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.
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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.
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.
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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.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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
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
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.
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.)
ATAC-seq counts matrix and cell count
Featurecounts raw matrix and DESEQ2 analysis from pairwise comparisons
Top markers from Seurat analysis including raw matrix file. Source files available on GEO series.
ATAC-seq counts matrix
Statistical P values for combinatorial analysis and differential binding DESEQ2 analysis
Featurecounts raw matrix
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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