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Self-organizing neuruloids model developmental aspects of Huntington’s disease in the ectodermal compartment


Harnessing the potential of human embryonic stem cells to mimic normal and aberrant development with standardized models is a pressing challenge. Here we use micropattern technology to recapitulate early human neurulation in large numbers of nearly identical structures called neuruloids. Dual-SMAD inhibition followed by bone morphogenic protein 4 stimulation induced self-organization of neuruloids harboring neural progenitors, neural crest, sensory placode and epidermis. Single-cell transcriptomics unveiled the precise identities and timing of fate specification. Investigation of the molecular mechanism of neuruloid self-organization revealed a pulse of pSMAD1 at the edge that induced epidermis, whose juxtaposition to central neural fates specifies neural crest and placodes, modulated by fibroblast growth factor and Wnt. Neuruloids provide a unique opportunity to study the developmental aspects of human diseases. Using isogenic Huntington’s disease human embryonic stem cells and deep neural network analysis, we show how specific phenotypic signatures arise in our model of early human development as a consequence of mutant huntingtin protein, outlining an approach for phenotypic drug screening.

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Fig. 1: Generation of neural rosettes on micropatterned colonies.
Fig. 2: Self-organization of ectoderm into neuruloids on micropatterns.
Fig. 3: Molecular characterization of the neuruloid cell populations by single-cell RNA sequencing.
Fig. 4: Neuruloid formation requires coupling of cell fate specification with a two-step morphogenesis mechanism.
Fig. 5: Phenotypic signatures associated with Huntington disease using deep neural networks.
Fig. 6: Misregulation of cytoskeleton organization genes implicated in the impairment of HD neuruloid morphogenesis.

Data availability

The accession number for the single-cell RNA-seq data reported in this paper is GSE118682.

Code availability

The code is available upon request or directly from the GitHub submission:


  1. 1.

    Ozair, M. Z., Kintner, C. & Brivanlou, A. H. Neural induction and early patterning in vertebrates. Wiley Interdiscip. Rev. Dev. Biol. 2, 479–498 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Chambers, S. M. et al. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat. Biotechnol. 27, 275–280 (2009).

    CAS  Article  Google Scholar 

  3. 3.

    Ozair, M. Z., Noggle, S., Warmflash, A., Krzyspiak, J. E. & Brivanlou, A. H. SMAD7 directly converts human embryonic stem cells to telencephalic fate by a default mechanism. Stem Cells 31, 35–47 (2013).

    CAS  Article  Google Scholar 

  4. 4.

    Dincer, Z. et al. Specification of functional cranial placode derivatives from human pluripotent stem cells. Cell Rep. 5, 1387–1402 (2013).

    CAS  Article  Google Scholar 

  5. 5.

    Tchieu, J. et al. A modular platform for differentiation of human PSCs into all major ectodermal lineages. Cell Stem Cell 21, 399–410 e397 (2017).

    CAS  Article  Google Scholar 

  6. 6.

    Hemmati-Brivanlou, A. & Melton, D. A. Inhibition of activin receptor signaling promotes neuralization in Xenopus. Cell 77, 273–281 (1994).

    CAS  Article  Google Scholar 

  7. 7.

    Munoz-Sanjuan, I. & Brivanlou, A. H. Neural induction, the default model and embryonic stem cells. Nat. Rev. Neurosci. 3, 271–280 (2002).

    CAS  Article  Google Scholar 

  8. 8.

    Wilson, P. A., Lagna, G., Suzuki, A. & Hemmati-Brivanlou, A. Concentration-dependent patterning of the Xenopus ectoderm by BMP4 and its signal transducer Smad1. Development 124, 3177–3184 (1997).

    CAS  PubMed  Google Scholar 

  9. 9.

    Bhattacharyya, S. & Bronner-Fraser, M. Hierarchy of regulatory events in sensory placode development. Curr. Opin. Genet. Dev. 14, 520–526 (2004).

