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Neuronal defects in a human cellular model of 22q11.2 deletion syndrome

Abstract

22q11.2 deletion syndrome (22q11DS) is a highly penetrant and common genetic cause of neuropsychiatric disease. Here we generated induced pluripotent stem cells from 15 individuals with 22q11DS and 15 control individuals and differentiated them into three-dimensional (3D) cerebral cortical organoids. Transcriptional profiling across 100 days showed high reliability of differentiation and revealed changes in neuronal excitability-related genes. Using electrophysiology and live imaging, we identified defects in spontaneous neuronal activity and calcium signaling in both organoid- and 2D-derived cortical neurons. The calcium deficit was related to resting membrane potential changes that led to abnormal inactivation of voltage-gated calcium channels. Heterozygous loss of DGCR8 recapitulated the excitability and calcium phenotypes and its overexpression rescued these defects. Moreover, the 22q11DS calcium abnormality could also be restored by application of antipsychotics. Taken together, our study illustrates how stem cell derived models can be used to uncover and rescue cellular phenotypes associated with genetic forms of neuropsychiatric disease.

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Fig. 1: Cortical differentiation and transcriptomic changes in 22q11DS.
Fig. 2: Defects in neuronal excitability and calcium signaling in 22q11DS neurons.
Fig. 3: Calcium channel function and RMP in 22q11DS neurons
Fig. 4: Heterozygous loss of DGCR8 recapitulates functional phenotypes of 22q11DS neurons.
Fig. 5: Genetic rescue of phenotypes in 22q11DS neurons.
Fig. 6: Pharmacological modulation of phenotypes in 22q11DS neurons.

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Data availability

Gene expression data are available in the Gene Expression Omnibus (GEO) under accession nos. GSE142041 and GSE145122. The data that support the findings of this study are available on request from the corresponding author.

References

  1. Sullivan, P. F., Daly, M. J. & O’Donovan, M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat. Rev. Genet. 13, 537–551 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Sanders, S. J. et al. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat. Med. 25, 1477–1487 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Zinkstok, J. R. et al. Neurobiological perspective of 22q11.2 deletion syndrome. Lancet Psychiatry 6, 951–960 (2019).

    PubMed  PubMed Central  Google Scholar 

  4. Murphy, K. C., Jones, L. A. & Owen, M. J. High rates of schizophrenia in adults with velo-cardio-facial syndrome. Arch. Gen. Psychiatry 56, 940–945 (1999).

    CAS  PubMed  Google Scholar 

  5. Monks, S. et al. Further evidence for high rates of schizophrenia in 22q11.2 deletion syndrome. Schizophr. Res. 153, 231–236 (2014).

    PubMed  Google Scholar 

  6. Schneider, M. et al. Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome. Am. J. Psychiatry 171, 627–639 (2014).

    PubMed  PubMed Central  Google Scholar 

  7. Vorstman, J. A. et al. The 22q11.2 deletion in children: high rate of autistic disorders and early onset of psychotic symptoms. J. Am. Acad. Child Adolesc. Psychiatry 45, 1104–1113 (2006).

    PubMed  Google Scholar 

  8. Olsen, L. et al. Prevalence of rearrangements in the 22q11.2 region and population-based risk of neuropsychiatric and developmental disorders in a Danish population: a case-cohort study. Lancet Psychiatry 5, 573–580 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. Drew, L. J. et al. The 22q11.2 microdeletion: fifteen years of insights into the genetic and neural complexity of psychiatric disorders. Int. J. Dev. Neurosci. 29, 259–281 (2011).

    CAS  PubMed  Google Scholar 

  10. Karayiorgou, M., Simon, T. J. & Gogos, J. A. 22q11.2 microdeletions: linking DNA structural variation to brain dysfunction and schizophrenia. Nat. Rev. Neurosci. 11, 402–416 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Chun, S. et al. Specific disruption of thalamic inputs to the auditory cortex in schizophrenia models. Science 344, 1178–1182 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Sigurdsson, T., Stark, K. L., Karayiorgou, M., Gogos, J. A. & Gordon, J. A. Impaired hippocampal-prefrontal synchrony in a genetic mouse model of schizophrenia. Nature 464, 763–767 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Fernandez, A. et al. Mitochondrial dysfunction leads to cortical under-connectivity and cognitive impairment. Neuron 102, 1127–1142 e1123 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Demaerel, W. et al. The 22q11 low copy repeats are characterized by unprecedented size and structural variability. Genome Res. 29, 1389–1401 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Pasca, S. P. The rise of three-dimensional human brain cultures. Nature 553, 437–445 (2018).

