in calibration. Duplicated reads were marked and removed using Picard (ver. 2.21.1) from each sample to generate .sam files. Each file was sorted and filtered based on the read quality (“minQualityScore=2”) with samtools (ver. 1.14) and converted to .bam and .bedgraph files with bedtools (ver. 2.30.0). To evaluate the reproducibility among the samples, the read counts were split into 500 base pair (bp) bins and applied Spearman’s rank correlation between all pairwise brain samples and antibodies.

For the human prenatal cortical samples, sparse enrichment analysis for CUT&RUN (SEACR) was used for peak calling. Peaks for the reads from each antibody were identified using the reads from control IgG as a background. SEACR normalization “norm” and “stringent” arguments were used. For iPSC-derived NPCs and neurons, we used MACS2 (v. 2.2.9.1) using the command “-macs2 callpeak -t <sample_input.bam> -c <IgG_input.bedgraph> -f BAM -g hs --keep-dup all -n <sample_id> --outdir ./ -B –trackline” on .bam files of each sample. Peaks were visualized using Integrative Genomics Viewer (ver. 2.17.3). To annotate peaks into genomic regions, we initially used ChIPseeker (ver. 1.36.0) using “annotatePeak()” function. For annotation by ChIPseeker, promoters were defined as genomic regions between −1kb to 100bp of TSS for each gene. We then manually defined “active promoter” as H3K4me3 peaks overlapped with the promoters annotated by ChIPseeker and “active enhancer” as H3K27ac peaks not overlapped with our “active promoters”. Peaks outside active regulatory regions were annotated based on Chipseeker annotation and defined as “other genomic regions”. To find peaks between samples, we used GenomicRanges (ver. 1.52.0) and ChIPpeakAnno (ver. 3.28.1) using the “findOverlapsOfPeaks()” function with the arguments “ignore.strand=T” and “connectedPeaks=“merge””. Overrepresentation test was carried out using regioneR (ver. 1.26.1) with permTest() function with the arguments: function=randomizeRegions, evaluate.function=numOverlaps, count.once=TRUE and 10,000 permutations. We used a custom version of hg38 with 910 genomic regions removed based on the DAC exclusion list regions (https://www.encodeproject.org/files/ENCFF356LFX/) as a background genome. For SCZ GWAS loci, we used merged regions LD-r2 > 0.1 clumps plus 50kb window. SCZ GWAS loci and DRHs identified by SAR-seq were converted the loci to hg38 using liftOver package (ver. 1.30.0). Pathway enrichment analysis was performed using rGREAT (ver. 1.27.1). We selected pathways with p-value < 0.05 from binomial test as enriched pathways.

To compare H3K4me3 and SETD1A binding density between the genotypes, the diffbind package (ver. 3.10.0) was used to obtain RPKM value for each sample. The binding density was compared using the Wilcoxon signed-rank test in R (ver. 4.3.1).

For GO enrichment analysis of SCZ GWAS significant genes, we used g:profiler (ver. 0.2.0) by using all 682 SCZ GWAS significant (FINEMAP+SMR) genes as “background”. For synaptic gene enrichment, SynGO was used with “brain expressed” genes as background.

Linkage disequilibrium (LD) score regression
We performed stratified LD score (S-LDSC) regression to evaluate the enrichment of heritability of brain-related traits for CUT&Tag peaks. We selected eight brain-related traits with GWAS sample sizes > 40,000, including a wider range of behavioral traits, and neurodevelopmental, psychiatric, and neurological disorders. We also included five non-brain traits as controls. We downloaded GWAS summary statistics of each trait from the sources listed in Supplementary Data 19. Following recommendations from the LDSC resource website (https://alkesgroup.broadinstitute.org/LDSCORE), S-LDSC was run using LDSC (ver. 1.0.1) for each list of variants that included 97 annotations to control for the LD between variants with other functional annotations in the genome. We used HapMap Project Phase 3 SNPs as regression SNPs, and 1000 Genomes SNPs of European ancestry samples as reference SNPs, downloaded from the LDSC resource website.

