OSCR

Focal astrocyte loss reveals nuclear translocation during lesion repopulation.

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] § Methods › STs › Cell-type identification ↔ rmd/5.Cell_type.Rmd, lines 285–296 · score 0.81 · Slc1a2, S100b, Tubb2b, Aldh1l1, Gja1, SCT
  2. [2] § Methods › STs › Cell-type identification ↔ rmd/7.Comparaison.Rmd, lines 142–165 · score 0.73 · logfc.threshold, FindMarkers, min.pct, healthy, DEGs, reactive
  3. [3] § Results › Perilesional astrocytes display a transient injury-associated gene signature ↔ rmd/7.Comparaison.Rmd, lines 691–704 · score 0.72 · Tuba1a, Tmsb4x, Tubb2b, Marcks, Rtn4, Vim
  4. [4] § Methods › STs › Gene expression and pathway analysis ↔ rmd/7.Comparaison.Rmd, lines 437–465 · score 0.61 · clusterProfiler, db, BP, mm, enriched, enrichment
  5. [5] § Methods › STs › Astrocyte spatial gene expression ↔ rmd/utils_ST.R, lines 67–101 · score 0.54 · spot coordinates, lesion center, centroids, Seurat, distance
  6. [6] § Results › Perilesional astrocytes display a transient injury-associated gene signature ↔ rmd/5.Cell_type.Rmd, lines 285–296 · score 0.53 · S100b, Aldh1l1, Gja1, Gfap, filtered, Aqp4

