Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A human neurodevelopmental model for Williams syndrome

Abstract

Williams syndrome is a genetic neurodevelopmental disorder characterized by an uncommon hypersociability and a mosaic of retained and compromised linguistic and cognitive abilities. Nearly all clinically diagnosed individuals with Williams syndrome lack precisely the same set of genes, with breakpoints in chromosome band 7q11.23 (refs 1, 2, 3, 4, 5). The contribution of specific genes to the neuroanatomical and functional alterations, leading to behavioural pathologies in humans, remains largely unexplored. Here we investigate neural progenitor cells and cortical neurons derived from Williams syndrome and typically developing induced pluripotent stem cells. Neural progenitor cells in Williams syndrome have an increased doubling time and apoptosis compared with typically developing neural progenitor cells. Using an individual with atypical Williams syndrome6,7, we narrowed this cellular phenotype to a single gene candidate, frizzled 9 (FZD9). At the neuronal stage, layer V/VI cortical neurons derived from Williams syndrome were characterized by longer total dendrites, increased numbers of spines and synapses, aberrant calcium oscillation and altered network connectivity. Morphometric alterations observed in neurons from Williams syndrome were validated after Golgi staining of post-mortem layer V/VI cortical neurons. This model of human induced pluripotent stem cells8 fills the current knowledge gap in the cellular biology of Williams syndrome and could lead to further insights into the molecular mechanism underlying the disorder and the human social brain.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Characterization of participating individuals and iPSC differentiation.
Figure 2: Defect in apoptosis of WS-derived NPCs owing to haploinsufficiency of FZD9.
Figure 3: Altered morphology of WS-derived cortical neurons and network activity.
Figure 4: Neuroanatomical and morphological alterations in WS human brains.

References

  1. Korenberg, J. R. et al. VI. Genome structure and cognitive map of Williams syndrome. J. Cogn. Neurosci. 12 (Suppl. 1), 89–107 (2000)

    PubMed  Google Scholar 

  2. Meyer-Lindenberg, A. et al. Neural basis of genetically determined visuospatial construction deficit in Williams syndrome. Neuron 43, 623–631 (2004)

    CAS  PubMed  Google Scholar 

  3. Bellugi, U., Lichtenberger, L., Mills, D., Galaburda, A. & Korenberg, J. R. Bridging cognition, the brain and molecular genetics: evidence from Williams syndrome. Trends Neurosci . 22, 197–207 (1999)

    CAS  PubMed  Google Scholar 

  4. Bellugi, U., Lichtenberger, L., Jones, W., Lai, Z. & St George, M. I. The neurocognitive profile of Williams syndrome: a complex pattern of strengths and weaknesses. J. Cogn. Neurosci. 12 (Suppl. 1), 7–29 (2000)

    PubMed  Google Scholar 

  5. Doyle, T. F., Bellugi, U., Korenberg, J. R. & Graham, J. “Everybody in the world is my friend” hypersociability in young children with Williams syndrome. Am. J. Med. Genet. A 124, 263–273 (2004)

    Google Scholar 

  6. Dai, L. et al. Is it Williams syndrome? GTF2IRD1 implicated in visual-spatial construction and GTF2I in sociability revealed by high resolution arrays. Am. J. Med. Genet. A 149A, 302–314 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Edelmann, L. et al. An atypical deletion of the Williams–Beuren syndrome interval implicates genes associated with defective visuospatial processing and autism. J. Med. Genet. 44, 136–143 (2007)

    CAS  PubMed  Google Scholar 

  8. Chailangkarn, T., Acab, A. & Muotri, A. R. Modeling neurodevelopmental disorders using human neurons. Curr. Opin. Neurobiol. 22, 785–790 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ewart, A. K. et al. Hemizygosity at the elastin locus in a developmental disorder, Williams syndrome. Nature Genet . 5, 11–16 (1993)

    CAS  PubMed  Google Scholar 

  10. Järvinen-Pasley, A. et al. Defining the social phenotype in Williams syndrome: a model for linking gene, the brain, and behavior. Dev. Psychopathol. 20, 1–35 (2008)