WGCNA on human prenatal cortical transcriptomic data
To identify gene expression patterns in human prenatal cortices, we performed WGCNA. Transcriptome data of fetal cortices (n=50 individuals in the second trimester) (Supplementary Fig. 7a) were used. Genes with sufficient abundance (RPKM ≥ 0.1) in more than 80% of samples (n=20,675 genes) were then normalized by log2(RPKM+1) transformation. We then removed 10 samples outlying for the standardized connectivity Z.k for a threshold of min Z.k = −4; Z.k was computed by hierarchical clustering of the Euclidean distances measured from the expression data. Normalized expression data from all 40 samples were adjusted to remove unwanted variance potentially explained by RNA quality with cleaningY function (ver. 0.99.30). Residuals calculated from the “cleaning” model were then used as expression input in WGCNA. For the co-expression network analysis, we used the WGCNA package (ver. 1.68). The beta power threshold was selected by analysis of scale-free topology for multiple soft thresholding powers. The networks were automatically created in a step-wise manner with functions implemented in WGCNA: (1) Calculation of adjacency matrix: correlation type=bi-weight midcorrelation; type of network=signed; power=12 selected with soft thresholding to correspond to an R2 ≥ 0.8; (2) Calculation of topological overlap matrix dissimilarity; (3) Detect modules by hierarchical clustering (deep split parameter=2, minimum module size=50) and summarize them by the first principal component (i.e., the “eigengene”-ME). The MEs can be regarded as expression profiles that best characterize the gene correlations within modules. Biological inference can be drawn from the genes in the constructed modules by using gene set enrichment analyses and by correlating module eigengenes with biological covariates. (4) Merging modules whose eigengenes are highly correlated for a height of cut dendrogram of 0.15. Genes were finally assigned to 63 modules (gene size per module: minimum=61, maximum=1802); 159/20,675 genes were not assigned to modules (“grey” genes). Detected modules were labeled with pre-specified colors implemented in WGCNA routines. GO analysis was then carried out on the modules with g:profiler (ver. 0.2.0) to find biological processes and functions enriched by the gene sets of each cluster. The whole list of genes (n=20,675) was used as background. Expression for 875 genes: SETD1A and 874 SETD1A-bound genes in top 11 modules showing high correlation with SETD1A (|Pearson correlation coefficient (r)| > 0.65) were used to calculate a sub-network, respectively a co-expression matrix (875×875) represented by Pearson’s correlation coefficients. This sub-network was subsequently converted into a graph object-an adjacency matrix, by using functions from the iGraph package. Edges (i.e., correlation coefficients) less than the absolute value of 0.6 were removed. Essentially, the 875 genes represent nodes in the sub-network and correlation coefficients represent weights of edges (i.e., links between nodes weighted by the strength of correlations between nodes).

Gene expression correlation analysis
Postmortem DLPFC samples from 165 European ancestry individuals were analyzed (Supplementary Fig. 10a). Gene expression was normalized and transformed into log2(TMP+1). We used lmFit in limma package (ver. 3.54.2) to remove covariate effects, including diagnosis, gender, five genetic principal components (PCs), and 17 expression PCs. The number of expression PCs was determined using sva package (ver. 3.46.0). Co-expressions were examined using Pearson correlation (cor.test). GO enrichment analysis was then performed with g:profiler (ver. 0.2.0).

Differential gene expression analysis of psychENCODE (PEC) and BrainSeq datasets
We analyzed DEGs in postmortem cortices between neurotypical control individuals and individuals with SCZ identified in the PEC project. Fisher’s exact test was performed for the enrichment analyses. Neurotypical control and SCZ cohorts were stratified into three subgroups based on SETD1A expression level on the quantile ranges (<25% [low], >25 to <75% [middle] and >75% quantiles [high]). Differential expression analysis between Control-high vs. SCZ-low was performed following the PEC study. Briefly, we used DESeq2 (ver. 1.40.2) by using RIN, sex, ethnicity, and age of death as covariates based on principal component analysis using factoextra (v.1.0.7) (https://CRAN.R-project.org/package=factoextra) and singular vector decomposition using “svd()” function in R. Genes were filtered to include only those on autosomes longer than 250 base pairs with transcripts per million reads (TPM) > 0.1 in at least 25% of samples, removing immunoglobulin isotypes. For the BrainSeq datasets, transcriptome profiles of postmortem DLPFC tissue from 226 neurotypical control individuals and 155 individuals with SCZ (≥17-year-old at the age of death) were analyzed. SETD1A expression levels were evaluated by generating an edgeR (ver. 4.2.2) object [DGEList()] using raw counts and normalized to library-size using the calcNormFactors() function to generate trimmed mean of M-values counts. The normalized SETD1A counts were applied to the voom normalization using limma package (ver. 3.62.1) with a linear model. Voom-normalized counts were fitted into the null model using the limma’s function lmfit(). After regressing out covariates, the z-score transformation was applied on the residualized counts. We then stratified both neurotypical control and SCZ cohort into three subgroups on the quantile ranges (<25% [low], >25 to <75% [middle] and >75% quantiles [high]) of the SETD1A residualized expression. Differential gene expression analysis was performed using edgeR and limma by using RIN, sex, age of death, mtDNA rate, rRNA rate, and number of total assigned genes as covariates. DEGs were defined with a threshold of FDR < 0.05. For GO enrichment analysis, we used g:profiler (ver. 0.2.0) by using all 682 SCZ GWAS significant genes as “background”. For synaptic gene enrichment, SynGO was used with “brain expressed” genes as background.

