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Reconstruction of the Global Neural Crest Gene Regulatory Network in Vivo

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Reconstruction of the Global Neural Crest Gene Regulatory Network in Vivo Reconstruction of the global neural crest gene regulatory network in vivo Ruth M Williams1, Ivan Candido-Ferreira1, Emmanouela Repapi2, Daria Gavriouchkina1,4, Upeka Senanayake1, Jelena Telenius2,3, Stephen Taylor2, Jim Hughes2,3, and Tatjana Sauka-Spengler1,∗ Supplemental Material ∗Lead and corresponding author: Tatjana Sauka-Spengler ([email protected]) 1University of Oxford, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, Oxford, OX3 9DS, UK 2University of Oxford, MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 3University of Oxford, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK 4Present Address: Okinawa Institute of Science and Technology, Molecular Genetics Unit, Onna, 904-0495, Japan A 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R1_5-6ss) log2(R1_8-10ss) log2(R1_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.92 0 r=0.99 0 r=0.96 0 r=0.99 0 r=0.96 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R2_non-NC) log2(R2_5-6ss) log2(R3_5-6ss) log2(R2_8-10ss) log2(R3_8-10ss) 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R1_5-6ss) log2(R2_5-6ss) log2(R1_8-10ss) log2(R2_8-10ss) log2(R1_non-NC) 5 5 5 5 5 0 r=0.94 0 r=0.96 0 r=0.95 0 r=0.96 0 r=0.95 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R3_non-NC) log2(R4_5-6ss) log2(R3_5-6ss) log2(R4_8-10ss) log2(R3_8-10ss) 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 10 10 10 10 10 log2(R2_5-6ss) log2(R3_5-6ss) log2(R2_8-10ss) log2(R3_8-10ss) log2(R3_non-NC) 5 5 5 5 5 0 r=0.97 0 r=0.95 0 r=0.98 0 r=0.95 0 r=0.98 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 log2(R2_non-NC) log2(R4_5-6ss) log2(R4_5-6ss) log2(R4_8-10ss) log2(R4_8-10ss) B C Downregulated 8-10ss vs 5-6ss Upregulated 8-10ss vs 5-6ss vi vii viii 10 10 10 CLDN1 35 CYP26A1 9 9 9 8 8 8 30 OTOGL ed counts z mali 7 r 7 7 CadM1 ADAMTS1 Col6A1 No 25 Col2a1 EDNRB CYP26A1 6 6 6 Lmo4 Pax3 PPP1R1C FGFR2/3 Wnt7a/b 234 DACT1 PCDH18 202 FZD6 244 alue) FST v 5-6ss 8-10ss non-NC 5-6ss 8-10ss non-NC 5-6ss 8-10ss non-NC 20 PTN ix x xi 10 10 BPIFB3 10 −log10(p 15 AGTRAP MEF2C 9 9 9 ADAMTS1 NOXO1 8 10 LTK ADAMTSL1 8 8 BMP5 ed Counts DMBX1 CITED4 MYL4 WNT7B z BRINP2 CXCR4 RXRG PPL mali r EDNRB 7 7 NEMP1 7 HES5 SOX10 BMP5 Oct1 BRPF3 FBXO2 No Draxin TFAP2d 5 COL9A1 LHX9 FZD7 PCDH12 Notch2 Sox2 Chd2,4,5 Id2 TFAP2e 6 6 FGF8 Zic2 Cramp1 ILK Wnt8c 6 Kit Wnt’s3-9b LIMK1/2 WISP1 393 FST 341 Pcdh8 Zic1 762 5-6ss 8-10ss non-NC 5-6ss 8-10ss non-NC 5-6ss 8-10ss non-NC −3 −2 −1 0 1 2 3 log2FoldChange xii xiii 10 10 9 9 8 8 ed Counts z FGF22 RARB Cldn16-18 Wnt5 7 mali FGFBP1 Hand1 7 Cx3cr1 FZD8 r NR2E3 E2F1 No SFRP5 6 6 223 323 5-6ss 8-10ss non-NC 5-6ss 8-10ss non-NC Supplementary Figure S1. RNA-seq quality control and reproducibility. Related to figure 1 (A) Scatter plots showing correlation of RNA-seq replicas. r= Pearson correlation co-efficient. (B) Volcano plot of genes up and downregulated at 8-10ss compared to 5-6ss. (C) Clusters (vi-xiii) of highly correlated genes identified by WGCNA. 