Abstract
Given the large number of genes significantly associated with risk for neuropsychiatric disorders, a critical unanswered question is the extent to which diverse mutations—sometimes affecting the same gene—will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, which encodes a presynaptic cell-adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain and are differentially affected by unique (non-recurrent) deletions1. We contrast the cell-type-specific effect of patient-specific mutations in NRXN1 using human-induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Through distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/− deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Reciprocal isogenic manipulations causally demonstrate that aberrant splicing drives these changes in synaptic activity. For NRXN1 deletions, and perhaps more broadly, precision medicine will require stratifying patients based on whether their gene mutations act through LOF or GOF mechanisms, to achieve individualized restoration of NRXN1 isoform repertoires by increasing wild-type and/or ablating mutant isoforms. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disorders, our findings add nuance to future considerations of precision medicine.
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Data availability
All source donor hiPS cells have been deposited at the National Institute of Mental Health/Sampled Repository (https://studyreg.nimhgenetics.org/ListOfStudies.jsp; study 160). All bulk and single-cell transcriptome sequencing data can be accessed via the Gene Expression Omnibus under the accession codes GSE288880, GSE288881 and GSE288964. Source data will be provided on request from the corresponding authors.
Code availability
To facilitate improved reproducibility of our data, analytical scripts have been deposited to GitHub (https://github.com/mbfernando/NRXN1).
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Acknowledgements
M.B.F. was supported by a Gilliam Fellowship from the Howard Hughes Medical Institute. This work was supported by the National Institute of Mental Health grants RO1 MH121074 (to K.J.B., G.F. and P.A.S.), RO1 MH125579 (to G.F. and K.J.B.), R01MH123155 (to K.J.B.) and RM1MH132648 (to K.J.B.). S.G. is supported by grants from the Trond Mohn Research Foundation (grant numbers TMS2021TMT07 and TMS2023TMT06). D.A.K. is supported by the National Science Foundation under grant number DBI2146398. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We thank the Stem Cell Engineering Core at the Icahn School of Medicine at Mount Sinai; the laboratories of N. Yang (R. Hu and X. Zhou) and S. Marro (M. Durens) for assistance in primary glial preparations; K. G. Townsley and M. G. Baxter for advice on statistical testing; L. Yang for critical advice on single-cell RNA-seq analyses; D. Weinberger and the Lieber Institute for Brain Development at Johns Hopkins School of Medicine for sharing post-mortem materials; and all members of the Brennand, Slesinger and Fang laboratories for critical feedback and discussions throughout the course of this work.
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M.B.F., S.K., A.N.M., R.O., C.P. and A.P. performed and/or analysed the experiments supervised by P.A.S. and K.J.B.; Y.Z. and Y.F. performed the bioinformatic analyses supervised by G.F. A.T. performed the bioinformatic analysis of alternative splicing estimates, supervised by D.A.K. S.E.W. produced virus for the generation of iGABA neurons. S.G. and L.C. made substantial contributions to experimental design, data analysis and interpretation of results. P.J.M.D. performed high-content imaging experiments. E.K.F. processed post-mortem tissue and generated long-read data. I.A.P. assisted in statistical analyses. M.B.F., G.F., P.A.S. and K.J.B. wrote the paper with input from all authors.
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Extended data figures and tables
Extended Data Fig. 1 Transcriptomic analysis of differentially expressed genes in iGLUT and iGABA neurons.
(a) Splicegraph displaying significant gene wide splicing clusters at NRXN1 SS3 (p = 0.0196), compared via Dirichlet-multinomial generalized linear model with Bonferroni corrections. (b, d) Volcano plots of differential gene expression (DE) analysis across both genotypes in iGLUT and (i, k) iGABA neurons, significant genes defined by FDR correction. Vertical dashed lines represent DE genes ±1.5 Log2FC. Horizontal dashed lines represent FDR = 0.1 cutoff (lower) and Bonferroni corrected cutoff (upper). (c, e) Sunburst plots of all FDR corrected DEGs with SynGO annotated synapse function for iGLUT and (j, l) iGABA neurons. (f, m) Overlap of DEGs and (g,n) gene set enrichment analysis (GSEA) between genotypes. (h,o) Distinct gene expression patterns by hierarchal clustering of all patient specific DEGs. Sample information correspond to Fig. 1.
