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 well represent 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 a greater than twofold reduction in half of the wild-type NRXN1α isoforms and express dozens of novel isoforms 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.
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To facilitate improved sharing between stem cell laboratories, all hiPSCs have already been deposited at the Rutgers University Cell and DNA Repository (study 160; http://www.nimhstemcells.org/) and all sequencing data have been deposited to GEO (GSE137101, whole short-read RNA-seq and scRNA-seq; and GSE137127, targeted short-read RNA-seq) or SRA (PRJNA563972, Iso-seq).
To facilitate improved reproducibility of our data analyses, all code has been deposited at https://github.com/zhushijia/STAR2bSMRT.
Ching, M. S. L. et al. Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders. Am. J. Med. Genet. B 153, 937–947 (2010).
Marshall, C. R. et al. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat. Genet. 49, 27–35 (2017).
Matsunami, N. et al. Identification of rare recurrent copy number variants in high-risk autism families and their prevalence in a large ASD population. PLoS ONE 8, e52239 (2013).
Moller, R. S. et al. Exon-disrupting deletions of NRXN1 in idiopathic generalized epilepsy. Epilepsia 54, 256–264 (2013).
Lowther, C. et al. Molecular characterization of NRXN1 deletions from 19,263 clinical microarray cases identifies exons important for neurodevelopmental disease expression. Genet. Med. 19, 53–61 (2017).
Etherton, M. R., Blaiss, C. A., Powell, C. M. & Südhof, T. C. Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc. Natl Acad. Sci. USA 106, 17998–18003 (2009).
Grayton, H. M., Missler, M., Collier, D. A. & Fernandes, C. Altered social behaviours in Neurexin 1α knockout mice resemble core symptoms in neurodevelopmental disorders. PLoS ONE 8, e67114 (2013).
Missler, M. et al. Alpha-neurexins couple Ca2+ channels to synaptic vesicle exocytosis. Nature 423, 939–948 (2003).
Pak, C. et al. Human neuropsychiatric disease modeling using conditional deletion reveals synaptic transmission defects caused by heterozygous mutations in NRXN1. Cell Stem Cell 17, 316–328 (2015).
Jenkins, A. K. et al. Neurexin 1 (NRXN1) splice isoform expression during human neocortical development and aging. Mol. Psychiatry 21, 701–706 (2016).
Harkin, L. F. et al. Neurexins 1–3 each have a distinct pattern of expression in the early developing human cerebral cortex. Cereb. Cortex 27, 1–17 (2016).
Treutlein, B., Gokce, O., Quake, S. R. & Südhof, T. C. Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. Proc. Natl Acad. Sci. USA 111, E1291–E1299 (2014).
Schreiner, D. et al. Targeted combinatorial alternative splicing generates brain region-specific repertoires of neurexins. Neuron 84, 386–398 (2014).
Nguyen, T.-M. et al. An alternative splicing switch shapes neurexin repertoires in principal neurons versus interneurons in the mouse hippocampus. eLife 5, e22757 (2016).
Fuccillo, M. V. et al. Single-cell mRNA profiling reveals cell-type-specific expression of neurexin isoforms. Neuron 87, 326–340 (2015).
Traunmuller, L., Gomez, A. M., Nguyen, T.-M. & Scheiffele, P. Control of neuronal synapse specification by a highly dedicated alternative splicing program. Science 352, 982–986 (2016).
Au, K. F., Underwood, J. G., Lee, L. & Wong, W. H. Improving PacBio long read accuracy by short read alignment. PLoS ONE 7, 1–8 (2012).
Au, K. F. et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc. Natl Acad. Sci. USA 110, E4821–E4830 (2013).
Ahn, K., An, S. S., Shugart, Y. Y. & Rapoport, J. L. Common polygenic variation and risk for childhood-onset schizophrenia. Mol. Psychiatry 21, 94–96 (2016).
Ahn, K. et al. High rate of disease-related copy number variations in childhood onset schizophrenia. Mol. Psychiatry 19, 568–572 (2014).
Sudhof, T. C. Synaptic neurexin complexes: a molecular code for the logic of neural circuits. Cell 171, 745–769 (2017).
Brennand, K. J. et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature 479, 556–556 (2011).
