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Neuronal impact of patient-specific aberrant NRXN1α splicing

NRXN1 undergoes extensive alternative splicing, and non-recurrent heterozygous deletions in NRXN1 are strongly associated with neuropsychiatric disorders. We establish that human induced pluripotent stem cell (hiPSC)-derived neurons represent well the diversity of NRXN1α alternative splicing observed in the human brain, cataloguing 123 high-confidence in-frame human NRXN1α isoforms. Patient-derived NRXN1+/− hiPSC-neurons show greater than two-fold reduction of half of the wild-type NRXN1α isoforms and express dozens of novel isoforms expressed from the mutant allele. Reduced neuronal activity in patient-derived NRXN1+/− hiPSC-neurons is ameliorated by overexpression of individual control isoforms in a genotype-dependent manner, whereas individual mutant isoforms decrease neuronal activity levels in control hiPSC-neurons. In a genotype-dependent manner, the phenotypic impact of patient-specific NRXN1+/− mutations can occur through a reduction in wild-type NRXN1α isoform levels as well as the presence of mutant NRXN1α isoforms.

We applied new methods 17,18 to integrate targeted long-and short-read sequencing data, cataloguing and quantifying NRXN1α isoform repertoires in human postmortem brain and across hiPSC-neuronal subtypes. hiPSC-neurons derived from psychosis cases with heterozygous NRXN1 deletions exhibited significant perturbations in the NRXN1α isoform repertoire, including decreased levels of many wild-type isoforms and unexpected expression of patient-specific mutant NRXN1α isoforms. Single cell (sc) RNA-seq revealed that NRXN1 +/− hiPSC-neurons failed to reach full maturity. Reduced neuronal activity in NRXN1 +/− hiPSC-neurons could be ameliorated by overexpression of individual wild-type NRXN1α isoforms only in patient-derived hiPSC-neurons lacking expression of mutant isoforms, whereas neuronal activity was decreased in control hiPSC-neurons following expression of individual mutant NRXN1α isoforms, suggesting a possible novel dominantnegative mode of action for heterozygous NRXN1 +/− mutations. In summary, we report and functionally evaluate psychiatric disease-associated changes in the NRXN1α isoform repertoire.

RESULTS
We generated hiPSCs from four individuals with rare heterozygous intragenic deletions in NRXN1 19,20 and diagnosed with a psychosis disorder (two sharing ~136-kb deletions in the 3'-region of NRXN1 and two sharing ~115-kb deletions in the 5'-region of NRXN1; available clinical information provided in Table 1) as well as one related and three unrelated age-, sex-and ethnicity-matched controls ( Table 1, Fig. 1a, and Extended Data Fig. 1a-f). The 3'-NRXN1 +/− deletions encompassed three exons, impacting canonical splice site 4 (SS4), thought to impact post-synaptic binding affinities 21 , but none of the other five known canonical splice sites. Two-to-three hiPSC clones per individual were reprogrammed from fibroblasts using a non-integrating sendai virus approach, followed by the differentiation of neural progenitor cells (NPCs) through dual SMAD inhibition [22][23][24] . NESTIN and SOX2positive hiPSC-NPC populations robustly differentiated into MAP2-positive hiPSC-neurons (Fig. 1b), a forebrain-like population consisting primarily of glutamatergic neurons, but also GABAergic neurons and astrocytes 25 . hiPSC-NPCs were also rapidly induced into glutamatergic and GABAergic neurons through NGN2- 26 and ASCL1/DLX2-induction 27 , respectively.