    PubMed  PubMed Central  Google Scholar 

  11. Marchetto, M. C. et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 143, 527–539 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Beltrão-Braga, P. C. B. et al. Feeder-free derivation of induced pluripotent stem cells from human immature dental pulp stem cells. Cell Transplant . 20, 1707–1719 (2011)

    PubMed  Google Scholar 

  13. Adamo, A. et al. 7q11.23 dosage-dependent dysregulation in human pluripotent stem cells affects transcriptional programs in disease-relevant lineages. Nature Genet . 47, 132–141 (2015)

    CAS  PubMed  Google Scholar 

  14. Van Raay, T. J. et al. frizzled 9 is expressed in neural precursor cells in the developing neural tube. Dev. Genes Evol. 211, 453–457 (2001)

    CAS  PubMed  Google Scholar 

  15. Zhao, C. et al. Hippocampal and visuospatial learning defects in mice with a deletion of frizzled 9, a gene in the Williams syndrome deletion interval. Development 132, 2917–2927 (2005)

    CAS  PubMed  Google Scholar 

  16. Fujimoto, T., Tomizawa, M. & Yokosuka, O. SiRNA of frizzled-9 suppresses proliferation and motility of hepatoma cells. Int. J. Oncol. 35, 861–866 (2009)

    CAS  PubMed  Google Scholar 

  17. Lian, X. et al. Efficient differentiation of human pluripotent stem cells to endothelial progenitors via small-molecule activation of WNT signaling. Stem Cell Rep . 3, 804–816 (2014)

    CAS  Google Scholar 

  18. Jho, E. H. et al. Wnt/beta-catenin/Tcf signaling induces the transcription of Axin2, a negative regulator of the signaling pathway. Mol. Cell. Biol. 22, 1172–1183 (2002)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Fujimura, N. et al. Wnt-mediated down-regulation of Sp1 target genes by a transcriptional repressor Sp5. J. Biol. Chem. 282, 1225–1237 (2007)

    CAS  PubMed  Google Scholar 

  20. Srinivasan, K. et al. A network of genetic repression and derepression specifies projection fates in the developing neocortex. Proc. Natl Acad. Sci. USA 109, 19071–19078 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen, B. et al. The Fezf2–Ctip2 genetic pathway regulates the fate choice of subcortical projection neurons in the developing cerebral cortex. Proc. Natl Acad. Sci. USA 105, 11382–11387 (2008)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Leone, D. P., Srinivasan, K., Chen, B., Alcamo, E. & McConnell, S. K. The determination of projection neuron identity in the developing cerebral cortex. Curr. Opin. Neurobiol. 18, 28–35 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hutsler, J. J. & Zhang, H. Increased dendritic spine densities on cortical projection neurons in autism spectrum disorders. Brain Res . 1309, 83–94 (2010)

    CAS  PubMed  Google Scholar 

  24. Spitzer, N. C., Root, C. M. & Borodinsky, L. N. Orchestrating neuronal differentiation: patterns of Ca2+ spikes specify transmitter choice. Trends Neurosci . 27, 415–421 (2004)

    CAS  PubMed  Google Scholar 

  25. Chiang, M. C. et al. 3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry. Neuroimage 36, 1096–1109 (2007)

    PubMed  Google Scholar 

  26. Lawless, J. F. & Fredette, M. Frequentist prediction intervals and predictive distributions. Biometrika 92, 529–542 (2005)

    MathSciNet  MATH  Google Scholar 

  27. Chen, J. et al. Transcriptome comparison of human neurons generated using induced pluripotent stem cells derived from dental pulp and skin fibroblasts. PLoS ONE 8, e75682 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Marinho, P. A., Chailangkarn, T. & Muotri, A. R. Systematic optimization of human pluripotent stem cells media using Design of Experiments. Sci. Rep. 5, 9834 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004)

    CAS  PubMed  Google Scholar 

  30. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔC T method. Methods 25, 402–408 ( 2001)