Paper

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The authors' code

R Markdown · 850 lines · 28 KB · no license · 3 matches

  1. ---
  2. title: "Reactive Astrocyte Gene Signature and Pathway Dynamics Across Timepoints"
  3. author: "Anna Lasne"
  4. date: "2025-05-28"
  5. output: html_document
  6. ---
  8. # Setup
  10. ```{r}
  11. knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, cache = TRUE)
  12. set.seed(123)
  14. suppressPackageStartupMessages({
  15. library(reticulate)
  16. library(grid)
  17. library(SeuratDisk)
  18. library(patchwork)
  19. library(Matrix)
  20. library(data.table)
  21. library(Seurat)
  22. library(dplyr)
  23. library(igraph)
  24. library(ggcorrplot)
  25. library(hdf5r)
  26. library(png)
  27. library(ggplot2)
  28. library(arrow)
  29. library(cluster)
  30. library(Banksy)
  31. library(readxl)
  32. library(textshape)
  33. library(radiant)
  34. library(scales)
  35. library(xml2)
  36. library(sf)
  37. library(ComplexHeatmap)
  38. library(RColorBrewer)
  39. library(future)
  40. library(EnhancedVolcano)
  41. library(tibble)
  42. library(circlize)
  43. library(purrr)
  44. library(clusterProfiler)
  45. library(org.Mm.eg.db)
  46. library(DOSE)
  48. })
  50. source("~/Documents/ST/resequencing/rmd/utils_ST.R") # Put all helper fns here!
  52. options(future.globals.maxSize = 8e9)
  53. mem.maxVSize(300000)
  54. ```
  56. # Data Loading
  58. ```{r}
  59. # Load per-timepoint Seurat objects for astrocyte subsets
  60. threedpi <- readRDS("~/Documents/ST/resequencing/Visium_obj/astrocyte_subset_labeled_0.5_3dpi.rds")
  61. fivedpi <- readRDS("~/Documents/ST/resequencing/Visium_obj/astrocyte_subset_labeled_0.5_5dpi.rds")
  62. seventeendpi <- readRDS("~/Documents/ST/resequencing/Visium_obj/astrocyte_subset_labeled_0.5_17dpi.rds")
  63. ```
  65. ## Timepoint labels
  67. ```{r}
  68. threedpi$timepoint <- "3dpi"
  69. fivedpi$timepoint <- "5dpi"
  70. seventeendpi$timepoint <- "17dpi"
  71. ```
  73. # Reactive/Healthy Classification
  75. ## Define 17 dpi Cutoffs (Quantile & Regression)
  77. ```{r}
  78. # Calculate quantile-based and regression-based thresholds for defining "reactive" at 17dpi.
  79. df_3_reactive <- [email hidden] %>% filter(astro_status == "reactive")
  80. q3_3dpi <- quantile(df_3_reactive$min_distance_microns, 0.75, na.rm = TRUE)
  81. df_5_reactive <- [email hidden] %>% filter(astro_status == "reactive")
  82. q3_5dpi <- quantile(df_5_reactive$min_distance_microns, 0.75, na.rm = TRUE)
  84. threshold_17_quantile <- mean(c(q3_3dpi, q3_5dpi))
  85. seventeendpi$astro_status <- ifelse([email hidden]$min_distance_microns <= threshold_17_quantile, "reactive", "healthy")
  87. message("Q₃(3 dpi reactive) = ", round(q3_3dpi, 1))
  88. message("Q₃(5 dpi reactive) = ", round(q3_5dpi, 1))
  89. message("Threshold for 17 dpi (quantile‐avg) = ", round(threshold_17_quantile, 1))
  90. ```
  92. ## Visualize Distance Distributions & Cutoffs
  94. ```{r}
  95. # Plot the distribution of min-distance to lesion at each timepoint,
  96. # including vertical lines for thresholds used to call "reactive".
  97. df_plot <- bind_rows(
  98. [email hidden] %>% filter(astro_status == "reactive") %>% dplyr::select(timepoint, min_distance_microns) %>% mutate(group = "3dpi reactive"),
  99. [email hidden] %>% filter(astro_status == "reactive") %>% dplyr::select(timepoint, min_distance_microns) %>% mutate(group = "5dpi reactive"),
  100. [email hidden] %>% dplyr::select(timepoint, min_distance_microns) %>% mutate(group = "17dpi all")
  101. )
  102. vline_info <- data.frame(
  103. label = c("Q3 (3 dpi)", "Q3 (5 dpi)", "Cutoff (quantile)"),
  104. x = c(q3_3dpi, q3_5dpi, threshold_17_quantile),
  105. color = c("#1B9E77", "#D95F02", "#7570B3"),
  106. linetype = c("dashed", "dashed", "solid"),
  107. stringsAsFactors = FALSE
  108. )
  110. ggplot(df_plot, aes(x = min_distance_microns)) +
  111. geom_density(aes(fill = group), color = NA, alpha = 0.3, size = 0.8) +
  112. geom_vline(
  113. data = vline_info,
  114. aes(xintercept = x, color = label, linetype = label),
  115. size = 1.2, inherit.aes = FALSE
  116. ) +
  117. scale_fill_manual(
  118. name = "Density group",
  119. values = c("3dpi reactive" = "#1B9E77", "5dpi reactive" = "#D95F02", "17dpi all" = "#7570B3")
  120. ) +
  121. scale_color_manual(
  122. name = "Reference lines",
  123. values = c("Q3 (3 dpi)" = "#1B9E77", "Q3 (5 dpi)" = "#D95F02", "Cutoff (quantile)" = "#7570B3")
  124. ) +
  125. scale_linetype_manual(
  126. name = "Reference lines",
  127. values = c("Q3 (3 dpi)" = "dashed", "Q3 (5 dpi)" = "dashed", "Cutoff (quantile)" = "solid")
  128. ) +
  129. labs(
  130. title = "Min‐Distance Densities (Reactive 3 dpi, Reactive 5 dpi, All 17 dpi)",
  131. x = "Min Distance to Lesion (µm)",
  132. y = "Density"
  133. ) +
  134. theme_minimal() +
  135. theme(
  136. legend.box = "vertical",
  137. legend.title = element_text(size = 10),
  138. legend.text = element_text(size = 9)
  139. )
  140. ```
  142. # Differential Expression Analyses
  144. ## Run FindMarkers (Reactive vs. Healthy)
  146. ```{r}
  147. # Find DEGs for each timepoint using "integrated" assay; compare "reactive" vs "healthy".
  148. seurat_list <- list("3dpi" = threedpi, "5dpi" = fivedpi, "17dpi" = seventeendpi)
  149. de_results <- lapply(names(seurat_list), function(tp) {
  150. obj <- seurat_list[[tp]]
  151. DefaultAssay(obj) <- "integrated"
  152. Idents(obj) <- "astro_status"
  153. FindMarkers(
  154. object = obj,
  155. ident.1 = "reactive",
  156. ident.2 = "healthy",
  157. test.use = "wilcox",
  158. logfc.threshold = 0.25,
  159. min.pct = 0.1
  160. ) %>%
  161. rownames_to_column("gene")
  162. })
  163. names(de_results) <- names(seurat_list)
  164. lapply(de_results, head, n = 5)
  165. ```
  167. ## Fold Change Heatmaps
  169. ```{r}
  170. # Load identified noise-prone genes for timepoint of interest
  171. noise_prone_genes <- readRDS("~/Documents/ST/resequencing/Visium_obj/Noise_prone_genes_3dpi.rds")
  172. ```
  174. ```{r}