    CAS  Article  Google Scholar 

  10. 10.

    Litsiou, A., Hanson, S. & Streit, A. A balance of FGF, BMP and WNT signalling positions the future placode territory in the head. Development 132, 4051–4062 (2005).

    CAS  Article  Google Scholar 

  11. 11.

    Sauka-Spengler, T. & Bronner-Fraser, M. A gene regulatory network orchestrates neural crest formation. Nat. Rev. Mol. Cell Biol. 9, 557–568 (2008).

    CAS  Article  Google Scholar 

  12. 12.

    Betancur, P., Bronner-Fraser, M. & Sauka-Spengler, T. Genomic code for Sox10 activation reveals a key regulatory enhancer for cranial neural crest. Proc. Natl Acad. Sci. USA 107, 3570–3575 (2010).

    CAS  Article  Google Scholar 

  13. 13.

    Kwon, H. J., Bhat, N., Sweet, E. M., Cornell, R. A. & Riley, B. B. Identification of early requirements for preplacodal ectoderm and sensory organ development. PLoS Genet. 6, e1001133 (2010).

    Article  Google Scholar 

  14. 14.

    Schlosser, G. Early embryonic specification of vertebrate cranial placodes. Wiley Interdiscip. Rev. Dev. Biol. 3, 349–363 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Stuhlmiller, T. J. & Garcia-Castro, M. I. FGF/MAPK signaling is required in the gastrula epiblast for avian neural crest induction. Development 139, 289–300 (2012).

    CAS  Article  Google Scholar 

  16. 16.

    Reichert, S., Randall, R. A. & Hill, C. S. A BMP regulatory network controls ectodermal cell fate decisions at the neural plate border. Development 140, 4435–4444 (2013).

    CAS  Article  Google Scholar 

  17. 17.

    Patthey, C. & Gunhaga, L. Signaling pathways regulating ectodermal cell fate choices. Exp. Cell Res. 321, 11–16 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Leung, A. W. et al. WNT/beta-catenin signaling mediates human neural crest induction via a pre-neural border intermediate. Development 143, 398–410 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Greenberg, F. DiGeorge syndrome: an historical review of clinical and cytogenetic features. J. Med. Genet. 30, 803–806 (1993).

    CAS  Article  Google Scholar 

  20. 20.

    Roizen, N. J. & Patterson, D. Down’s syndrome. Lancet 361, 1281–1289 (2003).

    Article  Google Scholar 

  21. 21.

    Sarkozy, A., Digilio, M. C. & Dallapiccola, B. Leopard syndrome. Orphanet J. Rare Dis. 3, 13 (2008).

    Article  Google Scholar 

  22. 22.

    Ferner, R. E. Neurofibromatosis 1. Eur. J. Hum. Genet. 15, 131–138 (2007).

    CAS  Article  Google Scholar 

  23. 23.

    Greene, N. D. & Copp, A. J. Neural tube defects. Ann. Rev. Neurosci. 37, 221–242 (2014).

    CAS  Article  Google Scholar 

  24. 24.

    Elkabetz, Y. et al. Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage. Genes Dev. 22, 152–165 (2008).

    CAS  Article  Google Scholar 

  25. 25.

    Ozair, M. Z. et al. hPSC modeling reveals that fate selection of cortical deep projection neurons occurs in the subplate. Cell Stem Cell 23, 60–73 e66 (2018).

    CAS  Article  Google Scholar 

  26. 26.

    Warmflash, A., Sorre, B., Etoc, F., Siggia, E. D. & Brivanlou, A. H. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat. Methods 11, 847–854 (2014).

    CAS  Article  Google Scholar 

  27. 27.