    CAS  Article  PubMed  Google Scholar 

  31. Marchetto, M. C. et al. Differential L1 regulation in pluripotent stem cells of humans and apes. Nature 503, 525–529 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang, B., Kirov, S. & Snoddy, J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res . 33, W741–W748 (2005)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res . 13, 2498–2504 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Llorens-Bobadilla, E. et al. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell 17, 329–340 (2015)

    CAS  PubMed  Google Scholar 

  35. Livak, K. J. et al. Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods 59, 71–79 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hermann, B. P. et al. Transcriptional and translational heterogeneity among neonatal mouse spermatogonia. Biol. Reprod. 92, 54 (2015)

    PubMed  PubMed Central  Google Scholar 

  37. Jacobs, B. et al. Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study. Cereb. Cortex 11, 558–571 (2001)

    CAS  PubMed  Google Scholar 

  38. Semendeferi, K. et al. Spatial organization of neurons in the frontal pole sets humans apart from great apes. Cereb. Cortex 21, 1485–1497 (2011)

    PubMed  Google Scholar 

  39. Marin-Padilla, M. Structural abnormalities of the cerebral cortex in human chromosomal aberrations: a Golgi study. Brain Res . 44, 625–629 (1972)

    CAS  PubMed  Google Scholar 

  40. Takashima, S., Becker, L. E., Armstrong, D. L. & Chan, F. Abnormal neuronal development in the visual cortex of the human fetus and infant with down’s syndrome. A quantitative and qualitative Golgi study. Brain Res . 225, 1–21 (1981)

    CAS  PubMed  Google Scholar 

  41. Jay, V., Chan, F. W. & Becker, L. E. Dendritic arborization in the human fetus and infant with the trisomy 18 syndrome. Brain Res. Dev. Brain Res. 54, 291–294 (1990)

    CAS  PubMed  Google Scholar 

  42. Marin-Padilla, M. Prenatal and early postnatal ontogenesis of the human motor cortex: a golgi study. II. The basket-pyramidal system. Brain Res . 23, 185–191 (1970)

    CAS  PubMed  Google Scholar 

  43. Vukšić, M., Petanjek, Z., Rasin, M. R. & Kostović, I. Perinatal growth of prefrontal layer III pyramids in Down syndrome. Pediatr. Neurol. 27, 36–38 (2002)

    PubMed  Google Scholar 

  44. Jacobs, B. et al. Quantitative analysis of cortical pyramidal neurons after corpus callosotomy. Ann. Neurol. 54, 126–130 (2003)

    PubMed  Google Scholar 

  45. Riley, J. N. A reliable Golgi-Kopsch modification. Brain Res. Bull. 4, 127–129 (1979)

    CAS  PubMed  Google Scholar 

  46. Williams, R. S., Ferrante, R. J. & Caviness, V. S., Jr. The Golgi rapid method in clinical neuropathology: the morphologic consequences of suboptimal fixation. J. Neuropathol. Exp. Neurol. 37, 13–33 (1978)

    CAS  PubMed  Google Scholar 

  47. Jacobs, B. & Scheibel, A. B. A quantitative dendritic analysis of Wernicke’s area in humans. I. Lifespan changes. J. Comp. Neurol. 327, 83–96 (1993)

    CAS  PubMed  Google Scholar 

  48. Uylings, H. B., Ruiz-Marcos, A. & van Pelt, J. The metric analysis of three-dimensional dendritic tree patterns: a methodological review. J. Neurosci. Methods 18, 127–151 (1986)

    CAS  PubMed  Google Scholar 

  49. White, N. et al. PROMO: Real-time prospective motion correction in MRI using image-based tracking. Magn. Reson. Med. 63, 91–105 (2010)

    ADS  PubMed  PubMed Central  Google Scholar 

  50. Brown, T. T. et al. Prospective motion correction of high-resolution magnetic resonance imaging data in children. Neuroimage 53, 139–145 (2010)

    PubMed  Google Scholar 

  51. Kuperman, J. M. et al. Prospective motion correction improves diagnostic utility of pediatric MRI scans. Pediatr. Radiol. 41, 1578–1582 (2011)

