OSCR

Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development.

Code ↔ Paper

17 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 17 matches
  1. [1] § Materials and methods › Kinetics of branch-specific CREs across neuronal trajectories ↔ scripts/pl/figures/Fig2.r, lines 34–94 · score 0.89 · iCPN_late, CPN_L2, iCPN_early, cpn l5, ipc cycs, gene module
  2. [2] § Materials and methods › Kinetics of branch-specific CREs across neuronal trajectories ↔ scripts/pl/figures/FigS2.r, lines 1–89 · score 0.89 · iCPN_late, CPN_L2, iCPN_early, cpn l5, ipc cycs, gene module
  3. [3] § Materials and methods › Metacell analysis and filtering ↔ scripts/pl/figures/FigS1.r, lines 63–107 · score 0.85 · C1qb, Aif1, Dlx2, Gad2, Gsx2, Hexb
  4. [4] § Materials and methods › NSC gene module analysis ↔ scripts/pl/figures/Fig2.r, lines 34–94 · score 0.74 · Top2a, IPC_cyc, gene module, Mcm4, Mki67, Pcna
  5. [5] § Materials and methods › NSC gene module analysis ↔ scripts/pl/figures/FigS2.r, lines 1–89 · score 0.74 · Top2a, IPC_cyc, gene module, Mcm4, Mki67, Pcna
  6. [6] § Materials and methods › Preparation of training data for accessibility prediction model ↔ scripts/pl/figures/FigS6.r, lines 103–179 · score 0.71 · IPC NSC ATAC, ATAC UMIs, SHAP, XGBoost, R2, prediction
  7. [7] § Results › Modeling IPC chromatin accessibility using TF affinities and epigenomics ↔ scripts/pl/figures/FigS6.r, lines 103–179 · score 0.70 · NSC ATAC, NSC methylation, SHAP, IPC NSC, S6C, R2
  8. [8] § Materials and methods › Selection of neuron branch-specific CREs ↔ scripts/pl/figures/Fig4.r, lines 1–75 · score 0.69 · CthPN, CPN_L2, cpn l5, CfuPN, SCPN, CREs
  9. [9] § Materials and methods › Selection of neuron branch-specific CREs ↔ scripts/pl/figures/FigS4.r, lines 1–51 · score 0.69 · CthPN, CPN_L2, cpn l5, CfuPN, SCPN, CREs
  10. [10] § Materials and methods › Identification of cell type-specific CREs for methylation analysis ↔ scripts/pl/figures/Fig4.r, lines 1–75 · score 0.62 · CthPN, CPN_L2, cpn l5, SCPN, methylation, IPCs
  11. [11] § Materials and methods › Identification of cell type-specific CREs for methylation analysis ↔ scripts/pl/figures/FigS4.r, lines 1–51 · score 0.62 · CthPN, CPN_L2, cpn l5, SCPN, methylation, IPCs
  12. [12] § Results › Modeling IPC chromatin accessibility using TF affinities and epigenomics ↔ scripts/pl/figures/Fig6.r, lines 47–127 · score 0.60 · box energy, SHAP, high affinities, bars, neighboring, methylation
  13. [13] § Materials and methods › Stratification of CREs by E-box affinity, number of proximal elements with high E-box/T-box, and methylation ↔ scripts/pl/figures/FigS6.r, lines 43–99 · score 0.60 · T_box_1, E_box_1, quantile, ATAC, CREs, methylation
  14. [14] § Materials and methods › Stratification of CREs by E-box affinity, number of proximal elements with high E-box/T-box, and methylation ↔ scripts/pl/figures/Fig6.r, lines 47–127 · score 0.57 · IPC ATAC, high affinity, box, neighboring, proximal, CREs
  15. [15] § Materials and methods › Metacell UMAP and noise cleanup ↔ scripts/pl/figures/FigS2.r, lines 91–149 · score 0.54 · NSC Gene Module, cell cycle, S2, RNA, cluster, metacell
  16. [16] § Materials and methods › Single-cell cell cycle analysis ↔ scripts/pl/figures/FigS2.r, lines 91–149 · score 0.52 · NSC gene modules, cell cycle, stem, matrix, IPC
  17. [17] § Materials and methods › TSS enrichment score ↔ scripts/pl/figures/FigS3.r, lines 1–75 · score 0.51 · TSS enrichment score, RNA, metacell