    Deglincerti, A., Etoc, F., Ozair, M. Z. & Brivanlou, A. H. Self-organization of spatial patterning in human embryonic stem cells. Curr. Top. Dev. Biol. 116, 99–113 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Etoc, F. et al. A balance between secreted inhibitors and edge sensing controls gastruloid self-organization. Dev. Cell. 39, 302–315 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Knight, G. T. et al. Engineering induction of singular rosette emergence within hPSC-derived tissues. eLife 7, e37549 (2018).

    Article  Google Scholar 

  30. 30.

    Broccoli, V., Colombo, E. & Cossu, G. Dmbx1 is a paired-box containing gene specifically expressed in the caudal most brain structures. Mech. Dev. 114, 219–223 (2002).

    CAS  Article  Google Scholar 

  31. 31.

    Grenningloh, G., Soehrman, S., Bondallaz, P., Ruchti, E. & Cadas, H. Role of the microtubule destabilizing proteins SCG10 and stathmin in neuronal growth. J. Neurobiol. 58, 60–69 (2004).

    CAS  Article  Google Scholar 

  32. 32.

    Mallika, C., Guo, Q. & Li, J. Y. Gbx2 is essential for maintaining thalamic neuron identity and repressing habenular characters in the developing thalamus. Dev. Biol. 407, 26–39 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Quina, L. A., Wang, S., Ng, L. & Turner, E. E. Brn3a and Nurr1 mediate a gene regulatory pathway for habenula development. J. Neurosci. 29, 14309–14322 (2009).

    CAS  Article  Google Scholar 

  34. 34.

    Inoue, T., Chisaka, O., Matsunami, H. & Takeichi, M. Cadherin-6 expression transiently delineates specific rhombomeres, other neural tube subdivisions, and neural crest subpopulations in mouse embryos. Dev. Biol. 183, 183–194 (1997).

    CAS  Article  Google Scholar 

  35. 35.

    Van de Putte, T. et al. Mice lacking ZFHX1B, the gene that codes for Smad-interacting protein-1, reveal a role for multiple neural crest cell defects in the etiology of hirschsprung disease-mental retardation syndrome. Am. J. Hum. Genet. 72, 465–470 (2003).

    Article  Google Scholar 

  36. 36.

    Bolos, V. et al. The transcription factor slug represses E-cadherin expression and induces epithelial to mesenchymal transitions: a comparison with snail and E47 repressors. J. Cell Sci. 116, 499–511 (2003).

    CAS  Article  Google Scholar 

  37. 37.

    Huang, M. et al. Generating trunk neural crest from human pluripotent stem cells. Sci. Rep. 6, 19727 (2016).

    CAS  Article  Google Scholar 

  38. 38.

    Bhatt, S., Diaz, R. & Trainor, P. A. Signals and switches in mammalian neural crest cell differentiation. Cold Spring Harbor Perspect. Biol. 5, pii: a008326 (2013).

    Article  Google Scholar 

  39. 39.

    Ma, Q., Chen, Z., del Barco Barrantes, I., de la Pompa, J. L. & Anderson, D. J. neurogenin1 is essential for the determination of neuronal precursors for proximal cranial sensory ganglia. Neuron 20, 469–482 (1998).

    CAS  Article  Google Scholar 

  40. 40.

    Bae, S., Bessho, Y., Hojo, M. & Kageyama, R. The bHLH gene Hes6, an inhibitor of Hes1, promotes neuronal differentiation. Development 127, 2933–2943 (2000).

    CAS  PubMed  Google Scholar 

  41. 41.

    Byrne, C., Tainsky, M. & Fuchs, E. Programming gene expression in developing epidermis. Development 120, 2369–2383 (1994).

    CAS  PubMed  Google Scholar 

  42. 42.

    Moll, R., Divo, M. & Langbein, L. The human keratins: biology and pathology. Histochem. Cell Biol. 129, 705–733 (2008).

    CAS  Article  Google Scholar 

  43. 43.

