OSCR

TNF-α induces type I IFN signalling to suppress neurogenesis and recruit T cells.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › Normalisation, dimensionality reduction, and clustering ↔ scripts/02_normalisation_and_clustering.Rmd, lines 67–79 · score 0.80 · FindClusters, FindNeighbors, RunUMAP, neighbour, resolutions, dims
  2. [2] § Results › Chronic TNF-α disrupts neuronal differentiation and upregulates a transcriptional type I IFN signature ↔ scripts/03_analysis.Rmd, lines 544–578 · score 0.76 · IFN responsive glial, wound healing, immature neuron, reactive astrocyte, IPC, RGL
  3. [3] § Results › Chronic TNF-α disrupts neuronal differentiation and upregulates a transcriptional type I IFN signature ↔ scripts/03_analysis.Rmd, lines 544–578 · score 0.67 · IFN responsive glial, wound healing, reactive astrocyte, high TNF, IPC, RGL
  4. [4] § Methods › Quality control ↔ scripts/01_load_and_qc.Rmd, lines 401–410 · score 0.66 · mitochondrial gene expression, novelty score, quality cells, QC
  5. [5] § Results › Chronic TNF-α disrupts neuronal differentiation and upregulates a transcriptional type I IFN signature ↔ scripts/03_analysis.Rmd, lines 171–178 · score 0.61 · S100B, AQP4, CD44, CLU, GFAP, ID4
  6. [6] § Results › Chronic TNF-α disrupts neuronal differentiation and upregulates a transcriptional type I IFN signature ↔ scripts/03_analysis.Rmd, lines 146–169 · score 0.61 · Stacked bar, Cluster proportions, low TNF, high TNF, UMAP, treatment
  7. [7] § Methods › Gene module scoring for human immature dentate granule cell signature ↔ scripts/03_analysis.Rmd, lines 181–204 · score 0.51 · AddModuleScore, immature, scored, human, signature, genes