Paper

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The authors' code

R · 331 lines · 16 KB · MIT · 4 matches

  1. library(metacell)
  2. # devtools::load_all('~/src/metacell.flow')
  3. library(metacell.flow)
  4. suppressPackageStartupMessages(library(ComplexHeatmap))
  5. library(matrixStats)
  6. # wd <- '/net//mraid20//export/tgdata/users/yonshap/proj/mmcortex/'
  7. wd <- '.'
  8. # setwd(wd)
  9. db_path <- file.path(wd, 'scdb')
  10. scdb_init(db_path, force_reinit = T)
  11. scdb_flow_init()
  12. SEED <- 1337
  13. K <- 16
  14. set.seed(SEED)
  15. scfigs_init("figs/")
  16. doMC::registerDoMC(60)
  17. nm <- 'pl_cort'
  18. source(file.path(wd,'scripts/util.r'))
  20. mc <- scdb_mc(nm)
  21. mat <- scdb_mat(nm)
  23. mcmd <- readr::read_tsv(file.path(wd, 'output/metacell_model/mcmd_pl_cort.tsv'))
  24. col_key <- tibble::deframe(unique(mcmd[,c('cell_type', 'color')]))
  25. color_key <- unique(mcmd[,c('cell_type', 'color')])
  27. cust_st_ord <- c('OPCs', 'Astrocytes', 'NSC', 'IPC','iCPN/CfuPN', 'iCPN_early','iCPN_late',
  28. 'CPN_L2-3','CPN_L5_6','iCfuPN','SCPN','CthPN')
  29. cust_mc_ord_st <- unlist(lapply(cust_st_ord, function(s) setNames(which(mcmd$cell_type == s)[order(mcmd$mean_day[which(mcmd$cell_type == s)])],
  30. rep(s, length(which(mcmd$cell_type == s)))
  31. )))
  33. cust_st_ord2 <- c('OPCs', 'Astrocytes', 'NSC', 'IPC_cyc', 'IPC', 'iCPN_early','iCPN_late',
  34. 'CPN_L2-3','CPN_L5_6','iCPN/CfuPN','iCfuPN','SCPN','CthPN')
  35. cust_mc_ord_st2 <- unlist(lapply(cust_st_ord2, function(s) setNames(which(mcmd$cell_type == s)[order(mcmd$mean_day[which(mcmd$cell_type == s)])],
  36. rep(s, length(which(mcmd$cell_type == s)))
  37. )
  38. )
  39. )
  40. goi <- c('Pou3f1', 'Pou3f2', 'Cux1', 'Cux2', 'Neurod1', 'Neurog2', 'Id4',
  41. 'Eomes', 'Hes1', 'Apoe', 'Sox5', 'Tbr1', 'Foxp2', 'Foxp1', 'Nfia', 'Islr2',
  42. 'Zbtb20', 'Bcl11b', 'Fezf2', 'Satb2', 'Mef2c', 'Nhlh1', 'Tle4',
  43. 'Rnd2', 'Runx1t1', 'Mapt', 'Mki67', 'Pcna',
  44. 'Fabp7', 'Olig1', 'Ldb2', 'Gadd45g', 'Syt4')
  45. marks_filt <- goi
  47. m_genes <- c("Mki67","Cenpf","Top2a","Smc4","Ube2c","Ccnb1","Cdk1","Arl6ip1","Ankrd11","Hmmr",
  48. "Cenpa","Tpx2","Aurka","Kif4", "Kif2c","Bub1b","Ccna2", "Kif23","Kif20a","Sgo2a",
  49. "Sgo2b","Smc2", "Kif11", "Cdca2","Incenp","Cenpe")
  50. s_genes <- c("Pcna", "Rrm2", "Mcm5", "Mcm6", "Mcm4", "Ung", "Mcm7", "Mcm2","Uhrf1", "Orc6", "Tipin")
  51. cc_genes <- union(m_genes, s_genes)
  53. col_annot <- mcmd[,c('metacell', 'cell_type', 'mean_day')]
  54. col_annot <- tibble::column_to_rownames(col_annot, 'metacell')
  56. clrmp <- colorRampPalette(c('red', 'orange', 'yellow', 'green', 'blue1', 'blue4', 'purple3'))(1000)
  58. clrmp_abs <- colorRampPalette(c('white', 'orange', 'red', 'purple', 'black'))(1000)
  59. brks_abs <- seq(-16.6,-10, l=1000)
  61. clrmp_rel <- colorRampPalette(c('blue3', 'white','red3'))(1000)
  62. brks_rel <- seq(-3,3, l=1000)
  64. ann_colors <- list('cell_type' = tibble::deframe(unique(mcmd[,c('cell_type', 'color')])),
  65. 'mean_day' = setNames(colorRampPalette(c('red', 'orange', 'yellow', 'green', 'blue', 'purple'))(100),
  66. seq(13,18,l=100)))
  68. legc <- log2(1e-05 + mc@e_gc)
  71. dir.create('./output/paper_figs/FigS2/')
  72. device <- 'pdf'
  73. fig_s2a_path <- glue::glue('./output/paper_figs/FigS2/FigS2A.{device}')