    Lavery, D. L., Davenport, I. R., Turnbull, Y. D., Wheeler, G. N. & Hoppler, S. Wnt6 expression in epidermis and epithelial tissues during Xenopus organogenesis. Dev. Dyn. 237, 768–779 (2008).

    CAS  Article  Google Scholar 

  44. 44.

    Arabzadeh, A., Troy, T. C. & Turksen, K. Role of the Cldn6 cytoplasmic tail domain in membrane targeting and epidermal differentiation in vivo. Mol. Cell. Biol. 26, 5876–5887 (2006).

    CAS  Article  Google Scholar 

  45. 45.

    Kaufman, C. K. et al. GATA-3: an unexpected regulator of cell lineage determination in skin. Genes Dev. 17, 2108–2122 (2003).

    CAS  Article  Google Scholar 

  46. 46.

    Onorati, M. et al. Molecular and functional definition of the developing human striatum. Nat. Neurosci. 17, 1804–1815 (2014).

    CAS  Article  Google Scholar 

  47. 47.

    Selleck, M. A. & Bronner-Fraser, M. Origins of the avian neural crest: the role of neural plate-epidermal interactions. Development 121, 525–538 (1995).

    CAS  PubMed  Google Scholar 

  48. 48.

    Lo Sardo, V. et al. An evolutionary recent neuroepithelial cell adhesion function of huntingtin implicates ADAM10-N-cadherin. Nat. Neurosci. 15, 713–721 (2012).

    CAS  Article  Google Scholar 

  49. 49.

    Conforti, P. et al. Faulty neuronal determination and cell polarization are reverted by modulating HD early phenotypes. Proc. Natl Acad. Sci. USA 115, E762–E771 (2018).

    CAS  Article  Google Scholar 

  50. 50.

    Ruzo, A. et al. Chromosomal instability during neurogenesis in Huntington’s disease. Development 145, pii: dev156844 (2018).

    Article  Google Scholar 

  51. 51.

    Molero, A. E. et al. Selective expression of mutant huntingtin during development recapitulates characteristic features of Huntington’s disease. Proc. Natl Acad. Sci. USA 113, 5736–5741 (2016).

    CAS  Article  Google Scholar 

  52. 52.

    Arteaga-Bracho, E. E. et al. Postnatal and adult consequences of loss of huntingtin during development: implications for Huntington’s disease. Neurobiol. Dis. 96, 144–155 (2016).

    CAS  Article  Google Scholar 

  53. 53.

    Siebzehnrubl, F. A. et al. Early postnatal behavioral, cellular, and molecular changes in models of Huntington disease are reversible by HDAC inhibition. Proc. Natl Acad. Sci. USA 115, E8765–E8774 (2018).

    Article  Google Scholar 

  54. 54.

    Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017).

    Article  Google Scholar 

  55. 55.

    Barnat, M., Le Friec, J., Benstaali, C. & Humbert, S. Huntingtin-mediated multipolar-bipolar transition of newborn cortical neurons is critical for their postnatal neuronal morphology. Neuron 93, 99–114 (2017).

    CAS  Article  Google Scholar 

  56. 56.

    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  Article  Google Scholar 

  57. 57.

    Labadorf, A. et al. RNA sequence analysis of human Huntington disease brain reveals an extensive increase in inflammatory and developmental gene expression. PloS ONE 10, e0143563 (2015).

    Article  Google Scholar 

  58. 58.

    Lancaster, M. A. & Knoblich, J. A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).

    Article  Google Scholar 

  59. 59.

    Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).

    CAS  Article  Google Scholar 

  60. 60.

    Xue, X. et al. Mechanics-guided embryonic patterning of neuroectoderm tissue from human pluripotent stem cells. Nat. Mater. 17, 633–641 (2018).

    CAS  Article  Google Scholar 

  61. 61.

    Hikosaka, O. The habenula: from stress evasion to value-based decision-making. Nat. Rev. Neurosci. 11, 503–513 (2010).

    CAS  Article  Google Scholar 

  62. 62.