Paper

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The authors' code

R Markdown · 1,162 lines · 43 KB · MIT · 5 matches

  1. ---
  2. title: "03 Analysis"
  3. output: html_document
  4. ---
  7. ```{r setup, include=FALSE}
  8. # If running interactively from within /scripts, move up to project root
  9. # If running interactively from within /scripts, move up to project root
  10. if (basename(getwd()) == "scripts") setwd("..")
  12. # Now we are in the project root
  13. knitr::opts_knit$set(root.dir = normalizePath("."))
  14. knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
  17. source("scripts/00_setup.R")
  18. ```
  20. # Load the seurat object
  22. ```{r}
  23. # load diff
  24. seurat_pipeline_cluster <- readRDS(file.path(RESULTS_DIR, "seurat_pipeline_cluster.rds"))
  26. seurat_pipeline_cluster$active_ident <- Idents(seurat_pipeline_cluster)
  27. ```
  29. # generating figure 2b
  31. ```{r}
  32. new_order <- c("Astro-like 1", "Astro-like 2", "Astro-like 3", "Astro-like 4", "RGL-like 1", "RGL-like 2", "IPC-like 1", "IPC-like 2", "IPC-like 3", "IPC-like 4", "Neuroblast-like", "Immature neuron-like", "IFN-responsive glial/progenitor-like", "Reactive astrocyte-like", "Wound healing-like")
  34. seurat_pipeline_cluster <- SetIdent(seurat_pipeline_cluster, value = factor(Idents(seurat_pipeline_cluster), levels = new_order))
  36. colors_UMAP <- c(
  37. "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
  38. "#E55AA4", "#43BDB2", "#EECAA4", "#050B8D", "#FF6347",
  39. "#9C73B4", "#B3D500", "#27601C", "#DC95E3", "#995D07")
  41. UMAP_plot <- DimPlot(seurat_pipeline_cluster, cols = colors_UMAP) + labs(x = "UMAP1", y = "UMAP2")
  43. ggsave(filename = "Fig2b.pdf", path =FIGURES_DIR , plot = UMAP_plot, width = 200, height = 100, device = cairo_pdf, units = "mm")
  44. ```
  46. # generating figure 2c
  48. Add timepoint to metadata:
  50. ```{r}
  51. # Make a new metadata column
  52. seurat_pipeline_cluster$timepoint <- 'Time'
  54. # Fix meta data
  55. metadata_seurat_pipeline_cluster <- [email hidden] %>%
  56. mutate(Time = case_when(
  57. sample.ID == "Proliferation control" ~ "Proliferation (2 days)",
  58. sample.ID == "Proliferation low TNF" ~ "Proliferation (2 days)",
  59. sample.ID == "Proliferation high TNF" ~ "Proliferation (2 days)",
  60. sample.ID == "Differentiation control (7 days)" ~ "Differentiation (7 days)",
  61. sample.ID == "Differentiation low TNF (7 days)" ~ "Differentiation (7 days)",
  62. sample.ID == "Differentiation high TNF (7 days)" ~ "Differentiation (7 days)",
  63. sample.ID == "Differentiation control (14 days)" ~ "Differentiation (14 days)",
  64. sample.ID == "Differentiation low TNF (14 days)" ~ "Differentiation (14 days)",
  65. sample.ID == "Differentiation high TNF (14 days)" ~ "Differentiation (14 days)"))
  67. # Add new metadata
  68. [email hidden] <- metadata_seurat_pipeline_cluster
  69. ```
  70. Add treatment column
  72. ```{r}
  73. # Make a new metadata column
  74. seurat_pipeline_cluster$treatment <- NULL
  76. # Fix meta data
  77. metadata_seurat_pipeline_cluster <- [email hidden] %>%
  78. mutate(treatment = case_when(
  79. sample.ID == "Proliferation control" ~ "Control",
  80. sample.ID == "Proliferation low TNF" ~ "Low TNF",
  81. sample.ID == "Proliferation high TNF" ~ "High TNF",
  82. sample.ID == "Differentiation control (7 days)" ~ "Control",
  83. sample.ID == "Differentiation low TNF (7 days)" ~ "Low TNF",
  84. sample.ID == "Differentiation high TNF (7 days)" ~ "High TNF",
  85. sample.ID == "Differentiation control (14 days)" ~ "Control",
  86. sample.ID == "Differentiation low TNF (14 days)" ~ "Low TNF",
  87. sample.ID == "Differentiation high TNF (14 days)" ~ "High TNF"))
  89. # Add new metadata
  90. [email hidden] <- metadata_seurat_pipeline_cluster
  91. ```
  92. Take a look at number of cells belonging to each cluster by line and timepoint. For this we make a contingency table.
  94. ```{r}
  95. Prol <- subset(seurat_pipeline_cluster, Time =="Proliferation (2 days)")
  96. Dif7 <- subset(seurat_pipeline_cluster,Time == "Differentiation (7 days)")
  97. Dif14 <- subset(seurat_pipeline_cluster,Time == "Differentiation (14 days)")
  99. Idents(Prol) <- "active_ident"
  100. Idents(Dif7) <- "active_ident"
  101. Idents(Dif14) <- "active_ident"
  102. ```
  104. Look at cell frequencies in each sample:
  106. ```{r}
  107. table(Idents(Prol), Prol$treatment)
  108. table(Idents(Dif7), Dif7$treatment)
  109. table(Idents(Dif14), Dif14$treatment)
  111. to_df <- function(df) {
  112. df %>%
  113. as.data.frame() %>%
  114. as_tibble() %>%
  115. dplyr::rename("active_ident"=Var1,"treatment"=Var2,"count"=Freq) %>%
  116. arrange(active_ident)
  117. }
  118. ```
  120. Turn frequency tables into tidy data
  122. ```{r}
  123. Prol <- table(Idents(Prol), Prol$treatment) %>%
  124. to_df() %>%
  125. mutate(sample = "2-Day Proliferation")
  127. Dif7 <- table(Idents(Dif7), Dif7$treatment) %>%
  128. to_df() %>%
  129. mutate(sample = "7-Day Differentiation")
  131. Dif14 <- table(Idents(Dif14), Dif14$treatment) %>%
  132. to_df() %>%
  133. mutate(sample = "14-Day Differentiation")
  135. my_data <- Prol %>%
  136. bind_rows(Dif7) %>%
  137. bind_rows(Dif14) %>%
  138. mutate(active_ident = factor(active_ident,
  139. levels=c("Astro-like 1","Astro-like 2","Astro-like 3","Astro-like 4","RGL-like 1","RGL-like 2","IPC-like 1","IPC-like 2", "IPC-like 3","IPC-like 4", 'Neuroblast-like', 'Immature neuron-like', 'IFN-responsive glial/progenitor-like', 'Reactive astrocyte-like', 'Wound healing-like'))) %>%
  140. mutate(sample = factor(sample,levels = c("2-Day Proliferation",
  141. "7-Day Differentiation",
  142. "14-Day Differentiation")))
  144. ```
  146. Make stacked bar plots of cell type proportions
  148. ```{r}
  149. my_data <- my_data %>%
  150. mutate(treatment = factor(treatment, levels = c("High TNF", "Low TNF", "Control")))
  152. bar_plot <- my_data %>%
  153. ggplot(aes(y=count,x=treatment,fill=active_ident)) +
  154. geom_bar(position="fill",stat="identity") +
  155. coord_flip() +
  156. scale_fill_manual(values = colors_UMAP,name="") +
  157. scale_y_continuous(expand = c(0,0),
  158. labels = percent_format()) +
  159. facet_wrap(~sample,ncol = 1) +
  160. theme(axis.line.y = element_blank(),
  161. axis.text.y = element_text(size = 10, color = "black", face = "bold"),
  162. axis.ticks.y = element_blank(),
  163. strip.text = element_text(size = 10,face = "bold")) +
  164. labs(x="",y="Cluster Proportion") +
  165. theme(plot.margin = margin(0,1,0.5,0, "cm"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(),plot.background = element_blank() )
  167. ggsave(filename = "Fig2c.pdf", path =FIGURES_DIR , plot = bar_plot, width = 200, height = 120, device = cairo_pdf, units = "mm")
  169. ```
  171. # generating figure 2d
  172. ```{r}
  173. all_markers <- c('AQP4','S100B', 'GFAP', 'BCAN', 'CLU', 'ID4', 'FABP7', 'PTN', 'CDH4', 'CD44' ,'HES1','STMN1', 'MKI67', 'HMGB2','DCX','IGFBPL1' ,'PROX1','MAP2', 'NRXN3', 'TUBB3','SYNPR', 'DSCAM', 'CALB2')
  175. dotplot <- DotPlot_scCustom(seurat_pipeline_cluster,features = all_markers, x_lab_rotate = TRUE)
  177. ggsave(filename = "Fig2d.pdf", path =FIGURES_DIR , plot = dotplot, width = 240, height = 120, device = cairo_pdf, units = "mm")
  178. ```
  181. #generating figure 2e
  182. ```{r}
  183. human_genes <- read.csv("scripts/NIHMS1815814-supplement-1815814_Tab_4.csv")
  185. top_twenty <- human_genes[1:20, ]
  186. top_twenty_genes <- top_twenty$Gene
  188. seurat_pipeline_cluster <- AddModuleScore(
  189. seurat_pipeline_cluster,
  190. features = list(top_twenty_genes),
  191. name = c('Immature DG cell signature'),