NRXN1 +/− hiPSC-neurons display aberrant NRXN1 isoform expression
To survey the molecular impact of patient-specific NRXN1 +/− deletions, we performed whole transcriptome RNA-seq on hiPSC-NPCs and 6-week hiPSC-neurons from two-tothree hiPSC clones per individual (Supplementary Table 1). Following confirmation of sample identity 24 and cell type composition (Extended Data Figs. 1 and 2), we analyzed 37 hiPSC-NPC and hiPSC-neuron samples. In the combined dataset, differential expression (DE) analysis between NRXN1 +/− and controls identified 49 genes with FDR < 10% and common variants in ASD exhibit enhanced effects when found in combination with rare variant(s) 28 , and genetic risk underlying many neuropsychiatric disorders is thought to converge at pathways involved in synaptic function 2,[29][30][31] and epigenetic regulation 31,32 , our cohort is underpowered to examine this question in greater detail. We observed a significant correlation between the overall t-statistics from our case-control NRXN1 +/− hiPSC-NPC/ neuron comparison with postmortem brain comparisons of schizophrenia, major depressive disorder, bipolar disorder, and autism spectrum disorder (CommonMind 33 , NIMH HBCC and UCLA datasets 34 ) ( Fig. 1e and Supplementary Table 6). This enrichment for GWASlinked genes and correlation with postmortem datasets highlighted that patient-specific NRXN1 +/− neurons captured some of the biological underpinnings associated more broadly with psychiatric disease 35 .
Alternatively, 3'-NRXN1 +/− hiPSC-neurons demonstrated a more subtle increase in NRXN1α isoform usage ( Fig. 1g; P = 0.01 by one-way ANOVA with Dunnett's test), while usage of NRXN1β isoforms were decreased ( Fig. 1h; P = 0.01 by one-way ANOVA with Dunnett's test). NRXN1 exon expression is highly correlated with its own gene expression; therefore, to identify genes associated with NRXN1 splicing, we calculated partial correlation coefficients between all genes and each NRXN1 exon (irrespective of genotype), adjusting for NRXN1 gene transcription. Genes with significant correlation coefficients (FDR < 0.01) to perturbations in NRXN1α splicing in hiPSC neurons (regardless of NRXN1 gene expression) were enriched for synaptic gene sets (Fig. 1i). Similar results were observed in postmortem adult brains 33 (Extended Data Fig. 1o), in donors lacking rare NRXN1 +/− deletions, suggesting a generalizable correlation between NRXN1 splicing patterns and global expression of synaptic genes.
Because isoform quantification by short-read RNA-seq is limited 36 and current annotations do not include all possible NRXN1 isoforms, such as potentially novel isoforms arising from the deletion allele, we developed a targeted hybrid sequencing approach to more carefully examine the impact of patient-specific deletions in NRXN1 on isoform expression.
We catalogued 123 human NRXN1α isoforms (predicted to be translated, ≥7 read-count threshold) across two control hiPSC-neuron, three adult dlPFC and three fetal PFC samples. Fetal PFC exhibited the highest NRXN1 expression (whole transcriptome sequencing: Extended Data Fig. 1k) and the greatest NRXN1α isoform diversity (targeted hybrid  Table 7). Overall, hiPSC-neurons modeled well the NRXN1α alternative splicing diversity found in vivo, particularly the high abundance isoforms (Fig. 2g,h).

NRXN1α isoforms are differentially expressed in NRXN1 +/− hiPSC-neurons
We next quantified NRXN1α isoforms in patient-specific 3'-NRXN1 +/− neurons (Extended Data Figs. 3 and 4); because this targeted strategy relied upon the amplification of transcripts containing the first and last coding exons of NRXN1α (Supplementary Note and Extended encompassed the first coding exon. The correlation of NRXN1α isoform expression was higher between the two 3'-NRXN1 +/− cases (r = 0.59) or between the two controls (r = 0.60) than across NRXN1 genotypes (r = 0.03-0.37) (Extended Data Fig. 6a).
Across control and 3'-NRXN1 +/− hiPSC-neurons, alternative splicing of NRXN1α occurred at the six previously annotated canonical splice sites, with almost no occurrences of noncanonical splicing (Extended Data Fig. 6b,c). In fact, there was very similar inclusion of all NRXN1α exons between control hiPSC-neurons and 3'-NRXN1 +/− hiPSC-neurons, except for those exons encompassed by the 3'-deletion (Extended Data Fig. 6c). Overall, the 3'-NRXN1 +/− deletion impacted the NRXN1α transcriptional repertoire by decreasing levels of wild-type isoforms and also by introducing novel isoforms not otherwise identified in control hiPSC-neurons or in postmortem brain.
Interestingly, C14, a mature neuronal cluster, was also identified and contained almost exclusively 5'-NRXN1 +/− hiPSC-neurons ( Fig. 5a,b,e). Conversely, the clusters (C0, C10) with highest expression of astrocyte markers, including GFAP and s100B (Supplementary   Tables 13 and 14), along with the more immature neuronal clusters (C1, C2, and C3), were equally comprised of control and NRXN1 +/− cells (Fig. 5d,e). We found no dramatic differences in the expression level and percent of cells expressing common synaptic markers in the immature neuronal clusters (Extended Data Fig. 7) or impact on the relative patterning of glutamatergic, GABAergic or glial fates by scRNA-seq ( Fig. 5a-e) or immunocytochemical analysis (Extended Data Fig. 2c,d). Within the largest and most mature neuron cluster (C4), patient NRXN1 +/− neurons showed significantly reduced expression of NRXN1 (P adj = 3.07 × 10 −24 ), as well as genes related to the development of the nervous system, development of axons and neuronal projection regulation, and regional patterning ( Fig. 5f and Supplementary Table 15).