Brennand, K. et al. Phenotypic differences in hiPSC NPCs derived from patients with schizophrenia. Mol. Psychiatry 20, 361–368 (2014).
Hoffman, G. E. et al. Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains. Nat. Commun. 8, 2225 (2017).
Topol, A., Tran, N. N. & Brennand, K. J. A guide to generating and using hiPSC derived NPCs for the study of neurological diseases. J. Vis. Exp. e52495 (2015).
Ho, S., Topol, A. & Brennand, K. J. From ‘directed differentiation’ to ‘neuronal induction’: modeling neuropsychiatric disease. Biomark. Insights 10, 31 (2015).
Yang, N. et al. Generation of pure GABAergic neurons by transcription factor programming. Nat. Methods 14, 621–628 (2017).
Ballouz, S. & Gillis, J. Strength of functional signature correlates with effect size in autism. Genome Med. 9, 64 (2017).
Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).
Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Steijger, T. et al. Assessment of transcript reconstruction methods for RNA-seq. Nat. Methods 10, 1177–1184 (2013).
Zhang, Y. et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 78, 785–798 (2013).
Ho, S. M. et al. Rapid Ngn2-induction of excitatory neurons from hiPSC-derived neural progenitor cells. Methods 101, 113–124 (2016).
Aoto, J., Martinelli, D. C., Malenka, R. C., Tabuchi, K. & Südhof, T. C. Presynaptic neurexin-3 alternative splicing trans-synaptically controls postsynaptic AMPA receptor trafficking. Cell 154, 75–88 (2013).
Aoto, J., Földy, C., Ilcus, S. M. C., Tabuchi, K. & Südhof, T. C. Distinct circuit-dependent functions of presynaptic neurexin-3 at GABAergic and glutamatergic synapses. Nat. Neurosci. 18, 997–1007 (2015).
Graf, E. R., Zhang, X., Jin, S.-X. X., Linhoff, M. W. & Craig, A. M. Neurexins induce differentiation of GABA and glutamate postsynaptic specializations via neuroligins. Cell 119, 1013–1026 (2004).
Uemura, T. et al. Trans-synaptic interaction of GluRdelta2 and Neurexin through Cbln1 mediates synapse formation in the cerebellum. Cell 141, 1068–1079 (2010).
Pettem, K. L. et al. The specific alpha-neurexin interactor calsyntenin-3 promotes excitatory and inhibitory synapse development. Neuron 80, 113–128 (2013).
Linhoff, M. W. et al. An unbiased expression screen for synaptogenic proteins identifies the LRRTM protein family as synaptic organizers. Neuron 61, 734–749 (2009).
Scheiffele, P., Fan, J., Choih, J., Fetter, R. & Serafini, T. Neuroligin expressed in nonneuronal cells triggers presynaptic development in contacting axons. Cell 101, 657–669 (2000).
Anderson, G. R. et al. beta-neurexins control neural circuits by regulating synaptic endocannabinoid signaling. Cell 162, 593–606 (2015).
Quinn, D. P. et al. Pan-neurexin perturbation results in compromised synapse stability and a reduction in readily releasable synaptic vesicle pool size. Sci. Rep. 7, 42920 (2017).
Mountoufaris, G., Canzio, D., Nwakeze, C. L., Chen, W. V. & Maniatis, T. Writing, reading, and translating the clustered protocadherin cell surface recognition code for neural circuit assembly. Annu. Rev. Cell Dev. Biol. 34, 471–493 (2018).
Ding, X. et al. Activity-induced histone modifications govern Neurexin-1 mRNA splicing and memory preservation. Nat. Neurosci. 20, 690–699 (2017).
Rozic, G., Lupowitz, Z., Piontkewitz, Y. & Zisapel, N. Dynamic changes in neurexins’ alternative splicing: Role of rho-associated protein kinases and relevance to memory formation. PLoS ONE 6, e18579 (2011).
Iijima, T. et al. SAM68 regulates neuronal activity-dependent alternative splicing of neurexin-1. Cell 147, 1601–1614 (2011).
Boucard, A. A., Chubykin, A. A., Comoletti, D., Taylor, P. & Sudhof, T. C. A splice code for trans-synaptic cell adhesion mediated by binding of neuroligin 1 to alpha- and beta-neurexins. Neuron 48, 229–236 (2005).
