OSCR

Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue.

Code ↔ Paper

19 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 19 matches
  1. [1] § Results › PU.1-Based Approach Preserves Microglial Heterogeneity in Frozen Tissue ↔ 2V_02_mg_cells_UMAP_dot_plot.Rmd, lines 244–254 · score 0.91 · C1qa, C1qb, P2ry12, Clec7a, Ccl4, Ifit3
  2. [2] § Methods › Data Analysis › 3’ Bias Metrics ↔ 1DP_06_library_qc.Rmd, lines 153–169 · score 0.91 · additional_sequence_annot.gtf, Gene body coverage, RSeQC, mm10_RefSeq.bed, usegalaxy.eu, mRNA
  3. [3] § Methods › Data Analysis › Data Filtering ↔ 1DP_05_all_cells_LC_FN_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 236–268 · score 0.80 · UMIfraction, percent.spike, nCount_RNA, nFeature_RNA, ERCC, SIRV
  4. [4] § Methods › Data Analysis › Data Filtering ↔ 1DP_00_all_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 268–302 · score 0.80 · UMIfraction, percent.spike, nCount_RNA, nFeature_RNA, ERCC, SIRV
  5. [5] § Methods › Data Analysis › Cell–Cell Communication Analysis ↔ 1DP_04_CellChat.Rmd, lines 168–188 · score 0.80 · computeNetSimilarityPairwise, netEmbedding, mergeCellChat, netClustering, LiveNuclei, LiveCells
  6. [6] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_01_mg_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 115–144 · score 0.79 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  7. [7] § Methods › Data Analysis › Gene Ontology Enrichment Analysis ↔ 1DP_03_GO.Rmd, lines 10–47 · score 0.78 · clusterProfiler, Gene ontology, GO terms, functional pathways, biological process, LiveCells
  8. [8] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_00_all_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 268–302 · score 0.78 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  9. [9] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_05_all_cells_LC_FN_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 236–268 · score 0.78 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  10. [10] § Results › PU.1-Based Enrichment Enhances snRNA-seq Profiling of Microglia in Frozen Brain Tissue ↔ 2V_00_all_cells_UMAP_dot_plot.Rmd, lines 156–168 · score 0.77 · Cx3cr1, endothelial cells, mural cells, neutrophils, astrocytes, monocytes
  11. [11] § Methods › Data Analysis › Normalization, Integration, and Clustering ↔ 1DP_01_mg_cells_raw_data_to_seurat_merged_integrated_UMAP.Rmd, lines 115–144 · score 0.77 · FindClusters, FindNeighbors, SCTransform, CCAIntegration, UMAP, resolution
  12. [12] § Results › PU.1-Based Protocol Enables Detection of Inflammatory Microglial States ↔ 2V_04_DEG.Rmd, lines 168–203 · score 0.69 · Hif1a, Clec7a, ARM population, Cd63, Ctsb, Apoe
  13. [13] § Methods › Data Analysis › Differential Gene Expression Analysis ↔ 1DP_02_DEG.Rmd, lines 158–243 · score 0.62 · FindMarkers, RNA assay, isolation protocol, HM, Seurat, ARM
  14. [14] § Methods › Data Analysis › Identification of Cluster Markers ↔ 2V_02_mg_cells_UMAP_dot_plot.Rmd, lines 623–666 · score 0.60 · module scores, AddModuleScore, Seurat, UMAP, genes
  15. [15] § Results › PU.1-Based Enrichment Enhances snRNA-seq Profiling of Microglia in Frozen Brain Tissue ↔ 2V_00_all_cells_UMAP_dot_plot.Rmd, lines 156–168 · score 0.57 · endothelial cells, mural cells, neutrophils, astrocytes, monocytes, PVM
  16. [16] § Results › PU.1-Based Protocol Enables Detection of Inflammatory Microglial States ↔ 2V_04_DEG.Rmd, lines 33–134 · score 0.55 · log2 fold change, Volcano, log10, overlap, HM, LiveNuclei
  17. [17] § Methods › Data Analysis › Differential Gene Expression Analysis ↔ 2V_04_DEG.Rmd, lines 33–134 · score 0.54 · log2 fold change, HM, isolation protocol, ARM, gene, cells
  18. [18] § Methods › Data Analysis › 3’ Bias Metrics ↔ 1DP_06_library_qc.Rmd, lines 138–150 · score 0.51 · prefixes, internal, ERCC, SIRV, header, BAM
  19. [19] § Results › PU.1-Based Profiling Captures Cell–Cell Interactions Among Microglia ↔ 2V_06_CellChat.Rmd, lines 131–191 · score 0.50 · CellChat, chord diagrams, interactions, LiveNuclei, signaling, LiveCells