  175. # Build a matrix of top up/down DEGs at 3dpi and plot fold changes for all timepoints
  176. markers_3dpi_df <- de_results[["3dpi"]] %>% column_to_rownames("gene")
  177. markers_5dpi_df <- de_results[["5dpi"]] %>% column_to_rownames("gene")
  178. markers_17dpi_df <- de_results[["17dpi"]] %>% column_to_rownames("gene")
  179. ```
  181. ```{r}
  182. sig3 <- markers_3dpi_df %>%
  183. rownames_to_column("gene") %>%
  184. filter(p_val_adj < 0.05)
  186. # Filter out the noise‐prone genes
  187. sig3_clean <- sig3 %>%
  188. filter(! gene %in% noise_prone_genes)
  190. top_up_clean <- sig3_clean %>%
  191. arrange(desc(avg_log2FC)) %>%
  192. slice_head(n = 20) %>%
  193. pull(gene)
  195. top_down_clean <- sig3_clean %>%
  196. arrange(avg_log2FC) %>%
  197. slice_head(n = 20) %>%
  198. pull(gene)
  200. top_genes <- c(top_up_clean, top_down_clean)
  201. ```
  203. ```{r}
  204. get_log2fc <- function(markers_df, genes) {
  205. sapply(genes, function(g) {
  206. if (g %in% rownames(markers_df)) {
  207. markers_df[g, "avg_log2FC"]
  208. } else {
  209. NA_real_
  210. }
  211. })
  212. }
  214. fc_matrix <- cbind(
  215. `3 dpi` = get_log2fc(markers_3dpi_df, top_genes),
  216. `5 dpi` = get_log2fc(markers_5dpi_df, top_genes),
  217. `17 dpi` = get_log2fc(markers_17dpi_df, top_genes)
  218. )
  219. rownames(fc_matrix) <- top_genes
  221. raw_fun <- colorRamp2(
  222. c(min(fc_matrix, na.rm = TRUE), 0, max(fc_matrix, na.rm = TRUE)),
  223. c("#7EA3DE", "#E6E3E3", "#DB3C4C")
  224. )
  225. z_fun <- colorRamp2(c(-2, 0, 2), c("#7EA3DE", "#E6E3E3", "#DB3C4C"))
  227. ht_raw <- Heatmap(
  228. fc_matrix,
  229. name = "Fold−change on SCT residuals",
  230. col = raw_fun,
  231. cluster_rows = FALSE,
  232. cluster_columns = FALSE,
  233. show_row_names = TRUE,
  234. show_column_names = TRUE,
  235. row_names_side = "left",
  236. column_names_side = "top",
  237. column_names_rot = 0,
  238. column_title = "Fold−change on SCT residuals",
  239. na_col = "grey90"
  240. )
  242. ht_raw
  243. ```
  244. ```{r}
  245. # Save HM data
  246. mat <- ht_raw@matrix
  248. df <- as.data.frame(mat)
  249. df$Gene <- rownames(df)
  251. df <- df[, c("Gene", setdiff(names(df), "Gene"))]
  253. write.csv(
  254. df,
  255. file = "top20_3dpi_FC_across_timepoints.csv",
  256. row.names = FALSE,
  257. quote = FALSE
  258. )
  259. ```
  261. # DEG Summary and Evolution
  263. ```{r}
  264. # Summarize number/proportion of up/down/NS DEGs at each timepoint and plot stacked bars
  265. classify_deg <- function(df, pval_cut = 0.05, fc_cut = 0.25) {
  266. df %>%
  267. mutate(regulation = case_when(
  268. p_val_adj < pval_cut & avg_log2FC > fc_cut ~ "Upregulated",
  269. p_val_adj < pval_cut & avg_log2FC < -fc_cut ~ "Downregulated",
  270. TRUE ~ "NS"
  271. ))
  272. }
  274. # marker dataframes in list with timepoint names
  275. markers_list <- list(
  276. "3dpi" = markers_3dpi_df,
  277. "5dpi" = markers_5dpi_df,
  278. "17dpi" = markers_17dpi_df
  279. )
  281. markers_list_clean <- map(markers_list, function(df) {
  282. df %>%
  283. # bring gene names into a column
  284. rownames_to_column("gene") %>%
  285. # keep only genes NOT in the noise list
  286. filter(! gene %in% noise_prone_genes) %>%
  287. # back to rownames
  288. column_to_rownames("gene")
  289. })
  291. # DEG summary: classify & bind all timepoints
  292. deg_all <- map2_dfr(markers_list_clean, names(markers_list_clean),
  293. ~ classify_deg(.x) %>% mutate(timepoint = .y)
  294. )
  296. deg_summary <- deg_all %>%
  297. group_by(timepoint, regulation) %>%
  298. summarise(n = n(), .groups = "drop") %>%
  299. group_by(timepoint) %>%
  300. mutate(prop = n / sum(n)) %>%
  301. ungroup() %>%
  302. mutate(
  303. timepoint = factor(timepoint, levels = names(markers_list_clean)),
  304. regulation = factor(regulation, levels = c("Upregulated", "NS", "Downregulated"))
  305. )
  307. # Plot DEG summary
  308. ggplot(deg_summary, aes(timepoint, prop, fill = regulation)) +
  309. geom_col() +
  310. geom_text(aes(label = n),
  311. position = position_stack(vjust = 0.5),
  312. color = "white", size = 5) +
  313. scale_fill_manual(values = c(
  314. Upregulated = "#E44E5A",
  315. NS = "lightgrey",
  316. Downregulated = "#7CA2DE"
  317. )) +
  318. labs(
  319. title = "Proportion and Number of DEGs",
  320. x = "Timepoint",
  321. y = "Proportion of Genes",
  322. fill = "Regulation"
  323. ) +
  324. theme_minimal(base_size = 14)
  325. ```
  327. ```{r}
  328. # Sava data
  329. write.csv(
  330. deg_summary,
  331. file = "DEG_time.csv",
  332. row.names = FALSE,
  333. quote = FALSE
  334. )
  335. ```
  337. ## DEG Evolution (Up and Down Signatures)
  339. ```{r}
  340. # Identify 3dpi signatures
  341. sig_3dpi <- deg_summary %>%
  342. filter(timepoint == "3dpi" & regulation != "NS") %>%
  343. split(.$regulation) %>%
  344. map(~ rownames(markers_list_clean[["3dpi"]])[
  345. markers_list_clean[["3dpi"]]$p_val_adj < 0.05 &
  346. sign(markers_list_clean[["3dpi"]]$avg_log2FC) == ifelse(.x$regulation[1]=="Upregulated", 1, -1) &
  347. abs(markers_list_clean[["3dpi"]]$avg_log2FC) > 0.25
  348. ])
  350. # Build gene‐evolution table for a signature
  351. make_evolution <- function(genes, init_label, markers, pval_cut = 0.05, fc_cut = 0.25) {
  352. map_dfr(names(markers), function(tp) {
  353. df <- markers[[tp]]
  354. stats <- df[genes, c("p_val_adj","avg_log2FC"), drop = FALSE]
  356. regulation <- if (tp == "3dpi") {
  357. init_label
  358. } else {
  359. case_when(
  360. !is.na(stats$p_val_adj) & stats$p_val_adj < pval_cut & stats$avg_log2FC > fc_cut ~ "Upregulated",
  361. !is.na(stats$p_val_adj) & stats$p_val_adj < pval_cut & stats$avg_log2FC < -fc_cut ~ "Downregulated",
  362. TRUE ~ "NS"
  363. )
  364. }
  366. tibble(
  367. gene = genes,
  368. timepoint = factor(tp, levels = names(markers)),
  369. regulation = factor(regulation, levels = c("Upregulated", "NS", "Downregulated"))
  370. )
  371. })
  372. }
  374. # Summarize evolution for plotting
  375. summarize_evol <- function(evo_df) {
  376. evo_df %>%
  377. count(timepoint, regulation, name = "n") %>%