Statistics
Raw data were tested for normality of distribution, and statistical analyses were performed with a method described in figure legends using GraphPad Prism (ver. 10), Python (ver. 3.11.3), and R (ver. 4.3.1). P-values of less than 0.05 were considered to indicate a statistically significant difference between groups unless otherwise indicated.

Statistics and reproducibility
The results shown as representative images (Figs. 1g and 2f) were validated and reproduced in at least two independent batches of experiments.

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

Data availability
The raw sequencing data generated in this study have been deposited in the GEO database under accession codes GSE293154 for CUT&Tag, GSE293155 for bulk RNA-seq, and GSE293325 for scRNA-seq. All other data generated in this study are provided in the Source Data file. Processed data from the previous studies are available from BrainSeq Phase 2 and PsychENCODE Integrative Analysis [http://resource.psychencode.org/]. Source data are provided with this paper. BED files are available on GitHub [https://github.com/paquolalab/Sawada_et_al_SETD1A]. Source data are provided with this paper.

Code availability
The code is available on GitHub [https://github.com/paquolalab/Sawada_et_al_SETD1A].

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have made significant corrections, including addressing a technical point I raised, however the paper remains unduly long and challenging to read. Below I make additional suggestions for the text that would help the readers.\nMy major technical concern of the previous review was that the authors did not knockout/down validate the antibodies they used for Cut&Tag. Here they have added validation by western, but that is not as rigorous as validation in the same assay where the antibody is used (Cut&Tag). This is especially important because 1) the binding data on Setd1 is a major feature of the study and 2) the data from these authors is not fully consistent with a prior published study. The authors did include an IgG control, which at least shows they are not seeing nonspecific open chromatin. At a minimum the authors should add a statement of limitations of their study, saying that they did not perform the Cut&Tag from a knockout/down condition and thus cannot entirely rule out cross-reaction of their antibody with other chromatin proteins. In the section where they compare their data to the prior paper they may also wish to comment that compared with ChIP, Cut&Tag tends to emphasize promoters over enhancers because promoters are more accessible (for example a discussion of limitations can be found here - https://www.activemotif.com/blog-cut-tag) which could explain the different results.\nLine 57 – just as the authors show here that H3K4me3 regulatory proteins do not always act enzymatically, the involvement of H3K4me3 regulatory proteins in NDDs does not “[show] that H3K4me3 is essential for human neurodevelopment.”\nLine 74 Recent studies of isogenic human induced pluripotent stem cell (iPSCs) carrying nonpathogenic SETD1A LoF mutations demonstrated…It is not clear what “nonpathogenic” means. If these were benign they would presumably have no effect. I think the author means this is a mutation that was not derived from a patient, but “nonpathogenic” does not convey the right meaning.\nLine 116 “patient mutant NPCs” is vague. Are these heterozygous? Are the mutations loss of function? Is there a different phenotype from different (likely) loss of function mutations? The mutations should be specified.\nThe data in the section that begins at line 119 is not consistent with the title. The authors do not present data that shows “accelerated neuronal differentiation”. The title should be more specific to the data.\nLine 122 and beyond “Hetero” is used frequently, which is vague and nonspecific. The authors should write out the gene name and genotype.\nLine 124 “Patient mutant neurons” again is very vague and non-specific. The mutation should be specified.\nLine 131 “non-patient mutation” again is awkward language.\nLine 167 It is not clear what the authors mean by “while the resolution of 2D bulk sequencing reduced the power to detect DEGs…” What is “resolution” in this case?\nLine 188 the firing changes reported in Figure 1 could arise from excitability changes as much as synapse changes.\nAll sections that report gene expression data (such as the one beginning on line 137, the one starting at line 399, and the one starting at Line 552) have substantial discussion merged in with the reporting of data. (Any sentence that says “this suggests…”) These sections would be stronger (and the paper would be much shorter and easier to read) if they were comprised only of results, with the discussion left for the end.