OSCR

Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina.

Code ↔ Paper

16 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 16 matches
  1. [1] § Results › Neuron‐Associated Müller Glia Subtypes Are Evolutionarily Conserved, while Spatial Signaling Axes Are Likely Specific to the Zebrafish ↔ python/cross_species_comparison.ipynb, lines 145 to 294 · score 0.91 · nrn1a, ascl1a, calb2b, gad1b, adult human, human fetal · Report a mismatch
  2. [2] § Results › Neuron‐Associated Müller Glia Subtypes Are Evolutionarily Conserved, while Spatial Signaling Axes Are Likely Specific to the Zebrafish ↔ python/cross_species_comparison.ipynb, lines 145 to 294 · score 0.90 · Aldh1a1, bmpr1ba, cyp26a1, aldh1a2, aldh1a3, fetal human · Report a mismatch
  3. [3] § Results › Müller Glia Subtypes that Define Spatially Distributed BMP and RA Signaling Axes Remain into Adulthood ↔ R/figures.R, lines 699 to 754 · score 0.89 · gdf6a, tbx5a, dhrs3a, bmpr1ba, stc2a, rdh10a · Report a mismatch
  4. [4] § Methods › Cross‐Species Bioinformatics Analysis ↔ python/intra_species_integration.ipynb, lines 145 to 259 · score 0.84 · highly variable genes, donor_id, Scanpy, scVI, Leiden, batch · Report a mismatch
  5. [5] § Methods › Bioinformatic Analysis ↔ R/doublets.R, lines 2 to 42 · score 0.84 · FindClusters, doublet rate, glia subset, PCA, Seurat, PCs · Report a mismatch
  6. [6] § Results › Spatially Distinct Müller Glia Subpopulations Describe a Dorso‐Ventral Axis of Morphogen Patterning ↔ R/figures.R, lines 453 to 523 · score 0.79 · avpr2ab, stc2a, cyp26a1, rdh10a, cyp26c1, foxi2 · Report a mismatch
  7. [7] § Methods › Riboprobe Design ↔ R/figures.R, lines 699 to 754 · score 0.76 · stc2a, aldh1a2, aldh1a3, rdh10a, cyp26c1, bambia · Report a mismatch
  8. [8] § Results › Single‐Cell RNA Sequencing Reveals Müller Glia Transcriptional Heterogeneity in the Larval Retina ↔ R/figures.R, lines 31 to 102 · score 0.72 · her4.2, her4.1, fabp7a, rlbp1a, her12, glula · Report a mismatch
  9. [9] § Methods › Bioinformatic Analysis ↔ python/intra_species_integration.ipynb, lines 44 to 141 · score 0.70 · highly variable genes, PCA, batch, UMAP, embeddings, AnnData · Report a mismatch
  10. [10] § Results › Several Müller Glia Subpopulations Are Defined by Coherent Neuronal Gene Programs ↔ R/figures.R, lines 105 to 152 · score 0.69 · pou4f1, isl2b, gad1b, chata, glia clusters, GABA · Report a mismatch
  11. [11] § Results › Several Müller Glia Subpopulations Are Defined by Coherent Neuronal Gene Programs ↔ R/figures.R, lines 308 to 356 · score 0.67 · neuronal signature score, module score, neuronal gene, heatmap, glia clusters, cell · Report a mismatch
  12. [12] § Results › Spatially Distinct Müller Glia Subpopulations Describe a Dorso‐Ventral Axis of Morphogen Patterning ↔ R/figures.R, lines 453 to 523 · score 0.66 · stc2a, cyp26a1, rdh10a, cyp26c1, bambia, Axis · Report a mismatch
  13. [13] § Results › Several Müller Glia Subpopulations Are Defined by Coherent Neuronal Gene Programs ↔ R/figures.R, lines 263 to 306 · score 0.65 · AddModuleScore, Glyc AC, GABA AC, Seurat, HC, scoring · Report a mismatch
  14. [14] § Results › Single‐Cell RNA Sequencing Reveals Müller Glia Transcriptional Heterogeneity in the Larval Retina ↔ R/figures.R, lines 31 to 102 · score 0.63 · her4.1, fabp7a, rlbp1a, cdk1, glula, pcna · Report a mismatch
  15. [15] § Results › Several Müller Glia Subpopulations Are Defined by Coherent Neuronal Gene Programs ↔ R/figures.R, lines 105 to 152 · score 0.54 · slc4a5a, gad1b, onecut2, GABA, elavl3, gad2 · Report a mismatch
  16. [16] § Results › Several Müller Glia Subpopulations Are Defined by Coherent Neuronal Gene Programs ↔ R/figures.R, lines 757 to 801 · score 0.53 · fads2, gnat1, gngt1, jun, saga, sagb · Report a mismatch