    PubMed  PubMed Central  Google Scholar 

  52. Jovicich, J. et al. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30, 436–443 (2006)

    PubMed  Google Scholar 

  53. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999)

    CAS  PubMed  Google Scholar 

  54. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999)

    CAS  PubMed  Google Scholar 

  55. Fischl, B. et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage 23 (Suppl 1), S69–S84 (2004)

    PubMed  Google Scholar 

  56. Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004)

    PubMed  Google Scholar 

  57. Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl Acad. Sci. USA 97, 11050–11055 (2000)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006)

    PubMed  Google Scholar 

  59. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010)

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the California Institute for Regenerative Medicine (CIRM) TR2-01814 and TR4-06747, the National Institutes of Health (NIH) through P01 NICHD033113, NIH Director’s New Innovator Award Program 1-DP2-OD006495-01, R01MH094753, R01MH103134, U19MH107367, U19MH106434, R01MH095741, a National Alliance for Research on Schizophrenia and Depression (NARSAD) Independent Investigator Grant to A.R.M., grants from the Bob and Mary Jane Engman, the JPB Foundation, Paul G. Allen Family Foundation, the Leona M. and Harry B. Helmsley Charitable Trust grant 2012-PG-MED002, Annette C. Merle-Smith, the G. Harold & Leila Y. Mathers Foundation, the Royal Thai Government Scholarship to T.C., a CIRM postdoctoral fellowship to C.A.T., the Rita L. Atkinson Graduate fellowship to B.H.-M and the University of California San Diego Kavli Institute for Brain and Mind. Human tissue was obtained from the University of Maryland Brain and Tissue Bank, which is a brain and tissue repository of the NIH NeuroBioBank. We acknowledge K. Jepsen for the DNA bead arrays and members of the Willert laboratory for assistance with the Wnt pathway experiments. We thank all the participants and their families.

Author information

Authors and Affiliations

Authors

Contributions

A.R.M., T.C. and C.A.T. designed the experiments and wrote the manuscript with input from K.S. and all authors. T.C. processed DPCs, generated and characterized iPSCs, NPCs and neurons, and performed cell number, proliferation, and apoptosis experiments as well as FZD9 knockdown and overexpression and statistical analysis. C.A.T. performed C1 single-cell analyses, synaptic quantification, calcium imaging, cell density experiments, live neuronal morphology analysis and statistical analysis. B.C.F. performed MEA recording, PCR for retrovirus silencing and Wnt pathway gene-expression analysis. B.C.F. and S.E.R. prepared astrocytes for co-culture experiments, NPC characterization by flow cytometry and CHIR 98014 experiments. K.S. designed all morphometry experiments with B.H.-M. and B.J., and co-wrote the manuscript to link the various levels of investigation from the whole-brain imaging findings to the cellular level. L.S. prepared Golgi staining for post-mortem neurons with help from K.L.H. and B.J. B.H.-M. obtained morphometric data on iPSC-derived neurons and post-mortem neurons. D.X.Y., M.C.M., C.A.T. and L.M. performed calcium transient experiments and statistical analysis. T.T.B. performed brain scan and statistical analysis with help from A.M.D. C.B. performed electrophysiological tests. M.D., W.W., P.L. and Y.M.S. performed neurocognitive and social tests. A.J., Y.M.S., and M.C.A. performed analyses and interpretation of social/neurocognitive tests. R.H.H. performed bioinformatics analysis. L.D. and J.R.K. confirmed deletion of all cells from participants with WS who donated them for reprogramming. E.H., U.B., F.H.G., K.S. and A.R.M. edited the manuscript for publication.

Corresponding authors

Correspondence to Katerina Semendeferi or Alysson R. Muotri.