  74. fig_s2b_path <- glue::glue('./output/paper_figs/FigS2/FigS2B.{device}')
  75. fig_s2c_path <- glue::glue('./output/paper_figs/FigS2/FigS2C.{device}')
  76. fig_s2d_path <- glue::glue('./output/paper_figs/FigS2/FigS2D.{device}')
  77. fig_s2e_path <- glue::glue('./output/paper_figs/FigS2/FigS2E.{device}')
  78. fig_s2f_path <- glue::glue('./output/paper_figs/FigS2/FigS2F.{device}')
  79. fig_s2g_path <- glue::glue('./output/paper_figs/FigS2/FigS2G.{device}')
  80. fig_s2h_path <- glue::glue('./output/paper_figs/FigS2/FigS2H.{device}')
  81. fig_s2i_path <- glue::glue('./output/paper_figs/FigS2/FigS2I.{device}')
  82. fig_s2j_path <- glue::glue('./output/paper_figs/FigS2/FigS2J.{device}')
  84. astro_module <- readLines('./output/metacell_model/nsc_gene_modules/astro_module.txt')
  85. ipc_module <- readLines('./output/metacell_model/nsc_gene_modules/ipc_module.txt')
  86. stem_module <- readLines('./output/metacell_model/nsc_gene_modules/stem_module.txt')
  88. load('./output/metacell_model/nsc_gene_modules/figs2_data.rda')
  89. load('./output/metacell_model/nsc_gene_modules/phase_info.rda')
  91. ## Fig S2A
  92. st_legc <- as.data.frame(t(tgstat::tgs_matrix_tapply(legc, mcmd$cell_type, mean)))
  95. cluster_names <- setNames(c('Cell cycle 1',
  96. 'Temp. decreasing 1',
  97. 'Cell cycle 2',
  98. 'Temp. increasing 1',
  99. 'Temp. increasing 2',
  100. 'Cell cycle 3',
  101. 'Cell cycle 4',
  102. 'Temp. decreasing 2'), sort(unique(ct_hc_cor_nsc)))
  104. ## Gene module table for MCV and supp table 1
  105. all_genes_in_modules <- multunion(names(ct_hc_cor_nsc), astro_module, ipc_module, stem_module)
  106. gene_module_table <- tibble::enframe(ct_hc_cor_nsc, name = 'gene', value = 'nsc_gene_module')
  107. gene_module_table$nsc_gene_module_name <- cluster_names[gene_module_table$nsc_gene_module]
  108. gene_module_table[,c('IPC', 'astro', 'stem')] <- cbind(ifelse(gene_module_table$gene %in% ipc_module, TRUE, FALSE),
  109. ifelse(gene_module_table$gene %in% astro_module, TRUE, FALSE),
  110. ifelse(gene_module_table$gene %in% stem_module, TRUE, FALSE))
  111. gene_module_table[,colnames(legc_by_day_n)] <- legc_by_day_n[gene_module_table$gene,]
  112. gene_module_table <- gene_module_table %>% dplyr::arrange(nsc_gene_module_name, gene)
  114. readr::write_tsv(gene_module_table, './output/metacell_model/nsc_gene_modules/supp_table_1_nsc_gene_modules.tsv')
  117. pdf(fig_s2a_path, h = 500/71, w = 1000/71)
  120. EXPAND_FACTOR <- 3
  121. RATIO <- 1.3
  122. layout_mat = matrix(c(rep(1:4, EXPAND_FACTOR), rep(5:8, round(EXPAND_FACTOR*RATIO))),
  123. nrow = EXPAND_FACTOR + round(EXPAND_FACTOR*RATIO),
  124. ncol = 4,
  125. byrow = T)
  126. layout(layout_mat)
  127. mari <- c(9,6,3,0.5)
  128. par(las = 2, cex.main = 2, cex.lab = 2, cex.axis = 1.52, mar = mari)
  129. vvv <- lapply(sort(cluster_names), function(cnj) {
  130. clj <- as.numeric(names(cluster_names)[cluster_names == cnj])
  131. gnj <- names(ct_hc_cor_nsc)[ct_hc_cor_nsc == clj]
  132. if (grepl('cell', cnj, ign = T)) {
  133. xaxti <- 'n'
  134. mari[[1]] <- 0.5
  135. } else {
  136. xaxti <- 's'
  137. mari[[1]] <- 10
  138. }
  139. par(mar = mari)
  140. boxplot(st_legc[gnj,cust_st_ord2], col = col_key[cust_st_ord2],
  141. main = cluster_names[[clj]],