Extended Data Fig. 2 Transcriptomic profile of NRXN1 RNA-binding proteins.
(a) Gene expression fold-change of select NRXN1 predicted RNA-binding proteins (RBP) across patients and control iGLUT neurons with statistical comparisons for STAR-Family RBPs, (n/d: Control = 6/2; 5′-Del = 6/2; 3′-Del = 6/2 | 1 batch), compared via two-tailed 1-way ANOVA, with Dunnett’s test (Sam68 F2, 15 = 15.36, 5′-Del p = 0.0001, 3′-Del p = 0.0181; Slm1 F2, 15 = 15.29, 5′-Del p = 0.0002, 3′-Del p = 0.0014; Slm2 F2, 15 = 20.22, 5′-Del p = 5.34E-05, 3′-Del p = 0.0003. (b) Gene expression fold-change of select NRXN1 predicted RNA-binding proteins (RBP) across patients and control iGABA neurons with statistical comparisons for STAR-Family RBPs, (n/d: Control = 5/2; 5′-Del = 6/2; 3′-Del = 6/2 | 1 batch), compared via two-tailed 1-way ANOVA, with Dunnett’s test (Sam68 F2, 14 = 1.449, 5′-Del p = 0.8826, 3′-Del p = 0.2172; Slm1 F2, 14 = 6.421, 5′-Del p = 0.0109, 3′-Del p = 0.0158; Slm2 F2, 14 = 21.06, 5′-Del p = 5.53E-05, 3′-Del p = 0.0002. n reported as samples/donors | independent batches.
Extended Data Fig. 3 Extended transcriptomics analysis on disease risk associated genes.
(a) Summary table of overlapping DEGs with risk enrichments across publicly curated datasets for autism (ASD), bipolar disorder (BD) and schizophrenia. (b, e) Enrichment of genes across neuropsychiatric disorders for iGLUT and iGABA neurons. (c) Interaction maps of risk genes for 5′-Del iGLUT, (d) 3′-Del iGLUT, (f) 5′-Del iGABA and (g) 5′-iGABA. Sample information correspond to Fig. 1.
Extended Data Fig. 4 Extended data on human organoid generation and characterization.
(a) Timeline of dorsal organogenesis for hCOs and (b,c) representative images of hiPSC aggregation and immature spheroids post dislodging and normalized organoid perimeters over time, relative to averaged control (n/d: Control = 136/2; 5′-Del = 128/2; 3′-Del = 150/2 | 2 batches) (d) hCO RT-qPCR results from 4-month organoids of genes for pluripotency, neuronal, and cell-type specific markers (n/d: Control = 4/2; 5′-Del = 4/2; 3′-Del = 4/2 | 2 batches), compared via two-tailed 1-way ANOVA, with Dunnett’s test (SOX2 F2, 9 = 3.921, 5′-Del p = 0.065, 3′-Del p = 0.9981; MAP2AB F2, 9 = 0.7201, 5′-Del p = 0.5479, 3′-Del p = 0.4688; vGLUT1 F2, 9 = 1.594, 5′-Del p = 0.6353, 3′-Del p = 0.6331; vGLUT2 F2, 9 = 1.710, 5′-Del p = 0.3992, 3′-Del p = 0.7901). (e,f) hCO RT-qPCR results of NRXN1 WT and MT expression (n/d: Control = 4/2; 5′-Del = 4/2; 3′-Del = 4/2 | Representative batch) compared via two-tailed 1-way ANOVA, with Dunnett’s test (NRXN1-WT F2, 9 = 0.5873, 5′-Del p = 0.9256, 3′-Del p = 0.6961). (g) Timeline of ventral organogenesis for hSOs and (h,i) representative images of hiPSC aggregation and immature spheroids post dislodging and normalized organoid perimeters over time, relative to averaged control (n/d: Control = 118/2; 5′-Del = 92/2; 3′-Del = 120/2 | 2 batches). (j) hSO RT-qPCR results from 4-month organoids of genes for pluripotency, neuronal, and cell-type specific markers (n/d: Control = 4/2; 5′-Del = 4/2; 3′-Del = 4/2 | 2 batches), compared via 1-way ANOVA, with Dunnett’s test (SOX2 F2, 9 = 5.683, 5′-Del p = 0.1563, 3′-Del p = 0.0152; MAP2AB F2, 9 = 4.800, 5′-Del p = 0.0875, 3′-Del p = 0.0285; GAD65 F2, 9 = 1.641, 5′-Del p 0.3573, 3′-Del p = 0.8806; DLX5 F2, 9 = 0.01755, 5′-Del p = 0.981, 3′-Del p = 0.9809). (k,l) hSO RT-qPCR results of NRXN1 WT and MT expression (n/d: Control = 4/2; 5′-Del = 4/2; 3′-Del = 4/2 | Representative batch) compared via 1-way ANOVA, with Dunnett’s test (NRXN1-WT F2, 9 = 4.818, 5′-Del p = 0.7447, 3′-Del p = 0.0824). Data represented as mean ± sem. n reported as samples/donors | independent batches.