Paper

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The authors' code

R · 863 lines · 25 KB · no license · 11 matches

  1. library(Seurat)
  2. library(dplyr)
  3. library(tidyr)
  4. library(tibble)
  5. library(scales)
  6. source("path/to/functions.R")
  9. # Load data. These data can be found at Zenodo with the DOI commented at the end of each load
  11. Zebrafish_WholeEye_5dpf <- ReadRDS('Storey_2026_Zebrafish_Whole_Eye_5dpf.rds') # Zenodo DOI:
  12. MG_5dpf <- readRDS('Storey_2026_Zebrafish_MG_5dpf.rds') # Zenodo DOI:
  13. MG_integrated <- readRDS('Zebrafish_5dpf_9dpf_adult_integrated_MG.rds') # Zenodo DOI
  14. # The raw data can be found by SRA at NCBI GEO GSE319514
  16. ## Directory setup
  18. root <- 'path/to/figure/dir'
  19. dest_dir_1 <- file.path(root, 'Figure1')
  20. dest_dir_2 <- file.path(root, 'Figure2')
  21. dest_dir_3 <- file.path(root, 'Figure3')
  22. dest_dir_4 <- file.path(root, 'Figure4')
  23. dest_dir_5 <- file.path(root, 'Figure5')
  24. dest_dir_6 <- file.path(root, 'Figure6')
  25. dest_dir_7 <- file.path(root, 'Figure7')
  26. dest_dir_8 <- file.path(root, 'Figure8')
  27. des_dir_sup <- file.path(root, 'FigureSup')
  31. ######################
  32. ###### Figure 1 ######
  33. ######################
  35. we_dimplot <- DimPlot(Zebrafish_WholeEye_5dpf, label = TRUE, repel = T)
  37. save_pub_plot(
  38. we_dimplot,
  39. file_name = "Figure_1A_DimPlot",
  40. type = "dim",
  41. size_preset = "double",
  42. force_square_panel = TRUE,
  43. dest_dir = dest_dir_1
  44. )
  46. rlbp1a_fp_we <- FeaturePlot(Zebrafish_WholeEye_5dpf, features = "glula")
  47. save_pub_plot(rlbp1a_fp_we,
  48. file_name = "Figure_1A_glula_fp_we",
  49. type = "feature",
  50. size_preset = "square",
  51. force_square_panel = TRUE,
  52. dest_dir = dest_dir_1)
  54. mg_dimplot <- DimPlot(MG_5dpf, label = TRUE, repel = T)
  57. save_pub_plot(mg_dimplot,
  58. file_name = "Figure_1C_dimplot_mg",
  59. type = "dim",
  60. size_preset = "square",
  61. force_square_panel = TRUE,
  62. dest_dir = dest_dir_1)
  64. c4_c5_genes <- c("cdk1", "cdk2", "mki67", "pcna", "ascl1a", "hes2.2", "her4.1", "her4.2", "her12", "rlbp1a", "fabp7a", "apoeb", "glulb", "glula")
  66. AxisElementTextSize <- 25
  68. dot_plot_f1e <-DotPlot(MG_5dpf, features = c4_c5_genes)
  69. save_pub_plot(
  70. dot_plot_f1e,
  71. file_name = "Figure_1D_DotPlot",
  72. type = "dot",
  73. size_preset = "double",
  74. x_title = "Müller Glia Cluster",
  75. y_title = "Gene",
  76. x_axis_face = "bold",
  77. y_axis_face = "bold.italic",
  78. x_angle = 0,
  79. x_hjust = 0.5,
  80. force_square_panel = FALSE,
  81. dest_dir = dest_dir_1
  82. )
  85. f_plot_F1E <- FeaturePlot(MG_5dpf, features = "pcna", order = TRUE)
  86. save_pub_plot(
  87. plot = f_plot_F1E,
  88. file_name = "Figure_1E_mg_pcna",
  89. type = "feature",
  90. size_preset = "square",
  91. force_square_panel = TRUE,
  92. dest_dir =dest_dir_1 )
  95. f_plot_F1H <- FeaturePlot(MG_5dpf, features = "her4.1", order = TRUE)
  96. save_pub_plot(
  97. plot = f_plot_F1H,
  98. file_name = "Figure_1H_mg_her4.1",
  99. type = "feature",
  100. size_preset = "square",
  101. force_square_panel = TRUE,
  102. dest_dir =dest_dir_1 )
  105. ######################
  106. ###### Figure 2 ######
  107. ######################
  109. NA_genes <- c("opn1mw1", "rho", "vsx2", "crx", "isl2b", "pou4f1", "calb2b", "gad2", "gad1b", "slc4a5a", "onecut2", "chata", "elavl3")
  110. cell_types <- c("RGC", "GABA AC", "Glyc AC", "HC", "Rods", "Cones")
  111. desired_order <- c("Cones", "Rods", "RGC", "GABA AC", "Glyc AC","HC")
  112. subset_obj <- subset(Zebrafish_WholeEye_5dpf, idents = desired_order)
  115. [email hidden] <- factor(x = [email hidden], levels = desired_order)
  117. dot_plot_f2a <- DotPlot(
  118. subset_obj,
  119. features = NA_genes,
  120. scale = TRUE,
  121. scale.max = 100
  122. )
  123. save_pub_plot(
  124. dot_plot_f2a,
  125. file_name = "Figure_2A_DotPlot",
  126. type = "dot",
  127. custom_width_cm = 10.5,
  128. custom_height_cm = 10,
  129. x_axis_face = "bold",
  130. y_axis_face = "bold.italic",
  131. x_title = "Cell type",
  132. y_title = "Gene",
  133. force_square_panel = FALSE,
  134. dest_dir = dest_dir_2
  135. )
  136. #2b
  138. dot_plot_f2b <-DotPlot(MG_5dpf, features = NA_genes, scale = FALSE, scale.max = 100)
  139. save_pub_plot(
  140. dot_plot_f2b,
  141. file_name = "Figure_2B_DotPlot",
  142. type = "dot",
  143. x_title = "Müller Glia Cluster",
  144. y_title = "Gene",
  145. x_axis_face = "bold",
  146. y_axis_face = "bold.italic",
  147. x_angle = 0,
  148. x_hjust = 0.5,
  149. size_preset = "double",
  150. force_square_panel = FALSE,
  151. dest_dir = dest_dir_2
  152. )