  142. ylab = '',
  143. xaxt = xaxti,
  144. ylim = quantile(unlist(st_legc[gnj,cust_st_ord2]), c(0.1,0.96)))
  145. title(ylab = 'Mean RNA', line = 4)
  146. })
  147. dev.off()
  149. cluster_names <- setNames(gsub(' ', '\n', cluster_names), names(cluster_names))
  152. ## Fig S2B
  153. ct_hc_cor_nsc_h <- setNames(cluster_names[ct_hc_cor_nsc], names(ct_hc_cor_nsc))
  155. ca <- columnAnnotation(df = tibble::column_to_rownames(tibble::enframe(ct_hc_cor_nsc_h[hc_cor_nsc$order], name = 'gene', value = 'cluster'), 'gene'),
  156. show_legend = c('cluster' = F),
  157. col = list(cluster = setNames(chameleon::distinct_colors(8)$name, cluster_names[1:8])))
  159. ra <- rowAnnotation(df = tibble::column_to_rownames(tibble::enframe(ct_hc_cor_nsc_h[hc_cor_nsc$order], name = 'gene', value = 'cluster'), 'gene'),
  160. show_legend = c('cluster' = F),
  161. col = list(cluster = setNames(chameleon::distinct_colors(8)$name, cluster_names[1:8])))
  163. ac <- list(cluster = setNames(chameleon::distinct_colors(8)$name, 1:8))
  165. ch_cor_dyn_genes_nsc <- ComplexHeatmap::Heatmap(cor_nsc_legc_dyn_genes[hc_cor_nsc$order,hc_cor_nsc$order], name = ' ',
  166. column_split = ct_hc_cor_nsc_h[hc_cor_nsc$order],
  167. row_split = ct_hc_cor_nsc_h[hc_cor_nsc$order],
  168. top_annotation = ca, left_annotation = ra,
  169. show_row_names = F, show_column_names = F,
  170. col = circlize::colorRamp2(colors = c('blue3', 'white', 'red3'), breaks = c(-1,0,1)),
  171. cluster_columns = F, cluster_rows = F)
  173. pdf(fig_s2b_path, h = 10, w = 10)
  174. draw(ch_cor_dyn_genes_nsc)
  175. dev.off()
  178. ## Fig S2C
  180. tbl_pba_by_ct <- t(table(sc_data_df$cell_type, sc_data_df$pba))
  181. tbl_pba_by_ct_norm <- t(t(tbl_pba_by_ct)/colSums(tbl_pba_by_ct))
  183. rownames(tbl_pba_by_ct_norm) <- gsub('\\d_', '', rownames(tbl_pba_by_ct_norm))
  185. p_pba_by_ct <- pheatmap::pheatmap(tbl_pba_by_ct_norm[,cust_st_ord], cluster_cols = F,
  186. annotation_legend = F, col = clrmp_abs, fontsize = 12,
  187. treeheight_row = 0, treeheight_col = 0)
  188. save_pheatmap_pdf(p_pba_by_ct, fig_s2c_path, h = 500/71, w = 800/71)
  190. ## Fig S2D
  191. nsc_sc <- intersect(names(mc@mc[mc@mc %in% which(mcmd$cell_type == 'NSC')]), colnames(mat_ds))
  193. nsc_sc_by_day <- lapply(tail(sort(unique(mat@cell_metadata$day)),-1), function(di) intersect(nsc_sc, rownames(mat@cell_metadata)[mat@cell_metadata$day == di]))
  194. names(nsc_sc_by_day) <- tail(sort(unique(mat@cell_metadata$day)),-1)
  196. nsc_sc_by_day_vec <- setNames(unlist(sapply(names(nsc_sc_by_day), function(x) rep(x, length(nsc_sc_by_day[[x]])))), do.call('c', nsc_sc_by_day))
  199. pdf(fig_s2d_path, h = 7, w = 28)
  200. par(mfrow = c(1,4), mar = c(6,7,4,1), cex.lab = 3, cex.axis = 3, cex.main = 5)
  202. NUM_PARTITION <- 13
  203. phase_qs <- seq(1-1e-2,max(phase),l=NUM_PARTITION)
  204. bin_borders <- setNames(phase_qs[c(1,2,4,5,8,10,12, 13)], c('G1', 'G1_0', 'G1', 'S', 'G2', 'M', 'G1'))
  205. heights_text <- c(1.5, 2.2, 2.2, 4.2)
  206. ttt <- sapply(1:nrow(mat_ds_cc_genes_select_ord_phase), function(i) {
  207. plot(sort(phase[nsc_sc]), mat_ds_cc_genes_select_ord_phase[i,],
  208. col = 'white',
  209. cex = 1, pch = 16, ylim = quantile(mat_ds_cc_genes_select_ord_phase[i,], c(0.05, 0.95)),
  210. xlab = '', ylab = ''
