OSCR

Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics.

Code ↔ Paper

6 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 6 matches
  1. [1] § Results › Oligodendrocyte co-methylation module genes show mostly upregulation in AD OLGs ↔ ExpressionPlots.Rmd, lines 419–469 · score 0.71 · hd turquoise, hd blue, HIPPO grey60, ERC tan, module membership, DLPFC greenyellow
  2. [2] § Results › AD-associated OLG co-methylation networks show dysregulation of gene expression in mouse models of early AD stages ↔ ExpressionPlots.Rmd, lines 325–418 · score 0.61 · HIPPO grey60, co methylation modules, ERC tan, DLPFC greenyellow, module genes, alpha
  3. [3] § Methods › DNA methylation datasets and pre-processing ↔ ERC.Rmd, lines 68–120 · score 0.60 · Sox10P, NeuNP, DoubleN, models
  4. [4] § Methods › DNA methylation datasets and pre-processing ↔ HIPPO.Rmd, lines 75–110 · score 0.60 · Sox10P, NeuNP, DoubleN, models
  5. [5] § Results › Oligodendrocyte co-methylation module genes show mostly upregulation in AD OLGs ↔ ExpressionPlots.Rmd, lines 87–124 · score 0.59 · late pathology, HIPPO grey60, ERC tan, pathology stage, DLPFC greenyellow, downregulated
  6. [6] § Methods › Human brain gene expression ↔ hdWGCNAPre-Processing.Rmd, lines 38–44 · score 0.59 · LogNormalize, NormalizeData, Seurat