  192. search = TRUE,
  193. )
  195. Immature_DG <- FeaturePlot_scCustom(
  196. seurat_pipeline_cluster,
  197. features = "Immature DG cell signature1",
  198. colors_use = viridis_plasma_dark_high,
  199. na_color = "grey90",
  200. na_cutoff = 0.15) + xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed() + ggtitle('Human imGCs signature')
  202. ggsave(filename = "Fig2e.pdf", path =FIGURES_DIR , plot = Immature_DG, width = 200, height =200, device = cairo_pdf, units = "mm")
  204. ```
  207. # generating figure 2f-g
  209. ```{r}
  210. DefaultAssay(seurat_pipeline_cluster) <- "SCT"
  211. seurat_pipeline_cluster <- PrepSCTFindMarkers(seurat_pipeline_cluster)
  212. markers_clusters <-FindAllMarkers(seurat_pipeline_cluster,assay = 'SCT', only.pos = TRUE, min.pct=0.25, test.use = "wilcox", logfc.threshold = 0.25)
  213. markers_clusters %>% group_by(cluster) %>% arrange(desc(avg_log2FC)) %>% slice_head(n = 20) %>% ungroup() -> top20
  214. ```
  216. ```{r}
  217. # get topp 100 genes from neuroblast and immature neuronal cluster
  218. neuroblast_top_100 <- markers_clusters %>% filter(cluster == 'Neuroblast-like') %>% arrange(desc(avg_log2FC)) %>% top_n(100, wt = avg_log2FC)
  219. neuro_top_100 <- markers_clusters %>% filter(cluster == 'Immature neuron-like') %>% arrange(desc(avg_log2FC)) %>% top_n(100, wt = avg_log2FC)
  221. # getting the genes
  222. neuroblast_genes <- neuroblast_top_100$gene
  223. neuro_genes <- neuro_top_100$gene
  225. # convert gene symbols to entrez IDs
  226. entrez_ids_neuroblast <- bitr(neuroblast_genes,
  227. fromType = "SYMBOL",
  228. toType = "ENTREZID",
  229. OrgDb = org.Hs.eg.db)
  231. entrez_ids_neuro<- bitr(neuro_genes,
  232. fromType = "SYMBOL",
  233. toType = "ENTREZID",
  234. OrgDb = org.Hs.eg.db)
  235. ```
  237. ```{r}
  238. # run GO enrinchment - BP
  239. go_results_neuroblasts <- enrichGO(gene = entrez_ids_neuroblast$ENTREZID,
  240. OrgDb = org.Hs.eg.db,
  241. keyType = "ENTREZID",
  242. ont = "BP", # Can be "BP", "MF", or "CC"
  243. pAdjustMethod = "BH",
  244. pvalueCutoff = 0.05,
  245. qvalueCutoff = 0.2,
  246. readable = TRUE)
  248. go_results_neuro <- enrichGO(gene = entrez_ids_neuro$ENTREZID,
  249. OrgDb = org.Hs.eg.db,
  250. keyType = "ENTREZID",
  251. ont = "BP", # Can be "BP", "MF", or "CC"
  252. pAdjustMethod = "BH",
  253. pvalueCutoff = 0.05,
  254. qvalueCutoff = 0.2,
  255. readable = TRUE)
  256. ```
  258. Make dotplots
  260. ```{r}
  261. neuroblast_bb <- dotplot(go_results_neuroblasts, showCategory = 15)
  262. ggsave(filename = "Fig2f.pdf", path = FIGURES_DIR , plot = neuroblast_bb, width = 200, height =200, device = cairo_pdf, units = "mm")
  263. ```
  265. ```{r}
  266. neuro_bb <-dotplot(go_results_neuro, showCategory = 15)
  267. ggsave(filename = "Fig2g.pdf", path = FIGURES_DIR , plot = neuro_bb, width = 200, height =200, device = cairo_pdf, units = "mm")
  268. ```
  270. # generating figure 2h
  272. ```{r}
  273. # Step 1: Subset to 7d and 14d differentiation samples
  274. diff_cells <- subset(seurat_pipeline_cluster, subset = Time %in% c("Differentiation (7 days)", "Differentiation (14 days)"))
  276. # Step 2: Subset only neuroblast and immature neurons
  277. target_cells <- [email hidden] %>%filter(active_ident %in% c("Neuroblast-like", "Immature neuron-like"))
  279. # Step 3: Count cells per treatment and type
  280. counts <- target_cells %>%
  281. group_by(treatment, active_ident) %>%
  282. summarise(n = n(), .groups = "drop")
  284. # Step 4: Get total number of cells per condition (in differentiation samples)
  285. total_per_condition <- [email hidden] %>%
  286. group_by(treatment) %>%
  287. summarise(total = n(), .groups = "drop")
  289. # Step 5: Merge counts and total, compute percentages
  290. counts <- left_join(counts, total_per_condition, by = "treatment") %>%
  291. mutate(percent = (n / total) * 100)
  293. counts$treatment <- factor(counts$treatment, levels = c("Control", "Low TNF", "High TNF"))
  295. # Step 6: Plot
  296. treatment_neurogenesis <- ggplot(counts, aes(x = treatment, y = percent, fill = active_ident)) +
  297. geom_bar(stat = "identity", position = "stack") +
  298. labs(x = "Treatment", y = "Percentage of cells in neurogenic clusters", fill = "Cluster") +
  299. scale_fill_manual(values = c("Neuroblast-like" = "#9C73B4", "Immature neuron-like" = "#B3D500")) +
  300. theme_minimal() + theme(
  301. axis.text.x = element_text(face = "bold"),
  302. axis.title.y = element_text(face = "bold"))
  304. ggsave(filename = "Fig2h.pdf", path = FIGURES_DIR , plot = treatment_neurogenesis, width = 200, height =200, device = cairo_pdf, units = "mm")
  305. ```
  307. # generate figure 3a
  309. Generate heatmap comparing RGL vs. RGL immune
  310. ```{r}
  311. # Use then SCT assay - find markers expressed in minimum 25% of cells in the cluster
  312. RGL_vs_RGL_immune <-FindMarkers(seurat_pipeline_cluster,ident.1 = 'RGL-like 2', ident.2 = 'RGL-like 1', assay = 'SCT', only.pos = TRUE, min.pct=0.25, test.use = "wilcox", logfc.threshold = 0.25) %>% rownames_to_column(var = "gene")
  314. # get the data from the top 40 genes in each cluster based on avg_log2FC
  315. RGL_vs_RGL_immune %>% arrange(desc(avg_log2FC)) %>% slice_head(n = 40) %>% ungroup() -> top40_RGL_vs_RGL_immune
  317. RGL_heatmap_genes <- top40_RGL_vs_RGL_immune$gene
  319. # subset the seurat object
  320. RGL_all <- subset(seurat_pipeline_cluster, idents = c('RGL-like 2', 'RGL-like 1'))
  322. # colors to use
  323. colors_RGL_heatmap <- c( "#0072B2", "#E55AA4")
  325. # do heatmap - genes expressed in 25% of cells
  326. heatmap_RGL_vs_RGL_immune <- DoHeatmap(RGL_all, features = RGL_heatmap_genes, assay = 'SCT', slot = "scale.data",label = FALSE, group.colors = colors_RGL_heatmap)
  328. # save the plot
  329. ggsave(filename = "Fig3a.pdf", path = FIGURES_DIR , plot = heatmap_RGL_vs_RGL_immune, width = 200, height =200, device = cairo_pdf, units = "mm")
  330. ```
  331. # generate figure 3b
  333. ```{r}
  334. # Use then SCT assay - find markers expressed in minimum 25% of cells in the cluster
  335. IPC_TNF_vs_IPCs <-FindMarkers(seurat_pipeline_cluster,ident.1 = 'IPC-like 4', ident.2 = c('IPC-like 1', 'IPC-like 2', 'IPC-like 3'), assay = 'SCT', only.pos = TRUE, min.pct=0.25, test.use = "wilcox", logfc.threshold = 0.25) %>% rownames_to_column(var = "gene")
  337. # get the data from the top 40 genes in each cluster based on avg_log2FC
  338. IPC_TNF_vs_IPCs %>% arrange(desc(avg_log2FC)) %>% slice_head(n = 40) %>% ungroup() -> topIPC_TNF_vs_IPCs
  340. IPC_heatmap_genes <- topIPC_TNF_vs_IPCs$gene
  342. # subset the seurat object
  343. IPC_all <- subset(seurat_pipeline_cluster, idents = c('IPC-like 1', 'IPC-like 2', 'IPC-like 3', 'IPC-like 4'))
  345. # colors to use
  346. colors_IPC_heatmap <- c("#43BDB2", "#EECAA4", "#050B8D", "#FF6347")
  348. # do heatmap - genes expressed in 25% of cells
  349. heatmap_IPC_vs_IPC_immune <- DoHeatmap(IPC_all, features = IPC_heatmap_genes, assay = 'SCT', slot = "scale.data",label = FALSE, group.colors = colors_IPC_heatmap)
  351. # save the plot
  352. ggsave(filename = "Fig3b.pdf", path = FIGURES_DIR , plot = heatmap_IPC_vs_IPC_immune, width = 200, height =200, device = cairo_pdf, units = "mm")
  353. ```
  355. # generate figure 3c
  357. ```{r}
  358. IFN_genes <- c('MX1', 'ISG15', 'OAS1', 'OAS2', 'IFI6', 'IFI27', 'IFI35','IFI44', 'IFI44L','IFI16', 'IFIT1', 'IFIT3', 'STAT1', 'STAT2', 'TRIM22', 'BST2', 'CXCL10','IFITM1', 'IFITM3', 'B2M', 'IL32', 'HLA-B')
  360. IFN_dotplot <- DotPlot_scCustom(seurat_pipeline_cluster,features = IFN_genes, x_lab_rotate = TRUE)