  211. )
  212. vvv <- sapply(head(seq_along(bin_borders), -1), function(j) {
  213. bj <- bin_borders[[j]]
  214. lines(rep(bj, 2), c(-1,10), lty = 2, lwd = 2)
  215. nmj <- names(bin_borders)[[j]]
  216. })
  217. lines(sort(phase[nsc_sc]), mat_ds_cc_genes_select_ord_phase_rm[i,], col = 'black', lwd = 3)
  218. if (i == 1) {legend('topleft', legend = glue::glue('Rollmean k = {K}'), lwd = 3, col = 'black', cex = 2, bg = 'white')}
  219. title(main = rownames(mat_ds_cc_genes_select_ord_phase)[[i]])
  220. title(xlab = 'phase', line = 4)
  221. title(ylab = 'Downsampled UMIs', line = 4)
  222. })
  223. dev.off()
  226. ## Fig S2E
  227. s_genes_sum <- Matrix::colSums(mat_ds[s_genes,])
  229. m_genes_sum <- Matrix::colSums(mat_ds[m_genes,])
  232. pdf(fig_s2e_path, h = 500/71, w = 1500/71)
  233. par(mfrow = c(1,3), cex.main = 2, cex.axis = 2, cex.lab = 2, mar = c(6,6,5,1), las = 2)
  234. boxplot(s_genes_sum[names(nsc_sc_by_day_vec)]/length(s_genes) ~ phase_cut[names(nsc_sc_by_day_vec)],
  235. main = 'UMIs per S gene per single NSC per bin\nn_{S genes} = 11', ylab = '', xlab = '')
  236. title(ylab = 'UMIs per gene', line = 4)
  237. boxplot(m_genes_sum[names(nsc_sc_by_day_vec)]/length(m_genes) ~ phase_cut[names(nsc_sc_by_day_vec)],
  238. main = 'UMIs per M gene per single NSC per bin\nn_{M genes} = 26', ylab = 'UMIs per gene', xlab = '')
  239. boxplot(s_genes_sum[names(nsc_sc_by_day_vec)]/length(s_genes) + m_genes_sum[names(nsc_sc_by_day_vec)]/length(m_genes) ~ phase_cut[names(nsc_sc_by_day_vec)],
  240. main = 'UMIs per S+M gene per cell per bin', ylab = 'UMIs per gene', xlab = '')
  241. dev.off()
  243. ## Fig S2F
  244. days <- unique(mat@cell_metadata[nsc_sc,'day'])
  246. nsc_inds <- which(sc_data_df$cell_type == 'NSC')
  247. lupc <- length(unique(phase_cut))
  248. bin_seq <- seq(lupc+0.5, 0.5+length(days)*lupc, 12)
  249. pdf(fig_s2f_path, h = 1050/71, w = 1600/71)
  250. par(mfrow = c(3,1), las = 2, cex.lab = 3, cex.axis = 1.5, mar = c(6,6,0.5,0.5))
  251. boxplot(ipc ~ ., data = sc_data_df[nsc_inds,c('ipc','phase_cut', 'day')], ylim = c(0,quantile(sc_data_df$ipc[nsc_inds], 0.94, na.rm = T)),
  252. col = rep(rainbow(lupc), length(days)), ylab = 'IPC module ds UMIs', xlab = '')
  253. vvv <- sapply(bin_seq, function(i) lines(rep(i,2), c(0,1000), lwd = 3))
  254. boxplot(astro ~ ., data = sc_data_df[nsc_inds,c('astro','phase_cut', 'day')], ylim = c(0,quantile(sc_data_df$astro[nsc_inds], 0.975, na.rm = T)),
  255. col = rep(rainbow(lupc), length(days)), ylab = 'Astro module ds UMIs', xlab = '')
  256. vvv <- sapply(bin_seq, function(i) lines(rep(i,2), c(0,1000), lwd = 3))
  257. boxplot(stem ~ ., data = sc_data_df[nsc_inds,c('stem','phase_cut', 'day')], ylim = c(0,quantile(sc_data_df$stem[nsc_inds], 0.95, na.rm = T)),
  258. col = rep(rainbow(lupc), length(days)), ylab = 'Stem module ds UMIs', xlab = '')
  259. vvv <- sapply(bin_seq, function(i) lines(rep(i,2), c(0,1000), lwd = 3))
  260. dev.off()
  264. ## Fig S2G
  265. nsc_late <- as.character(mcmd$metacell[mcmd$cell_type == 'NSC' & mcmd$mean_day > 16.5])
  266. nsc_early <- as.character(mcmd$metacell[mcmd$cell_type == 'NSC' & mcmd$mean_day < 14.5])
  267. nsc_late_rna <- rowMeans(legc[,as.numeric(nsc_late)])
  268. nsc_early_rna <- rowMeans(legc[,as.numeric(nsc_early)])