\nLine 210 the authors find differences in cell types generated in the organoids. Is this putative E/I balance consistent with the phenotypes of the heterozygous mice or could it be a culture artifact?\nLine 386 and 394 – In summary, In summary. Only one summary.\nLine 445 what do the authors mean when they say a region was “uniquely” bound in WT neurons? Or in line 451 “exclusively”. This is not clear. How do the authors know the difference between a weak site falling below threshold and a categorical bound/not bound?\nThe first two paragraphs of the section starting at Line 467 are background that is distracting to read in a results section. Again this could be streamlined.\nLine 498 lots of discussion in the results section.\nThe H3K4me3 data are hard to follow.\nThe final section starting on line 552 is correlations of correlations and it seems like this part could be cut to give room to make the rest easier to read.", "reviewer_3": "The authors have attempted to address many of my and other reviewers concerns in the manuscript, which I believe is improved. I am supportive of publication. I list below a point that could be addressed to improve the manuscript.\nThis reviewer still does not find the DNA Damage staining in neurons (yH2AX) (Fig 2i to be particularly convincing). They authors could further improve by co-staining for another marker of damage (eg 53BP1) to make the point more clearly. However, I do not believe this is required for publication- merely a suggestion to strengthen the data."}, "author_response": "Point-by-point response\nReviewer #1 (Remarks to the Author):\nReviewer Comment: The authors have made significant corrections, including addressing a technical point I raised,\nhowever the paper remains unduly long and challenging to read. Below I make additional suggestions for the text\nthat would help the readers.\nAuthor Response: We thank the reviewer for their thoughtful and constructive comments. We appreciate the\nconcern and have substantially revised the manuscript to improve clarity and conciseness. We have removed or\nshortened background passages from Results subsections (e.g., Lines 467–498), eliminated redundancies (e.g., “In\nsummary” at Lines 386 and 394), and provided a limitations paragraph in the discussion.\nReviewer Comment: My major technical concern of the previous review was that the authors did not\nknockout/down validate the antibodies they used for Cut&Tag. Here they have added validation by western, but that\nis not as rigorous as validation in the same assay where the antibody is used (Cut&Tag). This is especially important\nbecause 1) the binding data on Setd1 is a major feature of the study and 2) the data from these authors is not fully\nconsistent with a prior published study. The authors did include an IgG control, which at least shows they are not\nseeing nonspecific open chromatin. At a minimum the authors should add a statement of limitations of their study,\nsaying that they did not perform the Cut&Tag from a knockout/down condition and thus cannot entirely rule out\ncross-reaction of their antibody with other chromatin proteins. In the section where they compare their data to the\nprior paper they may also wish to comment that compared with ChIP, Cut&Tag tends to emphasize promoters over\nenhancers because promoters are more accessible (for example a discussion of limitations can be found here -\nhttps://www.activemotif.com/blog-cut-tag) which could explain the different results.\nAuthor Response: We appreciate the reviewer’s continued attention to antibody validation and acknowledge the\nimportance of rigorous specificity controls for chromatin profiling experiments. In response to the initial review, we\ninterpreted the comment as a request for antibody validation by performing knockdown and then assaying via\nwestern blotting, which we provided in the revision. We have included this point about validation in the same assay\n(i.e., CUT&Tag) using a knockout/down approach in our revised limitations paragraph (see below). Since the first\nsubmission of the manuscript, CST has now also validated their antibody for CUT&Tag. Importantly, we had already\nperformed CUT&Tag in isogenic SETD1Ac.4582-2delAG/+ haploinsufficient NPCs and neurons, which demonstrated fewer\nSETD1A peaks at active promoters (Fig. 3g) and genome-wide (Supplementary Fig. 9a) and reduced intensity by\nwestern. These findings all support the specificity of the antibody in the context of CUT&Tag in our exact cell system\nand demonstrate that the observed signal is indeed dependent on SETD1A dosage.