  154. #2c
  155. Zebrafish_WholeEye_5dpf.markers <- FindAllMarkers(Zebrafish_WholeEye_5dpf, only.pos = T, min.pct = 0.25, logfc.threshold = 0.25)
  157. top5Zebrafish_WholeEye_5dpf.markers <- Zebrafish_WholeEye_5dpf.markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = abs(avg_log2FC)) %>% print(n=100)
  159. cell_type_markers <- top5Zebrafish_WholeEye_5dpf.markers %>%
  160. summarise(genes = list(gene), .groups = 'drop') %>%
  161. deframe()
  162. str(cell_type_markers)
  163. cell_type_markers$`CMZ`
  166. cell_types_to_include_ordered <-c(
  167. "RGC",
  168. "CMZ",
  169. "GABA AC",
  170. "HC",
  171. "Glyc AC",
  172. "Cones",
  173. "Rods"
  174. )
  176. subset_list <- cell_type_markers[cell_types_to_include_ordered]
  179. dotplot<- DotPlot(
  180. MG_5dpf,
  181. features = subset_list,
  182. scale = TRUE
  183. )
  185. ### swapping the axes
  186. gene_group_lookup <- enframe(subset_list, name = "cell_type", value = "gene") %>%
  187. unnest(cols = c(gene))
  189. # run DotPlot just to get its calculated data, not for the plot itself
  190. plot_data <- DotPlot(MG_5dpf, features = subset_list, scale = TRUE)$data
  192. merged_plot_data <- left_join(
  193. plot_data,
  194. gene_group_lookup,
  195. by = c("features.plot" = "gene")
  196. )
  198. merged_plot_data$cell_type <- factor(
  199. merged_plot_data$cell_type,
  200. levels = cell_types_to_include_ordered
  201. )
  202. merged_plot_data$features.plot <- factor(
  203. merged_plot_data$features.plot,
  204. levels = rev(unlist(subset_list))
  205. )
  208. final_dotplot <- ggplot(merged_plot_data, aes(x = id, y = features.plot)) +
  209. geom_point(aes(size = pct.exp, color = avg.exp.scaled)) +
  211. facet_grid(
  212. rows = vars(cell_type),
  213. scales = "free_y",
  214. space = "free_y",
  215. switch = "y"
  216. ) +
  218. scale_y_discrete(position = "right") +
  221. scale_color_gradientn(
  222. colors = c("lightgrey", "#203864"),
  223. name = "Avg. Expr."
  224. ) +
  226. labs(
  227. title = "Neuronal Signature in Müller Glia Clusters",
  228. x = "Müller Glia Clusters",
  229. y = NULL,
  230. size = "Pct. Expr." # Shortened for cleaner legend
  231. ) +
  233. theme_minimal() +
  234. theme(
  236. plot.background = element_rect(fill = "white", color = NA),
  237. panel.background = element_rect(fill = "white", color = NA),
  238. text = element_text(color = "black"),
  239. plot.title = element_text(size = 23, face = "bold", hjust = 0.5, margin = margin(b = 20), color = "black"),
  240. axis.title.x = element_text(size = 23, face = "bold", color = "black"),
  241. axis.text.x = element_text(size = 23, face = "bold", color = "black", hjust = 1),
  242. axis.text.y = element_text(size = 23, face = "bold", color = "black"),
  243. axis.text.y.right = element_text(angle = 0, size = 23, face = "italic", color = "black"),
  244. panel.grid.major = element_line(color = "#444444", size = 0.3),
  245. panel.grid.minor = element_blank(),
  246. strip.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
  247. strip.text = element_text(color = "black", face = "bold"),
  248. strip.text.y.left = element_text(angle = 0, size = 20, face = "bold", color = "black"),
  249. legend.position = "right",
  250. legend.background = element_rect(fill = "white", color = NA),
  251. legend.key = element_rect(fill = "white", color = NA),
  252. legend.title = element_text(size = 23, face = "bold", color = "black"),
  253. legend.text = element_text(size = 23, color = "black")
  254. ) +
  255. guides(
  256. size = guide_legend(title.position = "top", override.aes = list(color = "black")),
  257. color = guide_colorbar(title.position = "top", title.hjust = 0.5, frame.colour = "black", ticks.colour = "black")
  258. )
  260. print(final_dotplot)
  261. exportPlotPDF(plot = final_dotplot, file_name = "Figure2C_Neuronal_markers_in_MG_Clusters_large" ,width = 16, height = 11.52, dest_dir = dest_dir_2)
  263. #2d
  265. top50_Zebrafish_WholeEye_5dpf.markers <- Zebrafish_WholeEye_5dpf.markers%>% group_by(cluster) %>% slice_max(n = 50, order_by = abs(avg_log2FC)) %>% print(n=100)
  266. top50_marker_list <- top50_Zebrafish_WholeEye_5dpf.markers %>%
  267. summarise(genes = list(gene), .groups = 'drop') %>%
  268. deframe()
  270. str(top50_marker_list)
  271. top50_marker_list$`CMZ`
  273. target_cell_types <- c(
  274. "MG",
  275. "GABA AC",
  276. "Glyc AC",
  277. "HC",
  278. "CMZ",
  279. "RGC",
  280. "Cones",
  281. "Rods"
  282. )
  284. scoring_list <- top50_marker_list[target_cell_types]
  287. #Seurat will create:
  288. # cell_type_signature1 (corresponding to MG)
  289. # cell_type_signature2 (corresponding to GABA AC)
  290. # etc.
  291. MG_5dpf <- AddModuleScore(