Expression of specific NRXN1α isoforms may impact neuronal activity in a genotypedependent manner
We tested the extent to which decreased neuronal activity in NRXN1 +/− hiPSC-neurons reflected a decrease in wild-type NRXN1α isoforms and/or potential dominant-negative activity of mutant patient-specific NRXN1α isoforms. Individual NRXN1α isoforms were selected for overexpression studies: four wild-type isoforms shared between all hiPSCneurons but significantly decreased in 3'-NRXN1 +/− hiPSC-neurons (Extended Data Fig.  8n), and four mutant isoforms unique to 3'-NRXN1 +/− hiPSC-neurons and not detected in controls (Extended Data Fig. 8n). Lentiviral expression of the wild-type isoform resulted in a 2-to 7-fold increase of NRXN1 expression after 48 hours (Extended Data Fig. 8o).
Expression of all four wild-type NRXN1α isoforms tested significantly increased population-wide neuronal activity after 2 weeks of expression in 5'-NRXN1 +/− deletion hiPSC-neurons ( Fig. 6i; P < 0.05 by one-way ANOVA with Dunnett's test to GFP-alone). Reciprocally, expression of two of the four mutant NRXN1α isoforms evaluated significantly decreased neuronal activity after two weeks of expression in control hiPSCneurons ( Fig. 6h; P < 0.05 by one-way ANOVA with Dunnett's test). Surprisingly, neither the wild-type nor mutant isoforms tested impacted 3'-NRXN1 +/− hiPSC-neuron activity ( Fig. 6j by one-way ANOVA with Dunnett's test).
Overall, our data support a model whereby functional deficits in 5'-NRXN1 +/− neurons arise from NRXN1 haploinsufficiency and can therefore be rescued by overexpression of wildtype NRXN1α isoforms, but unexpectedly, haploinsufficiency in 3'-NRXN1 +/− neurons is exacerbated by novel dominant-negative activity of mutant splice isoforms, and so cannot be rescued by simply increasing wild-type NRXN1α levels ( Fig. 6k). Non-recurrent heterozygous deletions in NRXN1 +/− seem to impact neuronal function through different mechanisms, dependent on the precise patient-specific mutations involved.