\nRegarding the comparison to Mukai et al., we note that this may be due to species differences. Mukai et al (PMID:\n31606247) analyzed mouse tissue, and we are assaying human material. Our data does not conflict with any\nreported human SETD1A brain binding, as our paper is the first study of this. In addition, Mukai et al. did not perform\nany western blot with their antibody to demonstrate how many bands are detected even in wild-type brain. They also\ndid not perform knockout/down validation of their antibody by western blotting, CUT&Tag, or ChIP. Furthermore,\nbased on our western blot analysis, the antibody used by Mukai et al. (PMID: 31606247) demonstrates considerably\nless specificity to human compared to either the Atlas or CST antibodies used in our study (Supplementary Fig. 5c).\nAs suggested, we have also added a note to the discussion acknowledging that CUT&Tag, unlike ChIP, tends to bias\ntoward promoter-proximal regions due to differences in chromatin accessibility, which may also contribute to\ndiscrepancies between datasets.\nWe have updated the discussion to include the following revised limitations paragraph:\n“Despite these insights, our study has limitations. While we used γH2AX staining to detect increased DNA damage\nin SETD1Ac.4582-2delAG/+ neurons, additional markers such as 53BP1 would provide stronger validation. For our CUT&Tag\nexperiments, although we validated antibody specificity by western blot and observed reduced peak numbers in\nisogenic SETD1Ac.4582-2delAG/+ haploinsufficient lines (Fig. 3g, Supplementary Fig. 9a), we did not perform antibody\nvalidation using knockout or knockdown cells in the same assay, limiting our ability to completely exclude potential\ncross-reactivity. Moreover, differences between our CUT&Tag findings and ChIP-seq findings in Mukai et al.16 may\nreflect both antibody specificity, mouse vs. human differences, and methodological differences, as CUT&Tag is\nknown to preferentially enrich for promoter regions due to accessibility biases, whereas ChIP may capture broader\nregulatory landscapes including enhancers.”\nWe hope this addresses the reviewer’s concerns and clarifies the steps we have taken to ensure the reliability of our\ndata.\nReviewer Comment: Line 57 – just as the authors show here that H3K4me3 regulatory proteins do not always act\nenzymatically, the involvement of H3K4me3 regulatory proteins in NDDs does not “[show] that H3K4me3 is\nessential for human neurodevelopment.”\nAuthor Response: We thank the reviewer for this important clarification. We have revised the sentence to better\nreflect this nuance:\n“The frequent implication of H3K4me3 regulatory proteins in neurodevelopmental disorders suggests a critical role\nfor H3K4me3-associated pathways in human brain development, which may occur through both enzymatic and\nnon-enzymatic mechanisms.”\nReviewer Comment: Line 74 Recent studies of isogenic human induced pluripotent stem cell (iPSCs) carrying\nnonpathogenic SETD1A LoF mutations demonstrated…It is not clear what “nonpathogenic” means. If these were\nbenign they would presumably have no effect. I think the author means this is a mutation that was not derived from\na patient, but “nonpathogenic” does not convey the right meaning.\nAuthor Response: We have changed the phrase to “heterozygous SETD1A mutation not found in patients” to clarify\nthat these mutations were generated in vitro and not identified in patients.\nReviewer Comment: Line 116 “patient mutant NPCs” is vague. Are these heterozygous? Are the mutations loss of\nfunction? Is there a different phenotype from different (likely) loss of function mutations? The mutations should be\nspecified.\nAuthor Response: We have clarified the text and updated all references to the various mutations to the exact\nnomenclature of the allele (i.e., SETD1Ac.4582-2delAG/+). The first section of results demonstrates that SETD1Ac.4582-2delAG/+\nand the other heterozygous mutations described are haploinsufficient loss-of-function alleles.\nReviewer Comment: The data in the section that begins at line 119 is not consistent with the title. The authors do\nnot present data that shows “accelerated neuronal differentiation”. The title should be more specific to the data.\nAuthor Response: We have revised the subsection title to: “SETD1A haploinsufficiency disrupts NPC proliferation\nand alters neuronal morphology and network activity.”\nReviewer Comment: Line 122 and beyond “Hetero” is used frequently, which is vague and nonspecific. The authors\nshould write out the gene name and genotype. Line 124 “Patient mutant neurons” again is very vague and non-\nspecific. The mutation should be specified. Line 131 “non-patient mutation” again is awkward language.