    CAS  PubMed  Google Scholar 

  16. 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).

    PubMed  PubMed Central  Google Scholar 

  17. Toyoshima, M. et al. Analysis of induced pluripotent stem cells carrying 22q11.2 deletion. Transl. Psychiatry 6, e934 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Lin, M. et al. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC Syst. Biol. 10, 105 (2016).

    PubMed  PubMed Central  Google Scholar 

  19. Pasca, A. M. et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat. Methods 12, 671–678 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Sloan, S. A., Andersen, J., Pasca, A. M., Birey, F. & Pasca, S. P. Generation and assembly of human brain region-specific three-dimensional cultures. Nat. Protoc. 13, 2062–2085 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Sloan, S. A. et al. Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron 95, 779–790 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Yoon, S. J. et al. Reliability of human cortical organoid generation. Nat. Methods 16, 75–78 (2019).

    CAS  PubMed  Google Scholar 

  23. Pașca, S. P. et al. Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat. Med. 17, 1657–1662 (2011).

    PubMed  PubMed Central  Google Scholar 

  24. Sun, Y. et al. A deleterious Nav1.1 mutation selectively impairs telencephalic inhibitory neurons derived from Dravet syndrome patients. eLife 5, e13073 (2016).

    PubMed  PubMed Central  Google Scholar 

  25. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).

    PubMed Central  Google Scholar 

  26. Moskvina, V. et al. Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk. Mol. Psychiatry 14, 252–260 (2009).

    CAS  PubMed  Google Scholar 

  27. Yasumoto, F. et al. Dopamine receptor 2 regulates L-type voltage-gated calcium channel in primary cultured mouse midbrain neural network. Cell. Mol. Neurobiol. 24, 877–882 (2004).

    CAS  PubMed  Google Scholar 

  28. Morrow, B. E., McDonald-McGinn, D. M., Emanuel, B. S., Vermeesch, J. R. & Scambler, P. J. Molecular genetics of 22q11.2 deletion syndrome. Am. J. Med. Genet. A 176, 2070–2081 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Sun, Z., Williams, D. J., Xu, B. & Gogos, J. A. Altered function and maturation of primary cortical neurons from a 22q11.2 deletion mouse model of schizophrenia. Transl. Psychiatry 8, 85 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Han, J. et al. The Drosha–DGCR8 complex in primary microRNA processing. Genes Dev. 18, 3016–3027 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Gokhale, A. et al. Systems analysis of the 22q11.2 microdeletion syndrome converges on a mitochondrial interactome necessary for synapse function and behavior. J. Neurosci. 39, 3561–3581 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    CAS  PubMed  Google Scholar 

  35. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Moore, A. R. et al. Electrical excitability of early neurons in the human cerebral cortex during the second trimester of gestation. Cereb. Cortex 19, 1795–1805 (2009).

    PubMed  Google Scholar 

  39. Drew, L. J. et al. Evidence for altered hippocampal function in a mouse model of the human 22q11.2 microdeletion. Mol. Cell Neurosci. 47, 293–305 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Zamponi, G. W., Striessnig, J., Koschak, A. & Dolphin, A. C. The physiology, pathology and pharmacology of voltage-gated calcium channels and their future therapeutic potential. Pharm. Rev. 67, 821–870 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Wheeler, D. G. et al. CaV1 and CaV2 channels engage distinct modes of Ca2+ signaling to control CREB-dependent gene expression. Cell 149, 1112–1124 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Schofield, C. M. et al. Monoallelic deletion of the microRNA biogenesis gene Dgcr8 produces deficits in the development of excitatory synaptic transmission in the prefrontal cortex. Neural Dev. 6, 11 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gritz, S. M. & Radcliffe, R. A. Genetic effects of ATP1A2 in familial hemiplegic migraine type II and animal models. Hum. Genomics 7, 8 (2013).

    PubMed  PubMed Central  Google Scholar 

  44. McQuate, A., Latorre-Esteves, E. & Barria, A. A Wnt/calcium signaling cascade regulates neuronal excitability and trafficking of NMDARs. Cell Rep. 21, 60–69 (2017).

    CAS  PubMed  Google Scholar 

  45. Bassett, A. S. & Chow, E. W. Schizophrenia and 22q11.2 deletion syndrome. Curr. Psychiatry Rep. 10, 148–157 (2008).

    PubMed  PubMed Central  Google Scholar 

  46. Seeman, P., Lee, T., Chau-Wong, M. & Wong, K. Antipsychotic drug doses and neuroleptic/dopamine receptors. Nature 261, 717–719 (1976).