DISCUSSION
Our report links patient-specific, heterozygous intragenic deletions in NRXN1 to isoform dysregulation and impaired neuronal maturation and activity in a human and diseaserelevant context. NRXN1α alternative splicing is disrupted in NRXN1 +/− hiPSC-neurons; patient-specific perturbations include decreased expression of wild-type NRXN1α isoforms, and, in a subset of cases, the production of mutant NRXN1α isoforms. Mutant NRXN1α isoforms may be particularly biologically relevant as our experimental data demonstrated that overexpression of even a single mutant isoform was sufficient to perturb neuronal activity in control neurons and that overexpression of control isoforms could only ameliorate activity deficits in patient hiPSC-neurons in the absence of mutant isoform expression. Overall, our data, when combined with the published literature (reviewed in ref. 21), suggest that NRXN1α haploinsufficiency impairs neuronal maturation in addition to synaptic activity.
The consensus roles for neurexins are as organizers of neurotransmitter release by coupling calcium channels to the presynaptic machinery 8 and trans-synaptic organizers of postsynaptic proteins resulting in stabilization of AMPARs 39,40 . Although neurexins and their ligands have also been linked to synapse formation [41][42][43][44][45] , deletion of all three α-Nrxns 8 or all three β-Nrxns 46 impaired neurotransmitter release without altering glutamatergic synapse density in mice, and pan-Nrxn disruption of all αand β-Nrxns dramatically reduced synapse stabilization and functional maturation 47 . Our scRNA-seq, morphological, and population-wide neuronal activity analyses fit a model whereby NRXN1 +/− hiPSC-neurons do not mature along the same trajectory as control hiPSC-neurons. Moreover, the lack of observed deficits in SYN1-puncta number and synaptic marker gene expression in NRXN1 +/− hiPSC-neurons suggests that synapses form but remain immature, consistent with the expression of synaptic-associated proteins in the immature neuronal clusters by scRNA-seq. While these data provides insights into the neuronal impact of patient-specific NRXN1 +/− deletions, the causal mechanisms remain unclear: does altered neuronal maturation lead to impaired synaptic function, or does aberrant synaptic activity limit neuronal maturation through an activity-dependent mechanism? Future experiments are needed to disentangle the extent to which perturbations in NRXN1 splicing independently perturb neuronal maturation, synaptic initiation, stability and activity across neurodevelopment and in a cell-type-specific manner. It is critical to disentangle the potentially additive effects of loss of wild-type NRXN1 isoforms and aberrant generation of mutant NRXN1 isoforms, particularly whether mutant isoforms are localized to the synapse, and the extent to which mutant isoforms impair the function of wild-type isoforms (perhaps similar to protocadherins 48 ) or directly interfere with neuronal function.
We demonstrated that hiPSC-neurons to a large extent recapitulate the in vivo splice repertoire. We observed significantly reduced expression of 49 (50%) wild-type NRXN1α isoforms in two 3'-NRXN1 +/− cases and identified 31 mutant isoforms not detected in controls. Increasing even a single differentially expressed wild-type NRXN1α isoform ameliorated neuronal activity in NRXN1 +/− hiPSC-neurons that did not express mutant isoforms, whereas mutant isoforms decreased neuronal activity levels in control hiPSCneurons. Protein models partially predicated protein structure of the top ten most abundant wild-type and mutant isoforms, suggesting that, whereas wild-type isoforms are largely conserved (Extended Data Fig. 9a,d), 3'-NRXN1 +/− mutant isoform models may be more variable (Extended Data Fig. 9b-c,e), particularly near the C-terminus where the 3'-NRXN1 +/− deletion is located (Extended Data Fig. 9 insets). Dominant-negative activity by patient-specific mutant NRXN1α may be a widespread phenomenon: up to 8/35 (23%) of identified NRXN1 +/− exonic deletions in the PGC dataset may be capable of producing novel translated (mutant) isoforms. Moving forward, it will be important to empirically evaluate the extent to which these putative mutant isoforms can exacerbate NRXN1 related phenotypes.
Psychosis risk reflects a complex interaction of rare 57 and common variants 35 . While idiopathic postmortem and hiPSC-based studies remain under-powered 24,33 , the effect sizes of NRXN1 deletions are dramatically larger [2][3][4][5] . Because patient-recruitment is limiting for this rare mutation (0.21% of a clinical population 5 ) and the size of the deletions with no flanking segmental duplications make isogenic strategies unavailable, we generated multiple independent hiPSC clones per donor. We note the extent that analyses were repeated between donors, hiPSC clones and independent differentiations, while carefully controlling for spurious RNA-seq effects that can arise when using multiple independent samples per donor 24,58 . Although our cohort size would be unsuitable as an idiopathic study, it is comparable to recent hiPSC-based psychiatric studies of rare variant effects [59][60][61][62][63][64][65] . Multielectrode arrays do not provide synaptic-level resolution, and the differences we observed in population-wide neuronal activity in NRXN1 +/− hiPSC-neurons likely reflect the delayed maturation of NRXN1 +/− hiPSC-neurons we identified through scRNA-seq, but may also be impacted by changes in synaptic activity. We unexpectedly also observed decreased neurite branching in NRXN1 +/− hiPSC-neurons, a phenotype similar to observations in Xenopus models 66 and a mouse model of double α-Nrxn deletions 67 . Overall, larger NRXN1 +/− cohorts are necessary to better discriminate molecular, structural and synaptic effects of distinct patient-specific mutations.
Our NRXN1-specific findings may yield broader insights into schizophrenia. Identifying the repertoire and abundance of NRXN1α transcriptional variants is important for understanding protein diversity at human synapses, which will ultimately help to reveal how patient-specific deletions in NRXN1 contribute to a number of psychiatric and neurodevelopmental disorders. Evaluating how loss and/or gain of specific NRXN1 isoforms impact neuronal fate, maturation and function in a cell type-specific and activity-dependent manner represents a critical first step towards a more genetics-based form of precision medicine. Understanding how NRXN1 +/− deletions perturb the splice repertoire and alter neuronal function could ultimately improve genetic diagnosis, prognosis and/or lead to new therapeutic targets.

Cohort
From a cohort of cases recruited to study childhood-onset schizophrenia (COS), four individuals with NRXN1 deletions were identified by IlluminaOmni 2.5 bead chip genotyping. The first two were a mother/son pair carrying 5'-deletions (~115 kb) in NRXN1; the son was diagnosed with childhood-onset bipolar disorder with psychosis and the mother with adult-onset bipolar disorder with psychosis. The others were two monozygotic twins carrying 3'-deletions (~136 kb) in NRXN1, one diagnosed with childhood-onset schizophrenia, the other with adult-onset schizoaffective disorder (Table 1). All human sample collection was done de-identified in accordance with the policies of Icahn School of Medicine at Mount Sinai and in regulation with its institutional review board.
Briefly, hiPSC-NPCs were seeded at low density and transduced with a lentivirus expressing TetO-NGN2-GFP-Puro or TetO-NGN2-Puro along with rtTA. Twenty-four hours after transduction, doxycycline was added to initiate NGN2 expression, and then 24 hours later the cells were selected with puromycin. NGN2-glutamatergic hiPSC-neurons were cultured in neuronal differentiation media and assayed after 3 weeks unless otherwise noted. A similar strategy was used to produce ASCL1/DLX2-GABAergic neurons. hiPSC-NPCs were seeded at low density and transduced with lentiviruses expressing TetO-ASCL1-GFP-Puro and TetO-DLX2-Hygro, along with rtTA. Twenty-four hours after transduction, doxycycline was added to initiate ASCL1 and DLX2 expression followed by selection with puromycin and hygromycin after an additional 24 hours. ASCL1/DLX2-GABAergic hiPSC-neurons were cultured in neuronal differentiation media with doxycycline for 2 weeks then neuronal differentiation media alone for an additional 3 weeks unless otherwise noted. ASCL1/ DLX2-GABAergic hiPSC-neurons were treated with Ara-C between weeks 1-3.