  269. astro_rna <- rowMeans(legc[,mcmd$metacell[mcmd$cell_type == 'Astrocytes']])
  272. pdf(fig_s2g_path, h = 500/71, w = 1000/71)
  273. par(mfrow = c(1,2), cex.lab = 1.52, cex.main = 1.5)
  274. plot(nsc_late_rna, astro_rna, pch = 16, cex = .15, xlab = 'NSC (mean day > 16.5) RNA', ylab = 'Astrocytes RNA', main = 'Astro vs late NSC - RNA')
  275. abline(a =-2,b = 1,col='red', lty= 2, lwd= 1)
  276. abline(a =+2,b = 1,col='red', lty= 2, lwd= 1)
  277. abline(a =-1,b = 1,col='green', lty= 2, lwd= 1)
  278. abline(a =+1,b = 1,col='green', lty= 2, lwd= 1)
  279. abline(a =+0,b = 1,col='blue', lty= 2, lwd= 1)
  280. legend('bottomright', legend = c('0 LFC', '1 LFC','2 LFC'), col = c('blue', 'green', 'red'), lty = 2, lwd =1)
  281. corh <- cor(nsc_late_rna, astro_rna, method = 'pearson')
  282. text(-13.5,-6, labels = paste0('R^2 = ', signif(corh**2, 2)), cex = 1.5)
  283. plot(nsc_late_rna, nsc_early_rna, pch = 16, cex = .15, xlab = 'NSC (mean day > 16.5) RNA', ylab = 'NSC (mean day < 14.5) RNA', main = 'Early vs late NSC - RNA')
  284. abline(a =-2,b = 1,col='red', lty= 2, lwd= 1)
  285. abline(a =+2,b = 1,col='red', lty= 2, lwd= 1)
  286. abline(a =-1,b = 1,col='green', lty= 2, lwd= 1)
  287. abline(a =+1,b = 1,col='green', lty= 2, lwd= 1)
  288. abline(a =+0,b = 1,col='blue', lty= 2, lwd= 1)
  289. legend('bottomright', legend = c('0 LFC', '1 LFC','2 LFC'), col = c('blue', 'green', 'red'), lty = 2, lwd =1)
  290. corh <- cor(nsc_late_rna, nsc_early_rna, method = 'pearson')
  291. text(-13.5,-9, labels = paste0('R^2 = ', signif(corh**2, 2)), cex = 1.5)
  292. dev.off()
  295. ## Fig S2H
  296. ### Plot IPC vs stem and astro vs stem signatures
  298. pdf(fig_s2h_path, w = 400/71, h = 400/71)
  299. par(mfrow = c(1,1), mar = c(5,5,1,1), cex.lab = 1, cex.axis = 1)
  300. plot(sc_data_df[names(matched_coords_bins_astro),'astro'], sc_data_df[names(matched_coords_bins_astro),'stem'], col = mcmd$color[mc@mc[names(matched_coords_bins_astro)]], ylab = 'Stem module UMIs', xlab = 'Astro module UMIs', pch =16, cex = 0.5)
  301. dev.off()
  304. ## Fig S2I
  305. astro_mcs <- which(mcmd$cell_type == 'Astrocytes')
  307. oligo_mcs <- which(mcmd$cell_type == 'OPCs')
  309. cpnl23_mcs <- which(mcmd$cell_type == 'CPN_L2-3')
  311. gh <- names(ct_hc_cor_nsc[ct_hc_cor_nsc == 4])
  312. pltmt <- cbind(legc_by_day_n[gh,], rowMeans(legc[gh,astro_mcs]),
  313. rowMeans(legc[gh,oligo_mcs]), rowMeans(legc[gh,cpnl23_mcs]))
  314. colnames(pltmt)[(ncol(pltmt)-2):ncol(pltmt)] <- c('Astrocytes', 'OPCs', 'CPN_L2-3')
  315. genes_hi <- rownames(pltmt)[which(rowMaxs(subset(pltmt, select = -c(Astrocytes, OPCs, `CPN_L2-3`))) > pltmt[,'Astrocytes'])]
  317. genes_norm <- setdiff(rownames(pltmt), genes_hi)
  319. pltmt2 <- rbind(pltmt[genes_hi[hclust(dist(pltmt[genes_hi,] - pltmt[genes_hi,'Astrocytes']), method = 'ward.D2')$order],],
  320. pltmt[genes_norm[hclust(dist(pltmt[genes_norm,] - pltmt[genes_norm,'Astrocytes']), method = 'ward.D2')$order],])
  322. p_nsc_cl4 <- pheatmap::pheatmap(pltmt2 - pltmt2[,'Astrocytes'],
  323. gaps_row = length(genes_hi),
  324. cluster_rows = F,
  325. cluster_cols = F,
  326. col = clrmp_rel, breaks = seq(-2,2,l=1000),
  327. clustering_method = 'ward.D2', treeheight_row = 0, fontsize_row = 4)
  331. save_pheatmap_pdf(p_nsc_cl4, fig_s2i_path, h= 1200/71, w = 300/71)