\nAuthor Response: We have clarified the text and updated all references to use exact nomenclature for each\nmutation (i.e., SETD1Ac.4582-2delAG/+ for the patient mutation and SETD1A+/− when referring collectively to mutation\nlines). All specific mutations are detailed in the supplemental material (Supplementary Fig. 1b; Supplementary\nTable 1).\nReviewer Comment: Line 167 It is not clear what the authors mean by “while the resolution of 2D bulk sequencing\nreduced the power to detect DEGs…” What is “resolution” in this case?\nAuthor Response: We have revised this sentence: “While the heterogeneous cell types and states in the 2D\nneuronal cultures reduced the power of bulk RNA-seq to detect DEGs,”\nReviewer Comment: Line 188 the firing changes reported in Figure 1 could arise from excitability changes as much\nas synapse changes.\nAuthor Response: We deleted this text.\nReviewer Comment: All sections that report gene expression data (such as the one beginning on line 137, the one\nstarting at line 399, and the one starting at Line 552) have substantial discussion merged in with the reporting of\ndata. (Any sentence that says “this suggests…”) These sections would be stronger (and the paper would be much\nshorter and easier to read) if they were comprised only of results, with the discussion left for the end.\nAuthor Response: We appreciate this suggestion and have revised the manuscript to substantially shorten these\nresults and remove interpretation.\nReviewer Comment: Line 210 the authors find differences in cell types generated in the organoids. Is this putative\nE/I balance consistent with the phenotypes of the heterozygous mice or could it be a culture artifact?\nAuthor Response: Currently, no studies have investigated the impact of Sedt1a loss-of-function mutations on\nprenatal mouse brain development. Existing single-cell RNA sequencing studies have focused on adult stages.\nWhile Nagahama et al. (PMID: 32937141) proposed an \"elevated inhibition/excitation (I/E) balance of synaptic\ninputs in Setd1a-KD neurons,\" this finding pertains to the function of excitatory neurons, not neuronal subtype\nproportions. Furthermore, it is crucial to recognize that findings in mice may not directly translate to humans due to\ninherent species differences.\nReviewer Comment: Line 386 and 394 – In summary, In summary. Only one summary.\nAuthor Response: Corrected and condensed.\nReviewer Comment: Line 445 what do the authors mean when they say a region was “uniquely” bound in WT\nneurons? Or in line 451 “exclusively”. This is not clear. How do the authors know the difference between a weak site\nfalling below threshold and a categorical bound/not bound?\nAuthor Response: We have updated the names to provide clarity. Uniquely/exclusively bound in WT neurons was\nchanged to regions where binding was lost in SETD1Ac.4582-2delAG/+. Uniquely/exclusively bound in patient mutant\nneurons was changed to regions where binding was gained in SETD1Ac.4582-2delAG/+. As specified in the methods, peaks\nwere identified by MACS2 and the set of loci termed lost in SETD1Ac.4582-2delAG/+ refers to peaks found in SETD1A+/+ and\nnot identified in SETD1Ac.4582-2delAG/+. In Fig 3j, we demonstrate that the set of loci called lost in SETD1Ac.4582-2delAG/+ have\nsignificantly fewer reads in SETD1Ac.4582-2delAG/+ compared to the isogenic SETD1A+/+. Therefore, this set of loci is\nstatistically significantly depleted for binding, which justifies the subsequent analysis of these loci as a set. We\nagree with the reviewer’s point and added this statement to the results:\n“We classified SETD1A binding sites at active promoters into three groups: those lost or gained in cells from\nSETD1Ac.4582-2delAG/+ compared to WT, or shared between two genotypes (Fig. 3h, i). Even though some low-signal\npeaks might individually fall below detection thresholds, analyzing these loci as a group revealed a significant\nalteration of SETD1A binding overall. As expected, regions lost in SETD1Ac.4582-2delAG/+ cells showed significantly less\nSETD1A binding, while gained regions showed significantly more binding compared to WT (Fig. 3j). ”\nReviewer Comment: The first two paragraphs of the section starting at Line 467 are background that is distracting\nto read in a results section. Again this could be streamlined.\nAuthor Response: We moved the background to the introduction. For the results section, we condensed this to\nintroduce the terminology and citations for the comparison: “DNA damage can lead to somatic mutations,\nespecially in RDCs that overlap with CFSs and mosaic CNVs23,27. In mature neurons, thousands of DRHs occur in\nopen chromatin30,31.”\nReviewer Comment: Line 498 lots of discussion in the results section.\nAuthor Response: We have condensed this section. In order to follow the rationale, test and results some\ndiscussion is required within the context of the results. We have removed as much discussion as possible while still\nallowing for the results to be understandable.