    Labbadia, J. & Morimoto, R. I. Huntington’s disease: underlying molecular mechanisms and emerging concepts. Trends Biochem. Sci. 38, 378–385 (2013).

    CAS  Article  Google Scholar 

  63. 63.

    Landles, C. & Bates, G. P. Huntingtin and the molecular pathogenesis of Huntington’s disease. fourth in molecular medicine review series. EMBO Rep. 5, 958–963 (2004).

    CAS  Article  Google Scholar 

  64. 64.

    Ruzo, A. et al. Discovery of novel isoforms of huntingtin reveals a new hominid-specific exon. PloS ONE 10, e0127687 (2015).

    Article  Google Scholar 

  65. 65.

    Tourette, C. et al. A large-scale huntingtin protein interaction network implicates Rho GTPase signaling pathways in Huntington disease. J. Biol. Chem. 289, 6709–6726 (2014).

    CAS  Article  Google Scholar 

  66. 66.

    Caviston, J. P. & Holzbaur, E. L. Huntingtin as an essential integrator of intracellular vesicular trafficking. Trends Cell Biol. 19, 147–155 (2009).

    CAS  Article  Google Scholar 

  67. 67.

    Mehler, M. F. et al. Loss-of-huntingtin in medial and lateral ganglionic lineages differentially disrupts regional interneuron and projection neuron subtypes and promotes Huntington’s disease-associated behavioral, cellular and pathological hallmarks. J. Neurosci. 39, 1892–1909 (2019).

    CAS  Article  Google Scholar 

  68. 68.

    Paulsen, J. S. et al. Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: a decade of the PREDICT-HD study. Front. Aging Neurosci. 6, 78 (2014).

    Article  Google Scholar 

  69. 69.

    Ciarochi, J. A. et al. Patterns of co-occurring gray matter concentration loss across the Huntington disease prodrome. Front. Neurol. 7, 147 (2016).

    Article  Google Scholar 

  70. 70.

    Harrington, D. L. et al. Network topology and functional connectivity disturbances precede the onset of Huntington’s disease. Brain 138, 2332–2346 (2015).

    Article  Google Scholar 

  71. 71.

    Paulsen, J. S. et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J. Neurol. Neurosurg. Psychiatry 79, 874–880 (2008).

    CAS  Article  Google Scholar 

  72. 72.

    Ernst, A. et al. Neurogenesis in the striatum of the adult human brain. Cell 156, 1072–1083 (2014).

    CAS  Article  Google Scholar 

  73. 73.

    Shi, Y., Kirwan, P. & Livesey, F. J. Directed differentiation of human pluripotent stem cells to cerebral cortex neurons and neural networks. Nat. Protoc. 7, 1836–1846 (2012).

    CAS  Article  Google Scholar 

  74. 74.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  Article  Google Scholar 

  75. 75.

    Sommer, C. et al. Ilastik: Interactive learning and segmentation toolkit. In Proc. 8th IEEE International Symposium on Biomedical Imaging (ed Sommer, C. et al.) 230–233 (ISBI, 2011).

  76. 76.

    Jegou, S. et al. The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. Preprint at (2016).

  77. 77.

    Kingma, D. et al. Adam A method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ed Kingma, D. P. & Ba, J.) (ICLR, 2015).

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We thank J. Auerbach for technical assistance, as well as J. Naftaly and C. Lara. We would like to thank C. Zhao and the Genomics Resource Center at the Rockefeller University for help and advice regarding single-cell RNA sequencing. We thank E. Siggia for constructive criticism and the members of the Brivanlou-Siggia laboratories for critical discussions and comments on the manuscript. We appreciated discussions and comments from D. Felsenfeld, T. Vogt and I. Muñoz-Sanjuán (CHDI Foundation). This work was supported by the CHDI Foundation (A-9423), an SRA from RUMI Scientific and private funding from the Rockefeller University.