  378. group_by(timepoint) %>%
  379. mutate(prop = n / sum(n)) %>%
  380. ungroup()
  381. }
  383. # Plot
  384. plot_evol <- function(sum_df, title, hide_legend = FALSE) {
  385. ggplot(sum_df, aes(timepoint, prop, fill = regulation)) +
  386. geom_col() +
  387. geom_text(aes(label = n),
  388. position = position_stack(vjust = .5),
  389. color = "white", size = 5) +
  390. scale_fill_manual(values = c(
  391. Upregulated = "#E44E5A",
  392. NS = "lightgrey",
  393. Downregulated = "#7CA2DE"
  394. )) +
  395. labs(title = title, x = "Timepoint", y = "Proportion of Genes") +
  396. theme_minimal(base_size = 14) +
  397. guides(fill = if (hide_legend) "none" else "legend")
  398. }
  400. evol_up <- make_evolution(sig_3dpi$Up, "Upregulated", markers_list_clean)
  401. evol_down <- make_evolution(sig_3dpi$Down, "Downregulated", markers_list_clean)
  403. sum_up <- summarize_evol(evol_up)
  404. sum_down <- summarize_evol(evol_down)
  406. p_up <- plot_evol(sum_up, "3 dpi Upregulated Signature Evolution", hide_legend = TRUE)
  407. p_down <- plot_evol(sum_down, "3 dpi Downregulated Signature Evolution")
  409. (p_up / p_down) + plot_layout(heights = c(1, 1))
  410. ```
  412. ```{r}
  413. # Save data
  414. write.csv(
  415. sum_up,
  416. file = "DEG_UP_time.csv",
  417. row.names = FALSE,
  418. quote = FALSE
  419. )
  420. ```
  422. # Pathway Enrichment (GO)
  424. ## GO Enrichment for Upregulated Genes
  426. ```{r}
  427. # Get upregulated genes for each timepoint and run GO enrichment (biological process)
  428. deg_3dpi_up <- subset(markers_list_clean[["3dpi"]], p_val_adj < 0.05 & avg_log2FC > 0.25)
  429. deg_5dpi_up <- subset(markers_list_clean[["5dpi"]], p_val_adj < 0.05 & avg_log2FC > 0.25)
  430. deg_17dpi_up <- subset(markers_list_clean[["17dpi"]], p_val_adj < 0.05 & avg_log2FC > 0.25)
  432. genes_3dpi_up <- rownames(deg_3dpi_up)
  433. genes_5dpi_up <- rownames(deg_5dpi_up)
  434. genes_17dpi_up <- rownames(deg_17dpi_up)
  435. ```
  437. ```{r}
  438. # Map gene symbols to Entrez IDs (required for clusterProfiler)
  439. genes_3dpi_entrez <- bitr(genes_3dpi_up, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  440. genes_5dpi_entrez <- bitr(genes_5dpi_up, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  441. genes_17dpi_entrez <- bitr(genes_17dpi_up, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  443. # Run enrichment
  444. ego_3dpi_up <- enrichGO(gene = genes_3dpi_entrez$ENTREZID,
  445. OrgDb = org.Mm.eg.db,
  446. ont = "BP",
  447. pAdjustMethod = "BH",
  448. pvalueCutoff = 0.05,
  449. qvalueCutoff = 0.05,
  450. readable = TRUE)
  451. ego_5dpi_up <- enrichGO(gene = genes_5dpi_entrez$ENTREZID,
  452. OrgDb = org.Mm.eg.db,
  453. ont = "BP",
  454. pAdjustMethod = "BH",
  455. pvalueCutoff = 0.05,
  456. qvalueCutoff = 0.05,
  457. readable = TRUE)
  458. ego_17dpi_up <- enrichGO(gene = genes_17dpi_entrez$ENTREZID,
  459. OrgDb = org.Mm.eg.db,
  460. ont = "BP",
  461. pAdjustMethod = "BH",
  462. pvalueCutoff = 0.05,
  463. qvalueCutoff = 0.05,
  464. readable = TRUE)
  465. ```
  467. ## GO Enrichment for Downregulated Genes
  469. ```{r}
  470. # Repeat for downregulated DEGs
  471. deg_3dpi_down <- subset(markers_list_clean[["3dpi"]], p_val_adj < 0.05 & avg_log2FC < -0.25)
  472. deg_5dpi_down <- subset(markers_list_clean[["5dpi"]], p_val_adj < 0.05 & avg_log2FC < -0.25)
  473. deg_17dpi_down <- subset(markers_list_clean[["17dpi"]], p_val_adj < 0.05 & avg_log2FC < -0.25)
  475. genes_3dpi_down <- rownames(deg_3dpi_down)
  476. genes_5dpi_down <- rownames(deg_5dpi_down)
  477. genes_17dpi_down <- rownames(deg_17dpi_down)
  478. ```
  480. ```{r}
  481. genes_3dpi_down_entrez <- bitr(genes_3dpi_down, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  482. genes_5dpi_down_entrez <- bitr(genes_5dpi_down, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  483. genes_17dpi_down_entrez <- bitr(genes_17dpi_down, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  485. ego_3dpi_down <- enrichGO(gene = genes_3dpi_down_entrez$ENTREZID,
  486. OrgDb = org.Mm.eg.db,
  487. ont = "BP",
  488. pAdjustMethod = "BH",
  489. pvalueCutoff = 0.05,
  490. qvalueCutoff = 0.05,
  491. readable = TRUE)
  492. ego_5dpi_down <- enrichGO(gene = genes_5dpi_down_entrez$ENTREZID,
  493. OrgDb = org.Mm.eg.db,
  494. ont = "BP",
  495. pAdjustMethod = "BH",
  496. pvalueCutoff = 0.05,
  497. qvalueCutoff = 0.05,
  498. readable = TRUE)
  499. ego_17dpi_down <- enrichGO(gene = genes_17dpi_down_entrez$ENTREZID,
  500. OrgDb = org.Mm.eg.db,
  501. ont = "BP",
  502. pAdjustMethod = "BH",
  503. pvalueCutoff = 0.05,
  504. qvalueCutoff = 0.05,
  505. readable = TRUE)
  506. ```
  508. ## Pathway Bubble Plots (Up/Down)
  510. ### Upregulated
  512. ```{r}
  513. # Extract and plot the top 3dpi upregulated pathways and show their trajectory across timepoints.
  514. df_3dpi <- as.data.frame(ego_3dpi_up@result) %>% mutate(timepoint = "3dpi")
  515. df_5dpi <- as.data.frame(ego_5dpi_up@result) %>% mutate(timepoint = "5dpi")
  516. df_17dpi <- as.data.frame(ego_17dpi_up@result) %>% mutate(timepoint = "17dpi")
  517. top10_pathways <- df_3dpi %>% arrange(p.adjust) %>% head(15)
  518. topIDs <- top10_pathways$ID
  519. top10_pathways <- top10_pathways %>% mutate(Pathway = paste(ID, Description, sep=": "))
  520. ordered_pathways <- top10_pathways %>% arrange(p.adjust) %>% pull(Pathway)
  522. df_3 <- df_3dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  523. df_5 <- df_5dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  524. df_17 <- df_17dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  525. combined_df <- bind_rows(df_3, df_5, df_17)
  527. combined_df <- combined_df %>%
  528. mutate(
  529. GeneRatio_numeric = sapply(GeneRatio, function(x){
  530. parts <- as.numeric(unlist(strsplit(x, "/")))
  531. if (length(parts) == 2) parts[1] / parts[2] else NA
  532. }),
  533. neg_log10_p = -log10(p.adjust)
  534. )
  535. combined_df$Pathway <- factor(combined_df$Pathway, levels = rev(ordered_pathways))