Extended data figures and tables

Extended Data Figure 1 Participants with WS in iPSC study and their neurocognitive and social profiles.

a, Summary of scores on the Diagnostic Score Sheet (DSS) for individuals with WS. b, Table showing allele number of genes in WS-deleted region in each participant obtained from qPCR. c, Summary of all neurocognitive and social behavioural tests used on this study. d, e, WS neurocognitive profiles. Log of predictive likelihood ratio for iPSC participants (identified by participant number) calculated as the log of the ratio of the likelihoods for each individual test score based on the predictive distributions for TD individuals and those with WS (d). Values less than 0 indicate depressed scores consistent with expectations for WS. Predictive distributions for TD participants used published norms (means and standard deviations with assumed normality). Predictive distributions for individuals with WS were calculated using available WS data (VIQ/PIQ n = 81, VMI n = 56, PPVT n = 97) (e), assuming normality and least squares estimation, and according to the procedures described elsewhere26. WS parameter estimates for the VMI were calculated using censored regression owing to several individuals with WS scoring at the instrument floor. f, Description of population included in Benton Face Recognition and Judgment of Line Orientation in Fig. 1b (TD n = 22 versus WS n = 65). g, Boxplots for WS (red) and TD (blue) participants on complex syntax (WS n = 45; TD n = 47) and social evaluation (WS n = 44; TD n = 49). Red and blue circles depict scores more than 1.5 times the interquartile range away from the median.

Extended Data Figure 2 Generation and characterization of iPSCs.

a, Summary of reprogramming protocol using retrovirus carrying Yamanaka transcription factors (see Supplementary Information for details). Scale bar, 200 μm. b, Representative images of iPSCs expressing pluripotent markers including Nanog, Lin28, Oct4 and SSEA4 assessed by immunofluorescence staining. Scale bar, 200 μm. c, Expression of three germ-layer markers in iPSC-derived embryoid bodies (EBs); PAX6 (ectoderm), MSX1 (mesoderm) and AFP (endoderm) assessed by semiquantitative RT–PCR. TBP, housekeeping control. d, Cluster analysis showing correlation coefficients of microarray profiles of three WS DPCs, three TD DPCs, three WS iPSCs, three TD iPSCs and one ESC. e, Representative PCR showing silencing of the four transgenes (exogenous) in iPSCs. f, Representative images of teratoma from iPSCs showing tissues of three germ layers; neural rosettes (ectoderm), cartilage (mesoderm), muscle cells (mesoderm) and goblet cells (endoderm). g, Representative image of iPSC chromosomes showing its genetic stability assessed by G-banding karyotype analysis. h, i, Spontaneous synaptic GABA events (h) and spontaneous synaptic AMPA events (i) in 1-month-old iPSC-derived neurons.

Extended Data Figure 3 Global gene expression analysis during neuronal differentiation.

a, PCA plot of embryonic stem cells (ES), induced pluripotent stem cells (iPS), neuronal progenitor cells (NPC) and neurons (NE) for TD, WS and pWS88. c, Euclidian matrix distance-based heat map and hierarchical clustering-based dendrogram of ES, NPC and NE cells for WD, WS and pWS88 samples. Expression variability between samples is indicated by Z-score, varying from green (negative variation) to red (positive variation). c, Euclidian matrix distance-based heat map and hierarchical clustering-based dendrogram of pluripotency gene markers for ES, NPC and NE cells for TD, WS and pWS88 samples. d, Euclidian matrix distance-based heat map and hierarchical clustering-based dendrogram of neuronal gene markers for iPS, NPC and NE cells for TD, WS and pWS88 samples. Expression variability between samples is indicated by Z-score, varying from green (negative variation) to red (positive variation). e, Specific cell type-based clustering analysis of biological replicates subjected to RNA-seq for the WS-related genes in three stages during differentiation (iPS, NPC and NE). f, Fold change variation of WS-related genes in different cell lines. Ideogram of chromosome 7 (band 7q11.23) corresponding to the commonly deleted region with the WS-related genes. Fold change variation of normalized WS-related gene expression in NPCs and neurons (NE) compared with TDs. Non-represented fold change corresponds to those genes having high expression variability between biological replicates, or having very low expression values. g, Expression of FZD9 gene in iPSC, NPCs and neurons from TD and WS. Error bars, s.e.m. h, Venn diagram showing correlation of significant differentially expressed genes between TD, pWS88 and WS during neuronal differentiation. Significantly enriched GO terms found for downregulated (red histogram) and upregulated (blue histogram) differentially expressed genes between TD and WS in NPC. Significantly enriched GO terms found for downregulated (red histogram) and upregulated (blue histogram) differentially expressed genes between TD and WS in neurons (NE). Vertical line (black) corresponds to a significant P value (<0.05). i, Enriched GO metabolic process terms found in NPC of WS samples correlated with the GO found by a similar comparison performed in ref. 13.