Author information




T.H. performed experiments. J.J.M. developed the image analysis pipeline. T.R. performed experiments and analyzed single-cell RNA-seq data. Z.O. created and validated transgenic hESC lines used for live imaging. F.E. directed the project. A.H.B. conceived and designed the project. F.E. and A.H.B. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Ali H. Brivanlou.

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Competing interests

A.H.B. is the co-founder of RUMI Scientific. Both A.H.B. and F.E. are shareholders of RUMI Scientific.

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Integrated supplementary information

Supplementary Figure 1 Analysis of apical markers and seeding density for neural rosettes on micropatterned colonies.

(A) Day 7 micropatterned culture after 7days with SB+LDN. Colonies of the following diameters are shown: 80, 200, 500, 800 and 1000 μm. Samples were stained with DAPI (gray), the neural differentiation marker PAX6 (green) and neural adhesion protein N-cadherin (N-CAD, orange). (B) Representative immunofluorescence images of PAX6 and N-CAD at day 7 of 200 and 500 μm micropatterned culture with multiple z from bottom to top. (C) Representative immunofluorescence images of atypical protein kinase C (aPKC), partitioning defective 3 (PAR3) at day 7 of 200 and 500 μm micropatterned culture with multiple z from bottom to top. Experiments in panels A-C have been repeated more than three times. (D) Representative immunofluorescence images of 80, 200, 500 and 1000 μm of day 7 micropatterned colonies by PAX6 (green) and N-CAD (orange) with 3 different initial cell seeding number: 0.38, 0.50 and 0.64 million cells. (Right) Quantification of lumen size at different initial seeding densities. N-CAD+ lumen sizes of 200 μm colonies are shown as a ratio of inner lumen area to colony area (n>80). Data are mean ± standard deviation. Data from panel D are from a single experiment with n>80 colonies for each condition. Scale bars: 50 μm except (A) 800 μm.

Supplementary Figure 2 Detailed characterization of neural rosettes and self-organized neuruloids on micropatterns.

(A) Representative immunofluorescence images of day 7 micropatterned colonies of 200 and 500 μm stained with N-CAD. Stars indicate the micropatterns with incomplete lumen. Representative images of the complete (above) and the incomplete (bottom) lumen in the 500 μm colonies are shown. Representative images of the satellite N-CAD+ loci are also shown. (B) Quantification of the proportion of closed lumens in 200 and 500 μm colonies, as defined by the continuity of the N-CAD ring at the center of the rosette as shown in panel (A). (C) Representative immunofluorescence images of the monolayer differentiation protocol from day 1 to 6 with PAX6 (green), OCT4 (red) and N-CAD (orange). (D) Quantitative PCR of various lineage-specific genes from day 1 to 7 of monolayer culture. Data were averaged from three technical replicates and error bars quantify standard deviations (E) Side view by resliced z-stack around N-CAD+ center region of day 7 neuruloid micropatterned colonies (BMP4 50 ng/ml) of 80, 200, 500 and 1000 μm. Samples were stained by DAPI (gray), PAX6 (green) and N-CAD (orange). The 1000 um diameter sample is shown from center to edge only. (F) Quantification of the area occupied by each marker across 145 neuruloid colonies from 3 independent experiments. Violin plots of the area positive for different markers (n=145). The violins are kernel density estimates of the underlying distribution. (G) Immunofluorescence analysis of 500 μm micropatterned colonies differentiated by BMP4 (50 ng/ml) as well as BMP4 (3 ng/ml) protocol. Samples were stained with DAPI (gray), PAX6 (green), neural crest marker SOX10 (red) and cranial placode marker SIX1 (orange). (H) 500 μm neuruloids at day 7 with different BMP4 concentrations: 0, 3, 13 and 50 ng/ml. SIX1 expression is shown within 25 patterns for comparison of the four different conditions. (I) Radially averaged marker expression from 3 different colonies show the TFAP2A+ only cells covering the full surface of the colony. Experiments in panels C,G,H and I have been repeated more than three times. Scale bars: (A) left 200 μm, middle 500 μm, right 100 μm; (C) 50 μm; (E) 50 μm except “80 μm” 10 μm; (G) 50 μm; (H) 500 μm; (I) 50 μm.