Extended Data Fig. 5 Single cell characterization of NRXN1+/− organoids.
(a, f), UMAPs of hCO and hSO organoid samples sequenced at 6 months, annotated by cell clusters. (b, g) Extended gene expression panel across sub-clusters of hCO and hSO samples across neuronal, cortical, subpallial and astroglia markers. (c, h) Validation of regionalization across forebrain (FOXG1), dorsal (EMX1) and ventral (DLX2) regions, with NRXN1 expression across all cells. (d, i) Relative proportions of cell clusters across pooled and individual genotypes, (hCO = 47,460 cells) and (hSO = 35,563 cells). (e, j) Gene ontological analysis results using DEGs from scRNASeq. *P < 0.05, **P < 0.01, ***P <0.001, two-tailed Wilcoxon’s rank sum test, FDR = 0.05.
Extended Data Fig. 6 Extended data on electrophysiological properties of 5′-Del and 3′-Del neurons.
(a) Voltage-gated potassium and channel kinetics across genotypes for iGLUT neurons (n/d = Control = 16/2; 5′-Del = 13/2; 3′-Del = 16/2 | 2 batches). (b) Comparative GLUT mEPSC kinetics of IEI (n/d: Control = 22/2; 5′-Del = 18/2; 3′-Del = 7/2 | 4 batches) compared via two-tailed 1-way ANOVA, with Dunnett’s test (F2,44 = 2.880; 5′-Del p = 0.0411, 3′-Del p = 0.8229, and (c) amplitude size (n/d: Control = 24/2; 5′-Del = 22/2; 3′-Del = 10/2 | 4 batches) compared via two-tailed 1-way ANOVA, with Dunnett’s test (F2,53 = 0.1996; 5′-Del p = 0.7806, 3′-Del p = 0.9972). (d) Voltage-gated potassium and channel kinetics across genotypes for iGABA neurons (n/d = Control = 11/2; 5′-Del = 12/2; 3′-Del = 11/2 | 2 batches). (e) Comparative iGABA mIPSC kinetics of IEI (n/d: Control = 10/2; 5′-Del = 9/2; 3′-Del = 8/2 | 3 batches) compared via two-tailed 1-way ANOVA, with Dunnett’s test (F2,24 = 0.9632; 5′-Del p = 0.6452, 3′-Del p = 0.7565, and (f) amplitude size (n/d: Control = 13/2; 5′-Del = 10/2; 3′-Del = 9/2 | 3 batches) compared via two-tailed 1-way ANOVA, with Dunnett’s test (F2,29 = 0.5097; 5′-Del p = 0.5141, 3′-Del p = 0.8618). Data represented as mean ± sem. n reported as samples/donors | independent batches.
Extended Data Fig. 7 Extended KCC2 related data from immature GABA neurons.