Paper

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The authors' code

R Markdown · 469 lines · 15 KB · no license · 3 matches

  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. ---
  6. This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
  8. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.
  10. ```{r}
  11. library(dplyr)
  12. library(ggplot2)
  13. library(tidyr)
  14. library(forcats)
  16. # Add regulation status and filter by adjusted p-value
  17. deg_all_complete <- deg_all_complete %>%
  18. filter(p_val_adj < 0.05) %>%
  19. mutate(Regulation = ifelse(avg_log2FC > 0, "Upregulated", "Downregulated"))
  21. # Function to extract significant gene counts for each module and region
  22. get_gene_counts <- function(module_genes, region_label) {
  23. deg_all_complete %>%
  24. filter(gene %in% module_genes) %>%
  25. count(Subcluster, Regulation) %>%
  26. mutate(Region = region_label)
  27. }
  29. # Apply for each region
  30. dlpfc_counts <- get_gene_counts(dlpfc_modgenes, "DLPFC-greenyellow")
  31. erc_counts <- get_gene_counts(erc_modgenes, "ERC-tan")
  32. hippo_counts <- get_gene_counts(hippo_modgenes, "HIPPO-grey60")
  34. # Combine all into one dataframe
  35. plot_df <- bind_rows(dlpfc_counts, erc_counts, hippo_counts)
  37. # Convert downregulated counts to negative for mirrored bars
  38. plot_df <- plot_df %>%
  39. mutate(GeneCount = ifelse(Regulation == "Downregulated", -n, n))
  41. # Plot
  42. ggplot(plot_df, aes(x = GeneCount, y = fct_rev(Subcluster), fill = Regulation)) +
  43. geom_col(width = 0.8) +
  44. facet_wrap(~Region, nrow = 1) +
  45. scale_fill_manual(values = c("Upregulated" = "firebrick", "Downregulated" = "steelblue")) +
  46. theme_minimal(base_size = 14) +
  47. labs(x = "Gene count (Up/Down)", y = "Subcluster", fill = NULL)
  51. subclusters <- unique([email hidden]$Subcluster)
  53. all_de_results <- list()
  55. for (sub in subclusters) {
  56. # Subset to one subcluster
  57. sub_obj <- subset(olg_AD, subset = Subcluster == sub & pathology.group %in% c("no-pathology", "early-pathology", "late-pathology"))
  59. # Early vs Control
  60. sub_early <- subset(sub_obj, subset = pathology.group %in% c("no-pathology", "early-pathology"))
  61. sub_early$comparison <- factor(sub_early$pathology.group, levels = c("no-pathology", "early-pathology"))
  62. Idents(sub_early) <- "comparison"
  63. de_early <- FindMarkers(sub_early, ident.1 = "early-pathology", ident.2 = "no-pathology", logfc.threshold = 0.1)
  64. de_early$comparison <- "early"
  65. de_early$subcluster <- sub
  66. de_early$gene <- rownames(de_early)
  68. # Late vs Control
  69. sub_late <- subset(sub_obj, subset = pathology.group %in% c("no-pathology", "late-pathology"))
  70. sub_late$comparison <- factor(sub_late$pathology.group, levels = c("no-pathology", "late-pathology"))
  71. Idents(sub_late) <- "comparison"
  72. de_late <- FindMarkers(sub_late, ident.1 = "late-pathology", ident.2 = "no-pathology", logfc.threshold = 0.1)
  73. de_late$comparison <- "late"
  74. de_late$subcluster <- sub
  75. de_late$gene <- rownames(de_late)
  77. # Combine
  78. all_de_results[[sub]] <- rbind(de_early, de_late)
  79. }
  81. # Combine all into one dataframe
  82. deg_by_sub <- do.call(rbind, all_de_results)
  85. ```
  87. ```{r}
  88. library(dplyr)
  90. # Add Upregulated / Downregulated label
  91. deg_counts_df <- deg_by_sub %>%
  92. filter(p_val_adj < 0.05, gene %in% c(dlpfc_modgenes, erc_modgenes, hippo_modgenes)) %>%
  93. mutate(Regulation = ifelse(avg_log2FC > 0, "Upregulated", "Downregulated")) %>%
  94. # Join with module labels
  95. left_join(data.frame(
  96. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  97. Module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  98. rep("ERC-tan", length(erc_modgenes)),
  99. rep("HIPPO-grey60", length(hippo_modgenes)))
  100. ), by = "gene") %>%
  101. group_by(Module, subcluster, comparison, Regulation) %>%
  102. summarise(GeneCount = n(), .groups = "drop")
  103. library(ggplot2)
  104. library(forcats)
  106. # Convert comparison to label
  107. deg_counts_df$comparison <- recode(deg_counts_df$comparison,
  108. "early" = "Early pathology", "late" = "Late pathology")
  110. # Set factor levels for facet order
  111. deg_counts_df$Module <- factor(deg_counts_df$Module,