Chih, B., Gollan, L. & Scheiffele, P. Alternative splicing controls selective trans-synaptic interactions of the neuroligin–neurexin complex. Neuron 51, 171–178 (2006).
Ko, J., Fuccillo, M. V., Malenka, R. C. & Südhof, T. C. LRRTM2 functions as a neurexin ligand in promoting excitatory synapse formation. Neuron 64, 791–798 (2009).
Siddiqui, T. J., Pancaroglu, R., Kang, Y., Rooyakkers, A. & Craig, A. M. LRRTMs and neuroligins bind neurexins with a differential code to cooperate in glutamate synapse development. J. Neurosci. 30, 7495–7506 (2010).
Boucard, Aa, Ko, J. & Südhof, T. C. High affinity neurexin binding to cell adhesion G-protein-coupled receptor CIRL1/latrophilin-1 produces an intercellular adhesion complex. J. Biol. Chem. 287, 9399–9413 (2012).
Germain, P. L. & Testa, G. Taming human genetic variability: transcriptomic meta-analysis guides the experimental design and interpretation of iPSC-based disease modeling. Stem Cell Rep. 8, 1784–1796 (2017).
Zhao, D. et al. MicroRNA profiling of neurons generated using induced pluripotent stem cells derived from patients with schizophrenia and schizoaffective disorder, and 22q11.2 Del. PLoS ONE 10, e0132387 (2015).
Marchetto, M. C. N. et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 143, 527–539 (2010).
Shcheglovitov, A. et al. SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients. Nature 503, 267–271 (2013).
Krey, J. F. et al. Timothy syndrome is associated with activity-dependent dendritic retraction in rodent and human neurons. Nat. Neurosci. 16, 201–209 (2013).
Wen, Z. et al. Synaptic dysregulation in a human iPS cell model of mental disorders. Nature 515, 414–418 (2014).
Deshpande, A. et al. Cellular phenotypes in human iPSC-derived neurons from a genetic model of autism spectrum disorder. Cell Rep. 21, 2678–2687 (2017).
Yeh, E. et al. Patient-derived iPSCs show premature neural differentiation and neuron type-specific phenotypes relevant to neurodevelopment. Mol. Psychiatry 23, 1687–1698 (2018).
Chen, S. X., Tari, P. K., She, K. & Haas, K. Neurexin–neuroligin cell adhesion complexes contribute to synaptotropic dendritogenesis via growth stabilization mechanisms in vivo. Neuron 67, 967–983 (2010).
Dudanova, I. et al. Deletion of alpha-neurexins does not cause a major impairment of axonal pathfinding or synapse formation. J. Comp. Neurol. 502, 261–274 (2007).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Roussos, P. et al. A role for noncoding variation in schizophrenia. Cell Rep. 9, 1417–1429 (2014).
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, 1–19 (2015).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Chen, L. & Zheng, S. Studying alternative splicing regulatory networks through partial correlation analysis. Genome Biol. 10, R3 (2009).
Zhu, S., Wang, G., Liu, B. & Wang, Y. Modeling exon expression using histone modifications. PLoS ONE 8, e67448 (2013).
Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).
Dorsett, C. R. et al. Traumatic brain injury induces alterations in cortical glutamate uptake without a reduction in glutamate transporter-1 protein expression. J. Neurotrauma 34, 220–234 (2017).
Mcguire, J. L. et al. Abnormalities of signal transduction networks in chronic schizophrenia. NPJ Schizophr. 3, 30 (2017).
Appuhamy, J. A. et al. Effects of AMP-activated protein kinase (AMPK) signaling and essential amino acids on mammalian target of rapamycin (mTOR) signaling and protein synthesis rates in mammary cells. J. Dairy Sci. 97, 419–429 (2013).