    CAS  PubMed  Google Scholar 

  47. Kapur, S., Zipursky, R., Jones, C., Remington, G. & Houle, S. Relationship between dopamine D2 occupancy, clinical response and side effects: a double-blind PET study of first-episode schizophrenia. Am. J. Psychiatry 157, 514–520 (2000).

    CAS  PubMed  Google Scholar 

  48. Hernandez-Lopez, S. et al. D2 dopamine receptors in striatal medium spiny neurons reduce L-type Ca2+ currents and excitability via a novel PLCβ1-IP3-calcineurin-signaling cascade. J. Neurosci. 20, 8987–8995 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Gee, S. et al. Synaptic activity unmasks dopamine D2 receptor modulation of a specific class of layer V pyramidal neurons in prefrontal cortex. J. Neurosci. 32, 4959–4971 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Santana, N., Mengod, G. & Artigas, F. Quantitative analysis of the expression of dopamine D1 and D2 receptors in pyramidal and GABAergic neurons of the rat prefrontal cortex. Cereb. Cortex. 19, 849–860 (2009).

    PubMed  Google Scholar 

  51. Perez, M. F., White, F. J. & Hu, X. T. Dopamine D2 receptor modulation of K+ channel activity regulates excitability of nucleus accumbens neurons at different membrane potentials. J. Neurophysiol. 96, 2217–2228 (2006).

    CAS  PubMed  Google Scholar 

  52. Gardner, D. M., Baldessarini, R. J. & Waraich, P. Modern antipsychotic drugs: a critical overview. CMAJ 172, 1703–1711 (2005).

    PubMed  PubMed Central  Google Scholar 

  53. Kobrynski, L. J. & Sullivan, K. E. Velocardiofacial syndrome, DiGeorge syndrome: the chromosome 22q11.2 deletion syndromes. Lancet 370, 1443–1452 (2007).

    CAS  PubMed  Google Scholar 

  54. Chun, S., Bayazitov, I. T., Blundon, J. A. & Zakharenko, S. S. Thalamocortical long-term potentiation becomes gated after the early critical period in the auditory cortex. J. Neurosci. 33, 7345–7357 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Eaton, C. B. et al. Epilepsy and seizures in young people with 22q11.2 deletion syndrome: prevalence and links with other neurodevelopmental disorders. Epilepsia 60, 818–829 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Yu, N., Tucker, K. R., Levitan, E. S., Shepard, P. D. & Canavier, C. C. Implications of cellular models of dopamine neurons for schizophrenia. Prog. Mol. Biol. Transl. Sci. 123, 53–82 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Anwar, I. J., Miyata, K. & Zsombok, A. Brain stem as a target site for the metabolic side effects of olanzapine. J. Neurophysiol. 115, 1389–1398 (2016).

    PubMed  Google Scholar 

  58. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    PubMed Central  Google Scholar 

  59. Akam, E. & Strange, P. G. Inverse agonist properties of atypical antipsychotic drugs. Biochem. Pharmacol. 67, 2039–2045 (2004).

    CAS  PubMed  Google Scholar 

  60. Li, P., Snyder, G. L. & Vanover, K. E. Dopamine targeting drugs for the treatment of Schizophrenia: past, present and future. Curr. Top. Med. Chem. 16, 3385–3403 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Yazawa, M. et al. Using induced pluripotent stem cells to investigate cardiac phenotypes in Timothy syndrome. Nature 471, 230–234 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Barreto-Chang, O. L. & Dolmetsch, R. E. Calcium imaging of cortical neurons using Fura-2 AM. J. Vis. Exp. 9, 1067 (2009).

    Google Scholar 

  64. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  65. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Oldham, M. C., Langfelder, P. & Horvath, S. Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst. Biol. 6, 63 (2012).

    PubMed  PubMed Central  Google Scholar 

  67. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    PubMed  Google Scholar 

  68. McKenna, A. et al. The genome analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Hansen, K. D., Irizarry, R. A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204–216 (2012).

    PubMed  PubMed Central  Google Scholar 

  71. Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv 060012 https://www.biorxiv.org/content/10.1101/060012v2 (2019).

  72. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Wu, H., Wang, C. & Wu, Z. PROPER: comprehensive power evaluation for differential expression using RNA-seq. Bioinformatics 31, 233–241 (2015).