Source data

Extended Data Figure 4 Defect in WS NPC apoptosis and role of FZD9.

a, Ratio of NPC number on day 4 over day 0 relative to TD. Data are shown as mean ± s.e.m.; n, number of clones. b, High percentage (>95%) of Sox1/Sox2-positive and Pax6/Nestin-positive cell population was comparably observed in TD, typical WS and pWS88 NPCs assessed by FACS. Data are shown as mean ± s.e.m.; n, number of clones. c, Microfluidics of C1 chip used to capture live single cells (calcein+ cell). d, Outlier exclusion based on the recommended/default limit of detection value of 24, analysed by Fluidigm Singular 3.0. Outliers were removed manually on the basis of the sample median log2(expression) values. e, Representative example of non-normalized Ct plot, indicated with the rectangle in the heat map. Cells are shown in rows and genes in columns. The range of cycle threshold (Ct) values is colour coded from low (blue) to high (red) and absent (black). f, Violin plots of all 96 genes showing the comparison between TD and WS NPCs from the single-cell analyses (log2(expression) values). The majority of genes show unimodal expression distribution. g, Volcano plot of single-cell expression data. Plot illustrates differences in expression patterns of target genes of iPSC-derived NPCs. The dotted lines represent more than or equal to 3.0-fold differentially expressed genes between the groups at P < 0.05 (unpaired two-sample t-test). h, Schematic diagram summarizing NPC preparation for proliferation assay and representative scatter plot showing cells in each cycle phase (G1, S and G2/M). i, No significant differences in percentage of the BrdU-positive population between TD, typical WS and pWS88 NPCs. j, Schematic diagram summarizing NPC preparation for apoptosis analysis and representative analysed data for DNA fragmentation (left) and caspase assay (right). km, Changes in ratio of NPC number on day 4 over day 0 relative to TD (k), percentage of subG1 population (l) and percentage of population with high caspase activity (m) of pWS88 NPCs when treated with shFZD9 and shControl. n, Increase in cell number day 4/day 0 upon overexpression of FZD9 in WS iPSC-derived NPCs. Data are shown as mean ± s.e.m. for each individual; n, technical replicates. For i and km, data are shown as mean ± s.e.m.; n, number of clones, *P < 0.05, **P < 0.01, ***P < 0.001, one-way ANOVA and Tukey’s post hoc test (i), Kruskal–Wallis test and Dunn’s multiple comparison test (km).

Source data

Extended Data Figure 5 Single-cell analysis of WS and TD iPSC-derived neurons.

a, b, Outlier exclusion based on limit of detection = 24, analysed by Fluidigm Singular 3.0. Outliers were removed manually on the basis of the sample median log2(expression) values. c, Heat map of number of genes with ANOVA P < 0.05 (82 genes in total). d, Unsupervised hierarchical clustering of 672 single-cell of WS and TD iPSC-derived neurons identified cell sub-populations not linked with the genotype. Cells are shown in rows and genes in columns. Log2(gene expression levels) were converted to a global Z-score (blue is the lowest value, red is highest). Genes were clustered using the Pearson correlation method and cells were clustered using the Euclidean method. e, PCA projections of the 96 genes, showing the contribution of each gene to the first two PCs. f, Violin plots of all 96 genes showing the comparison between TD, WS and pWS88 neurons from the single-cell analyses (log2(expression) values).