Supplementary Figure 3 Single-cell RNA-seq of neuruloid and validation of additional markers: neural populations.

(A) Cell numbers of the two single-cell RNA-seq RUES2 replicate transcriptomes produced in this study performed with Cell Ranger software and Seurat. Only the cells that passed QC were used in the analyses presented (see Methods). (B) UMAP plot showing the cell distributions of the two replicates. (C) Bar plot with the proportion of cells per cluster in both RUES2 replicates showing a difference of at most ~20% in the neural crest population. (D) UMAP graphs of: an overview of the clusters unbiasedly called using Louvain algorithm; OTX2 and PAX3 expression defining a dorsal anterior fate; EMX2 and LHX5 enriched in the caudal forebrain cluster NE1 and IRX3 and EN1 enriched in the midbrain (including the diencephalon) cluster NE2. (E) Expression region of forebrain and midbrain markers Pax6, Otx2, Emx2 and Dmbx1 from (Right) Scheme of markers in human embryo at 26 days post conception (d.p.c.). (F) 500 μm neuruloid (BMP4 3 ng/ml) at day 7 stained with DAPI, PAX6, LHX5. Zoomed and side views of the PAX6+ center region reveal a PAX6+LHX5+ population (forebrain) and a PAX6+LHX5- and PAX6-LHX5- populations corresponding to midbrain cells. (G) Characterization of the early-born neurons in the neuruloid. Battery of marker genes enriched in the neurons cluster. Zoomed immunofluorescence image of 500 μm neuruloid (BMP4 3 ng/ml) at day 7. Sample was stained with OTX2 and STMN2, a regulator of microtubule stability characteristic of early neurons. (H) UMAP plot and corresponding day 7 neuruloid stain of EBF3, specifically enriched in the diencephalic neurons cluster and not in the placode derived neurons, and STMN2. Immunofluorescence experiments in panels F and H have been repeated more than three times. Scale bars: 50 μm.

Supplementary Figure 4 Single-cell RNA-seq of neuruloid and validation of additional markers: neural crest, placodal and prospective epidermis.

(A) Single-cell gene expression UMAP plots with a battery of markers used to determine the anterior cranial nature of the neural crest population in the neuruloid. (B) Venn diagram with the overlap between isolated chick cranial neural crest genes (Simoes-Costa et al. 2014) and our collection of 149 NC marker genes (C) Immunofluorescence analysis of the 500 μm neuruloid (BMP4 3 ng/ml) NC at day 7. Sample was stained with DAPI, PAX6, SOX10, ETS1 and SNAIL. (D) Placodal cells express ISL1 and SIX1. (E) UMAP plot of POU4F1(BRN3A) and corresponding day 7 neuruloid stain of POU4F1(BRN3A), STMN2 and SIX1, marking specifically the sensory neurons coming from the trigeminal placode. (F) 500 μm neuruloid (BMP4 3 ng/ml) at day 7 stained with DAPI, PAX6, SOX10 and KRT18. Side view of the entire colony cross section was also shown. Immunofluorescence experiments in panels C, D, E and F have been repeated more than three times. Scale bars: 50 μm.

Supplementary Figure 5 Early human embryonic gene markers for each of the main cell types present in the neuruloid.

Marker genes enriched in the NE1, NE2, Neuron, Skin, Neural Crest and Placode populations. The complete marker list for each population can be found in Supplementary Table 1 and the associated statistical analysis is described in the method section. For each population, 5 markers are shown together with their expression on the UMAP plot.

Supplementary Figure 6 Reproducibility of the neuruloid in different cell lines.