  362. ggsave(filename = "Fig3c.pdf", path = FIGURES_DIR , plot = IFN_dotplot, width = 325, height =125, device = cairo_pdf, units = "mm")
  363. ```
  365. # generate figure d-f
  367. ```{r}
  368. IFN_astroglial_top_100 <- markers_clusters %>% filter(cluster == 'IFN-responsive glial/progenitor-like') %>% arrange(desc(avg_log2FC)) %>% top_n(100, wt = avg_log2FC)
  370. reactive_astro_top_100 <- markers_clusters %>% filter(cluster == 'Reactive astrocyte-like') %>% arrange(desc(avg_log2FC)) %>% top_n(100, wt = avg_log2FC)
  372. wound_healing_top_100 <- markers_clusters %>% filter(cluster == 'Wound healing-like') %>% arrange(desc(avg_log2FC)) %>% top_n(100, wt = avg_log2FC)
  374. # getting the genes
  375. IFN_astroglial_top_100_genes <- IFN_astroglial_top_100$gene
  376. reactive_astro_top_100_genes <- reactive_astro_top_100$gene
  377. wound_healing_top_100_genes <- wound_healing_top_100$gene
  379. # convert gene symbols to entrez IDs
  380. entrez_ids_IFN_astroglial_top_100_genes <- bitr(IFN_astroglial_top_100_genes,
  381. fromType = "SYMBOL",
  382. toType = "ENTREZID",
  383. OrgDb = org.Hs.eg.db)
  385. entrez_ids_reactive_astro_top_100_genes<- bitr(reactive_astro_top_100_genes,
  386. fromType = "SYMBOL",
  387. toType = "ENTREZID",
  388. OrgDb = org.Hs.eg.db)
  390. entrez_ids_wound_healing_top_100_genes<- bitr(wound_healing_top_100_genes,
  391. fromType = "SYMBOL",
  392. toType = "ENTREZID",
  393. OrgDb = org.Hs.eg.db)
  394. ```
  397. Go enrichment - BP - biologial process
  398. ```{r}
  399. # run GO enrinchment - BP
  400. go_results_IFN_astroglial <- enrichGO(gene = entrez_ids_IFN_astroglial_top_100_genes$ENTREZID,
  401. OrgDb = org.Hs.eg.db,
  402. keyType = "ENTREZID",
  403. ont = "BP", # Can be "BP", "MF", or "CC"
  404. pAdjustMethod = "BH",
  405. pvalueCutoff = 0.05,
  406. qvalueCutoff = 0.2,
  407. readable = TRUE)
  409. go_results_reactive_astro <- enrichGO(gene = entrez_ids_reactive_astro_top_100_genes$ENTREZID,
  410. OrgDb = org.Hs.eg.db,
  411. keyType = "ENTREZID",
  412. ont = "BP", # Can be "BP", "MF", or "CC"
  413. pAdjustMethod = "BH",
  414. pvalueCutoff = 0.05,
  415. qvalueCutoff = 0.2,
  416. readable = TRUE)
  418. go_results_wound_healing <- enrichGO(gene = entrez_ids_wound_healing_top_100_genes$ENTREZID,
  419. OrgDb = org.Hs.eg.db,
  420. keyType = "ENTREZID",
  421. ont = "BP", # Can be "BP", "MF", or "CC"
  422. pAdjustMethod = "BH",
  423. pvalueCutoff = 0.05,
  424. qvalueCutoff = 0.2,
  425. readable = TRUE)
  426. ```
  427. Dot plots:
  429. ```{r}
  430. go_results_IFN_astroglial_bb <- dotplot(go_results_IFN_astroglial, showCategory = 15)
  431. ggsave(filename = "Fig3d.pdf", path = FIGURES_DIR , plot = go_results_IFN_astroglial_bb, width = 150, height =250, device = cairo_pdf, units = "mm")
  432. ```
  434. ```{r}
  435. go_results_reactive_astro_bb <- dotplot(go_results_reactive_astro, showCategory = 15)
  436. ggsave(filename = "Fig3e.pdf", path = FIGURES_DIR , plot = go_results_reactive_astro_bb, width = 150, height =300, device = cairo_pdf, units = "mm")
  437. ```
  439. ```{r}
  440. go_results_wound_healing_bb <- dotplot(go_results_wound_healing, showCategory = 15)
  441. ggsave(filename = "Fig3f.pdf", path = FIGURES_DIR , plot = go_results_wound_healing_bb, width = 150, height =200, device = cairo_pdf, units = "mm")
  442. ```
  444. # generate figure 3g
  446. IFN-beta NSC signature - https://www.embopress.org/doi/full/10.15252/emmm.202216434
  448. ```{r}
  449. IFN_B_response_genes_df <- read.csv("scripts/NSC_IFN_b_genes.csv")
  450. IFN_B_response_genes <- IFN_B_response_genes_df$Genes
  452. seurat_pipeline_cluster <- AddModuleScore(
  453. seurat_pipeline_cluster,
  454. features = list(IFN_B_response_genes),
  455. name = c('NSC IFN-β signature'),
  456. search = TRUE,
  457. )
  459. IFN_signature <- FeaturePlot_scCustom(
  460. seurat_pipeline_cluster,
  461. features = "NSC IFN-β signature1", # your module score metadata
  462. colors_use = viridis_plasma_dark_high, # or your preferred gradient
  463. na_color = "grey90", # color for low module scores
  464. na_cutoff = 0.05) + xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed() + ggtitle('NSC IFN-β signature') # everything ≤ 0 is grey; >0 uses gradient
  466. ggsave(filename = "Fig3g.pdf", path = FIGURES_DIR , plot = IFN_signature, width = 325, height =125, device = cairo_pdf, units = "mm")
  467. ```
  471. # generate figure 3h
  473. ```{r}
  474. # generate net
  475. net <- get_collectri(organism='human', split_complexes=FALSE)
  477. # Extract the normalized log-transformed counts
  478. mat <- seurat_pipeline_cluster[["SCT"]]$data
  480. # Run ulm
  481. acts <- run_ulm(mat=mat, net=net, .source='source', .target='target',.mor='mor', minsize = 5)
  482. ```
  485. ```{r}
  486. # Extract ulm and store it in tfsulm
  487. seurat_pipeline_cluster[['tfsulm']] <- acts %>%
  488. pivot_wider(id_cols = 'source', names_from = 'condition',
  489. values_from = 'score') %>%
  490. column_to_rownames('source') %>%
  491. Seurat::CreateAssayObject(.)
  493. # Change assay
  494. DefaultAssay(object = seurat_pipeline_cluster) <- "tfsulm"
  496. # Scale the data
  497. seurat_pipeline_cluster <- ScaleData(seurat_pipeline_cluster)
  498. seurat_pipeline_cluster@assays$tfsulm@data <- seurat_pipeline_cluster@assays$[email hidden]
  499. ```
  502. ```{r}
  503. n_tfs <- 50
  505. Idents(seurat_pipeline_cluster) <- seurat_pipeline_cluster$active_ident
  507. # Extract activities from object as a long dataframe
  508. df <- t(as.matrix(seurat_pipeline_cluster@assays$tfsulm$data)) %>%
  509. as.data.frame() %>%
  510. mutate(cluster = Idents(seurat_pipeline_cluster)) %>%
  511. pivot_longer(cols = -cluster, names_to = "source", values_to = "score") %>%
  512. group_by(cluster, source) %>%
  513. summarise(mean = mean(score))
  515. # Get top tfs with more variable means across clusters
  516. tfs <- df %>%
  517. group_by(source) %>%
  518. summarise(std = sd(mean)) %>%
  519. arrange(-abs(std)) %>%
  520. head(n_tfs) %>%
  521. pull(source)
  523. # Subset long data frame to top tfs and transform to wide matrix
  524. top_acts_mat <- df %>%
  525. filter(source %in% tfs) %>%
  526. pivot_wider(id_cols = 'cluster', names_from = 'source',
  527. values_from = 'mean') %>%
  528. column_to_rownames('cluster') %>%
  529. as.matrix()
  531. # Choose color palette
  532. palette_length = 100
  533. my_color = colorRampPalette(c("Darkblue", "white","red"))(palette_length)
  535. my_breaks <- c(seq(-3, 0, length.out=ceiling(palette_length/2) + 1),
  536. seq(0.05, 3, length.out=floor(palette_length/2)))
  538. mat2 <- t(top_acts_mat)
  540. # Plot
  541. pheatmap(mat2,border_color = NA, color=my_color, breaks = my_breaks, filename = 'figures/fig3h.pdf',width = 7,height = 10)
  542. ```
  544. # extended data fig 1a
  546. ```{r}
  547. DefaultAssay(object = seurat_pipeline_cluster) <- "SCT"
  549. Idents(seurat_pipeline_cluster) <- 'sample.ID'
  551. seurat_pipeline_cluster$sample.ID <- factor(seurat_pipeline_cluster$sample.ID , levels = c("Proliferation control","Proliferation low TNF","Proliferation high TNF","Differentiation control (7 days)","Differentiation low TNF (7 days)","Differentiation high TNF (7 days)","Differentiation control (14 days)","Differentiation low TNF (14 days)","Differentiation high TNF (14 days)"))
  554. Idents(seurat_pipeline_cluster) <- 'active_ident'
  556. new_order <- c("Astro-like 1", "Astro-like 2", "Astro-like 3", "Astro-like 4", "RGL-like 1", "RGL-like 2", "IPC-like 1", "IPC-like 2", "IPC-like 3", "IPC-like 4", "Neuroblast-like", "Immature neuron-like", "IFN-responsive glial/progenitor-like", "Reactive astrocyte-like", "Wound healing-like")