\nReviewer Comment: The H3K4me3 data are hard to follow.\nAuthor Response: We have revised the statements regarding the H3K4me3 data:\n“In NPCs, loss of SETD1A binding at active promoters in SETD1Ac.4582-2delAG/+ was associated with reduced H3K4me3,\nwhile gain of SETD1A binding also correlated with reduced H3K4me3 (Fig. 3j). Unexpectedly, the opposite pattern\nwas observed in neurons: both loss and gain of SETD1A binding at active promoters in SETD1Ac.4582-2delAG/+ neurons\nwere associated with increased H3K4me3 (Fig. 3j). This underscores the distinct epigenetic consequences of the\nSETD1A mutation in NPCs versus neurons.”\nReviewer Comment: The final section starting on line 552 is correlations of correlations and it seems like this part\ncould be cut to give room to make the rest easier to read.\nAuthor Response: We agree with the need to condense the text and improve readability. We have reduced the text\n(introduction-discussion) by ~40% from 9,914 words to 6,192. The analyses associated with Fig. 5 are fundamental\nto the paper’s scope and particularly significant for translational research impacting the adult human patient\npopulation. As the ultimate goal of research in schizophrenia (SCZ) genetics is to translate research to actionable\nfindings in patients, in our opinion, this figure is the most important result of the entire paper, and therefore, we kept\nit in the manuscript. It extends our findings from isogenic models and fetal brain tissue, focused on SETD1A\nhaploinsufficiency, to large-scale adult postmortem transcriptomic datasets from individuals with sporadic SCZ\nwithout rare SETD1A mutations. We modified the title to: “SETD1A downregulation is associated with SCZ\npathogenesis in the broader patient population without rare LoF mutations” to more clearly convey its purpose.\nWe demonstrate that the SETD1A alterations in these datasets reflect modest reduced expression rather than rare\nmutations, and the inclusion of this analysis is essential for demonstrating that the dysregulation of SETD1A and its\ndownstream gene networks is potentially relevant to a broader adult patient population.\nSpecifically, this section highlights two key findings:\n1. SETD1A expression is significantly decreased in the adult cortex of individuals with SCZ who do not carry\nSETD1A mutations from the large psychENCODE dataset.\n2. SETD1A target genes, particularly SCZ GWAS-significant and synaptic genes, are disproportionately\ndownregulated with reduced SETD1A expression, implicating molecular pathways in common forms of the\ndisorder that are shared with those identified in stem cell models with SETD1Ac.4582-2delAG/+ patient mutation\nand fetal brain.\nThese results strengthen the central hypothesis that SETD1A acts as a convergent upstream regulator of a polygenic\nSCZ risk gene network. They also support the translational potential of targeting SETD1A or its associated chromatin\npathways as a therapeutic strategy for a larger segment of adult SCZ patients. Removing this section would\nsubstantially weaken the translational and conceptual impact of the manuscript.\nReviewer #3 (Remarks to the Author):\nReviewer Comment: The authors have attempted to address many of my and other reviewers concerns in the\nmanuscript, which I believe is improved. I am supportive of publication. I list below a point that could be addressed\nto improve the manuscript.\nThis reviewer still does not find the DNA Damage staining in neurons (yH2AX) (Fig 2i to be particularly convincing).\nThey authors could further improve by co-staining for another marker of damage (eg 53BP1) to make the point more\nclearly. However, I do not believe this is required for publication- merely a suggestion to strengthen the data.\nAuthor Response: We appreciate the reviewer’s suggestion to co-stain with an additional DNA damage marker\nsuch as 53BP1 to strengthen our findings. Dr. Erwin has since left the Lieber Institute, and this additional experiment\ncould not be completed. However, we acknowledge the limitation and have added the following statement to the\nmanuscript (added to the Limitations Section):\n\"While we used γH2AX staining to detect increased DNA damage in SETD1Ac.4582-2delAG/+ neurons, additional markers\nsuch as 53BP1 would provide stronger validation.\""}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revision the authors have substantially improved the manuscript, making their findings easier to follow and the significance of the study much more clear. It is an interesting collection of data that will be valuable to the field."}, "author_response": ""}]

Decision letter