K.J.B. is a New York Stem Cell Foundation—Robertson Investigator. This work was partially supported by National Institutes of Health (NIH) grants R01 MH121074 (K.J.B. and G.F.), R01 MH101454 (K.J.B.), R01 MH106056 (K.J.B.), R01 MH107487 (R.M.) and F31 MH112285 (E.F.), a Brain and Behavior Research Foundation Independent Investigator Grant (K.J.B.), a Brain Research Foundation Seed Grant (K.J.B.) and the New York Stem Cell Foundation (K.J.B.). We thank the Neuroscience and Stem Cell cores at Icahn School of Medicine at Mount Sinai. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. J. Simon drew the original illustrations used in the schematics shown in Figs. 3a and 6a,d,e. We acknowledge the Mount Sinai Neuropathology Research Core and Brain bank (J. Crary) for providing the human post-mortem brain tissue.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Validation of deletions in hiPSC cohort and whole transcriptome RNA-seq analysis.
a, Schematic showing the structure of the NRXN1 gene and location of 5’-((blue) and 3’-deletions (red). b,c, Mean and s.e. of Taqman CNV assay confirming 3’-CNV in fibroblasts from one 3’-NRXN1+/- case (b) and 5’-CNV in fibroblasts from both 5’-NRXN1+/- cases (c); two replicates per sample per probe. d, PCR of cDNA across exons encompassed by the 3’-deletion in controls and 3’-NRXN1+/- hiPSC-NPCs, two independent validations. e, Mean and s.e. of the log2FoldChange by qPCR across the novel junction (exon 20-24) created by 3’-deletion across 2 controls and 2 3’-NRXN1+/- hiPSC-NPCs compared using a two-sided t-test. f, Sanger sequencing result from a TOPO cloned NRXN1α isoform from 3’-NRXN1+/- hiPSC-neurons (n = 1) confirming presence of novel exon junction (exon 20-24). g, Confirmation of the sex of each sample. h, PCA plot of combined hiPSC-NPC (19 samples, 8 donors) and hiPSC-neuron (18 samples, 8 donors) RNA-seq dataset showing separation by cell type on PC1. i,j, Volcano plot showing differentially expressed genes within hiPSC-NPCs (i) and hiPSC-neurons (j) individually. k, FPKM of NRXN1 expression across fetal PFC (1), adult dlPFC (3) and control hiPSC-neurons (3). l, Circle plot showing hierarchical clustering of samples by cell type and by donor. m,n Pearson correlation of gene expression within and between donors in hiPSC-NPCs (19 samples, 8 donors) (m) and hiPSC-neurons (18 samples, 8 donors) (n) by one-sided Wilcoxon test. o, Gene set enrichments for genes correlated with NRXN1 spicing in the Common Mind Consortium dlPFC dataset.
a, Heatmap showing log2FPKM values of sub-type marker genes in hiPSC-neurons across genotypes. b, Cell type composition scores obtained using Cibersort4 in hiPSC-neurons (9 controls, 4 donors; 9 cases, 4 donors), by diagnosis. c, Images of GABA immunostaining overlaid with MAP2. d, Mean percent of GABA+ cells from control (8 images, 2 coverslips), 5’-NRXN1+/- (8 images, 2 coverslips) and 3’-NRXN1+/- (11 images, 3 coverslips). Error bars are s.e. e, Representative images of SYN1 immunostaining alone and overlaid with MAP2 immunostaining and DAPI. f, Mean intensity of SYN1+ puncta normalized to MAP2 intensity (3 images per coverslip, 8 coverslips per donor, 2 donors per genotype). Error bars are s.e.
a, Schematic of the sample preparation for the hybrid sequencing approach. b, Schematic of the computational pipeline developed for the hybrid sequencing approach. c, UCSC genome browser view of whole transcriptome Iso-seq data across the NRXN1 locus from five hiPSC-neuron samples. d,e, Representative bioanalyzer traces from Iso-seq library prep passing QC (d) compared to library prep failed QC (e). f, Targeted short-read sequencing counts per million for 3’-NRXN1+/- specific junction site showing 3’-NRXN1+/- hiPSC-neurons passing QC in green and failing QC in red. g,h, Pearson’s correlation of junction site expression from targeted long read vs. targeted short read data showing one of the samples passing QC (g) and one sample failing QC (h).