  558. seurat_pipeline_cluster <- SetIdent(seurat_pipeline_cluster, value = factor(Idents(seurat_pipeline_cluster), levels = new_order))
  560. sample_ID_plot <- DimPlot_scCustom(
  561. seurat_pipeline_cluster,
  562. label = FALSE,
  563. split.by = "sample.ID",
  564. repel = TRUE,
  565. num_columns = 3,
  566. split_seurat = TRUE,
  567. colors_use = colors_UMAP,
  568. pt.size = 0.1
  569. ) +
  570. xlab("UMAP1") +
  571. ylab("UMAP2") +
  572. NoAxes() +
  573. theme(
  574. strip.text = element_text(size =9), # 👈 changes panel title size
  575. legend.title = element_text(size = 5))
  577. ggsave(filename = "extended_fig1a.pdf", path = FIGURES_DIR , plot = sample_ID_plot, width = 325, height =125, device = cairo_pdf, units = "mm")
  578. ```
  580. # extended data fig 1b
  582. ```{r}
  583. # subset prol data
  584. prol <- subset(seurat_pipeline_cluster, subset = Time == 'Proliferation (2 days)')
  586. prol$treatment <- factor(x = prol$treatment, levels = c('Control','Low TNF','High TNF'))
  588. Idents(prol) <- 'treatment'
  590. # violin plots
  591. ISG15 <- VlnPlot(prol, features = 'ISG15')
  592. IFI27 <- VlnPlot(prol, features = 'IFI27')
  593. IFI6 <- VlnPlot(prol, features = 'IFI6')
  594. IFITM3 <- VlnPlot(prol, features = 'IFITM3')
  596. dose_dependent1 <- ISG15 |IFI27
  597. dose_dependent2 <- IFI6 |IFITM3
  599. ggsave(filename = "extended_fig1b1.pdf", path = FIGURES_DIR , plot = dose_dependent1, width = 325, height =125, device = cairo_pdf, units = "mm")
  600. ggsave(filename = "extended_fig1b2.pdf", path = FIGURES_DIR , plot = dose_dependent2, width = 325, height =125, device = cairo_pdf, units = "mm")
  601. ```
  603. # extended data fig 1c
  605. ```{r}
  606. RGLs_markers <-FindMarkers(seurat_pipeline_cluster,ident.1 = 'RGL-like 2', ident.2 = 'RGL-like 1', assay = 'SCT', only.pos = TRUE, min.pct=0.25, test.use = "wilcox", logfc.threshold = 0.25) %>% rownames_to_column(var = "gene")
  608. RGLs_markers %>% arrange(desc(avg_log2FC)) %>% slice_head(n = 100) %>% ungroup() -> RGL_results
  610. RGL_results_genes <- RGL_results$gene
  612. # convert gene symbols to entrez IDs
  613. RGL_entrez <- bitr(RGL_results_genes,
  614. fromType = "SYMBOL",
  615. toType = "ENTREZID",
  616. OrgDb = org.Hs.eg.db)
  618. RGL_BP <- enrichGO(gene = RGL_entrez$ENTREZID,
  619. OrgDb = org.Hs.eg.db,
  620. keyType = "ENTREZID",
  621. ont = "BP", # Can be "BP", "MF", or "CC"
  622. pAdjustMethod = "BH",
  623. pvalueCutoff = 0.05,
  624. qvalueCutoff = 0.2,
  625. readable = TRUE)
  627. RGL_downregulated_tnf <- dotplot(RGL_BP, showCategory = 15)
  629. ggsave(filename = "extended_fig1c.pdf", path = FIGURES_DIR , plot = RGL_downregulated_tnf, width = 200, height =150, device = cairo_pdf, units = "mm")
  630. ```
  632. # extended data fig 1d
  634. ```{r}
  635. IPC_down_TNF <-FindMarkers(seurat_pipeline_cluster,ident.1 = 'IPC-like 4', ident.2 = c('IPC-like 1', 'IPC-like 2', 'IPC-like 3'), assay = 'SCT', only.pos = TRUE, min.pct=0.25, test.use = "wilcox", logfc.threshold = 0.25) %>% rownames_to_column(var = "gene")
  637. IPC_down_TNF %>% arrange(desc(avg_log2FC)) %>% slice_head(n = 100) %>% ungroup() -> IPC_down_TNF_top_100
  639. IPC_down_TNF_top_100_genes <- IPC_down_TNF_top_100$gene
  641. IPC_TNF_down <- bitr(IPC_down_TNF_top_100_genes,
  642. fromType = "SYMBOL",
  643. toType = "ENTREZID",
  644. OrgDb = org.Hs.eg.db)
  646. IPC_TNF_down_BP <- enrichGO(gene = IPC_TNF_down$ENTREZID,
  647. OrgDb = org.Hs.eg.db,
  648. keyType = "ENTREZID",
  649. ont = "BP", # Can be "BP", "MF", or "CC"
  650. pAdjustMethod = "BH",
  651. pvalueCutoff = 0.05,
  652. qvalueCutoff = 0.2,
  653. readable = TRUE)
  655. extended_fig1d <- dotplot(IPC_TNF_down_BP, showCategory = 15)
  657. ggsave(filename = "extended_fig1d.pdf", path = FIGURES_DIR , plot = extended_fig1d, width = 200, height =150, device = cairo_pdf, units = "mm")
  658. ```
  660. # extended data fig 1e-f
  662. ```{r}
  663. DefaultAssay(object = seurat_pipeline_cluster) <- "tfsulm"
  665. STAT1 <- FeaturePlot_scCustom(
  666. seurat_pipeline_cluster,
  667. features = "STAT1", # your module score metadata
  668. colors_use = viridis_plasma_dark_high, # or your preferred gradient
  669. na_color = "grey90", # color for low module scores
  670. na_cutoff = 0.5) + xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed() + ggtitle('STAT1 activity') # everything ≤ 0 is grey; >0 uses gradient
  672. ggsave(filename = "extended_fig1e.pdf", path = FIGURES_DIR , plot = STAT1, width = 200, height =150, device = cairo_pdf, units = "mm")
  674. STAT2 <- FeaturePlot_scCustom(
  675. seurat_pipeline_cluster,
  676. features = "STAT2", # your module score metadata
  677. colors_use = viridis_plasma_dark_high, # or your preferred gradient
  678. na_color = "grey90", # color for low module scores
  679. na_cutoff = 0.5) + xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed() + ggtitle('STAT2 activity') # everything ≤ 0 is grey; >0 uses gradient
  681. ggsave(filename = "extended_fig1f.pdf", path = FIGURES_DIR , plot = STAT2, width = 200, height =150, device = cairo_pdf, units = "mm")
  682. ```
  684. # supplementary Fig. 8
  686. ```{r}
  687. go_results_neuro_cc <- enrichGO(gene = entrez_ids_neuro$ENTREZID,
  688. OrgDb = org.Hs.eg.db,
  689. keyType = "ENTREZID",
  690. ont = "CC", # Can be "BP", "MF", or "CC"
  691. pAdjustMethod = "BH",
  692. pvalueCutoff = 0.05,
  693. qvalueCutoff = 0.2,
  694. readable = TRUE)
  696. cc_neuro_dotplot <- dotplot(go_results_neuro_cc, showCategory = 15)
  698. ggsave(filename = "supplementary_fig8.pdf", path = FIGURES_DIR , plot = cc_neuro_dotplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  699. ```
  701. # supplementary Fig. 9a
  703. ```{r}
  704. DefaultAssay(seurat_pipeline_cluster) <- 'SCT'
  706. # Parameters - change gene and object name if needed
  707. gene <- "TNFRSF1A" # <- replace with your gene name
  708. seu_obj <- seurat_pipeline_cluster
  710. # Check gene presence
  711. if (!(gene %in% rownames(seu_obj))) {
  712. stop(paste0("Gene '", gene, "' not found in the Seurat object."))
  713. }
  715. # Pull expression data
  716. expr_mat <- GetAssayData(seu_obj, assay = DefaultAssay(seu_obj), slot = "data")
  717. expr_vec <- as.numeric(expr_mat[gene, ])
  719. # Build dataframe with cluster identity and expression
  720. df <- data.frame(
  721. cluster = as.character(Idents(seu_obj)),
  722. expr = expr_vec,
  723. stringsAsFactors = FALSE
  724. )
  726. # Convert cluster to numeric if possible (to ensure natural order)
  727. df <- df %>%
  728. mutate(cluster_num = suppressWarnings(as.numeric(cluster))) %>%
  729. mutate(cluster_num = ifelse(is.na(cluster_num), cluster, cluster)) # fallback for non-numeric
  731. # Summarise: percent of cells with expr > 0 per cluster
  732. summary_df <- df %>%
  733. group_by(cluster, cluster_num) %>%
  734. summarise(
  735. n_cells = n(),
  736. n_expressing = sum(expr > 0),
  737. pct_expressing = 100 * (n_expressing / n_cells),
  738. .groups = "drop"
  739. ) %>%
  740. arrange(as.numeric(cluster_num))
  742. # Ensure numeric ordering of x-axis
  743. summary_df$cluster <- factor(summary_df$cluster, levels = summary_df$cluster)
  745. # Plot
  746. p1 <- ggplot(summary_df, aes(x = cluster, y = pct_expressing)) +
  747. geom_col(width = 0.7, fill = "#4682B4") +
  748. geom_text(aes(label = paste0(round(pct_expressing, 1), "%")),
  749. vjust = -0.3, size = 3) +
  750. scale_y_continuous(expand = expansion(mult = c(0, 0.12)), limits = c(0, NA)) +
  751. labs(
  752. title = paste0("Percent of cells expressing ", gene, " per cluster"),
  753. x = "Cluster",
  754. y = "Percent expressing (%)"
  755. ) +
  756. theme_minimal(base_size = 14) +