  292. MG_5dpf,
  293. features = scoring_list,
  294. name = "cell_type_signature"
  295. )
  297. score_names <- paste0("cell_type_signature", 1:length(scoring_list))
  299. plot_data <- FetchData(MG_5dpf, vars = c("ident", score_names)) %>%
  300. tidyr::pivot_longer(
  301. cols = all_of(score_names),
  302. names_to = "features.plot",
  303. values_to = "score"
  304. ) %>%
  305. group_by(ident, features.plot) %>%
  306. summarise(avg.score = mean(score), .groups = "drop")
  308. # Create the lookup table using the same vector as the list
  309. name_lookup <- tibble::tibble(
  310. features.plot = score_names,
  311. cell_type_label = target_cell_types
  312. )
  314. plot_data <- left_join(plot_data, name_lookup, by = "features.plot")
  316. # Set levels for plot order
  317. plot_data$cell_type_label <- factor(plot_data$cell_type_label, levels = target_cell_types)
  319. diverging_colors <- c("#D73027", "white", "#203864") # Red -> White -> Royal Blue
  321. min_score <- min(plot_data$avg.score)
  322. max_score <- max(plot_data$avg.score)
  324. heatmap_plot <- ggplot(plot_data, aes(x = ident, y = cell_type_label, fill = avg.score)) +
  325. geom_tile(color = "black") +
  326. scale_fill_gradientn(
  327. colors = diverging_colors,
  328. name = "Score",
  329. # Using scales::rescale to center white at 0 is good practice for ModuleScores
  330. values = rescale(c(min_score, 0, max_score)),
  331. limits = c(min_score, max_score)
  332. ) +
  333. labs(
  334. title = "Neuronal Signature Scores",
  335. x = "Müller Glia Clusters",
  336. y = "Neuronal Gene Signature"
  337. ) +
  338. pub_theme + # Use your defined theme
  339. theme(
  340. axis.text.x = element_text(angle = 45, hjust = 1, size = base_size*0.75, face = "bold"),
  341. axis.text.y = element_text(size = base_size, face = "bold"),
  342. panel.grid = element_blank()
  343. )
  345. save_pub_plot(
  346. heatmap_plot,
  347. file_name = "Figure_2D_Scores",
  348. type = "heatmap",
  349. title_face = "bold",
  350. x_axis_face = "bold",
  351. y_axis_face = "bold",
  352. use_default_scale = FALSE,
  353. force_square_panel = FALSE,
  354. size_preset = "double",
  355. dest_dir = dest_dir_2
  356. )
  358. ### figure 2E Velocity
  360. # velocity is done in python, we used the excellent scCustomize package
  361. # by Samuel Marsh here to convert the Seurat Object to AnnData
  362. # if (!requireNamespace("remotes", quietly = TRUE)) {
  363. # install.packages("remotes")
  364. # }
  366. # # Install scCustomize from GitHub
  367. # remotes::install_github("samuel-marsh/scCustomize")
  368. # library(scCustomize)
  369. # library(reticulate)
  370. # reticulate::py_install("anndata", pip = TRUE)
  371. # reticulate::py_module_available(module = 'anndata') # if this doesn't work the first time, try running this line again
  373. # as.anndata(
  374. # x = MG_5dpf,
  375. # assay = "RNA",
  376. # main_layer = "data",
  377. # other_layers = "counts",
  378. # file_path = "path/to/dir",
  379. # file_name = "Storey_2026_MG_Subset.h5ad"
  380. # )
  382. # performed in the jupyter notebook velocity.ipynb
  384. ######################
  385. ###### Figure 3 ######
  386. ######################
  389. f_plot_F3A <- FeaturePlot(MG_5dpf, features = "gad2", order = TRUE)
  392. save_pub_plot(
  393. plot = f_plot_F3A,
  394. file_name = "Figure_3A_mg_gad2",
  395. type = "feature",
  396. size_preset = "square",
  397. force_square_panel = TRUE,
  398. dest_dir =dest_dir_3 )
  400. f_plot_F3C <- FeaturePlot(MG_5dpf, features = "gad1b", , order = TRUE)
  403. save_pub_plot(
  404. plot = f_plot_F3C,
  405. file_name = "Figure_3C_mg_gad1b",
  406. type = "feature",
  407. size_preset = "square",
  408. force_square_panel = TRUE,
  409. dest_dir =dest_dir_3 )
  412. f_plot_F3E <- FeaturePlot(MG_5dpf, features = "onecut2",order = TRUE)
  415. save_pub_plot(
  416. plot = f_plot_F3E,
  417. file_name = "Figure_3E_mg_onecut2",
  418. type = "feature",
  419. size_preset = "square",
  420. force_square_panel = TRUE,
  421. dest_dir =dest_dir_3 )
  425. ######################
  426. ###### Figure 4 ######
  427. ######################
  429. f_plot_F4A <- FeaturePlot(MG_5dpf, features = "calb2b", order = TRUE)
  432. save_pub_plot(
  433. plot = f_plot_F4A,
  434. file_name = "Figure_4A_mg_calb2b",
  435. type = "feature",
  436. size_preset = "square",
  437. force_square_panel = TRUE,
  438. dest_dir =dest_dir_4 )
  440. f_plot_F4D <- FeaturePlot(MG_5dpf, features = "elavl3",order = TRUE)
  443. save_pub_plot(
  444. plot = f_plot_F4D,
  445. file_name = "Figure_4D_mg_elavl3",
  446. type = "feature",
  447. size_preset = "square",
  448. force_square_panel = TRUE,
  449. dest_dir =dest_dir_4 )