(A) Day7 neuruloid showing a similar structure to Fig. 3f when using a different hESC line (RUES1) and the iPSC line BJ-1. A typical colony is stained with PAX6 (green), SOX10 (red) and SIX1 (orange). Immunofluorescence experiment in panels A has been repeated more than three times. (B) qPCR data for 10 marker genes of NE, NC, Placode, Skin and Neurons clusters. Expression levels are shown for 3 independent experiments across all the three lines. Data are plotted with a bar centered on the mean value and standard deviations (C) UMAP plots of MKI67 and TOP2A which are enriched in cluster U1 (see also Supplementary Table 1) suggesting this cluster still represents a population of dividing cells. Scale bars: 50 μm.

Supplementary Figure 7 Performance of our neural network analysis in neural rosettes and neuruloid HD phenotypes.

(A) Comparison of lumen segmentation by the filter-based machine-learning framework Ilastik and a deep neural network trained on the same images and applied to previously unseen data. (Left) Examples in which the two approaches have similar performance, (Right) examples where the neural network performs significantly better. The neural network almost perfectly segments the lumen in a wide range of conditions, even when the filter-based classifier fails due to the reasons indicated on the side of the right column. (B) Immunofluorescence analysis of 500 μm micropatterned colonies of RUES2, 20CAG and 56CAG by PAX6, SOX10 at day 7 of SB+BMP4 (3 ng/ml) protocol. (Right) Quantification of PAX6+ area. N>20 colonies for every condition. Data are centered around the mean, with standard deviations. One-way anova test was used to quantify p-values. Scale bars: 50 μm.

Supplementary Figure 8 Differentially expressed genes in the HD neuruloid (56CAG) and detailed analysis of the HD phenotype.

(A) Cell numbers and QC measures of the 56CAG single-cell RNA-seq. Only the cells that passed QC and were not proliferating were kept for subsequent analyses. Bar plot shows the proportion of cells allocated to each neuruloid cell population in both RUES2 replicates and 56CAG. (B) Gene ontology analyses using DAVID ( for NE- and NC-specific DEG between RUES2 and 56CAG, the associated statistical analysis is described in the method section. (C) Violin plots showing the distribution of normalized gene expression values for the genes in Fig. 9, f. The violins are kernel density estimates of the underlying distribution. (D) qPCR validation of the down-regulation of selected WNT/PCP and cytoskeleton-associated genes in the 56CAG neuruloid versus WT RUES2. (E) Representative image of a day 7 neuruloid treated with Blebbistatin (5 μM) and stained for DAPI, N-CAD, PAX6 and COL4. (Top) side view, (Bottom) top view. Profile plot of N-CAD and PAX6 is also shown. Experiment in panels E F have been repeated more than three times. Scale bars: 50 μm.

Supplementary Figure 9

Full Western Blot images for Fig. 7b.

Supplementary Information

Supplementary Information

Supplementary Figs. 1–9.

Reporting Summary

Supplementary Table 1

Neuruloid single-cell RNA-seq gene markers for the main cell populations and DEGs in HD neuruloid phenotype (56CAG).

Supplementary Video 1

Three-dimensional structure of the neuruloid immunofluorescence z-stack image of the neuruloid stained with PAX6 (Green), SOX10 (Red), SIX1 (Orange) and TFAP2A (Cyan). The movie starts from the bottom of the neuruloid to the top.

Supplementary Video 2

Live-cell imaging of the neuruloid formation. Time-lapse microscopy video from days 3–6 of neuruloid formation using a live reporter cell line (PAX6::H2B-Citrin and SOX10::H2B-tdTomato). PAX6 is colored in green and SOX10 is red.

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Haremaki, T., Metzger, J.J., Rito, T. et al. Self-organizing neuruloids model developmental aspects of Huntington’s disease in the ectodermal compartment. Nat Biotechnol 37, 1198–1208 (2019).

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