  757. theme(
  758. axis.text.x = element_text(
  759. angle = 45,
  760. hjust = 1
  761. )
  762. )
  764. ggsave(filename = "supplementary_fig9a.pdf", path = FIGURES_DIR , plot = p1, width = 200, height =150, device = cairo_pdf, units = "mm")
  765. ```
  767. # supplementary Fig. 9b
  769. ```{r}
  770. DefaultAssay(seurat_pipeline_cluster) <- 'SCT'
  772. # Parameters - change gene and object name if needed
  773. gene <- "TNFRSF1B" # <- replace with your gene name
  774. seu_obj <- seurat_pipeline_cluster
  776. # Check gene presence
  777. if (!(gene %in% rownames(seu_obj))) {
  778. stop(paste0("Gene '", gene, "' not found in the Seurat object."))
  779. }
  781. # Pull expression data
  782. expr_mat <- GetAssayData(seu_obj, assay = DefaultAssay(seu_obj), slot = "data")
  783. expr_vec <- as.numeric(expr_mat[gene, ])
  785. # Build dataframe with cluster identity and expression
  786. df <- data.frame(
  787. cluster = as.character(Idents(seu_obj)),
  788. expr = expr_vec,
  789. stringsAsFactors = FALSE
  790. )
  792. # Convert cluster to numeric if possible (to ensure natural order)
  793. df <- df %>%
  794. mutate(cluster_num = suppressWarnings(as.numeric(cluster))) %>%
  795. mutate(cluster_num = ifelse(is.na(cluster_num), cluster, cluster)) # fallback for non-numeric
  797. # Summarise: percent of cells with expr > 0 per cluster
  798. summary_df <- df %>%
  799. group_by(cluster, cluster_num) %>%
  800. summarise(
  801. n_cells = n(),
  802. n_expressing = sum(expr > 0),
  803. pct_expressing = 100 * (n_expressing / n_cells),
  804. .groups = "drop"
  805. ) %>%
  806. arrange(as.numeric(cluster_num))
  808. # Ensure numeric ordering of x-axis
  809. summary_df$cluster <- factor(summary_df$cluster, levels = summary_df$cluster)
  811. # Plot
  812. p2 <- ggplot(summary_df, aes(x = cluster, y = pct_expressing)) +
  813. geom_col(width = 0.7, fill = "#4682B4") +
  814. geom_text(aes(label = paste0(round(pct_expressing, 1), "%")),
  815. vjust = -0.3, size = 3) +
  816. scale_y_continuous(expand = expansion(mult = c(0, 0.12)), limits = c(0, NA)) +
  817. labs(
  818. title = paste0("Percent of cells expressing ", gene, " per cluster"),
  819. x = "Cluster",
  820. y = "Percent expressing (%)"
  821. ) +
  822. theme_minimal(base_size = 14) +
  823. theme(
  824. axis.text.x = element_text(
  825. angle = 45,
  826. hjust = 1
  827. )
  828. )
  829. ggsave(filename = "supplementary_fig9b.pdf", path = FIGURES_DIR , plot = p2, width = 200, height =150, device = cairo_pdf, units = "mm")
  830. ```
  832. # supplementary Fig. 9c
  834. ```{r}
  835. DefaultAssay(seurat_pipeline_cluster) <- 'SCT'
  836. TNFR1_violin <- VlnPlot_scCustom(seurat_pipeline_cluster,features = "TNFRSF1A", colors_use = colors_UMAP)
  837. ggsave(filename = "supplementary_fig9c.pdf", path = FIGURES_DIR , plot = TNFR1_violin, width = 200, height =150, device = cairo_pdf, units = "mm")
  838. ```
  840. # supplementary Fig. 9d
  841. ```{r}
  842. DefaultAssay(seurat_pipeline_cluster) <- 'SCT'
  843. TNFR2_violin <- VlnPlot_scCustom(seurat_pipeline_cluster,features = "TNFRSF1B", colors_use = colors_UMAP)
  844. ggsave(filename = "supplementary_fig9d.pdf", path = FIGURES_DIR , plot = TNFR2_violin, width = 200, height =150, device = cairo_pdf, units = "mm")
  845. ```
  847. # supplementary Fig. 9e
  849. ```{r}
  850. TNFRSF1A_dotplot <- DotPlot(seurat_pipeline_cluster,features = c("TNFRSF1A"))
  851. ggsave(filename = "supplementary_fig9e.pdf", path = FIGURES_DIR , plot = TNFRSF1A_dotplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  852. ```
  854. # supplementary Fig. 9f
  856. ```{r}
  857. TNFRSF1B_dotplot <-DotPlot(seurat_pipeline_cluster,features = c("TNFRSF1B"))
  858. ggsave(filename = "supplementary_fig9f.pdf", path = FIGURES_DIR , plot = TNFRSF1B_dotplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  860. ```
  862. # supplementary Fig. 10a and c
  864. ```{r}
  865. ensembl <- useMart("ensembl",dataset="hsapiens_gene_ensembl")
  867. apoptosis <- getBM(attributes=c('hgnc_symbol', 'reactome'),
  868. filters = 'reactome',
  869. values = 'R-HSA-109581', mart = ensembl)
  871. apoptosis_genes <- unique(apoptosis$hgnc_symbol)
  872. ```
  874. ```{r}
  875. seurat_pipeline_cluster <- AddModuleScore(
  876. seurat_pipeline_cluster,
  877. features = list(apoptosis_genes),
  878. pool = NULL,
  879. nbin = 24,
  880. ctrl = 100,
  881. k = FALSE,
  882. assay = NULL,
  883. name = c("apoptosis_score"),
  884. seed = 1,
  885. search = TRUE,
  886. )
  888. apoptosis_Violinplot <- VlnPlot(seurat_pipeline_cluster,features = "apoptosis_score1")+ labs(title = "Apoptosis (R-HSA-109581)")
  890. ggsave(filename = "supplementary_fig10a.pdf", path = FIGURES_DIR , plot = apoptosis_Violinplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  891. ```
  893. ```{r}
  894. seurat_pipeline_cluster$treatment <- factor(seurat_pipeline_cluster$treatment,
  895. levels = c("Control", "Low TNF", "High TNF"))
  897. apoptosis_score_1 <- VlnPlot(seurat_pipeline_cluster, features = "apoptosis_score1", group.by = "treatment") +
  898. labs(title = "Apoptosis") + theme_minimal(base_size = 14)
  900. ggsave(filename = "supplementary_fig10c.pdf", path = FIGURES_DIR , plot = apoptosis_score_1, width = 200, height =150, device = cairo_pdf, units = "mm")
  901. ```
  903. # supplementary Fig. 10b and d
  905. ```{r}
  906. ensembl <- useMart("ensembl",dataset="hsapiens_gene_ensembl")
  908. apoptosis_2 <- getBM(attributes=c('hgnc_symbol', 'reactome'),
  909. filters = 'reactome',
  910. values = 'R-HSA-5357801', mart = ensembl)
  912. apoptosis_genes_2 <- unique(apoptosis_2$hgnc_symbol)
  913. ```
  915. ```{r}
  916. seurat_pipeline_cluster <- AddModuleScore(
  917. seurat_pipeline_cluster,
  918. features = list(apoptosis_genes_2),
  919. pool = NULL,
  920. nbin = 24,
  921. ctrl = 100,
  922. k = FALSE,
  923. assay = NULL,
  924. name = c("apoptosis_score_2"),
  925. seed = 1,
  926. search = TRUE,
  927. )
  928. ```
  930. ```{r}
  931. programmed_cell_death_Violinplot_2 <- VlnPlot(seurat_pipeline_cluster,features = "apoptosis_score_21")+ labs(title = "Programmed Cell Death (R-HSA-5357801)")
  933. ggsave(filename = "supplementary_fig10b.pdf", path = FIGURES_DIR , plot = programmed_cell_death_Violinplot_2, width = 200, height =150, device = cairo_pdf, units = "mm")
  935. apoptosis_score_2 <- VlnPlot(seurat_pipeline_cluster, features = "apoptosis_score_21", group.by = "treatment") + labs(title = "Programmed Cell Death") +theme_minimal(base_size = 14)
  937. ggsave(filename = "supplementary_fig10d.pdf", path = FIGURES_DIR , plot = apoptosis_score_2, width = 200, height =150, device = cairo_pdf, units = "mm")
  938. ```
  940. # supplementary Fig. 11
  942. RGL
  943. ```{r}
  944. RGL_CC <- enrichGO(gene = RGL_entrez$ENTREZID,
  945. OrgDb = org.Hs.eg.db,
  946. keyType = "ENTREZID",
  947. ont = "CC", # Can be "BP", "MF", or "CC"
  948. pAdjustMethod = "BH",
  949. pvalueCutoff = 0.05,
  950. qvalueCutoff = 0.2,
  951. readable = TRUE)
  953. RGL_CC_dotplot <- dotplot(RGL_CC, showCategory = 15)
  955. ggsave(filename = "supplementary_fig11_RGL.pdf", path = FIGURES_DIR , plot = RGL_CC_dotplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  956. ```
  958. IPC:
  960. ```{r}
  961. IPC_CC <- enrichGO(gene = IPC_TNF_down$ENTREZID,
  962. OrgDb = org.Hs.eg.db,
  963. keyType = "ENTREZID",
  964. ont = "CC", # Can be "BP", "MF", or "CC"
  965. pAdjustMethod = "BH",
  966. pvalueCutoff = 0.05,
  967. qvalueCutoff = 0.2,
  968. readable = TRUE)
  970. IPC_CC_dotplot <- dotplot(IPC_CC, showCategory = 15)
  972. ggsave(filename = "supplementary_fig11_IPC.pdf", path = FIGURES_DIR , plot = IPC_CC_dotplot, width = 200, height =150, device = cairo_pdf, units = "mm")