Extended Data Fig. 4 Comparison of long and short read data for quantification and threshold testing.
a, Correlation of NRXN1α junction expression from long read and short read sequencing across control (control 1 n = 39, control 2 n = 37) and 3’-NRXN1+/- (3’-NRXN1+/- 1 n = 36, 3’-NRXN1+/- 2 n = 45) hiPSC-neuron samples. Red triangles represent canonical junctions while black represent non-canonical. b, Correlation of NRXN1α isoform expression from long read quantification and short read quantification across control (Control 1 n = 90, Control 2 n = 88) and 3’-NRXN1+/- (3’-NRXN1+/- 1 n = 89, 3’-NRXN1+/- 2 n = 96) hiPSC-neuron samples. Colored triangles represent in-frame isoforms, predicted to be translated (red), untranslated (black) and TOPO cloned (green). c, Correlation of mouse and human NRXN1α isoform expression and corresponding Venn diagrams for the number of isoforms across expression thresholds (≥2 n = 112, ≥3 n = 88, ≥4 n = 75, ≥5 n = 63, ≥6 n = 60, ≥7 n = 57, ≥8 n = 54, ≥9 n = 52, ≥10 n = 50).
a,b, PCA of isogenic samples differentiated into three hiPSC-neuronal cell types colored by cell type (ASCL1/DLX2 (5), hiPSC-neuron (6), and NGN2 (6) (a) or by NRXN1 genotype (control (8), case (9) from 3 donors each) (b). c, Representative confocal images from 2 independent differentiations of control and NRXN1+/- ASCL1/DLX2-GABAergic hiPSC-neurons. d, Expression of glutamatergic, GABAergic and pan-neuronal marker genes across NRXN1+/- (9) and control (8) NGN2-glutamatergic and ASCL1/DLX2-GABAergic hiPSC-neurons. Boxplot shows median and IQR. e, Sum of all NRXN1 transcripts expressed across control (2 donors), 3’-NRXN1+/- (2 donors) and 5’-NRXN1+/- (2 donors) in NGN2-glutamatergic and ASCL1/DLX2-GABAergic hiPSC-neurons. f,g, NRXN1α (f) and NRXN1β (g) isoform usage expressed across control, 3’-NRXN1+/- and 5’-NRXN1+/- donors in NGN2-glutamatergic and ASCL1/DLX2-GABAergic hiPSC-neurons. Boxplot displays median and range with P < 0.01 indicated by “**” from two way ANOVA with Holm-Sidak’s test.
Extended Data Fig. 6 Examination of NRXN1α canonical splice sites and total NRXN1 isoform expression.
a, Pearson’s correlation of NRXN1α isoform expression across control and 3’-NRXN1+/- hiPSC-neurons (n = 99 isoforms, r computed by t-statistics). b,c, Bar plot of the total read count for each NRXN1α exon along with the fraction that each NRXN1α junction is included in control hiPSC-neurons (b) and 3’-NRXN1+/- hiPSC-neurons. Red circle indicates the novel junction created by the 3’-NRXN1+/- deletion (c). d, Schematic of the experimental design to test activity induced regulation at NRXN1α canonical splice sites. e, Fold change of canonical splice site exclusion in controls (gray) and a 3’-NRXN1+/- hiPSC-neuron (red) plus KCl compared to PBS control (dotted line). f, Bar plot showing fold change of SS4 in KCl treated control and 3’-NRXN1+/- hiPSC-neurons (compared to PBS). g, Schematic of the experimental design to test developmental regulation at NRXN1α canonical splice sites. h, Fold change of canonical splice sites in control hiPSC-neurons at 2-weeks (light gray), 4-weeks (gray) and 6-weeks (dark gray) post-differentiation compared to NPCs (dotted line). i, Specific examination of developmental exclusion of SS4. Error bars are s.e. j-m, Expression of levels of all NRXN1 isoforms (j), NRXN1α (k), NRXN1β (l), NRXN1γ (m) across NRXN1 genotypes (8 control, 3 donors; 5 3’-NRXN1+/-, 2 donors; 5 5’-NRXN1+/-, 2 donors) in hiPSC-neurons. Violin plot displays density and range with P < 0.05 indicated by “*” from Wilcoxon Signed Rank Test. n, Pearson’s correlation of all NRXN1 isoforms (18 samples, 6 donors) with NRXN1α, NRXN1β and NRXN1γ (r values calculated using t-statistics).
a, Violin plot displaying density of the expression of NRXN and multiple synaptic marker genes in single cells across control (2 donors) and NRXN1+/- (3 donors) hiPSC-neurons. b, Violin plot displaying density and range of the expression of multiple synaptic genes identified as marker genes in immature neuronal clusters from scRNA-seq data across control (2 donors) and NRXN1+/- (3 donors) hiPSC-neurons.