RNA isolation and preparation
hiPSC-NPCs and hiPSC-neurons were lysed in 1 mL Trizol (Thermoscientific) according to the manufacturer's protocol. RNA was chloroform extracted, pelleted with isopropanol, washed with 70% ethanol, and re-suspended in water. RNA was treated with RNase-free DNaseI (Promega) for 30 min at 37 °C and then the reaction was inactivated by incubation with stop buffer at 65 °C for 10 min. Human post-mortem samples were homogenized and lysed in 1 mL Trizol. RNA was chloroform extracted and purified and DNAse treated using the RNA Clean & Concentrator with DNAseI Kit (Zymo Research).

Western blotting
Cells were isolated, suspended in 1× RIPA lyses buffer (Sigma) supplemented with Complete Protease Inhibitor tablets (1 per 50 ml) (Roche) and PhosSTOP phosphatase inhibitor tablets (1 per 10 ml), sonicated and then centrifuged at 14,000g for 30 min at 4 °C. Total protein was separated on 4-20% SDS-polyacrylamide gel (Bio-Rad) and transferred to a nitrocellulose membrane (Bio-Rad). The membrane was blocked with 4% bovine serum albumin (Sigma) in 1.0% Tween (Sigma) in PBS and probed with primary antibodies against FLAG (F7425-.2MG, Sigma) and overnight at 4 °C, then with HRP-conjugated secondary antibodies (Sigma) for 2 h at room temperature. The membrane was visualized using Amersham ECL prime western blotting kit (GE, RPN2232). Imaging was done using a Bio-Rad Gel Doc Imaging System; the membrane was cut during imaging.

Predicted NRXN1 isoform structures
The top 10 most abundant wild type and mutant splice isoform sequences were submitted to ExPasy translate tool (https://web.expasy.org/translate/), for translation into amino acid sequences. The amino acid sequence was submitted to Phyre 2 for predicting protein models 71 . All models returned with 100% confidence.

Whole transcriptome RNA sequencing parameters
RNA sequencing libraries were prepared using the Kapa Total RNA library prep kit with ribo-depletion and strand-specific cDNA library construction (Kappa Biosystems). Pairedend sequencing (125-bp reads) was performed using the Illumina HiSeq2500 platform (New York Genome Center). RNA-seq reads were aligned to GRCh37 with STAR 72 . Uniquely mapping reads overlapping genes were counted with featureCounts 73 using annotations from ENSEMBL v70 while isoform expression was obtained using Kallisto 74 .

Whole transcriptome RNA-seq analysis
Samples were analyzed using publically available code from Hoffman et al. 5 . Briefly, sample identity was check by calling variants from the RNA sequencing BAM files using GATK to produce gVCF files. These gVCF files were merged with variants from whole exome sequencing data from the same donor and variant concordance was examined using bcftools. One sample with variant discordance was excluded from downstream analysis. variancePartition software was used to determine and visualize the contribution of a given variable to the variance in expression of each gene 75 . Hierarchical clustering was implemented in R using complete linkage clustering. A pairwise distance matrix was computed for all samples, and the median distance between all samples in each category was used to create a summary distance matrix using to perform the final clustering. PCA was performed on each cell type individually as well as the combined hiPSC-NPC + hiPSCneuron data set. To investigate differences in cell type composition in the gene expression data, the cell type composition score was obtained using Cibersort 76 . Differential expression analysis was done with limma/voom 77 and duplicateCorrelation was applied to account for the analysis of multiple samples per donor. Gene set enrichment was performed using a hypergeometric test with gene sets from MSigDB 68 , MAGMA 78 and additional sets from Fromer et al. 33 using genes expressed within the combined dataset as background. We examined enrichment for genes with FDR < 30% in either cell type with EnrichR 79 using genes expressed within the combined dataset as background. Correlation of t-statistic was done using publically available datasets including CommonMind 33 , NIMH HBCC (https:// www.nimh.nih.gov/labs-at-nimh/research-areas/research-support-services/hbcc/index.shtml) and UCLA datasets 34 .
As NRXN1 exon expression is highly correlated with its own gene expression, the correlation between NRXN1 exon expression and other genes could be largely driven and biased by overall NRXN1 gene expression level. Therefore, to screen genes that are associated with NRXN1 splicing but not NRXN1 transcription, we calculated the partial correlation coefficients between all genes and each NRXN1 exon, with the NRXN1 gene transcriptional effect regressed away 80,81 . The genes that showed significant correlation coefficients (FDR < 0.01), were selected for further gene set enrichment analysis. This analysis was performed for hiPSC-neuron and CommonMind 33 dlPFC datasets.