  536. combined_df$timepoint <- factor(combined_df$timepoint, levels = c("3dpi", "5dpi", "17dpi"))
  538. ggplot(combined_df, aes(x = timepoint, y = Pathway)) +
  539. geom_point(aes(size = GeneRatio_numeric, color = neg_log10_p)) +
  540. scale_color_gradient(low = "#F7CACE", high = "#A01823", name = expression(-log[10](adj~p))) +
  541. scale_size(range = c(3, 8), name = "Gene Ratio") +
  542. labs(title = "Evolution of Top 3dpi Pathways Across Timepoints",
  543. x = "Timepoint",
  544. y = "Pathway") +
  545. theme_bw() +
  546. theme(text = element_text(size = 12),
  547. axis.text.y = element_text(face = "italic"))
  548. ```
  550. ```{r}
  551. # Save data
  552. write.csv(combined_df, "top15_GO_UP_5dpi_pathways_across_timepoints_noise_genes.csv", row.names = FALSE)
  553. ```
  556. ### Downregulated
  558. ```{r}
  559. # Repeat for top 3dpi downregulated pathways
  560. df_3dpi <- as.data.frame(ego_3dpi_down@result) %>% mutate(timepoint = "3dpi")
  561. df_5dpi <- as.data.frame(ego_5dpi_down@result) %>% mutate(timepoint = "5dpi")
  562. df_17dpi <- as.data.frame(ego_17dpi_down@result) %>% mutate(timepoint = "17dpi")
  563. top10_pathways <- df_5dpi %>% arrange(p.adjust) %>% head(15)
  564. topIDs <- top10_pathways$ID
  565. top10_pathways <- top10_pathways %>% mutate(Pathway = paste(ID, Description, sep=": "))
  566. ordered_pathways <- top10_pathways %>% arrange(p.adjust) %>% pull(Pathway)
  568. df_3 <- df_3dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  569. df_5 <- df_5dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  570. df_17 <- df_17dpi %>% filter(ID %in% topIDs) %>% mutate(Pathway = paste(ID, Description, sep=": "))
  571. combined_df <- bind_rows(df_3, df_5, df_17)
  573. combined_df <- combined_df %>%
  574. mutate(
  575. GeneRatio_numeric = sapply(GeneRatio, function(x){
  576. parts <- as.numeric(unlist(strsplit(x, "/")))
  577. if(length(parts) == 2) parts[1] / parts[2] else NA
  578. }),
  579. neg_log10_p = -log10(p.adjust)
  580. )
  581. combined_df$Pathway <- factor(combined_df$Pathway, levels = rev(ordered_pathways))
  582. combined_df$timepoint <- factor(combined_df$timepoint, levels = c("3dpi", "5dpi", "17dpi"))
  584. ggplot(combined_df, aes(x = timepoint, y = Pathway)) +
  585. geom_point(aes(size = GeneRatio_numeric, color = neg_log10_p)) +
  586. scale_color_gradient(low = "#DFE8F7", high = "#1D3D72", name = expression(-log[10](adj~p))) +
  587. scale_size(range = c(3, 8), name = "Gene Ratio") +
  588. labs(title = "Evolution of Top 3dpi Downregulated Pathways Across Timepoints",
  589. x = "Timepoint",
  590. y = "Pathway") +
  591. theme_bw() +
  592. theme(text = element_text(size = 12),
  593. axis.text.y = element_text(face = "italic"))
  594. ```
  596. ```{r}
  597. # Save data
  598. write.csv(combined_df, "top5_GO_DOWN_3dpi_pathways_across_timepoints_noisegenes.csv", row.names = FALSE)
  599. ```
  602. # Gene Signature Analysis (Migratory & Progenitor Genes)
  604. ```{r, warning=FALSE}
  605. # Plot the mean expression (±SE) and per-gene trends for curated migratory and progenitor gene lists
  606. obj_list <- list(
  607. "3dpi" = subset(threedpi, subset = astro_status == "reactive"),
  608. "5dpi" = subset(fivedpi, subset = astro_status == "reactive"),
  609. "17dpi" = subset(seventeendpi, subset = astro_status == "reactive")
  610. )
  612. get_overall_mean_expr <- function(seurat_obj, gene_list, assay = "SCT") {
  613. mat <- GetAssayData(seurat_obj, assay = assay, slot = "data")
  614. genes_present <- intersect(gene_list, rownames(mat))
  615. if (length(genes_present) == 0) return(c(mean=NA, se=NA))
  616. expr <- rowMeans(mat[genes_present, , drop=FALSE], na.rm=TRUE)
  617. overall_mean <- mean(expr, na.rm=TRUE)
  618. overall_se <- sd(expr, na.rm=TRUE) / sqrt(length(expr))
  619. c(mean=overall_mean, se=overall_se)
  620. }
  622. plot_signature_overall <- function(obj_list, gene_list, sig_name) {
  623. df <- do.call(rbind, lapply(names(obj_list), function(tp) {
  624. stats <- get_overall_mean_expr(obj_list[[tp]], gene_list)
  625. data.frame(Timepoint = tp, Mean_Expression = stats["mean"], SE = stats["se"])
  626. }))
  627. df$Timepoint <- factor(df$Timepoint, levels = c("3dpi","5dpi","17dpi"))
  629. ggplot(df, aes(x = Timepoint, y = Mean_Expression, group = 1)) +
  630. geom_line(size = 1, color = "black") +
  631. geom_point(size = 4, aes(color = Timepoint)) +
  632. geom_errorbar(aes(ymin = Mean_Expression - SE, ymax = Mean_Expression + SE),
  633. width = 0.1, color = "black") +
  634. scale_color_manual(values = c("3dpi" = "#931F1D",
  635. "5dpi" = "#EF959D",
  636. "17dpi" = "#F3B7BC")) +
  637. labs(title = paste(sig_name, "Signature: Mean Expression (±SE)"),
  638. x = "Timepoint", y = "Mean Expression") +
  639. theme_minimal() +
  640. theme(text = element_text(size = 14))
  641. }
  643. plot_signature_per_gene <- function(obj_list, gene_list, sig_name) {
  644. df <- bind_rows(lapply(names(obj_list), function(tp) {
  645. mat <- GetAssayData(obj_list[[tp]], assay="SCT", slot="data")
  646. genes_present <- intersect(gene_list, rownames(mat))
  647. if(length(genes_present) == 0) return(NULL)
  648. tibble(Timepoint = tp,
  649. Gene = genes_present,
  650. Mean_Expr = rowMeans(mat[genes_present, , drop=FALSE], na.rm=TRUE))
  651. }))
  652. df$Timepoint <- factor(df$Timepoint, levels = c("3dpi","5dpi","17dpi"))
  654. ggplot(df, aes(x = Timepoint, y = Mean_Expr, group = Gene, color = Gene)) +
  655. geom_line(size = 0.8, alpha = 0.8) +
  656. geom_point(size = 2, alpha = 0.8) +
  657. scale_color_viridis_d(option = "turbo") +
  658. labs(
  659. title = paste0(sig_name, " Signature Genes: Per-gene Trends"),
  660. subtitle = "Overlay of all signature genes",
  661. x = "Timepoint",
  662. y = "Mean Expression",
  663. color = "Gene"
  664. ) +
  665. theme_minimal(base_size = 12) +
  666. theme(
  667. axis.text.x = element_text(angle = 45, hjust = 1),
  668. legend.position = "right",
  669. legend.key.size = unit(0.4, "cm")
  670. )
  671. }
  672. ```
  674. ## Progenitor Genes: Per-gene and Overall Trends
  676. ```{r}