    CAS  PubMed  Google Scholar 

  74. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  75. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the families who participated in this study. We acknowledge J. Andersen, J. Y. Park and all members of the Pasca Laboratory for support, as well as A. Trevino, A. E. Urban and H. Gai at Stanford University for scientific discussions and technical support. This work was supported by the US National Institutes of Health (NIH) BRAINS Award (MH107800), the NARSAD Young Investigator Award (Behavioral and Brain Foundation), the MQ Fellow Award, the NYSCF Robertson Stem Cell Investigator Award, the CZI Ben Barres Investigator Award, the Stanford Human Brain Organogenesis Program and the Brain Rejuvenation Project, Uytengsu Research Funds, the Kwan Research Fund and the California Institute of Regenerative Medicine (CIRM) (to S.P.P.); NIH R01MH100900 (J.F.H., C.E.B., R.O., J.A.B., D.H.G. and S.P.P.) and R01MH100900-S1 (J.F.H. and S.P.P.); NIMH R01 MH085953 and R01 MH085953-08:S1 (CEB); NIMH 5R37 MH060233 and 5R01 MH094714 (D.H.G.); National Research Foundation of Korea grant no. NRF-2019R1A2C3002354 (to C.H.K); the Lucile Packard Foundation for Children’s Health (anonymous gift for 22q11DS research; M.H.P.); the National Institute of Mental Health (J.L.R.); Stanford Graduate Fellowship (SGF) and National Science Foundation (NSF) Fellowship (T.A.K.); the Stanford Dean’s Fellowship, the Feldman Gift Fund and the Maternal and Child Health Research Institute (MCHRI) Fellowship (O.R.); the Autism Science Foundation (ASF) and the Brain and Behavior Research Foundation (BBRF) Young Investigator award (A.G.); the HHMI Fellowship (A.K.K.); the Stanford Bio-X Undergraduate Fellowship (J.M.S.); the DGIST R&D Program of the Korean Ministry of Science and ICT & Future Planning, 14-BD-16 (C.-H.K.).

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Authors

Contributions

T.A.K., O.R. and S.P.P. designed experiments. T.A.K. and S.P.P. wrote the manuscript with input from all authors. T.A.K. and S.-J.Y. performed the differentiation experiments and characterization of hCSs. S.-J.Y., M.Y. and S.P.P. generated, validated and differentiated hiPS cell lines. T.A.K., O.R. and S.P.P. conducted and analyzed the calcium imaging experiments. O.R., M.-Y.L., C.G., Y.S. and J.R.H. conducted and analyzed electrophysiological experiments. A.K.K., C.H.K., S.P.P. and R.E.D. performed and characterized 2D cortical neural cultures and established assays. A.G., Y.T. and D.H.G. analyzed the bulk RNA sequencing. T.A.K., J.M.S. and N.D.A. carried out single-cell RNA-seq experiments and analysis. K.I and M.H.P. generated the DGCR8+/− hiPS cell line. N.S. and S.N. performed the HPLC experiments. J.L.R., J.A.B., R.O., C.E.B., J.F.H. and L.K. recruited and collected the cohort of subjects. S.P.P. supervised all aspects of the work.

Corresponding author

Correspondence to Sergiu P. Paşca.

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Competing interests

Stanford University was granted a patent that covers the generation of region-specific brain organoids (US patent 15/158,408). Y.S., C.G. and R.E.D. were employees of Novartis Institute of Biomedical Institutes for part of the duration of this study.

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Extended data

Extended Data Fig. 1 Characterization of hiPS cell lines derived from control and 22q11DS subjects.

a, Immunocytochemistry for pluripotency markers with antibodies against OCT4, NANOG, SSEA–3, TRA–1–60, TRA–2–49. Images from one experiment. Selected hiPS cell lines are shown, black indicates control lines and red indicates 22q11DS lines. Scale bar= 200 μm. b, Expression of endogenous and silencing of exogenous pluripotency genes (qPCRs) in Sendai, episomal or retroviral based reprogramming into hiPS cells. Tf FB or FB–SeV indicate fibroblasts transduced with the reprogramming vectors. Mean ± SEM. (c) Example of genome–wide SNP microarray genotyping of a control and a 22q11DS hiPS cell line. For further details on deletion coordinates for all 22q11DS subjects, see Supplementary Table 4.