Paper

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The authors' code

R Markdown · 561 lines · 21 KB · no license · 3 matches

  1. ---
  2. title: "2V_04_DEG"
  3. author: "Dominika Dostalova"
  4. date: "2024-11-27"
  5. output: html_document
  6. editor_options:
  7. chunk_output_type: console
  8. ---
  9. Introduction
  10. This script is dedicated to visualizing processed data from **1DP_02_DEG.Rmd**. The processed data are represented using **Volcano and Scatter plots** to illustrate DEG analysis.
  12. #Loading of improtant files
  13. ```{r Load packages, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  14. suppressMessages(library("Matrix"))
  15. suppressMessages(library("ggplot2"))
  16. suppressMessages(library("Seurat"))
  17. suppressMessages(library("sctransform"))
  18. suppressMessages(library("ggrepel"))
  19. suppressMessages(library("dplyr"))
  20. suppressMessages(library("magrittr"))
  21. suppressMessages(library("patchwork"))
  22. suppressMessages(library("scales"))
  23. suppressMessages(library("tidyr"))
  24. suppressMessages(library("openxlsx"))
  25. plots <- list()
  26. col_list <- list()
  27. orders <- list()
  28. source("0F_colours_orders.R", local = F, echo = F, print.eval = F)
  29. source("0F_general_functions.R", local = F, echo = F, print.eval = F)
  30. gene_symbol <- read.table("data/Smartseq3xpress.gene_names.txt", header = TRUE)#File generaetd with zUMIS
  31. ```
  33. #Volcano Plots for comparison DEG in HM and ARM across isolation protocols
  34. ```{r Volcano, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  35. # Load the combined RDS file containing all conditions
  36. de_results_all <- readRDS("ws/1DP_02_DEG_ARMvsHM_allconditions.rds")
  38. # Define genes of interest - ARM Frigerio2019
  39. genes_of_interest <- c(
  40. "Apoe", "Cst7", "Lpl", "Clec7a", "Lyz2", "Ccl3", "Lgals3bp", "Cd63", "H2-D1",
  41. "Ctsb", "Cd9", "Ctsl", "Fth1", "Ctsz", "Cd52", "Ctsd", "B2m", "Eef1a1", "Cd83",
  42. "Ccl6", "Hif1a", "Tyrobp", "Serpine2", "Cadm1", "H2-K1", "Npc2", "Pkm",
  43. "Aldoa", "Tpi1", "Pld3", "Gusb", "Plek", "Ldha"
  44. )
  46. # Define protocol-specific colors (ordered for use with list indices)
  47. protocol_colors <- c("#5F7048", "#8a5c1a", "#d9741a") # Dark Green, Brown, Orange
  49. # Get protocol names
  50. protocol_names <- names(de_results_all)
  52. # Initialize an empty list to store plots
  53. plots <- list()
  55. # Loop through each protocol
  56. for (i in seq_along(protocol_names)) {
  57. protocol <- protocol_names[i]
  59. # Extract DE results
  60. de_results <- de_results_all[[protocol]]
  62. # Map ENSEMBL IDs to gene symbols
  63. de_results$gene_symbol <- gene_symbol$gene_name[match(rownames(de_results), gene_symbol$gene_id)]
  65. # Add significance
  66. de_results$significant <- with(de_results, p_val_adj < 0.05 & abs(avg_log2FC) > 1)
  68. # Add coloring category
  69. de_results$color_group <- ifelse(
  70. de_results$avg_log2FC > -1 & de_results$avg_log2FC < 1,
  71. "lowFC",
  72. ifelse(de_results$significant, "significant", "nonsig")
  73. )
  75. # Filter genes of interest
  76. gene_inter <- de_results %>%
  77. filter(gene_symbol %in% genes_of_interest)
  79. # Volcano plot
  80. plots[[protocol]] <- ggplot(data = de_results,
  81. aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  82. geom_point(aes(colour = color_group),