  453. ######################
  454. ###### Figure 5 ######
  455. ######################
  458. c0_c1_c9_c11_genes <- c("foxi2", "rdh10a", "cyp26a1", "avpr2ab","cyp26c1","stc2a","ptgdsb.2","apoeb","atp1a1b", "cahz")
  460. F5A_dotplot <-DotPlot(MG_5dpf, features = c0_c1_c9_c11_genes, scale = T, scale.max = 100) #, cluster.idents = TRUE, col.min = 0, scale.max = 60
  462. save_pub_plot(
  463. F5A_dotplot,
  464. file_name = "Figure_5A_DotPlot",
  465. type = "dot",
  466. x_title = "Müller Glia Cluster",
  467. y_title = "Gene",
  468. x_axis_face = "bold",
  469. y_axis_face = "bold.italic",
  470. size_preset = "double",
  471. x_angle = 0,
  472. x_hjust = 0.5,
  473. force_square_panel = FALSE,
  474. dest_dir = dest_dir_5
  475. )
  478. f_plot_F5C <- FeaturePlot(MG_5dpf, features = "cyp26c1", order = TRUE)
  481. save_pub_plot(
  482. plot = f_plot_F5C,
  483. file_name = "Figure_5C_mg_cyp26c1",
  484. type = "feature",
  485. size_preset = "square",
  486. force_square_panel = TRUE,
  487. dest_dir =dest_dir_5)
  489. f_plot_F5E <- FeaturePlot(MG_5dpf, features = "cyp26a1", order = TRUE)
  492. save_pub_plot(
  493. plot = f_plot_F5E,
  494. file_name = "Figure_5E_mg_cyp26a1",
  495. type = "feature",
  496. size_preset = "square",
  497. force_square_panel = TRUE,
  498. dest_dir =dest_dir_5)
  501. f_plot_F5G <- FeaturePlot(MG_5dpf, features = "bambia", order = TRUE)
  504. save_pub_plot(
  505. plot = f_plot_F5G,
  506. file_name = "Figure_5G_mg_bambia",
  507. type = "feature",
  508. size_preset = "square",
  509. force_square_panel = TRUE,
  510. dest_dir =dest_dir_5)
  514. f_plot_F5I <- FeaturePlot(MG_5dpf, features = "rdh10a", order = TRUE)
  517. save_pub_plot(
  518. plot = f_plot_F5I,
  519. file_name = "Figure_5I_mg_rdh10a",
  520. type = "feature",
  521. size_preset = "square",
  522. force_square_panel = TRUE,
  523. dest_dir =dest_dir_5)
  527. ######################
  528. ###### Figure 6 ######
  529. ######################
  531. ## Drop the microglia clusters 15 and 16 from the data for plotting
  532. Idents(MG_integrated) <- 'seurat_clusters'
  533. mg_int_clean <- subset(MG_integrated, idents = c("15", "16"), invert = TRUE)
  534. [email hidden] <- droplevels([email hidden])
  535. MG_integrated <- mg_int_clean
  536. Idents(MG_integrated) <- 'annotation'
  538. d_plot_6A <- DimPlot(MG_integrated, label = TRUE, repel = F, label.size = 7.5, pt.size = 0.75, split.by = 'age') + NoLegend() + theme_void()
  539. exportPlotPDF(plot = d_plot_6A, file_name = "Figure_6A" , width = 15.75, height = 5, dest_dir = dest_dir_6)
  541. d_plot_6A_leg <- DimPlot(MG_integrated, label = TRUE, repel = F, label.size = 7.5, pt.size = 0.75)
  543. save_pub_plot(
  544. plot = d_plot_6A_leg,
  545. file_name = "Figure_6A_dim_leg",
  546. type = "dim",
  547. legend_position = "right",
  548. custom_width_cm = 30,
  549. custom_height_cm = 15,
  550. size_preset = "custom",
  551. force_square_panel = TRUE,
  552. dest_dir = dest_dir_6
  553. )
  555. # F6B
  556. d_plot_6A <- DimPlot(MG_integrated, label = TRUE, label.size = 6, pt.size = 0.75, repel = T) + NoLegend()
  558. save_pub_plot(
  559. plot = d_plot_6A,
  560. file_name = "Figure_6A_dim",
  561. type = "dim",
  562. legend_position = "none",
  563. base_size = 14,
  564. custom_width_cm = 12.7,
  565. custom_height_cm = 12.7,
  566. size_preset = "square",
  567. force_square_panel = TRUE,
  568. dest_dir = dest_dir_6
  569. )
  570. #
  572. #F6B
  573. plot_data <- [email hidden] %>%
  574. group_by(annotation, age) %>%
  575. summarise(cell_count = n(), .groups = "drop") %>%
  576. group_by(annotation) %>%
  577. mutate(proportion = cell_count / sum(cell_count)) %>%
  578. ungroup()
  581. age_colors <- c("5dpf" = "#D95F02", "9dpf" = "#1B9E77", "adult" = "#7570B3")
  583. composition_plot <- ggplot(plot_data, aes(x = annotation, y = proportion, fill = age)) +
  584. geom_bar(stat = "identity", position = "fill", width = 0.7, color = "black") +
  585. scale_fill_manual(values = age_colors) +
  586. scale_y_continuous(labels = scales::percent, expand = c(0,0)) +
  587. labs(
  588. title = "Age Composition by Cluster",
  589. x = "Integrated Müller glia clusters",
  590. y = "Proportion of Cells",
  591. fill = "Age"
  592. ) +
  593. # Apply your specific theme
  594. theme_minimal(base_size = 14) +
  595. theme(
  596. text = element_text(family = "sans", color = "black"),
  597. plot.title = element_text(face = "bold", size = 16, hjust = 0.5, margin = margin(b = 15), color = "black"),
  598. axis.title = element_text(face = "bold", size = 14, color = "black"),
  599. axis.text.x = element_text(face = "bold", angle = 45, hjust = 1, size = 12, color = "black"),