Extended Data Fig. 2 RNA–sequencing analysis of hCS in 22q11DS.

a, Number of differentiations into hCS from each hiPS cell lines; controls (black, n = 26) and 22q11DS (red, n = 17). Height of the bar shows the number of independent differentiation experiments. b, Spearman’s correlation coefficients (mean ~ 0.97) between differentiations of the same cell line (within individuals) or between different hiPS cell lines (between individuals) (two–sided Wilcoxon–Mann–Whitney test, P < 0.05, controls, n = 104 samples from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n = 71 samples derived from 15 patient hiPS cell lines derived from 12 subjects). c, No significant interaction between the split batch (see Methods) and presence of a 22q11.2 deletion in DEGs. Values are –log of the uncorrected P values (controls, n = 25 samples from the split batch and n = 79 from the other batches from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n = 33 samples from the split batch and n = 38 from the other batches from 15 patient hiPS cell lines derived from 12 subjects). d, Violin plots of the percent of variance explained by each of the covariates on the x–axis for each gene. e, Association of the expression principal components (PC) with biological and technical covariates. f, Overlap of genes in the 22q11.2 deletion locus that are expressed in vivo (BrainSpan dataset, red) and in hCS (green). g, Log2 fold change in 22q11DS neurons of genes in the 22q11.2 locus (red) compared to genes adjacent to the 22q11.2 deletion locus (black). h, Power calculation based on the samples in the first batch of sequencing. The sample size in this study (20 differentiations) leads to a power of 0.85 to detect a log2 fold change of 0.25 (~18% change in expression). i, Overlap of DEG in 22q11DS hCS and DEG reported in Lin et al., 2016 (left) (using P < 0.05 across all datasets). Right, correlation between log fold–changes overlapping DE (Spearman’s R= 0.91, P = 1.11e–30). For (b, d, e, f): Controls, n = 104 samples from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n =71 samples derived from 15 patient hiPS cell lines derived from 12 subjects. For (b, c, e): Center shows median, box shows 25th to 75th percentile, lower whisker shows smallest observation great than or equal to lower hinger –1.5x interquartile range, upper whisker shows largest observation less than or equal to upper hinger +1.5x interquartile range.

Extended Data Fig. 3 Single cell transcriptional profiling of hCS derived from 22q11DS and control hiPS cell lines.

a, Droplet–based single–cell transcriptomics (10x Genomics Chromium) in four hiPS cell lines (n = 41,555 cells from two control hiPS cell lines derived from two subjects, and two patient hiPS cell lines derived from two subjects). b, Control and 22q11DS samples shown by line (two control lines and two 22q11DS lines). c, Gene expression, shown by the presence of clusters neuronal clusters, of the aggregated data of two control lines and two 22q11DS lines. d, Pearson’s correlation of all genes across all four lines (two control lines and two 22q11DS lines, R2> 0.96). e, Gene expression across group and cluster (two control lines and two 22q11DS lines). f, Proportion of cells in each cluster across lines (two control lines and two 22q11DS lines). g, Expression across all lines (two control lines and two 22q11DS lines). h, Expression across cell clusters in single cell gene expression of DEG identified in the bulk RNA–seq (at day 75).

Extended Data Fig. 4 Temporal trajectory of gene expression (RNA–seq) for selected cortical markers and mitochondrial genes in intact hCS.

a, Selected cortical marker trajectories in 22q11DS (red) and control lines (black) in whole hCS. Controls, n = 26 (day 25 hCS), n = 25 (day 50 hCS), n = 28 (day 75 hCS), and n = 25 (day 100 hCS) samples from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n = 14 (day 25 hCS), n = 16 (day 50 hCS), n = 24 (day 75 hCS), and n = 17 (day 100 hCS) samples derived from 15 patient hiPS cell lines derived from 12 subjects. b, Selected mitochondria genes trajectories in 22q11DS (red) and control lines (black) in hCS. Asterisks indicate differential expression at the specified timepoints between control and 22q11DS lines (FDR< 0.05). Controls, n = 26 (day 25 hCS), n = 25 (day 50 hCS), n = 28 (day 75 hCS), and n = 25 (day 100 hCS) samples from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n = 14 (day 25 hCS), n = 16 (day 50 hCS), n = 24 (day 75 hCS), and n =17 (day 100 hCS) samples derived from 15 patient hiPS cell lines derived from 12 subjects.