  112. levels = c("DLPFC-greenyellow", "ERC-tan", "HIPPO-grey60"))
  114. ggplot(deg_counts_df, aes(x = GeneCount * ifelse(Regulation == "Downregulated", -1, 1),
  115. y = fct_rev(subcluster),
  116. fill = Regulation)) +
  117. geom_col(width = 0.8) +
  118. facet_wrap(~Module + comparison, nrow = 1) +
  119. scale_fill_manual(values = c("Upregulated" = "firebrick", "Downregulated" = "steelblue")) +
  120. theme_minimal(base_size = 14) +
  121. labs(x = "Gene count (Up/Down)", y = "Subcluster", fill = NULL,
  122. title = "Number of Significant Module Genes by Pathology Stage and Subcluster")
  124. ```
  125. ```{r}
  126. library(dplyr)
  128. # Combine module and pathology info
  129. deg_counts_stacked <- deg_by_sub %>%
  130. filter(p_val_adj < 0.05,
  131. gene %in% c(dlpfc_modgenes, erc_modgenes, hippo_modgenes)) %>%
  132. mutate(Regulation = ifelse(avg_log2FC > 0, "Upregulated", "Downregulated")) %>%
  133. left_join(data.frame(
  134. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  135. Module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  136. rep("ERC-tan", length(erc_modgenes)),
  137. rep("HIPPO-grey60", length(hippo_modgenes)))
  138. ), by = "gene") %>%
  139. group_by(Module, subcluster, comparison, Regulation) %>%
  140. summarise(GeneCount = n(), .groups = "drop")
  142. deg_counts_stacked <- deg_counts_stacked %>%
  143. mutate(RegulationStage = paste(Regulation, comparison, sep = "_"))
  144. fill_colors <- c(
  145. "Upregulated_early" = "#e41a1c",
  146. "Upregulated_late" = "#fb8072",
  147. "Downregulated_early" = "#377eb8",
  148. "Downregulated_late" = "#9ecae1"
  149. )
  150. library(ggplot2)
  151. library(forcats)
  153. ggplot(deg_counts_stacked, aes(
  154. x = GeneCount * ifelse(grepl("Downregulated", RegulationStage), -1, 1),
  155. y = fct_rev(subcluster),
  156. fill = RegulationStage
  157. )) +
  158. geom_col(width = 0.8) +
  159. facet_wrap(~Module, nrow = 1) +
  160. scale_fill_manual(values = fill_colors) +
  161. theme_minimal(base_size = 14) +
  162. labs(
  163. x = "Gene count (Up/Down)",
  164. y = "Subcluster",
  165. fill = "Regulation & Stage",
  166. title = "Module DEG Counts by Subcluster (Early vs Late Pathology)"
  167. )
  169. ```
  170. ```{r}
  171. # Combine module membership info
  172. module_df <- data.frame(
  173. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  174. module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  175. rep("ERC-tan", length(erc_modgenes)),
  176. rep("HIPPO-grey60", length(hippo_modgenes)))
  177. )
  179. # Filter for Oli3, both early and late
  180. deg_oli3 <- deg_by_sub %>%
  181. filter(subcluster == "Oli3") %>%
  182. left_join(module_df, by = "gene") %>%
  183. mutate(module = ifelse(is.na(module), "Not in methylation modules", module))
  185. library(ggplot2)
  187. deg_oli3_early <- deg_oli3 %>%
  188. filter(comparison == "early") %>%
  189. mutate(
  190. sig = ifelse(p_val_adj < 0.05 & abs(avg_log2FC) > 0.25, "Significant", "Not sig")
  191. )
  193. ggplot(deg_oli3_early, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  194. geom_point(aes(color = module, alpha = sig), size = 2) +
  195. scale_color_manual(values = c(
  196. "DLPFC-greenyellow" = "#1b9e77",
  197. "ERC-tan" = "#d95f02",
  198. "HIPPO-grey60" = "#7570b3",
  199. "Not in methylation modules" = "grey80"
  200. )) +
  201. scale_alpha_manual(values = c("Significant" = 1, "Not sig" = 0.2)) +
  202. geom_vline(xintercept = c(-0.25, 0.25), linetype = "dashed") +
  203. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  204. theme_minimal(base_size = 13) +
  205. labs(
  206. x = "Log2 Fold Change (Early pathology vs control)",
  207. y = "-log10 Adjusted P-value",
  208. color = "Module Membership",
  209. alpha = "Significance",
  210. title = "Volcano Plot: DEGs in Oli3 Highlighting Methylation Modules"
  211. )
  213. ```
  214. ```{r}
  215. # First, combine into a list of unique module memberships per gene
  216. module_df <- data.frame(
  217. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  218. module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  219. rep("ERC-tan", length(erc_modgenes)),
  220. rep("HIPPO-grey60", length(hippo_modgenes)))
  221. )
  223. # Collapse multiple memberships into one row per gene