Generation of NRXN1α cDNA for targeted sequencing
For targeted NRXN1α library preparation first strand cDNA synthesis was performed on 500 ng of RNA using 0.5 μM of a primer complementary to the last exon of NRXN1α (CAGAAACCGGGATGAAGGCT). The cDNA was amplified in a 50-μL reaction with 4 μL of cDNA as template, LA Taq, and primers complementary to the first (TGGTGCCCTAAAGAGGCAAG) and last (same as above) coding exons of NRXN1α with 35 cycles (Supplementary Tables 16 and 17).
For targeted Illumina sequencing, the PCR product was then run on a 1% agarose gel and gel extracted using the Genejet gel extraction and DNA cleanup micro kit (ThermoFisher). Gel extracted cDNA was library prepped using the Nextera XT DNA library preparation kit (Genewiz). Libraries from four samples were pooled and then sequenced using Illumina MiSeq or HiSeq (2 × 150-bp reads).
For Pacific Biosciences targeted Iso-seq preparation, the NRXN1α PCR products were purified with Agencourt AMPure XP magnetic beads (Beckman Coulter) at a volume ratio of 0.5:1 (magnetic beads:PCR product). The cDNA was quantified using the Qubit HS dsDNA Kit and quality was assessed using the Agilent 2100 bioanalyzer. SMRT bell sequencing libraries were prepared using Pacific Biosciences DNA Template Prep Kit according to the 5-kb template preparation and sequencing protocol provided by Pacific Biosciences prior to purification and size binning to proper molecular range using Sage BluePippin. SMRT bell templates were bound to polymerases using DNA/Polymerase Binding Kit XL 1.0 (100-150-800) and v2 primers. Sequencing was carried out on the Pacific Biosciences RSII real-time sequencer using P6 sequencing reagents with 10-hour movies. Each sample was sequenced on one SMRT cell, yielding a total of 165,753,313 reads of insert (average across all samples) for NRXN1α.

Analysis of human NRXN1α targeted short read data
The human NRXN1α targeted Illumina cDNA reads were aligned to the human genome (hg19) using the spliced gap aligner STAR 72 . Only reads mapped to the region between the 5' and 3' primers were kept for the following analysis. Read quantitation at the exon level was done using the Subread package featureCounts 73 based on the GENCODE genome annotation GTF file. The junction sites were extracted from the bam file according to JI tags. Given an exon with canonical junction sites, say exon i , its exclusion value was calculated from the junction reads as follows: where exon i−1 , exon i , and exon i+1 represent three adjacent exons, and junction(exon i−1 → exon i ) represents the number of reads covering the junction between exon i−1 and exon i .
Kallisto was used for isoform-level short read-based quantitation 74 .

Analysis of human NRXN1α targeted long read data
Subread filtering was performed using Pacific Biosciences SMRT analysis software (v2.3.0). The human NRXN1α PacBio Iso-seq raw reads were constructed into Circular Consensus Sequences (CCS) and non-CCS subreads. Next, CCS was classified as full-length if both the 5' and 3' primers were present. To further improve the accuracy, we used ICE (Iterative Clustering for Error Correction) to cluster isoforms into consensus and used Quiver to polish the full-length consensus sequences based on both full-length and non-full-length reads. This method generated full-length isoforms with an expected post-correction accuracy of >99%. The polished high QV (phqv) isoform consensuses were mapped to the human genome (hg19) using STARlong 72 . The junction sites were extracted from the bam file based on the JI tags, and further corrected by junction sites from both the targeted Illumina short reads and the genome annotation GTF file. Based on the corrected junction sites, the phqv consensuses were clustered. The number of full-length reads corresponding to an isoform consensus was used as its expression and thresholded for ≥7 full length reads as described above. DEGseq 82 was used to call differential gene expression.

Analysis of mouse Nrxn1α targeted long and short read data
We generated the mouse Nrxn1α targeted Illumina cDNA reads, and aligned the reads to the mouse genome (mm10) with STAR. The reads mapped between 5' and 3' primers were kept for the following analysis. The mouse Nrxn1α Isoseq CCS reads (≥2 passes) were downloaded from SRA (SRR1184043), and mapped to the mouse genome (mm10) with STARlong. The full-length reads covering both 5' and 3' primers were kept for the following analysis. The junction sites from the Iso-seq long reads were further corrected based on targeted Illumina short reads and the genome annotation GTF file. The number of full-length reads corresponding to an isoform consensus was used as its expression. Based on the correspondence between mouse and human Nrxn1α exon annotations, the overlap of Nrxn1α isoforms were compared between human and mouse.