Extended Data Fig. 5 Electrophysiological characterization of hCS–derived neurons in 22q11DS.

a, Immunostaining of hCS–plated neuronal cultures with anti–CTIP2 and anti–MAP2 antibodies. Representative images from over 3 independent experiments with similar results. Scale bar = 50 μm. b, Proportion of cells with spontaneous action potentials (controls, n = 44 cells derived from 6 control hiPS cell lines derived from 6 subjects; 22q11DS, n = 60 cells derived from 5 patient hiPS cell lines derived from 4 subjects; chi–square test for proportion of cells, χ2=11.52, ***P = 0.0006). c, Comparison of action potential properties (controls, n = 46 cells derived from 6 control hiPS cell lines; 22q11DS, n = 66 cells derived from 4 patient hiPS cell lines): amplitude (two–tailed t–test, P = 0.70), threshold (two–tailed t–test, P = 0.97), rheobase (two–tailed Mann–Whitney test, P = 0.22), maximal rate of rise (two–tailed t–test, P = 0.29) and minimal rate of decay (two–tailed Mann–Whitney test, P = 0.43). Center shows median, box shows SEM and whisker represent 10th to 90th percentile. d, Representative traces from dissociated hCS neurons showing spontaneous action potentials (cell–attached mode) from control and 22q11DS neurons (control, n = 3 cells from 3 hiPS cell lines; 22q11DS, n = 3 cells from 3 hiPS cell lines).

Extended Data Fig. 6 Monolayer cortical differentiation and functional characterization of 22q11DS neurons.

a, Scheme illustrating the 2D–based approach for the differentiation of hiPS cells into cortical neural progenitor cells (NPCs) and cortical neurons. b, Immunostaining of neuronal cultures with anti–VGLUT1 and anti–MAP2 antibodies. Scale bar = 10μm. Representative images from one experiment. c, d, Changes in Fura–2 ratio in response to depolarization with 67 mM KCl (control, n = 136 cells from 6 lines derived from 4 subjects; 22q11DS, n = 94 cells from 5 lines derived from 5 subjects) and (d) quantification of Fura–2 ratio amplitudes shown by group and by hiPS cell (****P < 0.0001). Mean ratio ± SEM for (c). Mean peak amplitude ± SEM (d). e, Co–cultured 22q11DS and controls neurons with fluorescent labeling with AAV–DJ–hSyn1::mCherry or AAV–DJ–hSyn1::YFP. Images from over one experiment. f, Changes in Fura–2 ratio in response to depolarization with 67 mM KCl in co–cultured neurons (two–tailed Mann–Whitney for peak amplitude, P < 0.0001). Mean ratio ± SEM. Scale bar = 10μm g, RMP comparison (control, n = 10 neurons from one control line derived from 1 subject; 22q11DS, n = 12 neurons from 2 lines derived from 2 subjects. Mann–Whitney test, P = 0.0005). Mean ± SEM. h, Voltage dependent barium currents in 2D-derived neurons. Superimposed current traces before and during an activating step to 10 mV. i, Barium currents amplitude (left) and charge (right) (controls, n = 30 cells derived from 5 control hiPS cell lines derived from 5 subjects; 22q11DS, n = 17 cells derived from 4 patient hiPS cell lines derived from 4 subjects. Two–tailed Mann–Whitney, P = 0.23 for amplitude; two–tailed Mann–Whitney, P = 0.28 for charge). Center shows median, box shows SEM and whisker represent 10th to 90th percentile. j, Voltage dependent calcium currents in 2D-derived neurons. Superimposed current traces before and during an activating step to 10 mV. k, Calcium currents amplitude (left) and charge (right) (controls, n = 11 cells derived from 2 control hiPS cell lines derived from 2 subjects; 22q11DS, n = 6 cells derived from one patient hiPS cell lines derived from one subject. Two–tailed Mann–Whitney, P = 0.17 for amplitude; two–tailed Mann–Whitney, *P = 0.04 for charge). Center shows median, box shows SEM and whisker represent 10th to 90th percentile. l, The effect of 10 mM KCl pretreatment on Fura–2 calcium response to 67 mM KCl application in 2D derived control and 22q11DS neurons (control, n = 11 cells from one control hiPS cell line; control + 10mM, n = 8 from one control hiPS cell line). Mean ratio ± SEM. m, Quantification of the effect of 10 mM KCl pretreatment on the Fura–2 ratio amplitude from (l) (two–tailed Mann–Whitney for peak amplitude, ****P < 0.0001.) Mean peak amplitude ± SEM.