(a) Transcriptomic comparison of SLC12A5 expression across DIV14 and DIV35 RNASeq timepoints, compared via a two-tailed mixed effects model-time (F1,28 = 499.1 p <2.2E-16), sample information correspond Fig. 1. (b,c) MEA tests from pre- and post- treatment of 10uM GABAzine (n/d = 28/2 | 1 representative batch) compared via two-tailed paired student’s t-test for time-linked comparison (t = 9.739, df = 27; p = 2.497E-10) and two-tailed unpaired student’s t-test for pre/post activity foldchange, (t = 2.380, df = 54; p = 0.0209). Data represented as mean ± sem. n reported as samples/donors | independent batches.
Extended Data Fig. 8 shRNA knockdown validation.
(a) Extent of shRNA knockdown on WT and in iGLUT neurons (n: shNT = 5/2; shWT = 6/2 | Representative) compared via two-tailed t-test (t = 4.186, df = 9, p = 0.0024), and (b) MT NRXN1 (n/d: shNT = 8/2; shMT = 7/2 | Representative) compared via two-tailed t-test (t = 9.342, df = 13, p = 4.0E-07). (c, d) iGLUT RNASeq DEG plots of WT (n/d: shNT = 5/2; shWT = 5/2 | Representative) and MT (n/d: shNT = 4/2; shMT = 5/2 | Representative) knockdowns performed in isogenic analyses. (e) iGABA RT-qPCR of shRNA knockdown on WT NRXN1 (n/d: shNT = 9/3; shWT = 8/3 | Representative) compared via two-tailed t-test (t = 5.294, df = 15, p = 9.00E-05), and (f) MT NRXN1 (n/d: shNT = 9/2; shMT = 10/2 | Representative) compared via two-tailed t-test (t = 2.011, df = 17, p = 0.0604). (g,h) iGABA RNASeq DEG plots of WT (n/d: shNT = 3/1; shWT = 3/1) and MT (n/d: shNT = 2/1; shMT = 2/1 | 2-4 wells pooled across 2 batches) knockdowns performed in isogenic analyses. Data represented as mean ± sem. n reported as samples/donors | independent batches.
Extended Data Fig. 9 ChIP-seq enrichment of ER1 binding at NRXN1 loci in rodent brain.
(a) Female and (b) male mus musculus ChIP tracts of NRXN1 locus, with red dashed areas highlighting binding enrichment across vehicle and estradiol treated mice. (c) Effect of β-estradiol on control donors (n = 16/4 | Representative, (n/d: Control-Vehicle = 8/4; Control-β-estradiol = 8/4 | Representative) compared via two-sided t-test (t = 1.270, df = 14, p = 0.2248).
Extended Data Fig. 10 In-vivo validation of MT isoform expression from an unrelated autism NRXN1+/− patient.
(a) Schematic of novel NRXN1 autism patient, and GOF therapeutic targeting pipeline, with (b) schematic of the NRXN1α isoform structures, with each row representing a unique NRXN1α isoform and each column representing a NRXN1 exon. The colored isoforms (navy, wildtype; peach, patient-specific) are spliced into the transcript while the blank exons are spliced out. The schematic in panel a was created using BioRender. Fernando, M. (2025) https://BioRender.com/f78d262. (c) The abundance of each NRXN1α isoform by sample.
Supplementary information
Supplementary Note
Generation and characterization of NRXN1+/− sub-regional organoids
Supplementary Table 1
Clinical information of all hiPSC and post-mortem donors and detailed summary of hiPSC lines used per experiment. Adapted from ref. 15, Springer Nature.
Supplementary Table 2
Leafcutter results of differential splicing at neurexin loci
Supplementary Table 3
Leafcutter results of genome-wide differential splicing
Supplementary Table 4
SynapseGO results of shRNA experiments
Supplementary Table 5
Standard Gene Ontology and SynapseGO of ASO experiment
Supplementary Table 6
Details on oligonucleotide sequences and antibodies used throughout the study
Supplementary Table 7
Concise summary of all statistical information presented throughout the study
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Fernando, M.B., Fan, Y., Zhang, Y. et al. Phenotypic complexities of rare heterozygous neurexin-1 deletions. Nature 642, 710–720 (2025). https://doi.org/10.1038/s41586-025-08864-9
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DOI: https://doi.org/10.1038/s41586-025-08864-9