  677. # Curated list of proliferative signature genes (from literature or own analysis)
  678. progenitor_genes <- c(
  679. "Actn4", "Akt1s1", "Anxa7", "Atp2b4", "Bcl2l1", "C1qa", "Cdk14", "Chst2",
  680. "Dact3", "Dlg1", "Efhd2", "Etv5", "Fgf13", "Fkbp1a", "Gfap", "Grn", "Hdac5",
  681. "Igfbp2", "Il34", "Manf", "Mast2", "Mcl1", "Ndfip1", "Pcna", "Ptprj", "Rbbp4",
  682. "Rimbp2", "Sema6b", "Smarca2", "Tmsb4x", "Trf", "Vim"
  683. )
  685. progenitor_genes_clean <- setdiff(progenitor_genes, noise_prone_genes)
  687. plot_signature_overall(obj_list, progenitor_genes_clean, "Progenitor")
  688. plot_signature_per_gene(obj_list, progenitor_genes_clean, "Progenitor")
  689. ```
  691. ## Migratory Genes: Per-gene Trends
  693. ```{r}
  694. # Curated migratory signature genes
  695. migratory_genes <- c(
  696. "Actn4", "Brk1", "Cotl1", "Emc10", "Evl", "Fam107a", "Marcks", "Pik3r2",
  697. "Rtn4", "Sdc4", "Sparc", "Stmn1", "Tmsb4x", "Tnr", "Tuba1a", "Tubb2b",
  698. "Usp9x", "Vim"
  699. )
  700. migratory_genes_clean <- setdiff(migratory_genes, noise_prone_genes)
  702. plot_signature_overall(obj_list, migratory_genes, "Migratory")
  703. plot_signature_per_gene(obj_list, migratory_genes, "Migratory")
  704. ```
  706. ## Signature Expression vs. Distance
  708. ```{r}
  709. get_sig_expr_cells <- function(obj, tp, genes, assay="SCT", slot="data", dist_col="min_distance_microns") {
  710. mat <- GetAssayData(obj, assay = assay, slot = slot)
  711. genes_present <- intersect(genes, rownames(mat))
  712. if (length(genes_present) == 0) return(NULL)
  714. # compute signature score per cell
  715. sig_vals <- colMeans(mat[genes_present, , drop = FALSE], na.rm = TRUE)
  717. # pull distance from metadata
  718. d <- [email hidden][names(sig_vals), dist_col]
  720. # assemble
  721. data.frame(
  722. Timepoint = tp,
  723. Cell = names(sig_vals),
  724. Expr = sig_vals,
  725. Distance = d,
  726. stringsAsFactors = FALSE
  727. )
  728. }
  730. # Progenitor signature vs. distance
  731. genes <- progenitor_genes_clean
  732. df3 <- get_sig_expr_cells(threedpi, "3dpi", genes)
  733. df5 <- get_sig_expr_cells(fivedpi, "5dpi", genes)
  734. df17 <- get_sig_expr_cells(seventeendpi, "17dpi",genes)
  735. df_cells <- bind_rows(df3, df5, df17)
  737. p_prol <- ggplot(df_cells, aes(x = Distance, y = Expr, color = Timepoint, fill = Timepoint)) +
  738. geom_smooth(method = "loess", se = TRUE, size = 1) +
  739. labs(
  740. title = "Progenitor Signature: Mean SCT Expression vs. Distance",
  741. x = "Min Distance (µm)",
  742. y = "Mean SCT Expression"
  743. ) +
  744. scale_color_manual(values = c(
  745. "3dpi" = "#931F1D",
  746. "5dpi" = "#EF959D",
  747. "17dpi" = "#F6CBCF"
  748. )) +
  749. scale_fill_manual(values = c(
  750. "3dpi" = "#F6CCCC",
  751. "5dpi" = "#FCD1D0",
  752. "17dpi" = "#FBEDEC"
  753. )) +
  754. theme_minimal(base_size = 14) +
  755. theme(text = element_text(size = 14))
  757. print(p_prol)
  760. # Migratory signature vs. distance
  761. genes <- migratory_genes
  762. df3 <- get_sig_expr_cells(threedpi, "3dpi", genes)
  763. df5 <- get_sig_expr_cells(fivedpi, "5dpi", genes)
  764. df17 <- get_sig_expr_cells(seventeendpi, "17dpi",genes)
  765. df_cells <- bind_rows(df3, df5, df17)
  767. p_migr <- ggplot(df_cells, aes(x = Distance, y = Expr, color = Timepoint, fill = Timepoint)) +
  768. geom_smooth(method = "loess", se = TRUE, size = 1) +
  769. labs(
  770. title = "Migratory Signature: Mean SCT Expression vs. Distance",
  771. x = "Min Distance (µm)",
  772. y = "Mean SCT Expression"
  773. ) +
  774. scale_color_manual(values = c(
  775. "3dpi" = "#931F1D",
  776. "5dpi" = "#EF959D",
  777. "17dpi" = "#F6CBCF"
  778. )) +
  779. scale_fill_manual(values = c(
  780. "3dpi" = "#F6CCCC",
  781. "5dpi" = "#FCD1D0",
  782. "17dpi" = "#FBEDEC"
  783. )) +
  784. theme_minimal(base_size = 14) +
  785. theme(text = element_text(size = 14))
  787. print(p_migr)
  788. ```
  790. ## Saving Data
  792. ### Save Expression vs. Distance
  794. ```{r}
  795. df_prol_export <- df_cells %>%
  796. dplyr::rename(
  797. timepoint = Timepoint,
  798. distance = Distance,
  799. `Mean expression` = Expr
  800. )
  802. write.csv(df_prol_export, "migratory_signature_distance.csv", row.names = FALSE)
  803. ```
  805. ### Save per-gene trends
  807. ```{r}
  808. # Save the per-gene trends for both signatures for further stats or plotting
  809. save_signature_per_gene <- function(obj_list, gene_list, filename) {
  810. df <- bind_rows(lapply(names(obj_list), function(tp) {
  811. mat <- GetAssayData(obj_list[[tp]], assay="SCT", slot="data")
  812. genes_present <- intersect(gene_list, rownames(mat))
  813. if(length(genes_present) == 0) return(NULL)
  814. tibble(Timepoint = tp,
  815. Gene = genes_present,
  816. Mean_Expr = rowMeans(mat[genes_present, , drop=FALSE], na.rm=TRUE))
  817. }))
  818. df$Timepoint <- factor(df$Timepoint, levels = c("3dpi","5dpi","17dpi"))
  819. write.csv(df, file = filename, row.names = FALSE)
  820. invisible(df)
  821. }
  823. save_signature_per_gene(obj_list, migratory_genes, "migratory_per_gene_noise.csv")
  824. save_signature_per_gene(obj_list, proliferative_genes, "proliferative_per_gene_noise.csv")
  825. ```
  827. ### Save overall mean (±SE) table
  829. ```{r}
  830. # Save overall mean and SE for signature plots (for bar/line graphs or supplement)
  831. save_signature_overall <- function(obj_list, gene_list, filename) {
  832. df <- do.call(rbind, lapply(names(obj_list), function(tp) {
  833. stats <- get_overall_mean_expr(obj_list[[tp]], gene_list)
  834. data.frame(Timepoint = tp, Mean_Expression = stats["mean"], SE = stats["se"])
  835. }))
  836. df$Timepoint <- factor(df$Timepoint, levels = c("3dpi","5dpi","17dpi"))
  837. write.csv(df, file = filename, row.names = FALSE)
  838. invisible(df)
  839. }
  840. save_signature_overall(obj_list, migratory_genes, "migratory_overal_noise.csv")
  841. save_signature_overall(obj_list, proliferative_genes, "proliferative_overall_noise.csv")
  842. ```
  844. # Session Info
  846. ```{r}
  847. sessionInfo()
  848. ```