  973. ```
  975. # supplementary Fig. 12
  977. ```{r}
  978. IFNAR1 <- FeaturePlot(seurat_pipeline_cluster, features = "IFNAR1", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  980. ggsave(filename = "supplementary_fig12_ifnar1.pdf", path = FIGURES_DIR , plot = IFNAR1, width = 200, height =150, device = cairo_pdf, units = "mm")
  982. IFNAR2 <- FeaturePlot(seurat_pipeline_cluster, features = "IFNAR2", order = TRUE)+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  984. ggsave(filename = "supplementary_fig12_ifnar2.pdf", path = FIGURES_DIR , plot = IFNAR2, width = 200, height =150, device = cairo_pdf, units = "mm")
  986. JAK1 <- FeaturePlot(seurat_pipeline_cluster, features = "JAK1", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  988. ggsave(filename = "supplementary_fig12_jak1.pdf", path = FIGURES_DIR , plot = JAK1, width = 200, height =150, device = cairo_pdf, units = "mm")
  990. TYK2 <- FeaturePlot(seurat_pipeline_cluster, features = "TYK2", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  992. ggsave(filename = "supplementary_fig12_tyk2.pdf", path = FIGURES_DIR , plot = TYK2, width = 200, height =150, device = cairo_pdf, units = "mm")
  994. STAT1 <- FeaturePlot(seurat_pipeline_cluster, features = "STAT1", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  996. ggsave(filename = "supplementary_fig12_stat1.pdf", path = FIGURES_DIR , plot = STAT1, width = 200, height =150, device = cairo_pdf, units = "mm")
  998. STAT2 <- FeaturePlot(seurat_pipeline_cluster, features = "STAT2", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  1000. ggsave(filename = "supplementary_fig12_stat2.pdf", path = FIGURES_DIR , plot = STAT2, width = 200, height =150, device = cairo_pdf, units = "mm")
  1002. IRF9 <- FeaturePlot(seurat_pipeline_cluster, features = "IRF9", order = TRUE )+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  1004. ggsave(filename = "supplementary_fig12_irf9.pdf", path = FIGURES_DIR , plot = IRF9, width = 200, height =150, device = cairo_pdf, units = "mm")
  1005. ```
  1007. # supplementary Fig. 14a
  1009. ```{r}
  1011. CGAS <- FeaturePlot(seurat_pipeline_cluster, features = "CGAS", order = TRUE)+ xlab('UMAP1') + ylab('UMAP2') + theme(plot.title = element_text(hjust = 0.5)) + coord_fixed()
  1013. ggsave(filename = "supplementary_fig14.pdf", path = FIGURES_DIR , plot = CGAS, width = 200, height =150, device = cairo_pdf, units = "mm")
  1014. ```
  1016. # supplementary Fig. 19a
  1018. ```{r}
  1019. # Parameters - change gene and object name if needed
  1020. gene <- "CXCL10"
  1021. seu_obj <- seurat_pipeline_cluster
  1023. # Check gene presence
  1024. if (!(gene %in% rownames(seu_obj))) {
  1025. stop(paste0("Gene '", gene, "' not found in the Seurat object."))
  1026. }
  1028. # Pull expression data
  1029. expr_mat <- GetAssayData(seu_obj, assay = DefaultAssay(seu_obj), slot = "data")
  1030. expr_vec <- as.numeric(expr_mat[gene, ])
  1032. # Build dataframe with cluster identity and expression
  1033. df <- data.frame(
  1034. cluster = as.character(Idents(seu_obj)),
  1035. expr = expr_vec,
  1036. stringsAsFactors = FALSE
  1037. )
  1039. # Convert cluster to numeric if possible (to ensure natural order)
  1040. df <- df %>%
  1041. mutate(cluster_num = suppressWarnings(as.numeric(cluster))) %>%
  1042. mutate(cluster_num = ifelse(is.na(cluster_num), cluster, cluster)) # fallback for non-numeric
  1044. # Summarise: percent of cells with expr > 0 per cluster
  1045. summary_df <- df %>%
  1046. group_by(cluster, cluster_num) %>%
  1047. summarise(
  1048. n_cells = n(),
  1049. n_expressing = sum(expr > 0),
  1050. pct_expressing = 100 * (n_expressing / n_cells),
  1051. .groups = "drop"
  1052. ) %>%
  1053. arrange(as.numeric(cluster_num))
  1055. # Ensure numeric ordering of x-axis
  1056. summary_df$cluster <- factor(summary_df$cluster, levels = summary_df$cluster)
  1058. # Plot
  1059. CXCL10 <- ggplot(summary_df, aes(x = cluster, y = pct_expressing)) +
  1060. geom_col(width = 0.7, fill = "#4682B4") +
  1061. geom_text(aes(label = paste0(round(pct_expressing, 1), "%")),
  1062. vjust = -0.3, size = 3) +
  1063. scale_y_continuous(expand = expansion(mult = c(0, 0.12)), limits = c(0, NA)) +
  1064. labs(
  1065. title = paste0("Percent of cells expressing ", gene, " per cluster"),
  1066. x = "Cluster",
  1067. y = "Percent expressing (%)"
  1068. ) +
  1069. theme_minimal(base_size = 14) +
  1070. theme(
  1071. axis.text.x = element_text(
  1072. angle = 45,
  1073. hjust = 1
  1074. )
  1075. )
  1077. ggsave(filename = "supplementary_fig19a.pdf", path = FIGURES_DIR , plot = CXCL10, width = 200, height =150, device = cairo_pdf, units = "mm")
  1078. ```
  1080. # supplementary Fig. 19b
  1082. ```{r}
  1083. # Parameters - change gene and object name if needed
  1084. gene <- "CXCL11"
  1085. seu_obj <- seurat_pipeline_cluster
  1087. # Check gene presence
  1088. if (!(gene %in% rownames(seu_obj))) {
  1089. stop(paste0("Gene '", gene, "' not found in the Seurat object."))
  1090. }
  1092. # Pull expression data
  1093. expr_mat <- GetAssayData(seu_obj, assay = DefaultAssay(seu_obj), slot = "data")
  1094. expr_vec <- as.numeric(expr_mat[gene, ])
  1096. # Build dataframe with cluster identity and expression
  1097. df <- data.frame(
  1098. cluster = as.character(Idents(seu_obj)),
  1099. expr = expr_vec,
  1100. stringsAsFactors = FALSE
  1101. )
  1103. # Convert cluster to numeric if possible (to ensure natural order)
  1104. df <- df %>%
  1105. mutate(cluster_num = suppressWarnings(as.numeric(cluster))) %>%
  1106. mutate(cluster_num = ifelse(is.na(cluster_num), cluster, cluster)) # fallback for non-numeric
  1108. # Summarise: percent of cells with expr > 0 per cluster
  1109. summary_df <- df %>%
  1110. group_by(cluster, cluster_num) %>%
  1111. summarise(
  1112. n_cells = n(),
  1113. n_expressing = sum(expr > 0),
  1114. pct_expressing = 100 * (n_expressing / n_cells),
  1115. .groups = "drop"
  1116. ) %>%
  1117. arrange(as.numeric(cluster_num))
  1119. # Ensure numeric ordering of x-axis
  1120. summary_df$cluster <- factor(summary_df$cluster, levels = summary_df$cluster)
  1122. # Plot
  1123. CXCL11 <- ggplot(summary_df, aes(x = cluster, y = pct_expressing)) +
  1124. geom_col(width = 0.7, fill = "#4682B4") +
  1125. geom_text(aes(label = paste0(round(pct_expressing, 1), "%")),
  1126. vjust = -0.3, size = 3) +
  1127. scale_y_continuous(expand = expansion(mult = c(0, 0.12)), limits = c(0, NA)) +
  1128. labs(
  1129. title = paste0("Percent of cells expressing ", gene, " per cluster"),
  1130. x = "Cluster",
  1131. y = "Percent expressing (%)"
  1132. ) +
  1133. theme_minimal(base_size = 14) +
  1134. theme(
  1135. axis.text.x = element_text(
  1136. angle = 45,
  1137. hjust = 1
  1138. )
  1139. )
  1141. ggsave(filename = "supplementary_fig19b.pdf", path = FIGURES_DIR , plot = CXCL11, width = 200, height =150, device = cairo_pdf, units = "mm")
  1142. ```
  1144. # supplementary Fig. 19c
  1146. ```{r}
  1147. CXCL10_violin <- VlnPlot_scCustom(seurat_pipeline_cluster,features = "CXCL10", colors_use = colors_UMAP)
  1149. ggsave(filename = "supplementary_fig19c.pdf", path = FIGURES_DIR , plot = CXCL10_violin, width = 200, height =150, device = cairo_pdf, units = "mm")
  1150. ```
  1152. # supplementary Fig. 19d
  1154. ```{r}
  1155. CXCL11_violin <- VlnPlot_scCustom(seurat_pipeline_cluster,features = "CXCL11", colors_use = colors_UMAP)
  1157. ggsave(filename = "supplementary_fig19d.pdf", path = FIGURES_DIR , plot = CXCL11_violin, width = 200, height =150, device = cairo_pdf, units = "mm")
  1158. ```