  224. library(dplyr)
  226. module_combined <- module_df %>%
  227. group_by(gene) %>%
  228. summarise(module_label = paste(sort(unique(module)), collapse = " & "))
  230. # Now join this to your DEG table
  231. deg_oli3 <- deg_by_sub %>%
  232. filter(subcluster == "Oli3") %>%
  233. left_join(module_combined, by = "gene") %>%
  234. mutate(module_label = ifelse(is.na(module_label), "Not in methylation modules", module_label))
  236. ggplot(
  237. deg_oli3 %>% filter(comparison == "early") %>%
  238. mutate(sig = ifelse(p_val_adj < 0.05 & abs(avg_log2FC) > 0.25, "Significant", "Not sig")),
  239. aes(x = avg_log2FC, y = -log10(p_val_adj))
  240. ) +
  241. geom_point(aes(color = module_label, alpha = sig), size = 2) +
  242. scale_alpha_manual(values = c("Significant" = 1, "Not sig" = 0.2)) +
  243. geom_vline(xintercept = c(-0.25, 0.25), linetype = "dashed") +
  244. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  245. theme_minimal(base_size = 13) +
  246. labs(
  247. x = "Log2 Fold Change (Early pathology vs control)",
  248. y = "-log10 Adjusted P-value",
  249. color = "Module Membership",
  250. title = "Volcano Plot of Oli3 DEGs with Methylation Module Labels"
  251. )
  253. ```
  255. ```{r}
  256. # Combine module gene lists
  257. module_df <- data.frame(
  258. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  259. module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  260. rep("ERC-tan", length(erc_modgenes)),
  261. rep("HIPPO-grey60", length(hippo_modgenes)))
  262. )
  264. # Collapse multi-module membership
  265. library(dplyr)
  266. module_combined <- module_df %>%
  267. group_by(gene) %>%
  268. summarise(module_label = paste(sort(unique(module)), collapse = " & "), .groups = "drop")
  270. # Add gene column if needed
  271. deg_all_complete$gene <- rownames(deg_all_complete)
  273. # Filter for Oli3
  274. deg_oli3_all <- deg_all_complete %>%
  275. filter(Subcluster == "Oli3") %>%
  276. left_join(module_combined, by = "gene") %>%
  277. mutate(
  278. module_label = ifelse(is.na(module_label), "Not in module", module_label),
  279. sig = ifelse(p_val_adj < 0.05 & abs(avg_log2FC) > 0.25, "Significant", "Not sig")
  280. )
  282. module_colors <- c(
  283. "DLPFC-greenyellow" = "#1b9e77",
  284. "ERC-tan" = "#d95f02",
  285. "HIPPO-grey60" = "#7570b3",
  286. "DLPFC-greenyellow & ERC-tan" = "#e7298a",
  287. "DLPFC-greenyellow & HIPPO-grey60" = "#66a61e",
  288. "ERC-tan & HIPPO-grey60" = "#e6ab02",
  289. "DLPFC-greenyellow & ERC-tan & HIPPO-grey60" = "#a6761d",
  290. "Not in module" = "lightgrey"
  291. )
  293. library(ggrepel)
  295. top_labels <- deg_oli3_all %>%
  296. filter(p_val_adj < 0.01, module_label != "Not in module") %>%
  297. arrange(p_val_adj) %>%
  298. slice_head(n = 15)
  300. library(ggplot2)
  302. ggplot(deg_oli3_all, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  303. geom_point(aes(color = module_label, alpha = sig), size = 2) +
  304. scale_color_manual(values = module_colors) +
  305. scale_alpha_manual(values = c("Significant" = 1, "Not sig" = 0.2)) +
  306. geom_text_repel(
  307. data = top_labels,
  308. aes(label = paste0("italic(", gene, ")")),
  309. size = 4,
  310. parse = TRUE, # ← enables italic formatting
  311. max.overlaps = 100
  312. ) +
  313. geom_vline(xintercept = c(-0.25, 0.25), linetype = "dashed") +
  314. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  315. theme_minimal(base_size = 13) +
  316. labs(
  317. x = "Log2 Fold Change (Oli3)",
  318. y = "-log10 Adjusted P-value",
  319. color = "Module Membership",
  320. alpha = "Significance",
  321. title = "DEGs in Oli3 with Co-Methylation Module Labels"
  322. )
  324. ```
  325. ```{r}
  326. library(dplyr)
  328. library(stringr)
  329. library(dplyr)
  331. # Clean DEG table
  332. deg_all_complete <- deg_all_complete %>%
  333. mutate(gene = str_remove(as.character(gene), "\\.\\.\\..*$") %>% toupper())
  335. # Clean module genes
  336. dlpfc_modgenes <- str_remove(toupper(dlpfc_modgenes), "\\.\\.\\..*$")
  337. erc_modgenes <- str_remove(toupper(erc_modgenes), "\\.\\.\\..*$")
  338. hippo_modgenes <- str_remove(toupper(hippo_modgenes), "\\.\\.\\..*$")
  340. # Build module info dataframe
  341. module_df <- data.frame(
  342. gene = c(dlpfc_modgenes, erc_modgenes, hippo_modgenes),