Extended Data Fig. 8 Investigation of hiPSC-neuron morphology, cellular signaling, and NRXN1 overexpression.
a, Strategy to label individual hiPSC-neurons. b-g, Mean neurite number across genotypes (control 71 neurons, 2 donors; 3’-NRXN1+/-- 72 neurons, 2 donors; 5’-NRXN1+/- 50 neurons 2 donors) “****” indicates P < 0.00001 and “***”indicates P < 0.0001 by one-way ANOVA with Holm-Sidak’s test; (b) or by coverslip (2 donors, 12 coverslips, 3 regions each) (c,d) mean neurite length across genotypes (control 71 neurons, 2 donors; 3’-NRXN1+/- 72 neurons, 2 donors; 5’-NRXN1+/- 50 neurons, 2 donors); (e) or by coverslip (2 donors, 12 coverslips, 3 regions each) (f,g). Two donors per genotype indicated by different shading within each plot. h, Differentially active kinases (3’- NRXN1+/- hiPSC-neurons: 6 samples, 2 donors; controls: 5 samples, 2 donors). i, Volcano plot of –log10(P-value) and log2(FoldChange) from linear model of RNA-seq (3’-NRXN1+/- hiPSC-neurons: 5 samples, 2 donors; controls 6 samples, 2 donors); DE kinase associated genes labeled. j, Violin plot of median and quartiles of RPKM for kinase hits with largest fold-change in RNA-seq (3’-NRXN1+/- hiPSC-neurons: 5 samples, 2 donors; controls: 6 samples, 2 donors). k, Differentially active kinases in (5’- NRXN1+/- hiPSC-neurons: 3 samples, 1 donors; controls 5 samples, 2 donors). l, Volcano plot of –log10(P-value) and log2(FoldChange) from linear model of RNA-seq (5’-NRXN1+/- hiPSC-neurons: 3 samples, 1 donors; controls 6 samples, 2 donors); DE kinase associated genes labeled. m, Violin plot of median and quartiles of RPKM for kinase hits with largest fold change values in RNA-seq (5’-NRXN1+/- hiPSC-neurons: 3 samples, 1 donors; controls 6 samples, 2 donors). n, Isoform constructs for overexpression with log2(FoldChange) from hybrid sequencing dataset. o, Mean fold-change from qPCR of NRXN1 expression (3 replicates per condition. p, Representative western blot (2 replicates) for anti-FLAG (48hr expression of control-enriched NRXN1α-FLAG). q, Representative western blot (2 replicates) for anti-FLAG (48hr expression of 3’-NRXN1+/- specific NRXN1α-FLAG). All error bars are s.e.
a, Superimposed image of the top ten most abundant wildtype NRXN1α isoforms in hiPSC-neurons. b, Superimposed predicted protein model of the top ten most abundant mutant isoforms. c, Superimposed predicted protein model of the top ten most abundant mutant isoforms compared to the most abundant wildtype isoform (grey). d, Individual predicted protein models of the top ten most abundant wild type isoforms. e, Individual predicted protein models of the top ten most abundant mutant isoforms. Insets in each panel highlight C-terminal region of NRXN1α isoforms where 3’-NRXN1+/- deletion is located.
a,b, Bar plot of the total read count for each NRXN1α exon along with the fraction that each NRXN1α junction is included in adult dlPFC samples (a) and fetal PFC samples (b); pink boxes represent potential developmentally regulated exons. c, Schematic of NRXN1α isoform structure, with each row representing a unique NRXN1α isoform and each column representing a NRXN1 exon. Colored exons (blue, fetal PFC specific; green, adult dlPFC specific; orange, shared) are spliced into the transcript while blank exons are spliced out. Abundance of each NRXN1α isoform across fetal PFC and adult dlPFC samples.
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Flaherty, E., Zhu, S., Barretto, N. et al. Neuronal impact of patient-specific aberrant NRXN1α splicing. Nat Genet 51, 1679–1690 (2019). https://doi.org/10.1038/s41588-019-0539-z
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