FigS2.r at commit 2f1658c, under MIT · at the source

Overview

Authors: Yonatan Shapira1,2, Florian Noack3, Silvia Vangelisti3, Faye Chong3, Aviezer Lifshitz1,2, Amos Tanay1,2, Boyan Bonev3
ORCID iDs: Amos Tanay, Boyan Bonev
  1. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
  2. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
  3. Research Unit Brain Epigenomics, Helmholtz Center Munich, Munich 81377, Germany
Institutions: Weizmann Institute of Science (Israel); Helmholtz Munich (Germany)
Journal: Genes & development, volume 40, issue 11-12, pages 956-977
Dates: received 11 June 2025; accepted 22 December 2025; published online 1 June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1101/gad.353090.125 · PMID 42020311 · PMCID PMC13224856 · OpenAlex W7155220424
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), developmental (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning
Keywords: brain development, computational modeling, epigenetics, single-cell omics, in vivo MPRA
MeSH: Cerebral Cortex*, Epigenesis, Genetic*, Epigenome*, Gene Expression Regulation, Developmental*, Neural Stem Cells*, Animals, Cell Differentiation, Cell Lineage, DNA Methylation, Mice, Neurodevelopment, Time Factors (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Israeli Science Foundation; European Research Council (Cells2Tissues, 101044469); ERA‐NET NEURON
Citations: cited by 1 paper (Europe PMC); 64 references in the paper

Abstract

During cortical development, neural stem cells (NSCs) combine self-renewal with the sequential production of different subtypes of projection neurons as well as glia cells. How the NSC epigenome accommodates this over time remains unresolved. Here, we address this gap by multimodal epigenomic profiling of mouse cortical development across six time points and five embryonic days. Single-cell gene expression and temporal modeling reveal that NSC self-renewal is not homeostatic, showing progressively stronger astrocytic preference over time. Chromosome accessibility, DNA methylation, and Hi-C show that this process involves major reorganization of the NSC epigenome. A model combining transcription factor motif affinities with epigenetic features, as well as integration of the results with a reporter assay in vivo, show that activation of the NSC neuronal fate regulatory program may be affected by a changing epigenome. Collectively, our findings uncover temporal epigenomic reprogramming that underlies the evolving differentiation potential of NSCs, providing insights into the intrinsic and extrinsic mechanisms that pattern cortical lineages.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