  600. axis.text.y = element_text(size = 12, color = "black"),
  601. plot.background = element_rect(fill = "white", color = NA),
  602. panel.background = element_rect(fill = "white", color = NA),
  603. panel.grid.major.x = element_blank(),
  604. panel.grid.major.y = element_line(color = "grey", linewidth = 0.5),
  605. panel.grid.minor = element_blank(),
  606. legend.position = "right",
  607. legend.background = element_rect(fill = "white", color = NA),
  608. legend.key = element_rect(fill = "white", color = NA),
  609. legend.title = element_text(face = "bold", color = "black", size = 12),
  610. legend.text = element_text(color = "black", size = 12)
  611. )
  613. exportPlotPDF(plot = composition_plot, file_name = "Figure6B_composition_plot" , width = 5, height = 5, dest_dir = dest_dir_6)
  616. # F6C
  618. DefaultAssay(MG_integrated) <- "RNA"
  619. DimPlot(MG_integrated)
  621. target_genes_age_NA <- c("her4.1", "pcna", "neurod4", "crx", "elavl3", "calb2a", "gad2")
  623. MG_integrated$cluster_age <- paste(Idents(MG_integrated), MG_integrated$age, sep = "::")
  625. Idents(MG_integrated) <- "cluster_age"
  627. raw_data <- DotPlot(MG_integrated, features = target_genes_age_NA, scale = TRUE, scale.max = 100)$data
  629. plot_data_age_NA <- raw_data %>%
  630. separate(id, into = c("cluster", "age"), sep = "::") %>%
  631. mutate(
  632. age = factor(age, levels = c("5dpf", "9dpf", "adult")),
  633. features.plot = factor(features.plot, levels = target_genes_age_NA)
  634. )
  637. age_comparative_dotplot <- ggplot(plot_data_age_NA, aes(x = age, y = cluster)) +
  638. geom_point(aes(size = pct.exp, color = avg.exp.scaled)) +
  639. facet_grid(cols = vars(features.plot)) +
  640. scale_color_gradientn(colors = pub_gradient_colors, name = "Avg. Expr.") +
  641. scale_size(range = c(1, 6), name = "Pct. Expr.",limits = c(0, 100)) +
  642. labs(
  643. title = "Expression Dynamics Across Age",
  644. x = "Age",
  645. y = "Age-Integrated Müller Glia Clusters"
  646. ) +
  648. theme_minimal() +
  649. theme(
  650. axis.text.x = element_text(angle = 45, hjust = 1),
  651. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
  652. panel.spacing = unit(0.5, "cm"),
  653. strip.text.x = element_text(face = "bold.italic", size = 14)
  654. )
  656. age_comparative_dotplot
  658. save_pub_plot(
  659. age_comparative_dotplot,
  660. file_name = "Figure_6C_na_comparative_dotplot",
  661. type = "heatmap",
  662. x_angle = 45,
  663. x_hjust = 1,
  664. strip_face = "bold.italic",
  665. title_face = "bold",
  666. custom_width_cm = 38,
  667. custom_height_cm = 10,
  668. force_square_panel = FALSE,
  669. use_default_scale = FALSE,
  670. dest_dir = dest_dir_6
  671. )
  674. Idents(MG_integrated) <- "annotation"
  676. f_plot_F6D <- FeaturePlot(MG_integrated, features = "gad2", order = FALSE)
  678. save_pub_plot(
  679. plot = f_plot_F6D,
  680. file_name = "Figure_6D_int_mg_gad2",
  681. type = "feature",
  682. size_preset = "square",
  683. force_square_panel = TRUE,
  684. dest_dir =dest_dir_6)
  686. f_plot_F6F <- FeaturePlot(MG_integrated, features = "elavl3", order = FALSE)
  688. save_pub_plot(
  689. plot = f_plot_F6F,
  690. file_name = "Figure_6F_int_mg_gad2",
  691. type = "feature",
  692. size_preset = "square",
  693. force_square_panel = TRUE,
  694. dest_dir =dest_dir_6)
  699. ######################
  700. ###### Figure 7 ######
  701. ######################
  704. target_genes_age_morphogen <- c( "aldh1a2", "bambia","gdf6a", "tbx5a", "bmp4", "cyp26c1", "cyp26a1", "stc2a", "foxi2","aldh1a3", 'dhrs3a', "rdh10a","bmpr1ba")
  706. MG_integrated$cluster_age <- paste(Idents(MG_integrated), MG_integrated$age, sep = "::")
  708. Idents(MG_integrated) <- "cluster_age"
  710. raw_data <- DotPlot(MG_integrated, features = target_genes_age_morphogen, scale = TRUE, scale.max = 100)$data
  712. plot_data_age_morphogen <- raw_data %>%
  713. separate(id, into = c("cluster", "age"), sep = "::") %>%
  714. mutate(
  715. age = factor(age, levels = c("5dpf", "9dpf", "adult")),
  716. features.plot = factor(features.plot, levels = target_genes_age_morphogen)
  717. )
  720. age_comparative_dotplot_morphogen <- ggplot(plot_data_age_morphogen, aes(x = age, y = cluster)) +
  721. geom_point(aes(size = pct.exp, color = avg.exp.scaled)) +
  722. facet_grid(cols = vars(features.plot)) +
  723. scale_color_gradientn(colors = pub_gradient_colors, name = "Avg. Expr.") +
  724. scale_size(range = c(1, 6), name = "Pct. Expr.",limits = c(0, 100)) +
  725. labs(
  726. title = "Expression Dynamics Across Age",
  727. x = "Age",
  728. y = "Age-Integrated Müller Glia Clusters"
  729. ) +
  731. theme_minimal() +
  732. theme(
  733. axis.text.x = element_text(angle = 45, hjust = 1),