Axion multi-electrode array
hiPSC-NPCs from two controls, one 5'-and two 3'-NRXN1 +/− cases were plated onto a 48well multi-electrode array (MEA) plate and differentiated as hiPSC-neurons or induced NGN2-neurons over seven weeks. Each 48-well MEA plate from Axion Biosystems was coated with 2 μg/mL Matrigel 24 hours prior to seeding. hiPSC-NPCs were spotted at a density of 8,000 cells per well onto 6-12 wells per line. Half media change was performed every other day using neuron differentiation media. For over-expression experiments, lentiviruses expressing specific NRXN1α isoforms synthesized by Genecopoeia were added to directly onto the MEA plate of 4-week hiPSC-neurons and left overnight. After 24 hours, a full media was performed and the lentiviruses were allowed to express for 2 weeks before recording hiPSC-neurons at 6-week time point. Extracellular 15-minute recordings of spontaneous action potentials were performed at 37 °C using a Maestro MEA system and AXIS software (Axion Biosystems). Data were sampled at rate of 12.5 kHz with a 200-2500 Hz single-order Butterworth band-pass filer and a threshold for spike detection at 5.5 times the rolling standard deviation of the filtered field potential on each electrode. Spike time stamps were exported to Neuroexplorer (Axion Biosystems) for creation of raster plots, and all other data was exported as csv file for further analysis. If data stratified normality assumptions, one-way ANOVA with Dunnett's test for multiple comparisons was performed on the spike counts. However, isoform overexpression data violated this assumption of normality and therefore spike counts were log-transformed performing the one-way ANOVA to satisfy this assumption.

Neuronal tracing
Unlabeled control hiPSC-NPCs were plated onto 24-well matrigel coated plates with coverslips. Control hiPSC-NPCs were transduced with a scramble_gRNA-eGFP-puro expressing lentivirus while NRXN1 +/− hiPSC-NPCs were transduced with scramble_gRNA-tdTomato-puro expressing lentivirus. A small number of control tdTomato labeled and NRXN1 +/− eGFP-labeled hiPSC-NPCs were spiked into the unlabeled background of hiPSC-NPCs upon plating. hiPSC-NPCs were then differentiated for 6 weeks into hiPSCneurons and immunostained using mouse anti-GFP (ThermoFisher), 1:1,000; rabbit anti-RFP (ThermoFisher) 1:1,000; with Alexa donkey anti-mouse-488 and Alexa donkey antirabbit-568 secondary antibodies as described above. Individual neurons were imaged at 20x on the Zeiss 780 upright confocal microscope, followed by tracing and analysis using ImageJ Simple Neurite Tracer Plugin. One-way ANOVA with Dunnett's test was used to test the impact of genotype on neuronal morphology. As only the eGFP 3'-NRXN1 +/− were imaged on the same coverslip as tdTomato labeled controls, we assess the impact of coverslip to coverslip variation between these lines using a two-way ANOVA.

qPCR validation
Total RNA was extracted using Trizol following the manufacturer's instructions and qPCR reactions were done on a QuantStudio™ 7 Flex Real-Time PCR System using the Power SYBR green RNA-to-Ct RT-qPCR kit and gene specific primers (ThermoFisher). 50 ng of RNA template was added to the PCR mix. (ThermoFisher). Reaction conditions were 48 °C for 30 min, 95 °C for 10 min followed by 40 cycles (95 °C for 15 s, 60 °C for 60 s). Primers used are listed in Supplementary Table 18.