  343. module = c(rep("DLPFC-greenyellow", length(dlpfc_modgenes)),
  344. rep("ERC-tan", length(erc_modgenes)),
  345. rep("HIPPO-grey60", length(hippo_modgenes)))
  346. )
  348. module_combined <- module_df %>%
  349. group_by(gene) %>%
  350. summarise(module_label = paste(sort(unique(module)), collapse = " & "), .groups = "drop")
  351. # Filter for selected clusters
  352. deg_faceted <- deg_all_complete %>%
  353. filter(Subcluster %in% c("Oli0", "Oli1", "Oli3")) %>%
  354. mutate(gene = rownames(.)) %>%
  355. left_join(module_combined, by = "gene") %>%
  356. mutate(
  357. module_label = ifelse(is.na(module_label), "Not in module", module_label),
  358. sig = ifelse(p_val_adj < 0.05 & abs(avg_log2FC) > 0.25, "Significant", "Not sig")
  359. )
  362. # Clean up deg_all_complete$gene
  363. deg_faceted <- deg_faceted %>%
  364. mutate(gene = str_remove(gene, "\\.\\.\\..*$"))
  366. # Merge co-methylation module info
  368. library(ggrepel)
  370. top_labels <- deg_faceted %>%
  371. filter(p_val_adj < 0.01, module_label != "Not in module") %>%
  372. group_by(Subcluster) %>%
  373. arrange(p_val_adj) %>%
  374. slice_head(n = 10) %>%
  375. ungroup() %>%
  376. mutate(label = paste0("italic(", gene, ")"))
  379. module_colors <- c(
  380. "DLPFC-greenyellow" = "#1b9e77",
  381. "ERC-tan" = "#d95f02",
  382. "HIPPO-grey60" = "#7570b3",
  383. "DLPFC-greenyellow & ERC-tan" = "#e7298a",
  384. "DLPFC-greenyellow & HIPPO-grey60" = "#66a61e",
  385. "ERC-tan & HIPPO-grey60" = "#e6ab02",
  386. "DLPFC-greenyellow & ERC-tan & HIPPO-grey60" = "#a6761d",
  387. "Not in module" = "lightgrey"
  388. )
  390. library(ggplot2)
  392. library(stringr)
  395. ggplot(deg_faceted, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  396. geom_point(aes(color = module_label, alpha = sig), size = 1.5) +
  397. scale_color_manual(values = module_colors) +
  398. scale_alpha_manual(values = c("Significant" = 1, "Not sig" = 0.2)) +
  399. geom_text_repel(
  400. data = top_labels,
  401. aes(label = label),
  402. parse = TRUE,
  403. size = 3,
  404. max.overlaps = 100
  405. ) +
  406. geom_vline(xintercept = c(-0.25, 0.25), linetype = "dashed") +
  407. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  408. facet_wrap(~Subcluster, ncol = 3, scales = "free") +
  409. theme_minimal(base_size = 13) +
  410. labs(
  411. x = "Log2 Fold Change",
  412. y = "-log10 Adjusted P-value",
  413. color = "Module Membership",
  414. alpha = "Significance",
  415. title = "DEGs in OLG Subclusters"
  416. )
  418. ```
  419. ```{r}
  420. library(ggrepel)
  422. top_labels <- deg_faceted %>%
  423. filter(p_val_adj < 0.01, module_label != "Not in module") %>%
  424. group_by(Subcluster) %>%
  425. arrange(p_val_adj) %>%
  426. slice_head(n = 10) %>%
  427. ungroup() %>%
  428. mutate(label = paste0("italic(", gene, ")"))
  430. module_colors <- c(
  431. "DLPFC-greenyellow" = "#1b9e77",
  432. "ERC-tan" = "#d95f02",
  433. "HIPPO-grey60" = "#7570b3",
  434. "DLPFC-greenyellow & ERC-tan" = "#e7298a",
  435. "DLPFC-greenyellow & HIPPO-grey60" = "#66a61e",
  436. "ERC-tan & HIPPO-grey60" = "#e6ab02",
  437. "DLPFC-greenyellow & ERC-tan & HIPPO-grey60" = "#a6761d",
  438. "Not in module" = "lightgrey"
  439. )
  441. library(ggplot2)
  443. ggplot(deg_faceted, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  444. geom_point(aes(color = module_label, alpha = sig), size = 1.5) +
  445. geom_text_repel(
  446. data = top_labels,
  447. aes(label = label),
  448. parse = TRUE,
  449. size = 3.5,
  450. max.overlaps = 100
  451. ) +
  452. geom_vline(xintercept = c(-0.25, 0.25), linetype = "dashed") +
  453. geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
  454. facet_wrap(~Subcluster, ncol = 3, scales = "free") +
  455. scale_color_manual(values = module_colors) +
  456. scale_alpha_manual(values = c("Significant" = 1, "Not sig" = 0.2)) +
  457. theme_minimal(base_size = 13) +
  458. labs(
  459. x = "Log2 Fold Change",
  460. y = "-log10 Adjusted P-value",
  461. color = "Module Membership",
  462. alpha = "Significance",
  463. title = "Faceted Volcano Plot of DEGs by Subcluster with Co-Methylation Module Labels"
  464. )
  466. hd_blue <- subset(hd_mm_assigned, hd_module == "blue")
  467. hd_turquoise <- subset(hd_mm_assigned, hd_module == "turquoise")
  469. ```