  734. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
  735. panel.spacing = unit(0.5, "cm"),
  736. strip.text.x = element_text(face = "bold.italic", size = 14)
  737. )
  739. age_comparative_dotplot_morphogen
  741. save_pub_plot(
  742. age_comparative_dotplot_morphogen,
  743. file_name = "Figure_7A_Morph_comparative_dotplot",
  744. type = "heatmap",
  745. x_angle = 45,
  746. x_hjust = 1,
  747. strip_face = "bold.italic",
  748. title_face = "bold",
  749. custom_width_cm = 42,
  750. custom_height_cm = 10,
  751. force_square_panel = FALSE,
  752. use_default_scale = FALSE,
  753. dest_dir = dest_dir_7
  754. )
  757. ######################
  758. ###### Figure 8 ######
  759. ######################
  761. ## done in python
  762. # see: velocity.ipynb
  767. ###########################
  768. ###### Supplementary ######
  769. ###########################
  771. # Table 1
  772. DefaultAssay(MG_5dpf) <- "RNA"
  773. MG_5dpf.markers <- FindAllMarkers(MG_5dpf, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  774. top5MG_5dpf.markers <- MG_5dpf.markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = abs(avg_log2FC)) %>% print(n=100)
  775. write.csv(MG_5dpf.markers, file.path(des_dir_sup, 'MGMarkers_McFarlane_2026.csv'))
  776. write.csv(top5MG_5dpf.markers, file.path(des_dir_sup, "Top5MGMarkers_McFarlane_2026.csv"))
  779. # S2
  780. sup_markers1 <- c("neurod1", "neurod4", "olig2", "prox1a", "ptf1a", "saga", "sagb", "gngt1", "gnat1","vsx2", "jun", "sox2", "fads2", "isg15")
  783. dot_plot_S2 <- DotPlot(
  784. MG_5dpf,
  785. features = sup_markers1,
  786. scale = TRUE,
  787. scale.max = 100
  788. )
  790. save_pub_plot(
  791. dot_plot_S2,
  792. file_name = "S2_DotPlot",
  793. type = "dot",
  794. size_preset = "double",
  795. x_axis_face = "bold",
  796. y_axis_face = "bold.italic",
  797. x_title = "Müller glia cluster",
  798. y_title = "Gene",
  799. force_square_panel = FALSE,
  800. dest_dir = des_dir_sup
  801. )
  804. # Table 2
  806. DefaultAssay(Zebrafish_WholeEye_5dpf) <- 'RNA'
  807. we.markers <- FindAllMarkers(Zebrafish_WholeEye_5dpf, only.pos = T, min.pct = 0.25, logfc.threshold = 0.25)
  808. top10we.markers <- we.markers %>% group_by(cluster) %>% slice_max(n = 10, order_by = abs(avg_log2FC)) %>% print(n=100)
  809. write.csv(we.markers, file.path(des_dir_sup, "WholeEyeMarkers_McFarlane_2026.csv"))
  810. write.csv(top10we.markers, file.path(des_dir_sup, "Top10WholeEyeMarkers_McFarlane_2026.csv"))
  814. # S3
  816. ## Simpsons index for integration success -- annotated clusters
  817. Idents(MG_integrated) <- 'seurat_clusters'
  818. meta <- [email hidden]
  820. simpsons_data <- meta %>%
  821. group_by(seurat_clusters, age) %>%
  822. summarise(n = n(), .groups = "drop_last") %>%
  823. mutate(p = n / sum(n)) %>%
  824. summarise(
  825. Simpsons_Index = 1 - sum(p^2),
  826. Dominant_Age = age[which.max(p)],
  827. .groups = "drop"
  828. ) %>%
  829. arrange(desc(Simpsons_Index))
  830. simpsons_plot <- ggplot(simpsons_data, aes(x = reorder(seurat_clusters, Simpsons_Index), y = Simpsons_Index, fill = Dominant_Age)) +
  831. geom_bar(stat = "identity", color = "black", width = 0.7) +
  833. # dashed line at theoretical max
  834. geom_hline(yintercept = 0.666, linetype = "dashed", color = "black", linewidth = 0.8) +
  836. annotate("text", x = 1, y = 0.68, label = "Theoretical maximum", hjust = 0, size = 4, fontface = "italic") +
  838. scale_fill_manual(values = c("5dpf" = "#D95F02", "9dpf" = "#1B9E77", "adult" = "#7570B3"), name = "Dom. Age") +
  840. scale_y_continuous(limits = c(0, 0.75), expand = c(0, 0), breaks = seq(0, 0.7, 0.1)) +
  842. labs(
  843. title = "Integration Fidelity",
  844. x = "Integrated Müller Glia Clusters",
  845. y = "Simpson's Diversity Index"
  846. ) +
  848. theme_minimal(base_size = 14) +
  849. theme(
  850. text = element_text(family = "sans", color = "black"),
  851. plot.title = element_text(face = "bold", size = 16, hjust = 0.5, margin = margin(b = 15), color = "black"),
  852. axis.title = element_text(face = "bold", size = 14, color = "black"),
  853. axis.text.x = element_text(face = "bold", angle = 45, hjust = 1, size = 12, color = "black"),
  854. axis.text.y = element_text(size = 12, color = "black"),
  855. legend.title = element_text(face = "bold", color = "black", size = 12),
  856. panel.grid.major.y = element_blank(),
  857. panel.grid.major.x = element_line(color = "gray90"),
  858. panel.border = element_rect(color = "black", fill = NA, linewidth = 1)
  859. )
  862. print(simpsons_plot)
  863. exportPlotPDF(plot = simpsons_plot, file_name = "Supplemental_3_Simpsons_Integration" , width = 5.25, height = 5, dest_dir = des_dir_sup)