2 15 15 A 15 B 8-10ss NC 1 8-10ss NC 0.95 5-6ss NC 0.9 10 10 10 HH4 0.85 HH4 0.8 5 5 5 log2(R1_HH4) log2(R1_5-6ss) 8-10ss_negs log2(R1_8-10ss) 0.75 r=0.92 r=0.90 r=0.97 8-10ss_negs 0.7 8-10ss_negs 0 0 0 0 5 10 15 0 5 10 15 0 5 10 15 5-6ss NC log2(R2_5-6ss) log2(R2_8-10ss) log2(R2_HH4) 5-6ss_negs 15 15 15 5-6ss_negs 5-6ss_negs 8-10ss NC 8-10ss NC 5-6ss NC HH4 HH4 8-10ss_negs 8-10ss_negs 8-10ss_negs 5-6ss NC 5-6ss_negs 5-6ss_negs 5-6ss_negs 10 10 10 5 5 5 C ● ● log2(R1_5-6ss_Negs) log2(R2_5-6ss_Negs) log2(R1_5-6ss_Negs) 5-6ss r=0.98 r=0.98 r=0.99 ● 5-6ss_neg 0 0 0 ● ● 8-10ss 2.5 ● ● 0 5 10 15 0 5 10 15 0 5 10 15 ● 8-10ss_neg ● log2(R2_5-6ss_Negs) log2(R3_5-6ss_Negs) log2(R3_5-6ssNegs) ● ● HH4 ● 15 15 15 iance r a v 0.0 ● ● 10 10 10 PC2: 20% ● ● ● -2.5 5 5 5 log2(R2_9ss_Negs) log2(R1_8-10ss_Negs) log2(R1_8-10ss_Negs) r=0.98 r=0.97 r=0.98 0 0 0 0 5 10 15 0 5 10 15 0 5 10 15 -6 -3 0 3 log2(R2_8-10ss_Negs) log2(R3_8-10ss_Negs) log2(R3_8-10ss_Negs) PC1: 36% variance 1 0 2 4 6 8 0 0 0 0 0 0 % D % % % % F % Cl1_20156 10 10 10 xpressed_5_6ss) Cl2_7816 e 5 5 5 Cl3_6623 Cl4_7681 0 0 0 log(readcount_closest_TSS_5_6ss) log(readcount_closest_ Cl5_7740 log(readcount_closest_TSS_8-10ss) Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 Cl6_6479 Cl7_5832 F’ 10 10 promoter-TSS TTS xpressed_non-NC) intron 5' UTR e Intergenic exon 5 5 E 3' UTR 0 0 R1 5-6ss R2 5-6ss R3 5ss (Pax7) R1 8-10ss R2 8-10ss R3 8-10ss 5-6ss non-NC 8-10ss non-NC HH10 Somites HH4 log(readcount_closest_TSS_non-NC) k-Cl1 log(readcount_closest_ k 10 -Cl2 Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 k-Cl3 Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 k-Cl4 6 k -Cl5 2 k-Cl6 k-Cl7 G 60 k-Cluster-2 12.0 10 k-Cluster-3 k-Cluster-7 -Cl1 k-Cluster-6 k 20156 el. 10.5 50 9.0 5 40 -Cl2 k 7816 el. 7.5 30 -Cl3 k 6623 el. 6.0 0 -Cl4 20 k 0 5 10 15 % genes with multiple enhancers 7681 el. 4.5 -Cl5 k 7740 el. 3.0 10 -Cl6 k 6479 el. 1.5 0 -Cl7 k 5 10 15 20 5832 el. 0.0 -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb -1.0 centre 1.0kb Number of enhancers per gene Supplementary Figure S2. ATAC-seq quality control and chromatin accessibility dynamics. Related to Figures 2 and 3. (A) Scatter plots showing correlation of ATAC-seq replicas, r= Pearson correlation co-efficient. (B) Matrix presenting the correlation coefficients to all possible pairwise comparisons of replicates/samples. (C) PCA comparing NC and non-NC cells at both stages and HH4 ATAC-seq samples. (D) Stacked bar plot showing genomic annotation of k-Cluster elements. The number of elements in each k-Cluster is also shown. (E) Heatmap and merged profiles depicting k-means linear enrichment clustering of ATAC signal across all samples/stages analysed. Pax7 sample is NC cells isolated using the Pax7-195 enhancer (Fig. S3). (F, F0) Violin plots showing correlation between k-Cluster elements and gene expression levels in NC cells (F, green, 5-6ss, purple, 8-10ss) and non-NC cells (F0) annotated to closest expressed gene and closest TSS. (G) Percentage of genes with multiple associated enhancers. 3 A B putative enhancer 12 Nanotagged Dissect cranial Nanostring Assay putative enhancers regions, (inc.controls) extract RNA BsmBI/T4 Ligase TK C Cerulean tag# 1-16 pA pro 1000 TK lacZ Citrine pA 900 pro tag#17-32 800 TK Cherry tag#33-48 pA pro 700 HH4 6-10ss 600 500 400 300 Nanostring count 200 100 0 D Nanotagged Enhancers Snai2 Ets1 Msx1 enh-332 enh-332 enh-241 enh-242 enh-370 enh-117 enh-264 Foxd3 Sox10 Tfap2a enh-372 enh-372 enh-193 enh-84 enh-84 enh-99a enh-185 Pax7 Tfap2a enh-194 enh-195 enh-195 enh-195 enh-199 enh-74 enh-249 Pax7 Tfap2b enh-216 enh-218 enh-143 enh-368 enh-32 enh-36 enh-226 Supplementary Figure S3. Multiplexed high-throughput enhancer screening. Related to Figures 3 and 4. (A) Schematic depiction of enhancer cloning strategy. (B) Cartoon showing ex ovo electroporation technique and Nanostring assay. (C) Bar graph representing typical Nanostring results. Nanostring count (of nanotag transcripts) above 50 (green) was determined to reflect in vivo enhancer activity.
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