ExpressionPlots.Rmd at commit 2118099, no license · at the source

Overview

Authors: Katherine Fodder1, Hannah M G Smith1, Umran Yaman2, Ignazio S Piras3, Megha Murthy4, John Hardy1,2, Tammaryn Lashley1, Rohan de Silva4,5, Dervis A Salih1,2, Conceição Bettencourt1
ORCID iDs: Umran Yaman, John Hardy, Tammaryn Lashley, Rohan de Silva, Dervis A Salih, Conceição Bettencourt
  1. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
  2. UK Dementia Research Institute, London, UK
  3. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ USA
  4. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
  5. Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK
Institutions: UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); UK Dementia Research Institute (United Kingdom); Translational Genomics Research Institute (United States)
Journal: Molecular psychiatry, volume 31, issue 10, pages 5966-5978
Dates: received 10 November 2025; accepted 3 June 2026; published online 18 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41380-026-03686-1 · PMID 42315917 · PMCID PMC13569428 · OpenAlex W4413805618
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.28.672821
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Neuroscience, Molecular biology
MeSH: Alzheimer Disease*, Oligodendroglia*, Animals, Brain, DNA Methylation, Epigenesis, Genetic, Epigenomics, Gene Expression Profiling, Gene Regulatory Networks, Humans, Mice, Mice, Transgenic, Neuroglia, Transcriptome (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Alzheimer&amp;apos;s Research UK; Medical Research Council; Multiple System Atrophy Trust; Alzheimer&amp;apos;s Society
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Much research into the aetiology of Alzheimer’s disease (AD) has focused on neuronal cell types, while studies on the contribution of glial cells, particularly oligodendrocytes (OLGs), are only starting to emerge. Altered brain DNA methylation, an epigenetic modification that provides the interplay between genetics and environmental cues to tightly regulate gene expression, is well documented in AD. Yet, cell-type-specific investigations remain limited. Here, we examine the role of DNA methylation and OLGs in AD, and how such changes may impact gene expression. We performed weighted-gene correlation network analysis (WGCNA) on multiple brain omics AD datasets across species: human DNA methylation data from 4 brain regions, human brain single-nuclei RNA sequencing data and mouse brain RNA sequencing data. We compared AD-associated network modules enriched for OLG genes across AD brain regions, as well as with other neurodegenerative disease DNA methylation datasets. We identified a DNA methylation signature associated with AD, enriched for OLGs, and preserved across brain regions representing early and late AD pathology stages. Genes within this signature showed altered expression in AD OLGs, confirming cell-type specificity and relevance to AD. This OLG signature was also preserved in transgenic mice with early Aβ pathology and in other neurodegenerative diseases without Aβ pathology. We reveal a consistent pattern of OLG dysfunction spanning early to late stages of AD, across DNA methylation and gene expression. Our findings highlight OLG-associated DNA methylation changes as important in AD pathogenesis, and possibly in other neurodegenerative diseases, opening new avenues for therapeutic development.