figures.R at commit d85b36a, no license · at the source

Overview

Authors: Samuel S Storey1, Carrie L Hehr1, Shaelene Standing1, Sarah McFarlane1
ORCID iDs: Samuel S Storey, Carrie L Hehr, Shaelene Standing, Sarah McFarlane
  1. Department of Cell Biology and Anatomy, Hotchkiss Brain Institute, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
Institutions: University of Calgary (Canada); Alberta Children's Hospital Research Institute (Canada); Hotchkiss Brain Institute (Canada)
Journal: Glia, volume 74, issue 11, article e70220
Dates: received 19 February 2026; accepted 24 August 2026; published online 8 September 2026; in print November 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/glia.70220 · PMID 42711659 · PMCID PMC13553735 · OpenAlex W7130396987
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), zebrafish (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Machine learning
Keywords: glial heterogeneity, Müller glia, neuro‐glial associations, retinoic acid, scRNAseq, spatial heterogeneity
MeSH: Ependymoglial Cells*, Neuroglia*, Retina*, Animals, Animals, Genetically Modified, Gene Expression Regulation, Developmental, Tretinoin, Zebrafish (* major topic)
Topic: Retinal Development and Disorders (Ophthalmology, Medicine), according to OpenAlex
Funding: Lions Sight Centre Fund; CIHR (PJT-178044); Canadian Institutes of Health Research (PJT‐178044); Roy and Joan Allen Chair in Vision and Visual Sciences
Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

s-storey/storey-muller-glia-heterogeneity-2026

License: none: the authors keep all their rights
State: the link answers, verified on 1 November 2026
Evidence: files inventoried
Commit: d85b36a08568da11f846fd5535eeabc2c31e9dc1, 24 August 2026
Languages: Jupyter (4), R (3), Python (1)
Size: 15 files, 8 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, environment (python/requirements.txt), 4 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), Scanpy (5 files), anndata (4 files), Matplotlib (3 files), SciPy (3 files), seaborn (2 files), Seurat (2 files), ggplot2 (1 file), Harmony (1 file), PyTorch (1 file), scVelo (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 1 November 2026: the link answers
  • 1 November 2026: the link answers
9 files
At the source: github.com/s-storey/storey-muller-glia-heterogeneity-2026

The paper's code and data availability statement is in the Data section.

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;
  • 8 scripts, each with its path and the digest of its content;
  • 16 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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1002/glia.70220.

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, 1 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 8 MeSH terms, 4 funders, 96 references.

Cite

This paper

Storey, S. S., Hehr, C. L., Standing, S., & McFarlane, S. (2026). Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina. Glia, 74(11), e70220. https://doi.org/10.1002/glia.70220

BibTeX

@article{storey2026muller,
author = {Storey, Samuel S and Hehr, Carrie L and Standing, Shaelene and McFarlane, Sarah},
title = {{Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina}},
journal = {Glia},
year = {2026},
month = nov,
volume = {74},
number = {11},
pages = {e70220},
publisher = {Wiley},
issn = {0894-1491},
doi = {10.1002/glia.70220},
url = {https://doi.org/10.1002/glia.70220},
pmid = {42711659},
pmcid = {PMC13553735}
}

RIS

TY - JOUR
AU - Storey, Samuel S
AU - Hehr, Carrie L
AU - Standing, Shaelene
AU - McFarlane, Sarah
TI - Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina
T2 - Glia
J2 - Glia
PY - 2026
DA - 2026/11/01
VL - 74
IS - 11
SP - e70220
SN - 0894-1491
PB - Wiley
DO - 10.1002/glia.70220
UR - https://doi.org/10.1002/glia.70220
LA - en
ER -

CSL-JSON

{
"id": "10.1002/glia.70220",
"type": "article-journal",
"title": "Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina",
"container-title": "Glia",
"author": [
{
"family": "Storey",
"given": "Samuel S"
},
{
"family": "Hehr",
"given": "Carrie L"
},
{
"family": "Standing",
"given": "Shaelene"
},
{
"family": "McFarlane",
"given": "Sarah"
}
],
"container-title-short": "Glia",
"volume": "74",
"issue": "11",
"page": "e70220",
"DOI": "10.1002/glia.70220",
"PMID": "42711659",
"PMCID": "PMC13553735",
"ISSN": "0894-1491",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/glia.70220",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
1
]
]
}
}

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

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