Molecular cloning procedures
NRXN1α cDNA was generated as previously described for targeted short-read sequencing. Gel extracted NRXN1α cDNA was ligated into the pCR4-TOPO backbone (ThermoFisher) following the manufacturers guidelines. Individual colonies were picked and screened for full length NRXN1α isoforms by Sanger sequencing (Genewiz). Two specific NRXN1α isoforms in the pCR4-TOPO backbone were chosen for further cloning into a lentiviral expression vector. A 4.5-kb fragment containing most of coding sequence of NRXN1α was amplified from the pCR4-TOPO backbone. An upstream region of the first coding exon and a downstream region of the last coding exon fused to a 3xFLAG tag were amplified from two synthetic plasmids containing these sequences (Thermofisher). Fragment amplification was performed using Q5 polymerase following manufactures protocol using appropriate primers (Supplementary Table 19). Fragments were assembled using the NEBuilder HiFi DNA Assembly Master Mix following the manufacturers protocol and the completed assembly was transformed into Stbl3 Chemically Competent E. coli (ThermoFisher), and positive clones were confirmed by restriction digest and Sanger sequencing (Genewiz). Lentiviruses were produced from these vectors as previously desceribed 22 . Physical titration of lentivirus was performed by using a qPCR Lentivirus Titration Kit (ABM).
Single cell sequencing-Six hiPSC-neuron (two control, two 3'-NRXN1 +/− , and two 5'-NRXN1 +/− ) samples were differentiated into hiPSC-neurons for six weeks as previously described. To harvest, Accutase was added to the hiPSC-neurons and incubated at 37 °C for 10-15 minutes. The hiPSC-neurons were then gently pipetted 2-3 times, transferred into DMEM for wash and spun at 600g for 4 minutes. The hiPSC-neurons were gently resuspended in 500 μL of PBS +0.04% BSA and passed through a 0.5-μM filter. The hiPSCneurons were then counted and re-suspended to a concentration of 1,000 cells/μL in 25 μL. Single-cell RNA-seq (sc-RNA-seq) was performed on these samples using the Chromium platform (10x Genomics) with the 3' gene expression (3' GEX) V2 kit, using an input of ~10,000 cells. Libraries were prepared as directed by the manufacturer and sequenced on a HiSeq2500 (Illumina) targeting a minimum depth of 50,000 reads per cell. Sequencing reads were then aligned to hg38 using the CellRanger pipeline from 10x Genomics. One of the 3'-NRXN1 +/− had a much lower mean reads per cell than the other samples and therefore was not considered in the final analysis. All downstream sc-RNA-seq analysis was done using the Seurat package in R. Reads were filtered for mitochondria, ribosome and HLA related genes, and for cells expressing a minimum of 200 genes. Each sample was then normalized, scaled and variable genes were identified individually. The variable genes across samples were then used in the RunMultiCCA function considering 20 CCS. Clusters were identified on the integrated dataset using the FindClusters function and considering 12 dimensions and a resolution of 0.8. tSNE plots were generated using the same 12 dimensions. Marker genes as well as differential expression analysis using a non-parametric Wilcoxon rank sum test within each cluster was performed.
Profiling of serine-threonine kinase activity was performed using the PamStation12 microarray (PamGene International) and STK (4-well) PamChips containing 144 consensus phosphopeptide sequences (3 of which are internal controls) per well, immobilized on porous ceramic membranes. Each PamChip well was blocked with 2% bovine serum albumin (BSA) before 2 μg of protein in the manufacturer's kinase buffer (PamGene), 157 μM adenosine triphosphate (ATP), and FITC-labeled anti-phospho serine-threonine antibodies (PamGene) were added in each well. The homogenized samples containing the active kinases and assay mix were pumped through the wells to facilitate interaction between kinases in the sample and specific peptide substrates immobilized on the chip. The degree of phosphorylation per well was measured in real time using Evolve (PamGene) kinetic image capture software. The software captures FITC-labeled anti-phospho antibodies binding to each phosphorylated peptide substrate every 6 sec for 60 min. Peptide spot intensity was captured across multiple exposure times (10, 20, 50, 100, 200 ms) during post-wash, and the linear regression slope was calculated and used as the signal (i.e. peptide phosphorylation intensity) in comparative analyses.
The signal ratio between pairs of samples (control vs. NRXN1 +/− ) was used to calculate fold change (FC) for each peptide. Peptides with a FC of at least 15% (i.e. FC > 1.15 or FC < 0.85) were considered changed in degree of phosphorylation. This threshold value was guided by previous reports suggesting that changes in kinase activity with similar (or smaller) orders of magnitude are sufficient to trigger biologically relevant changes [83][84][85] . Peptides that were undetectable or non-linear in the post-wash phase (i.e. the coefficient of determination R 2 of the corresponding linear regression less than 0.90) were excluded from subsequent analyses. Genes related to hits in the kinome array for either control vs. 3'-NRXN1 +/− or control vs. 5'-NRXN1 +/− were then intersected with whole-transcriptome RNA-seq results which compared the differential expression of the same hiPSC donors.
Statistical analysis-Data from all phenotypic described above were first organized in a Microsoft Excel spreadsheet and analyzed using GraphPad PRISM 8 software or Rstudio. For MEA, qPCR data analysis and imaging analysis, values are expressed as mean ± s.e.m. All boxplots and violin plots represent the median and quartiles of the data. Statistical significance was tested using either two-sided Student's t test, one-way ANOVA with Dunnett's post-hoc test or two-way ANOVA with Holm-Sidak's test as noted in the figure legends. Whole transcriptome RNA-seq analysis was performed in R using DeSeq2 and single-cell sequencing analysis was performed in R using Seurat. More information can be found in the Life Sciences Reporting Summary.

Extended Data
Extended Data Fig. 1 ). b, Bar plot of the total read count for each NRXN1α exon along with the fraction that each NRXN1α junction (row, 5' end; column, 3' end) is included in control NGN2glutamatergic hiPSC-neurons (green, left) and control ASCL1/DLX2-GABAergic hiPSCneurons (blue, right). c, Schematic of NRXN1α isoform structure, with each row   Nat Genet. Author manuscript; available in PMC 2020 August 27.