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 6 matches between paragraphs and lines of code.

CBettencourtLab/AD_OLGDNAm

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 21180998cc9f0cc843c858ae87a66c9fc7ea9a1f, 16 March 2026
Languages: R (6)
Size: 10 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: 6 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (6 files), tidyverse (4 files), WGCNA (4 files), data.table (3 files), limma (3 files), Seurat (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/CBettencourtLab/AD_OLGDNAm

Code availability

All code used for data preprocessing, network construction, preservation analysis, and figure generation has been deposited in a publicly accessible GitHub repository: https://github.com/CBettencourtLab/AD_OLGDNAm.

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;
  • 6 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

Human AD DNA methylation data were accessed through the Gene Expression Omnibus (GEO; accession number GSE125895 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125895)). The independent ROSMAP DNA methylation dataset and RNA sequencing data were accessed through Synapse (ID: syn7357283 and syn3388564, respectively). Mouse RNA-sequencing data are available under GEO accession number GSE137313 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137313). Human single-nucleus RNA sequencing data were accessed through Synapse (ID: syn18485175).

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Nature

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 14 MeSH terms, 4 funders, 78 references.

Cite

This paper

Fodder, K., Smith, H. M. G., Yaman, U., Piras, I. S., Murthy, M., Hardy, J., Lashley, T., de Silva, R., Salih, D. A., & Bettencourt, C. (2026). Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics. Molecular psychiatry, 31(10), 5966-5978. https://doi.org/10.1038/s41380-026-03686-1

BibTeX

@article{fodder2026early,
author = {Fodder, Katherine and Smith, Hannah M G and Yaman, Umran and Piras, Ignazio S and Murthy, Megha and Hardy, John and Lashley, Tammaryn and de Silva, Rohan and Salih, Dervis A and Bettencourt, Conceição},
title = {{Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics}},
journal = {Molecular psychiatry},
year = {2026},
month = jun,
volume = {31},
number = {10},
pages = {5966--5978},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03686-1},
url = {https://doi.org/10.1038/s41380-026-03686-1},
pmid = {42315917},
pmcid = {PMC13569428}
}

RIS

TY - JOUR
AU - Fodder, Katherine
AU - Smith, Hannah M G
AU - Yaman, Umran
AU - Piras, Ignazio S
AU - Murthy, Megha
AU - Hardy, John
AU - Lashley, Tammaryn
AU - de Silva, Rohan
AU - Salih, Dervis A
AU - Bettencourt, Conceição
TI - Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/06/18
VL - 31
IS - 10
SP - 5966
EP - 5978
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03686-1
UR - https://doi.org/10.1038/s41380-026-03686-1
LA - en
ER -

CSL-JSON

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