A human neurodevelopmental model for Williams syndrome (original) (raw)

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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

Author notes

1. Thanathom Chailangkarn and Cleber A. Trujillo: These authors contributed equally to this work.
2. Lisa Stefanacci: Deceased.

Authors and Affiliations

1. Department of Pediatrics/Rady Children’s Hospital San Diego, University of California San Diego, School of Medicine, UCSD Stem Cell Program, La Jolla, 92037, California, USA
   Thanathom Chailangkarn, Cleber A. Trujillo, Beatriz C. Freitas, Roberto H. Herai, Lisa Stefanacci, Sarah Romero & Alysson R. Muotri
2. Department of Cellular & Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, 92037, California, USA
   Thanathom Chailangkarn, Cleber A. Trujillo, Beatriz C. Freitas, Roberto H. Herai, Lisa Stefanacci, Sarah Romero & Alysson R. Muotri
3. Center for Academic Research and Training in Anthropogeny (CARTA), La Jolla, 92093, California, USA
   Thanathom Chailangkarn, Cleber A. Trujillo, Beatriz C. Freitas, Roberto H. Herai, Lisa Stefanacci, Sarah Romero & Alysson R. Muotri
4. National Center for Genetic Engineering and Biotechnology (BIOTEC), Virology and Cell Technology Laboratory, Pathum, 12120, Thani, Thailand
   Thanathom Chailangkarn
5. Department of Anthropology, University of California San Diego, La Jolla, 92093, California, USA
   Branka Hrvoj-Mihic, Lisa Stefanacci, Kari L. Hanson & Katerina Semendeferi
6. Graduate Program in Health Sciences, School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Paraná, Brazil
   Roberto H. Herai
7. The Salk Institute for Biological Studies, Laboratory of Genetics, La Jolla, 92037, California, USA
   Diana X. Yu, Maria C. Marchetto, Cedric Bardy, Lauren McHenry & Fred H. Gage
8. University of California San Diego, Multimodal Imaging Laboratory, La Jolla, 92093, California, USA
   Timothy T. Brown, Anders M. Dale & Eric Halgren
9. Department of Neurosciences, University of California San Diego, School of Medicine, La Jolla, 92093, California, USA
   Timothy T. Brown, M. Colin Ard & Eric Halgren
10. University of California San Diego, Center for Human Development, La Jolla, 92093, California, USA
    Timothy T. Brown
11. SAHMRI Mind & Brain Theme, Laboratory for Human Neurophysiology and Genetics, Flinders University School of Medicine, Adelaide, 5000, South Australia, Australia
    Cedric Bardy
12. The Salk Institute for Biological Studies, Laboratory for Cognitive Neuroscience, La Jolla, 92037, California, USA
    Anna Järvinen, Yvonne M. Searcy, Michelle DeWitt, Wenny Wong, Philip Lai & Ursula Bellugi
13. Department of Psychology, Colorado College, Colorado Springs, 80903, Colorado, USA
    Bob Jacobs
14. Department of Radiology, University of California San Diego, School of Medicine, La Jolla, 92093, California, USA
    Anders M. Dale
15. Department of Cognitive Science, University of California San Diego, La Jolla, 92093, California, USA
    Anders M. Dale
16. Department of Pediatrics, University of Utah, Salt Lake City, 84108, Utah, USA
    Li Dai & Julie R. Korenberg
17. University of Utah, The Brain Institute, Salt Lake City, 84108, Utah, USA
    Li Dai & Julie R. Korenberg
18. University of California San Diego, Kavli Institute for Brain and Mind, La Jolla, 92093, California, USA
    Fred H. Gage, Eric Halgren, Katerina Semendeferi & Alysson R. Muotri
19. University of California San Diego, School of Medicine, Neuroscience Graduate Program, La Jolla, 92093, California, USA
    Katerina Semendeferi & Alysson R. Muotri

Authors

1. Thanathom Chailangkarn
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2. Cleber A. Trujillo
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3. Beatriz C. Freitas
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4. Branka Hrvoj-Mihic
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5. Roberto H. Herai
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6. Diana X. Yu
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7. Timothy T. Brown
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8. Maria C. Marchetto
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9. Cedric Bardy
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10. Lauren McHenry
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11. Lisa Stefanacci
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12. Anna Järvinen
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13. Yvonne M. Searcy
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14. Michelle DeWitt
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15. Wenny Wong
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16. Philip Lai
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17. M. Colin Ard
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18. Kari L. Hanson
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19. Sarah Romero
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20. Bob Jacobs
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21. Anders M. Dale
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22. Li Dai
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23. Julie R. Korenberg
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24. Fred H. Gage
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25. Ursula Bellugi
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26. Eric Halgren
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27. Katerina Semendeferi
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28. Alysson R. Muotri
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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 toKaterina 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 _C_t plot, indicated with the rectangle in the heat map. Cells are shown in rows and genes in columns. The range of cycle threshold (_C_t) 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). k–m, 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 k–m, 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 (k–m).

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. c–f, 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. g–m, 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. o–r, 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. s–u, 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 c–m and o–u, data are shown as mean ± s.e.m.; n, number of traced neurons, *P < 0.05, **P < 0.01, Kruskal–Wallis test (c–f), one-way ANOVA and Tukey’s post hoc test (o–q, r–u).

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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. j–l, 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. m–s, 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.

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Extended Data Table 1 List of top ten most significant differentially expressed genes in WS compared with TD for NPC and neurons

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Extended Data Table 2 Most significant (P < 0.05) enriched GO terms in NPC of WS compared with TD samples

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Extended Data Table 3 Most significant (P < 0.05) enriched GO terms in neurons of WS compared with TD samples

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Supplementary information

Supplementary Information

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

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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

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• Received: 30 December 2014
• Accepted: 29 June 2016
• Published: 10 August 2016
• Issue Date: 18 August 2016
• DOI: https://doi.org/10.1038/nature19067