Extended Data Fig. 7 Functional calcium signaling characterization of hCS–derived neurons in 22q11DS.

a, The effect of nimodipine (10 μM) on the calcium (Fura–2) response to 67 mM KCl application in control and 22q11DS neurons (control, n = 68 cells from 2 lines derived from 2 subjects; 22q11DS, n = 192 cells from 2 lines derived from 2 subjects; control + nimodipine, n= 69 cells from 2 lines derived from 2 subjects; 22q11DS+nimodipine, n = 121 cells from 2 lines derived from 2 subjects). Mean ratio ± SEM. b, Quantification of the effect of nimodipine on the Fura–2 ratio amplitude (Fura–2 ratio decreased by ~64% in control cortical and by ~57% in 22q11DS neurons; two-sided Kruskal–Wallis test, P < 0.0001; followed by Dunn’s multiple comparison test; **P = 0.001, ****P < 0.0001). Mean peak amplitude ± SEM. c, d, The effect of TTX (1 μM) on the Fura–2 calcium response following 67 mM KCl application in control and 22q11DS neurons (control: n = 126 cells from 3 hiPS cell line derived from 3 subjects; 22q11DS: n = 230 from 3 control hiPS cell line derived from 3 subjects; control + TTX: n = 158 cells from 3 hiPS cell line derived from 3 subjects; 22q11DS + TTX: n = 107 cells derived from 3 hiPS cell line derived from 3 subjects). (Right) Quantification of the effect of TTX on the Fura–2 ratio amplitude (two–way ANOVA for TTX exposure, F1,8= 6.50, *P = 0.03; Tukey’s multiple comparison test: **P = 0.004 for 22q11DS versus control, *P = 0.04 for 22q11DS + TTX versus control + TTX). Mean ratio ± SEM for (c). Mean peak amplitude ± SEM for (d). e, f, The effect of APV (20 μM) and NBQX (20 μM) on the Fura–2 calcium response following 67 mM KCl application in control and 22q11DS neurons (control, n = 120 cells from 3 hiPS cell line derived from 3 subjects; 22q11DS, n = 167 from 3 control hiPS cell line derived from 3 subjects; control+NBQX+APV, n= 167 cells from 3 hiPS cell line derived from 3 subjects; 22q11DS + NBQX + APV, n = 146 cells derived from 3 hiPS cell line derived from 3 subjects). Peak amplitude ± SEM. (Right) Quantification of the effect of APV and NBQX on the Fura–2 ratio amplitude (two–way ANOVA for NBQX + APV exposure, F1,8= 0.08, P = 0.77). Mean ratio ± SEM for (e). Mean peak amplitude ± SEM for (f). g, G/Gmax activation curve for calcium channels mediated currents, using 2 mM external barium as the main charge carrier (control, n = 8 cells from two hiPS cell lines; 22q11DS, n = 7 from two hiPS cell lines). Mean ± SEM. h, i, Effect of 10 mM KCl pretreatment on the Fura–2 calcium response after 67 mM KCl application in dissociated hCS control and 22q11DS neurons. (Right) Quantification of the effect of 10 mM KCl pretreatment on the Fura–2 ratio amplitude (control, n = 44 cells from one control hiPS cell line; control + 10mM, n = 77 from one control hiPS cell line derived; two–tailed Mann–Whitney for peak amplitude, ****P < 0.0001). Mean ± SEM. Mean ratio ± SEM for (h). Mean peak amplitude ± SEM for (i).

Extended Data Fig. 8 HPLC analysis of catecholamines, glutamate and GABA after depolarization of cortical neurons derived from control and 22q11DS patients.

a, and c, example chromatograms with the peaks for injected standard solutions of norepinephrine (NE), dopamine (DA), glutamate (GLUT), and GABA, and example of chromatograms in neuronal samples at day 43 of differentiation following depolarization with 67 mM KCl. b, and d, quantification of the levels of these neurotransmitters in control and 22q11DS media samples (results are normalized to the total number of cells). Two-sided t–test, P = 0.72 for NE, P = 0.39 for GLUT, DA and GABA not detectable (control: 4 hiPS cell lines derived from 3 subjects; 22q11DS, 5 hiPS cell lines derived from 5 subjects). Mean ± SEM.

Extended Data Fig. 9 Generation and characterization of a DGCR8+/– hiPS cell line.