7.Comparaison.Rmd at commit a23950f, no license · at the source

Overview

Authors: Marina Herwerth1,2,3,4,5, Matthias T Wyss1,3, Nicola B Schmid1,3, Anna Lasne1,3, Jacqueline Condrau1,3, Luca Ravotto1,3, José María Mateos Melero6, Andres Kaech6, Gustav Bredell7, Carolina Thomas8,9, Rachel Kim10, Petra Kukanja10, Vladyslav L Korobeynyk3,11, Christine Stadelmann8,9, Thomas Misgeld4,12,13, Jeffrey L Bennett14, Sebastian Jessberger3,11, Aiman S Saab1,3, Shane A Liddelow10,15,16,17, Bruno Weber1,3
ORCID iDs: Marina Herwerth, Matthias T Wyss, Anna Lasne, Luca Ravotto, José María Mateos Melero, Andres Kaech, Carolina Thomas, Rachel Kim, Thomas Misgeld, Sebastian Jessberger, Aiman S Saab, Shane A Liddelow, Bruno Weber
17 affiliations
  1. Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
  2. Neurology Department, University Hospital Zurich, University of Zurich, Zurich, Switzerland
  3. Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
  4. Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
  5. Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
  6. Center for Microscopy and Image Analysis, University of Zurich, Zurich, Switzerland
  7. Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
  8. Department of Neuropathology, University Medical Center Göttingen, Göttingen, Germany
  9. Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Network of Excitable Cells’ (MBExC), University of Goettingen, Göttingen, Germany
  10. Institute for Translational Neuroscience, NYU Grossman School of Medicine, New York City, NY USA
  11. Brain Research Institute, University of Zurich, Zurich, Switzerland
  12. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
  13. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
  14. Departments of Neurology and Ophthalmology, Programs in Neuroscience and Immunology, University of Colorado School of Medicine, Aurora, CO USA
  15. Department of Neuroscience, NYU Grossman School of Medicine, New York City, NY USA
  16. Department of Ophthalmology, NYU Grossman School of Medicine, New York City, NY USA
  17. Parekh Center for Interdisciplinary Neurology, NYU Grossman School of Medicine, New York City, NY USA
Institutions: University of Zurich (Switzerland); TUM Klinikum (Germany); ETH Zurich (Switzerland); University Hospital Zurich (Switzerland); Technical University of Munich (Germany); Center for Microscopy and Image Analysis (Switzerland); Computer Vision Laboratory (Switzerland); Universitätsmedizin Göttingen (Germany); Multiscale Bioimaging; University of Göttingen (Germany); New York University (United States); German Center for Neurodegenerative Diseases (Germany); Munich Cluster for Systems Neurology (Germany); University of Colorado Anschutz (United States); University of Colorado Denver (United States); NYU Langone Health (United States)
Journal: Nature neuroscience, volume 29, issue 8, pages 1826-1840
Dates: received 21 October 2025; accepted 29 May 2026; published online 23 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02354-5 · PMID 42493549 · PMCID PMC13433311 · OpenAlex W7170195188
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Astrocyte, Neuroimmunology
MeSH: Astrocytes*, Cell Nucleus*, Somatosensory Cortex*, Animals, Aquaporin 4, Cell Proliferation, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (196869, 232028, 223118, 156965, 209272, 320030, 216616, 187000); NEI NIH HHS (R01 EY022936)
Citations: cited by 2 papers (Europe PMC); 85 references in the paper