03_analysis.Rmd at commit bdf43f5, under MIT · at the source

Overview

Authors: Tinne A. D. Nissen1,2, Arishma Baig1, Sahand Farmand2, Daniel T. Rock2, Sandra Shibu2, Hyunah Lee2, Lauren A. O’Neill1, Vikki Houghton2,3, Susan John3, Linda S. Klavinskis1, Sandrine Thuret2
ORCID iDs: Tinne A. D. Nissen, Sahand Farmand, Daniel T. Rock, Vikki Houghton, Susan John, Linda S. Klavinskis, Sandrine Thuret
  1. Department of Infectious Diseases, School of Immunology & Microbial Sciences, King’s College London,London, UK
  2. Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  3. Peter Gorer Department of Immunobiology, King’s College London,London, UK
Institutions: King's College London (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 5287
Dates: received 9 August 2025; accepted 26 May 2026; published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74104-x · PMID 42414275 · PMCID PMC13342090 · OpenAlex W7167611941
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Cellular neuroscience, Neural stem cells, Neurogenesis
MeSH: Interferon Type I*, Neurogenesis*, T-Lymphocytes*, Tumor Necrosis Factor-alpha*, Animals, Cell Movement, Cells, Cultured, Female, Hippocampus, Humans, Inflammation, Neural Stem Cells, Signal Transduction (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: UK Medical Research Council (MR/S00484X/1); Wellcome Trust (Wellcome) (218452/Z/19/Z); RCUK | MRC | Medical Research Foundation (MR/R015643/1, MR/N012345/1); Galen and Hilary Weston Foundation
Citations: not cited yet (Europe PMC); 120 references in the paper

Abstract

Adult hippocampal neurogenesis is essential for learning, memory, and mood regulation, and its disruption is implicated in ageing, neurodegeneration, and mood disorders. However, the mechanisms linking inflammation to adult hippocampal neurogenesis impairment remain unclear. Here, we identify chronic tumour necrosis factor-alpha signalling as a key driver of neurogenic dysregulation via a previously unrecognised type I interferon autocrine/paracrine loop in human hippocampal progenitor cells. Using a female-derived human in vitro neurogenesis model, single-cell RNA sequencing, and functional T cell migration assays, we show that tumour necrosis factor-alpha induces a robust type I interferon response in hippocampal progenitor cells, promoting chemokine-mediated and CXC motif chemokine receptor 3-dependent T cell recruitment and suppressing neurogenesis. This inflammatory signalling cascade drives a fate switch in hippocampal progenitor cells from a neurogenic trajectory towards an immune-defensive phenotype, with critical implications for infectious and inflammatory disease pathogenesis. These findings uncover a key inflammatory checkpoint regulating human adult hippocampal neurogenesis and highlight potential therapeutic targets to restore neurogenesis in chronic inflammatory states.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

thuretlabkcl/TNF-scRNAseq-neurogenesis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bdf43f5618a7eb675c8e753c7171e0419a88783b, 22 April 2026
Languages: R (6)
Size: 18 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (renv.lock), 3 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (3 files), Seurat (2 files), clusterProfiler (1 file), ggplot2 (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/thuretlabkcl/TNF-scRNAseq-neurogenesis

Code availability

The code used for the ScRNA-seq analysis is available at: https://github.com/thuretlabkcl/TNF-scRNAseq-neurogenesis.

Reproduced under the paper's license (CC BY), 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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 7 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

Source data are provided with this paper. Raw sequencing data have been deposited in the NCBI Sequence Read Archive under BioProject accession PRJNA1397568 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1397568). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), 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, 11 authors, 3 keywords, 13 MeSH terms, 4 funders, 120 references.

Cite

This paper

Nissen, T. A. D., Baig, A., Farmand, S., Rock, D. T., Shibu, S., Lee, H., O’Neill, L. A., Houghton, V., John, S., Klavinskis, L. S., & Thuret, S. (2026). TNF-α induces type I IFN signalling to suppress neurogenesis and recruit T cells. Nature communications, 17(1), 5287. https://doi.org/10.1038/s41467-026-74104-x

BibTeX

@article{nissen2026tnf,
author = {Nissen, Tinne A. D. and Baig, Arishma and Farmand, Sahand and Rock, Daniel T. and Shibu, Sandra and Lee, Hyunah and O’Neill, Lauren A. and Houghton, Vikki and John, Susan and Klavinskis, Linda S. and Thuret, Sandrine},
title = {{TNF-α induces type I IFN signalling to suppress neurogenesis and recruit T cells}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {5287},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74104-x},
url = {https://doi.org/10.1038/s41467-026-74104-x},
pmid = {42414275},
pmcid = {PMC13342090}
}

RIS

TY - JOUR
AU - Nissen, Tinne A. D.
AU - Baig, Arishma
AU - Farmand, Sahand
AU - Rock, Daniel T.
AU - Shibu, Sandra
AU - Lee, Hyunah
AU - O’Neill, Lauren A.
AU - Houghton, Vikki
AU - John, Susan
AU - Klavinskis, Linda S.
AU - Thuret, Sandrine
TI - TNF-α induces type I IFN signalling to suppress neurogenesis and recruit T cells
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/07
VL - 17
IS - 1
SP - 5287
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74104-x
UR - https://doi.org/10.1038/s41467-026-74104-x
LA - en
ER -

CSL-JSON

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"title": "TNF-α induces type I IFN signalling to suppress neurogenesis and recruit T cells",
"container-title": "Nature communications",
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"PMCID": "PMC13342090",
"ISSN": "2041-1723",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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