tanaylab/mmcortex

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2f1658c07b65b2821e2be73a76938c5a6a360dca, 6 April 2025
Languages: R (16), Shell (1)
Size: 20 files, 17 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, license file, environment (Dockerfile)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (13 files), pheatmap (8 files), ComplexHeatmap (5 files), circlize (3 files), cowplot (2 files), ggplot2 (2 files), patchwork (2 files), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/tanaylab/mmcortex

aidenlab/JuiceMe

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 597b364296547a396a91e2576cdd4d971f6554cf, 8 July 2021
Languages: Shell (27), Perl (4), Python (3)
Size: 66 files, 34 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), SAMtools (3 files), SciPy (3 files), Matplotlib (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/aidenlab/JuiceMe

tanaylab/shaman

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5be5ce2f514c38f53cbda2497b73bca9bf3bd126, 23 March 2022
Languages: R (14), C/C++ (7), C++ (6), Perl (1)
Size: 68 files, 28 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, environment (DESCRIPTION), documentation, 4 notebooks
Not found: license file, CITATION.cff, tests, continuous integration
Tools: data.table (1 file), ggplot2 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
29 files
At the source: github.com/tanaylab/shaman

Code availability

The code used for generating the analysis and all of the figures is available at https://www.github.com/tanaylab/mmcortex. Methyl Hi-C data were processed using the JuiceMe pipeline, available at https://www.github.com/aidenlab/JuiceMe. The R package to compute the expected tracks and the Hi-C scores is available at https://www.github.com/tanaylab/shaman.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 79 scripts, each with its path and the digest of its content;
  • 17 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The sequencing data generated in this study can be accessed at the GEO database with the accession numbers GSE292318, GSE292319, GSE292320, and GSE292321. The E14 data (scRNA, scATAC, and methyl-Hi-C) are available from GEO with accession number GSE155677 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155677).

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 12 MeSH terms, 3 funders, 64 references.

Cite

This paper

Shapira, Y., Noack, F., Vangelisti, S., Chong, F., Lifshitz, A., Tanay, A., & Bonev, B. (2026). Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development. Genes & development, 40(11-12), 956-977. https://doi.org/10.1101/gad.353090.125

BibTeX

@article{shapira2026neural,
author = {Shapira, Yonatan and Noack, Florian and Vangelisti, Silvia and Chong, Faye and Lifshitz, Aviezer and Tanay, Amos and Bonev, Boyan},
title = {{Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development}},
journal = {Genes \& development},
year = {2026},
month = jun,
volume = {40},
number = {11-12},
pages = {956--977},
publisher = {Cold Spring Harbor Laboratory Press},
issn = {0890-9369},
doi = {10.1101/gad.353090.125},
url = {https://doi.org/10.1101/gad.353090.125},
pmid = {42020311},
pmcid = {PMC13224856}
}

RIS

TY - JOUR
AU - Shapira, Yonatan
AU - Noack, Florian
AU - Vangelisti, Silvia
AU - Chong, Faye
AU - Lifshitz, Aviezer
AU - Tanay, Amos
AU - Bonev, Boyan
TI - Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development
T2 - Genes & development
J2 - Genes Dev
PY - 2026
DA - 2026/06/01
VL - 40
IS - 11-12
SP - 956
EP - 977
SN - 0890-9369
PB - Cold Spring Harbor Laboratory Press
DO - 10.1101/gad.353090.125
UR - https://doi.org/10.1101/gad.353090.125
LA - en
ER -

CSL-JSON

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"id": "10.1101/gad.353090.125",
"type": "article-journal",
"title": "Neural stem cell epigenomes and fate bias are temporally coordinated during mouse cortical development",
"container-title": "Genes & development",
"author": [
{
"family": "Shapira",
"given": "Yonatan"
},
{
"family": "Noack",
"given": "Florian"
},
{
"family": "Vangelisti",
"given": "Silvia"
},
{
"family": "Chong",
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"container-title-short": "Genes Dev",
"volume": "40",
"issue": "11-12",
"page": "956-977",
"DOI": "10.1101/gad.353090.125",
"PMID": "42020311",
"PMCID": "PMC13224856",
"ISSN": "0890-9369",
"publisher": "Cold Spring Harbor Laboratory Press",
"URL": "https://doi.org/10.1101/gad.353090.125",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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