Abstract

Astrocyte loss occurs in various neurological conditions and can disrupt local tissue homeostasis. While astrocytes surrounding border-forming lesions adopt reactive states without restoring astrocyte networks, how astrocytes respond to spatially confined astrocyte loss remains poorly understood. Here we used longitudinal in vivo two-photon microscopy, combined with spatiotemporal transcriptional profiling, to examine astrocyte responses following focal aquaporin-4 antibody-mediated ablation in the somatosensory cortex of adult mouse brain, a model of astrocytopathy relevant to neuromyelitis optica spectrum disorder. Here we show that perilesional astrocytes undergo pronounced structural remodeling during lesion repopulation, characterized by cell proliferation, prolonged multinucleated astrocyte states, polarized process extension into the depleted area and gradual displacement of nuclei into previously unoccupied astrocyte territories. Spatial transcriptomics reveal an injury-associated molecular response that resolves as the astrocyte network is restored. Together, our findings delineate the spatiotemporal dynamics of astrocyte regeneration after astrocyte loss, extending current understanding of astroglial plasticity in the adult brain.

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

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alasne-uzh/migratory-astrocytes-ST

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At the source: github.com/alasne-uzh/migratory-astrocytes-ST

doi:10.17632/xw8fv8gt8f.1

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At the source: doi.org/10.17632/xw8fv8gt8f.1

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This paper reports an original Python code for vector-based morphological analysis that has been deposited at the Mendeley repository (10.17632/xw8fv8gt8f.1).

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

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Data Availability Statement

Authors confirm that all relevant data are included in the paper/or its supplementary information files. Further information and requests for resources should be directed to the lead contact, B.W. This study did not generate new unique reagents. Mouse lines can be requested from the providing investigators and are protected by standard MTAs. Stained human tissue sections, whole slide scans thereof and data related to human tissue analysis will be provided upon reasonable request. Transcriptomics data generated and analyzed in this study are available under Gene Expression Omnibus accession GSE300434 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300434). Code and library details for the ST analysis pipeline are available at https://github.com/alasne-uzh/migratory-astrocytes-ST. Source data are provided with this paper.

This paper reports an original Python code for vector-based morphological analysis that has been deposited at the Mendeley repository (10.17632/xw8fv8gt8f.1).

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

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Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 2 keywords, 8 MeSH terms, 2 funders, 81 references.

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Herwerth, M., Wyss, M. T., Schmid, N. B., Lasne, A., Condrau, J., Ravotto, L., Mateos Melero, J. M., Kaech, A., Bredell, G., Thomas, C., Kim, R., Kukanja, P., Korobeynyk, V. L., Stadelmann, C., Misgeld, T., Bennett, J. L., Jessberger, S., Saab, A. S., Liddelow, S. A., & Weber, B. (2026). Focal astrocyte loss reveals nuclear translocation during lesion repopulation. Nature neuroscience, 29(8), 1826-1840. https://doi.org/10.1038/s41593-026-02354-5

BibTeX

@article{herwerth2026focal,
author = {Herwerth, Marina and Wyss, Matthias T and Schmid, Nicola B and Lasne, Anna and Condrau, Jacqueline and Ravotto, Luca and Mateos Melero, José María and Kaech, Andres and Bredell, Gustav and Thomas, Carolina and Kim, Rachel and Kukanja, Petra and Korobeynyk, Vladyslav L and Stadelmann, Christine and Misgeld, Thomas and Bennett, Jeffrey L and Jessberger, Sebastian and Saab, Aiman S and Liddelow, Shane A and Weber, Bruno},
title = {{Focal astrocyte loss reveals nuclear translocation during lesion repopulation}},
journal = {Nature neuroscience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {1826--1840},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02354-5},
url = {https://doi.org/10.1038/s41593-026-02354-5},
pmid = {42493549},
pmcid = {PMC13433311}
}

RIS

TY - JOUR
AU - Herwerth, Marina
AU - Wyss, Matthias T
AU - Schmid, Nicola B
AU - Lasne, Anna
AU - Condrau, Jacqueline
AU - Ravotto, Luca
AU - Mateos Melero, José María
AU - Kaech, Andres
AU - Bredell, Gustav
AU - Thomas, Carolina
AU - Kim, Rachel
AU - Kukanja, Petra
AU - Korobeynyk, Vladyslav L
AU - Stadelmann, Christine
AU - Misgeld, Thomas
AU - Bennett, Jeffrey L
AU - Jessberger, Sebastian
AU - Saab, Aiman S
AU - Liddelow, Shane A
AU - Weber, Bruno
TI - Focal astrocyte loss reveals nuclear translocation during lesion repopulation
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/07/23
VL - 29
IS - 8
SP - 1826
EP - 1840
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02354-5
UR - https://doi.org/10.1038/s41593-026-02354-5
LA - en
ER -

CSL-JSON

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