a, Scheme illustrating the CRISPR/Cas9 editing design of DGCR8 heterozygous knockout (DGCR8+/–). b, Example images showing DGCR8 overexpression with the lentivirus–EF1α–DGCR8::YFP. Scale bar = 10 μm. Images from two experiments with similar results. c, Data from Fig. 5d shown by hiPS cell line. DGCR8 overexpression in control and 22q11DS dissociated hCS neurons (control, n = 59 cells from 3 hiPS cell lines derived from 3 subjects; 22q11DS, n = 118 cells from 2 hiPS cell lines derived from 2 subjects; 22q11DS + Lenti–DGCR8, n = 64 cells from two 22q11DS hiPS cell lines derived from 2 subjects). Mean peak amplitude ± SEM. d, Relative gene expression (normalized to GAPDH) as determined by RT-qPCR (day 75 of in vitro differentiation) in DGCR8+/– (n = 7 independent differentiation experiments of one DGCR8+/− hiPS cell line) versus isogenic control (n = 7 independent differentiation experiments of one parental hiPS cell line) of selected differentially expressed genes of 22q11DS RNA–seq from Supplementary Table 5: ATP1A2 (two–tailed Wilcoxon test, *P= 0.02), WNT5A (two–tailed Wilcoxon test, **P= 0.01), and ZNF132 (two–tailed Wilcoxon test, *P= 0.02), CACNB4 (two–tailed Wilcoxon test, P= 0.94), KCNJ11 (two–tailed Wilcoxon test, P= 0.94), CALY (two–tailed Wilcoxon test, P= 0.22), CALCB (two–tailed Wilcoxon test, P= 0.22), PCP4 (two–tailed Wilcoxon test, P= 0.08). Mean ± SEM.

Extended Data Fig. 10 Pharmacological rescue with sulpiride and olanzapine.

a, Trajectory of DRD2 gene expression (RNA–seq) in 22q11DS (red) and control lines (black) in hCS (controls, n = 26 (day 25 hCS), n = 25 (day 50 hCS), n = 28 (day 75 hCS), and n = 25 (day 100 hCS) samples from 14 control hiPS cell lines derived from 11 subjects; 22q11DS, n = 14 (day 25 hCS), n = 16 (day 50), n = 24 (day 75), and n = 17 (day 100) samples derived from 15 patient hiPS cell lines derived from 12 subjects). b, Immunostaining of hiPS cell–derived cortical neurons with anti–MAP2 and anti–DRD2 antibodies. Scale bar = 20 μm. Image from one experiment. c, Changes in Fura–2 ratio in response to 67 mM KCl in the presence or absence of 10 μM sulpiride. Mean ± SEM. d, Quantification of the peak Fura–2 ratio amplitude (control, n = 133 cells from 3 hiPS cell lines derived from 3 subjects; 22q11DS, n = 215 cells from 3 hiPS cell lines derived from 3 subjects; control + Sulpiride, n = 166 cells from 3 control hiPS cell lines derived from 3 subjects; 22q11DS + Sulpiride, n = 253 cells from 3 hiPS cell lines derived from 3 subjects; two–way ANOVA for sulpiride exposure, F1,8= 12.56, **P = 0.008; Tukey’s multiple comparison test: P = 0.95 for 22q11DS + Sulpiride versus control, P = 0.08 for 22q11DS versus 22q11DS + Sulpiride; P = 0.20 for control versus control + Sulpiride; *P = 0.04 for 22q11DS versus control). Mean peak amplitude ± SEM. e, The effect of sulpiride on the membrane potential of control and 22q11DS neurons (n = 12 control neurons, one–way repeated measures ANOVA, F2, 22= 14.35, ****P < 0.0001; followed by Tukey’s multiple comparisons test: **P = 0.001 for pre–sulpiride versus sulpiride, ****P < 0.0001 for sulpiride versus post-wash, P = 0.56 for pre–sulpiride versus post–wash; n = 13 22q11DS neurons, one–way repeated measures ANOVA, F2, 22= 7.84, *P = 0.002; Tukey’s multiple comparisons test: *P = 0.01 for pre–sulpiride versus sulpiride, **P = 0.003 for sulpiride versus post–wash and P = 0.72 for pre–sulpiride versus post–wash). Center shows median, box shows SEM and whisker represent 10th to 90th percentile. f, The effect of olanzapine on the RMP of 22q11DS neurons (n = 10 neurons derived from one 22q11DS hiPS cell line, one–way repeated measures ANOVA, F2,18= 6.3, **P = 0.008; followed by Tukey’s multiple comparisons test: *P = 0.05 for pre–olanzapine versus olanzapine, **P = 0.008 for olanzapine versus post–wash, P = 0.69 for pre–olanzapine versus post–wash). Center shows median, box shows SEM and whisker represent 10th to 90th percentile.

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Supplementary Tables 5–7

Supplementary Tables 5–7.

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Khan, T.A., Revah, O., Gordon, A. et al. Neuronal defects in a human cellular model of 22q11.2 deletion syndrome. Nat Med 26, 1888–1898 (2020). https://doi.org/10.1038/s41591-020-1043-9

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