Extended Data Figure 6 Morphometric analysis of WS-derived CTIP2-positive cortical neurons.

a, Summary of preparation of neurons for evaluation by morphometric analysis. b, Representative images of EGFP- and CTIP2-positive neuron (arrowhead) and tracing. Scale bar, 200 μm. cf, No significant differences in dendritic segment numbers (c), number of branching points (d), dendritic spine density (e) and soma area (f) between TD, typical WS and pWS88 were observed. gm, Morphometric analysis shown as individual participant for total dendritic length (g), dendritic tree number (h), dendritic spine number (i), dendritic segment number (j), number of branching points (k), dendritic spine density (l) and soma area (m). n, Four-week-old neurons were dissociated and plated to trace total neurite length every hour, for a total of 6 h. Representative images of traced neurons plated after 0 and 6 h from TD, typical WS and atypical pWS88 iPSC-derived neurons. or, Morphometric analysis showing significant differences among TD, typical WS and pWS88 in the initial neurite growth velocity (6 h period). r, Morphometric analysis shown for individual participants for neurite growth velocity for 6 h interval. n, Number of traced neurons. su, No significant changes were observed in the total dendritic length (s), dendritic segment number (t) and dendritic spine number (u) of TD neurons plated in different densities (300–1,200 cells per square millimetre). v, Individual channels of puncta quantification of post- and presynaptic markers (Homer1/Vglut1). Scale bar, 2 μm. For cm and ou, data are shown as mean ± s.e.m.; n, number of traced neurons, *P < 0.05, **P < 0.01, Kruskal–Wallis test (cf), one-way ANOVA and Tukey’s post hoc test (o–q, ru).

Source data

Extended Data Figure 7 Alteration in calcium transient in WS iPSC-derived neurons and morphometric analysis of cortical layer V/VI pyramidal neurons in post-mortem tissue.

a, Puncta quantification of post- and presynaptic markers. The synaptic proteins Vglut (presynaptic) and Homer1 (postsynaptic) were used as markers and only co-localized puncta on MAP2+ cells were quantified and graphed. Data are shown as the mean ± s.e.m.; n, number of neurons. b, Summary of preparation of neurons for calcium transient analysis. Representative images of live neuronal culture expressing RFP driven by synapsin promoter and the uptake of Fluo-4AM calcium dye. c, Blockade of calcium transient by TTX inhibition of synaptic activity. d, Representative images of calcium transient in single neurons (RFP-positive, arrowhead) from TD (top), typical WS (middle) and pWS88 (bottom). Number in the lower right of each figure represents each time point (seconds) when change in Fluo-4AM occurs. e, f, Calcium transient analysis shown as individual for frequency (e) and percentage of signalling neurons (f). Data are shown as mean ± s.e.m.; n, number of fields analysed. g, MEA analyses revealed an increase in spontaneous neuronal spikes. Data show individual clones. h, Raster plot of TD and WS iPSC-derived neurons analysed by multi-electrode array. i, Details of individuals used for the analysis. jl, No significant differences in dendrite number (j), dendritic spine density (k) and soma area (l) between TD and typical WS were observed. Data are shown as mean ± s.e.m.; n, number of traced neurons, two-sided unpaired Student’s t test. ms, Morphometric analysis shown for each individual for total dendritic length (m), dendritic spine number (n), segment number (o), branching point number (p), dendrite number (q), dendritic spine density (r) and soma area (s). Data are shown as mean ± s.e.m.; n, number of traced neurons.

Source data

Extended Data Table 1 List of top ten most significant differentially expressed genes in WS compared with TD for NPC and neurons
Extended Data Table 2 Most significant (P < 0.05) enriched GO terms in NPC of WS compared with TD samples
Extended Data Table 3 Most significant (P < 0.05) enriched GO terms in neurons of WS compared with TD samples

Supplementary information

Supplementary Information

This file contains Supplementary Note 1, Supplementary Tables 1-13 and Supplementary References. (PDF 2673 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chailangkarn, T., Trujillo, C., Freitas, B. et al. A human neurodevelopmental model for Williams syndrome. Nature 536, 338–343 (2016). https://doi.org/10.1038/nature19067

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature19067

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing