Early chromatin shaping predetermines multipotent vagal neural crest into neural, neuronal and mesenchymal lineages

Article metrics

Abstract

The enteric nervous system (ENS) predominantly originates from vagal neural crest (VNC) cells that emerge from the caudal hindbrain, invade the foregut and populate the gastrointestinal tract. However, the gene regulatory network (GRN) orchestrating the early specification of VNC remains unknown. Using an EdnrB enhancer, we generated a comprehensive temporal map of the chromatin and transcriptional landscape of VNC in the avian model, revealing three VNC cell clusters (neural, neurogenic and mesenchymal), each predetermined epigenetically prior to neural tube delamination. We identify and functionally validate regulatory cores (Sox10/Tfap2B/SoxB/Hbox) mediating each programme and elucidate their combinatorial activities with other spatiotemporally specific transcription factors (bHLH/NR). Our global deconstruction of the VNC-GRN in vivo sheds light on critical early regulatory mechanisms that may influence the divergent neural phenotypes in enteric neuropathies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Defining regions of differential chromatin accessibility within VNC identifies NC-specific EdnrB enhancers.
Fig. 2: Two distinct NC populations, DP and E2, are revealed by differential enhancer activity.
Fig. 3: Distinct transcriptomic profiles of VNC derivatives.
Fig. 4: Single-cell analysis parses three distinct VNC lineages, revealing relative proportions in RNA-seq datasets by deconvolution analysis.
Fig. 5: Analysis of differential TF motifs within DA elements uncovers Tfap2, Sox10, Sox2/3 and Hbox/bHLH as core TFs.
Fig. 6: Combinatorial analysis of the top de novo motif reveals dynamic use of enhancers to drive cell lineage specification validated by Sox10 and Tfap2B differential binding.
Fig. 7: Functional validation of upstream TFs in VNC specification analysed at HH18.
Fig. 8: Deconstruction of the VNC-GRN.

Data availability

ChIP-seq, ATAC-seq, scRNA-seq and bulk RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE125711. Previously published sequencing data that were re-analysed here are available under accession codes SRP135960 and GSE129114. Source data for Figs. 1 to 8 are provided with the paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The custom Python script used for gene expression correlation is available at https://github.com/tsslab/ENS/. All other code used in the study can be obtained from the corresponding author on reasonable request.

References

  1. 1.

    Gershon, M. D. Development of the enteric nervous system: a genetic guide to the perplexed. Gastroenterology 154, 478–480 (2018).

  2. 2.

    Amiel, J. et al. Hirschsprung disease, associated syndromes and genetics: a review. J. Med. Genet. 45, 1–14 (2008).

  3. 3.

    Chatterjee, S. et al. Enhancer variants synergistically drive dysfunction of a gene regulatory network in Hirschsprung disease. Cell 167, 355–368 (2016).

  4. 4.

    Tilghman, J. M. et al. Molecular genetic anatomy and risk profile of Hirschsprung’s disease. N. Engl. J. Med. 380, 1421–1432 (2019).

  5. 5.

    Le Douarin, N. M. & Teillet, M. A. The migration of neural crest cells to the wall of the digestive tract in avian embryo. J. Embryol. Exp. Morphol. 30, 31–48 (1973).

  6. 6.

    Yntema, C. L. & Hammond, W. S. The origin of intrinsic ganglia of trunk viscera from vagal neural crest in the chick embryo. J. Comp. Neurol. 101, 515–541 (1954).

  7. 7.

    Burns, A. J. & Douarin, N. M. The sacral neural crest contributes neurons and glia to the post-umbilical gut: spatiotemporal analysis of the development of the enteric nervous system. Development 125, 4335–4347 (1998).

  8. 8.

    Young, H. M., Bergner, A. J. & Muller, T. Acquisition of neuronal and glial markers by neural crest-derived cells in the mouse intestine. J. Comp. Neurol. 456, 1–11 (2003).

  9. 9.

    Vohra, B. P. et al. Differential gene expression and functional analysis implicate novel mechanisms in enteric nervous system precursor migration and neuritogenesis. Dev. Biol. 298, 259–271 (2006).

  10. 10.

    Schriemer, D. et al. Regulators of gene expression in enteric neural crest cells are putative Hirschsprung disease genes. Dev. Biol. 416, 255–265 (2016).

  11. 11.

    Memic, F. et al. Transcription and signaling regulators in developing neuronal subtypes of mouse and human enteric nervous system. Gastroenterology 154, 624–636 (2018).

  12. 12.

    Heanue, T. A. & Pachnis, V. Expression profiling the developing mammalian enteric nervous system identifies marker and candidate Hirschsprung disease genes. Proc. Natl Acad. Sci. USA 103, 6919–6924 (2006).

  13. 13.

    Roy-Carson, S. et al. Defining the transcriptomic landscape of the developing enteric nervous system and its cellular environment. BMC Genomics 18, 290 (2017).

  14. 14.

    Lasrado, R. et al. Lineage-dependent spatial and functional organization of the mammalian enteric nervous system. Science 356, 722–726 (2017).

  15. 15.

    Soldatov, R et al. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364, eaas9536 (2019).

  16. 16.

    Betancur, P., Bronner-Fraser, M. & Sauka-Spengler, T. Assembling neural crest regulatory circuits into a gene regulatory network. Annu. Rev. Cell Dev. Biol. 26, 581–603 (2010).

  17. 17.

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

  18. 18.

    Williams, R. M. et al. Reconstruction of the global neural crest gene regulatory network in vivo. Dev. Cell 51, 255–276 (2019).

  19. 19.

    Simoes-Costa, M. & Bronner, M. E. Reprogramming of avian neural crest axial identity and cell fate. Science 352, 1570–1573 (2016).

  20. 20.

    Simoes-Costa, M. S., McKeown, S. J., Tan-Cabugao, J., Sauka-Spengler, T. & Bronner, M. E. Dynamic and differential regulation of stem cell factor FoxD3 in the neural crest is encrypted in the genome. PLoS Genet. 8, e1003142 (2012).

  21. 21.

    Tani-Matsuhana, S., Vieceli, F. M., Gandhi, S., Inoue, K. & Bronner, M. E. Transcriptome profiling of the cardiac neural crest reveals a critical role for MafB. Dev. Biol. 444, S209–S218 (2018).

  22. 22.

    Bondurand, N., Natarajan, D., Barlow, A., Thapar, N. & Pachnis, V. Maintenance of mammalian enteric nervous system progenitors by SOX10 and endothelin 3 signalling. Development 133, 2075–2086 (2006).

  23. 23.

    Choi, H. M. T. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

  24. 24.

    Kim, B. M., Buchner, G., Miletich, I., Sharpe, P. T. & Shivdasani, R. A. The stomach mesenchymal transcription factor Barx1 specifies gastric epithelial identity through inhibition of transient Wnt signaling. Dev. Cell 8, 611–622 (2005).

  25. 25.

    Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

  26. 26.

    Lukoseviciute, M. et al. From pioneer to repressor: bimodal FoxD3 activity dynamically remodels neural crest regulatory landscape in vivo. Dev. Cell 47, 608–628 (2018).

  27. 27.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  28. 28.

    Zhan, X. & Liu, D. J. SEQMINER: an R-package to facilitate the functional interpretation of sequence-based associations. Genet. Epidemiol. 39, 619–623 (2015).

  29. 29.

    Stefflova, K. et al. Cooperativity and rapid evolution of cobound transcription factors in closely related mammals. Cell 154, 530–540 (2013).

  30. 30.

    Bailey, T. L. et al. MEME suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

  31. 31.

    Gui, H. et al. Whole exome sequencing coupled with unbiased functional analysis reveals new Hirschsprung disease genes. Genome Biol. 18, 48 (2017).

  32. 32.

    Williams, R. M. et al. Genome and epigenome engineering CRISPR toolkit for in vivo modulation of cis-regulatory interactions and gene expression in the chicken embryo. Development 145, dev160333 (2018).

  33. 33.

    Lane, P. W. & Liu, H. M. Association of megacolon with a new dominant spotting gene (Dom) in the mouse. J. Hered. 75, 435–439 (1984).

  34. 34.

    Zhao, F., Bosserhoff, A. K., Buettner, R. & Moser, M. A heart-hand syndrome gene: Tfap2b plays a critical role in the development and remodeling of mouse ductus arteriosus and limb patterning. PLoS One 6, e22908 (2011).

  35. 35.

    Bylund, M., Andersson, E., Novitch, B. G. & Muhr, J. Vertebrate neurogenesis is counteracted by Sox1–3 activity. Nat. Neurosci. 6, 1162–1168 (2003).

  36. 36.

    Bansod, S., Kageyama, R. & Ohtsuka, T. Hes5 regulates the transition timing of neurogenesis and gliogenesis in mammalian neocortical development. Development 144, 3156–3167 (2017).

  37. 37.

    Le Lievre, C. S. & Le Douarin, N. M. Mesenchymal derivatives of the neural crest: analysis of chimaeric quail and chick embryos. J. Embryol. Exp. Morphol. 34, 125–154 (1975).

  38. 38.

    Maeda, K. et al. Postotic and preotic cranial neural crest cells differently contribute to thyroid development. Dev. Biol. 409, 72–83 (2016).

  39. 39.

    Kirby, M. L. & Stewart, D. E. Neural crest origin of cardiac ganglion cells in the chick embryo: identification and extirpation. Dev. Biol. 97, 433–443 (1983).

  40. 40.

    Burns, A. J. & Delalande, J. M. Neural crest cell origin for intrinsic ganglia of the developing chicken lung. Dev. Biol. 277, 63–79 (2005).

  41. 41.

    Espinosa-Medina, I. et al. Dual origin of enteric neurons in vagal Schwann cell precursors and the sympathetic neural crest. Proc. Natl Acad. Sci. USA 114, 11980–11985 (2017).

  42. 42.

    Fontaine, J., Le Lievre, C. & Le Douarin, N. M. What is the developmental fate of the neural crest cells which migrate into the pancreas in the avian embryo? Gen. Comp. Endocrinol. 33, 394–404 (1977).

  43. 43.

    Le Douarin, N. M. & Teillet, M. A. Experimental analysis of the migration and differentiation of neuroblasts of the autonomic nervous system and of neurectodermal mesenchymal derivatives, using a biological cell marking technique. Dev. Biol. 41, 162–184 (1974).

  44. 44.

    Faure, S., McKey, J., Sagnol, S. & de Santa Barbara, P. Enteric neural crest cells regulate vertebrate stomach patterning and differentiation. Development 142, 331–342 (2015).

  45. 45.

    Bockman, D. E. & Kirby, M. L. Dependence of thymus development on derivatives of the neural crest. Science 223, 498–500 (1984).

  46. 46.

    Hakami, R. M. et al. Genetic evidence does not support direct regulation of EDNRB by SOX10 in migratory neural crest and the melanocyte lineage. Mech. Dev. 123, 124–134 (2006).

  47. 47.

    Hamburger, V. & Hamilton, H. L. A series of normal stages in the development of the chick embryo. J. Morphol. 88, 49–92 (1951).

  48. 48.

    Sauka-Spengler, T. & Barembaum, M. Gain- and loss-of-function approaches in the chick embryo. Methods Cell. Biol. 87, 237–256 (2008).

  49. 49.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  50. 50.

    Labun, K., Montague, T. G., Gagnon, J. A., Thyme, S. B. & Valen, E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 44, W272–W276 (2016).

  51. 51.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  52. 52.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  53. 53.

    Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).

  54. 54.

    Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

  55. 55.

    Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

  56. 56.

    Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

  57. 57.

    Ye, T. et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).

  58. 58.

    Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

  59. 59.

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

  60. 60.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  61. 61.

    Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

  62. 62.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  63. 63.

    Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).

  64. 64.

    Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-seq analysis. Nucleic Acids Res. 46, D252–D259 (2018).

  65. 65.

    Longabaugh, W. J., Davidson, E. H. & Bolouri, H. Computational representation of developmental genetic regulatory networks. Dev. Biol. 283, 1–16 (2005).

  66. 66.

    Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  67. 67.

    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).

  68. 68.

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  69. 69.

    Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).

  70. 70.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

Download references

Acknowledgements

We thank all the members of the T.S.-S. laboratory for helpful discussions throughout this project, particularly R.M. Williams and I. Candido-Ferreira. We thank M. Bronner and A. Burns for insightful comments on the manuscript. Next-generation sequencing was performed at the MRC WIMM Sequencing Facility, FACS at the WIMM Flow Cytometry Facility and 10x single-cell sequencing at the WIMM Single Cell Core Facility. This work was supported by the MRC, The Lister Institute, John Fell Fund and Leverhulme Trust grants (to T.S.-S.) and a Newlife Charity for Disabled Children small research grant (to I.T.C.L.). I.T.C.L. was funded by an NIHR Academic Clinical Fellowship in partnership with Oxford University Clinical Academic Graduate School (OUCAGS). This publication presents independent research partially funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information

Conceptualisation was provided by I.T.C.L. and T.S.-S., investigation, validation and formal analysis by I.T.C.L., manuscript writing by I.T.C.L. and T.S.-S., visualisation by I.T.C.L. and T.S.-S., supervision by T.S.-S. and funding acquisition by T.S.-S. and I.T.C.L.

Correspondence to Tatjana Sauka-Spengler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Summary of ENS development and current genome wide transcriptomic datasets.

Comparative developmental time points during key ENS developmental events summarised from current literature. All the current genome-wide transcriptomic datasets where the majority of studies were carried out in the mouse using either Wnt1:Cre or Sox10:Cre/Ert2 lines and cells analysed later in ENS development are listed. Red box indicates gap of genome-wide profiles consisting of ATAC-seq, Biotin-ChIP-seq and scRNA-seq unique datasets. Related to Introduction.

Extended Data Fig. 2 Quality control for HH10 NC2 ATAC-seq experiments.

a, ATAC-seq fragment lengths. b, ATAC-seq complexity curves. c, Hierarchical clustering of ATAC-seq peaks. d, Principal Component Analysis based on ATAC-seq genome-wide normalise reads. Each point represents one replicate experiment. Three NC2+ and three NC2- experiments are shown. e, ATAC-seq TSS peak densities. f, ATAC-seq peak centre densities. g, Box and whiskers plot of ATAC-seq total normalised counts per peak (n=7877 peaks) showing min to max counts. h, Boxplot of Pearson correlation scores between samples with line at median obtained from Deeptools analysis of ATAC samples, n=3 biological triplicates per cell type per stage including negative fluorescent cells. i, GO-terms of the top 200 genes obtained from DA analysis. Over-representation analysis was performed using Panther database and p-values were calculated using binominal test with Bonferroni correction for multiple hypothesis. j, Optimisation of co-electroporation assay. E2:Citrine and E2:Cerulean constructs were co-electroporated at HH4 and embryos were incubated till HH12. Embryos were dissociated and subjected to FAC-sorting. Using appropriate gating strategy, only one cell was recovered in single fluorophore channel. Co-electroporation tests indicated that vast majority of cells receive both plasmids. 60 embryos used fin FAC-sort for each independent experiment. k-l, Representative FACS plot for negative gating (k) and Citrine and Cerulean positive gating strategy (l). For Citrine only sorts (Fig. 1), six independent experiments were carried out. m, Immunofluorescence for Citrine and mCherry to mark E2- and NC2-driven reporters respectively in a transverse section of a HH18 embryo at the pharynx level showing distinct E2-only activity at pharyngeal arch 2 (pa2, pink arrowhead). 3 embryos. Scale bars = 100 μm. n, Whole mount imaging of a HH15 embryo electroporated with E2:Citrine and NC2:mCherry showing lateral view of the cranial and vagal region. Of note, there are NC2:mCherry cells within the cranial region. Pink arrowheads point to distinct E2-only activity in pharyngeal arches. 3 embryos. Scale bars = 100 μm. Related to Figs. 1 and 2. Source data

Extended Data Fig. 3 Single cell analysis and mining of published single cell datasets.

a, Schematic for the cell preparation of the 10X scRNA-seq experiment. b, QC plots showing RNA features, counts and percentage of mitochondrial reads per cell. Black dotted line indicates cut off for filtering where only cells with >200 and <3000 features (genes) and <10% mitochondrial gene content were selected for analysis. 700 cells obtained in total with the violin plot indicating median and filtered to 570 cells. c, UMAP representation of 570 single cells assigned to four distinct clusters (scC1-4). d, Violin plots of the expression levels (log TPM) for NeuroD1 and NeuroD4 genes indicate no neural tube contamination of the single cell sample. e, Top 50 differential gene markers for each cluster plotted on the –log10(padj) values. Adjusted p-value (padj) was calculated using Wald statistics with a negative binominal model and corrected using Benjamin-Hochberg method for multiple hypothesis testing. f, Violin plots depicting selected cluster markers from Wnt1-traced NC single-cell datasets from post-otic trunk level at E9.5 (Publicly available data from SRP135960)15. Violin Y-axis represents expression levels (log TPM) from Seurat analysis. Individual data points in the plot represent individual cells expressing the gene in that cluster. EdnrB is expressed in all clusters similar to Wnt1 while FoxD3 is only found in a subset of clusters. g, h, Volcano plot depicting statistically significant DE genes obtained by RNA-seq comparing DP (left) vs E2 (right) populations at HH10 (g) and HH18 (h). Adjusted p-values (padj), calculated using Wald test with Benjamin-Hochberg correction are plotted against FoldChange enrichment on a log scale. Analysis shows differential enrichment of neural genes in DP and neuronal/mesenchymal ones in E2 population. i, Heatmap showing the triplicate average gene TPM expression of the selected differentiation markers enriched at HH25 highlighting their expression dynamics across developmental stages and segregation within E2 or DP populations. j, Heatmap depicting average gene expression levels per single cell clusters from Wnt1-traced NC at P21 mouse small bowel myenteric plexus25 corroborating our data shown in (i). Related to Figs. 3 and 4. Source data

Extended Data Fig. 4 Quality control for ATAC-seq and RNA-seq experiments and correlation of the two datasets.

a, Hierarchical clustering of ATAC-seq datasets. b, Hierarchical clustering of RNA-seq datasets. c, Stacked bar plots depicting genomic feature distributions of ATAC-seq peaks as annotated by ChIPseeker package. Annotated features identify a large proportion of peaks as distal intergenic elements. d, Principal component analysis for ATAC-seq samples. Each point represents one ATAC-seq replicate experiment. e, Principal component analysis for RNA-seq samples. Each point represents one RNA-seq replicate experiment. f, ATAC-seq complexity curves. g, ATAC-seq fragment length. h, ATAC-seq peak density profiles. i, Number of ATAC-seq peak called using MACS and consensus peaksets obtained from all three replicates. j, Cumulative number of DA peaks per gene. k, Correlation of DA promoter peaks with DE genes. Simply, promoter peak log2FoldChange scores from DA ATAC-seq Deseq2 analysis and log2FoldChange scores from gene DE RNA-seq Deseq2 analysis were tabulated and gene-matched. The associated Log2FoldChange values (statistically significant with padj<0.05, log2FoldChange>1) were plotted, R-squared coefficient score was generated from the slope and p-value from regression analysis (Wald test). Sig, statistically significant. Related to Figs. 3 and 4. Source data

Extended Data Fig. 5 Common ATAC-seq peaks across stages and single-cell RNA-seq quality control with selected gene expression in individual cells.

a, ATAC-seq UCSC genome browser tracks for NeuroD4, Mbp, Sox6 and Barx1 showing accessible genomic elements. NeuroD4 elements show an accessibility bias in the E2 population compared to Mbp that showed a bias to DP. Sox6 and Barx1 displayed opened elements in both populations. b, The total number of accessible elements across different categories of differentially expressed genes showing dynamic use of elements across stages but also cell types. Size of the bubble indicates total number of differentially accessible elements. c, Hierarchical clustering of Biotin ChIP-seq peak sets based on Pearson correlation scores from duplicate experiments. d, Hierarchical clustering of Biotin ChIP-seq peaks within DP peaks based on Pearson correlation scores from duplicate experiments. e, Hierarchical clustering of Biotin ChIP-seq peaks within E2 peaks based on Pearson correlation scores from duplicate experiments. Related to Figs. 5 and 6. Source data

Extended Data Fig. 6 CRISPR knockout validation and summary analyses.

a, Principal Component Analysis of all RNA-seq samples. Each dot represents one independent experiment. Duplicate KO experiments were performed per gene. One Sox3 KO experiment (arrow) was removed as a clear outlier. b, sgRNA oligo sequence for targeting individual genes. c, Temperature-Shifted melt curves representing profiles from Cas9-control individual embryos in red (8 embryos) and Sox3 KO embryos in green (8 embryos) all showing split profiles, thus suggesting high penetrance of efficient genome editing events Difference in Relative Fluorescence Units (RFU) between experimental and control samples is shown on y-axis. d, IGV snapshot of BAM-generated mapping around the region of gRNA target sites with the top panel showing one representative replicate of Cas9-only control and the bottom panel showing one representative replicate of the gene knockout. Snapshots obtained at the base of the plots for similar comparison. e, Summary of differential expression analysis obtained using DESeq2 analysis of KO and control samples for selected genes. Related to Fig. 7.

Extended Data Fig. 7 Deconstructing EdnrB GRN.

a, TF binding motif enrichment within EdnrB DA elements. b, Results of RNA-seq differential expression analysis comparing KO versus control embryos depicting genes involved in EdnrB gene regulatory circuitry. Related to Fig. 8. Source data

Supplementary information

Reporting Summary

Supplementary Table 1

All de novo motifs identified by Homer with a P value of less than 1 × 10−60 were included. Two motifs per gene were added where a larger motif sequence was identified. All position weighted matrices were then annotated using TOMTOM (HOCOMOCO Human Full P < 0.0005, q < 0.5) and HOMER (score > 0.8) to obtain a consensus name. Where no consensus annotation is reached, a general family TF name was allocated to the motif. P adjusted values were obtained to adjust for multiple comparisons. (Related to Figs. 4 and 5.)

Source data

Statistical Source Data Fig. 1

ATAC-seq counts matrix and cell count

Statistical Source Data Fig. 2

Cell counts

Statistical Source Data Fig. 3

Featurecounts raw matrix and DESEQ2 analysis from pairwise comparisons

Statistical Source Data Fig. 4

Top markers from Seurat analysis including raw matrix file. Source files available on GEO series.

Statistical Source Data Fig. 5

ATAC-seq counts matrix

Statistical Source Data Fig. 6

Statistical P values for combinatorial analysis and differential binding DESEQ2 analysis

Statistical Source Data Fig. 7

Featurecounts raw matrix

Statistical Source Data Fig. 8

Motif occurrences

Statistical Source Data Extended Data Fig. 2

Source data

Statistical Source Data Extended Data Fig. 3

Source data

Statistical Source Data Extended Data Fig. 4

Source data

Statistical Source Data Extended Data Fig. 5

Source data

Statistical Source Data Extended Data Fig. 6

Source data

Statistical Source Data Extended Data Fig. 7

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ling, I.T.C., Sauka-Spengler, T. Early chromatin shaping predetermines multipotent vagal neural crest into neural, neuronal and mesenchymal lineages. Nat Cell Biol 21, 1504–1517 (2019) doi:10.1038/s41556-019-0428-9

Download citation