  83. alpha = 0.6,
  84. shape = 16,
  85. size = 1) +
  86. geom_point(data = gene_inter,
  87. aes(x = avg_log2FC, y = -log10(p_val_adj)),
  88. shape = 21,
  89. fill = "black",
  90. color = "black",
  91. size = 1,
  92. stroke = 0.2) +
  93. geom_label_repel(data = gene_inter %>% filter(significant),
  94. aes(label = gene_symbol),
  95. box.padding = 0.22,
  96. label.padding = 0.10,
  97. point.padding = 0.1,
  98. color = "black",
  99. size = 1.8,
  100. max.iter = 20000,
  101. max.overlaps = 20,
  102. min.segment.length = 0,
  103. force = 30,
  104. nudge_y = 1,
  105. label.size = 0,
  106. segment.size = 0.1) +
  107. geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "#B6C7DB", linewidth = 0.1) +
  108. geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "#B6C7DB", linewidth = 0.1) +
  109. labs(
  110. title = paste(protocol, "\n(ARM vs HM)"),
  111. subtitle = (" down in ARM up in ARM"),
  112. x = "Log2 fold change",
  113. y = if (i == 1) "-Log10 adjusted p-value" else NULL
  114. ) +
  115. scale_color_manual(
  116. values = c(
  117. "nonsig" = "gray80",
  118. "lowFC" = "gray80",
  119. "significant" = protocol_colors[i]
  120. ),
  121. guide = "none"
  122. ) +
  123. scale_x_continuous(limits = c(-6.5, 9.1)) +
  124. scale_y_continuous(limits = c(-5, NA )) +#70
  125. theme_mk_title +
  126. remove_grid
  128. print(paste("Volcano plot created for:", protocol))
  129. }
  131. # Combine plots for viewing
  132. plots[["volcano"]] <- plots[["LiveCells"]] + plots[["LiveNuclei"]] + plots[["FixedNuclei"]]
  133. plots[["volcano"]]
  134. ```
  136. # Fig 03 A
  137. ```{r Fig03_1stline, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  138. layout_1stline <- "
  139. ABC
  140. "
  141. plots[["1st_line"]] <- plots[["LiveCells"]] + plots[["LiveNuclei"]] + plots[["FixedNuclei"]] +
  142. plot_layout(guides = "collect", design = layout_1stline) &
  143. theme(
  144. legend.position = "bottom",
  145. legend.justification = "center",
  146. legend.margin = margin(t = -10)
  147. ) &
  148. plot_annotation(
  149. tag_levels = list(c('A', " "),
  150. theme = theme(
  151. plot.tag = element_text(size = 7, face = "plain")
  152. )))
  154. plots[["1st_line"]]
  157. ggsave(
  158. filename = "results/2V_Fig03_1stline_VolcanoPlots.svg",
  159. plot = plots[["1st_line"]],
  160. device = "svg",
  161. width = 16.9,
  162. height = 7,
  163. units = "cm"
  164. )
  165. ```
  168. #SCATTER PLOTS - differences in mean expression of genes in ARM population
  169. #Scatter plot LiveCells vs FixedNuclei in ARM
  170. ```{r Table for ScatterPlot LiveCells vs FixedNuclei, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  171. #gene_table - helper for faster dividing of ARM genes into slots for ScatterPlot
  172. gene_table <- readRDS("ws/1DP_03_gene_table_ScatterPlot_LCvsFN.rds")
  174. list_gene_names_ARM_sym <- c(
  175. "Apoe", "Cst7", "Lpl", "Clec7a", "Lyz2", "Ccl3", "Lgals3bp", "Cd63", "H2-D1",
  176. "Ctsb", "Cd9", "Ctsl", "Fth1", "Ctsz", "Cd52", "Ctsd", "B2m", "Eef1a1", "Cd83",
  177. "Ccl6", "Hif1a", "Tyrobp", "Serpine2", "Cadm1", "H2-K1", "Npc2", "Pkm",
  178. "Aldoa", "Tpi1", "Pld3", "Gusb", "Plek", "Ldha"
  179. )
  182. # Assuming `gene_table` is your data frame
  183. # Initialize vectors for each category
  184. up2_fn <- gene_table$`Upregulated >2x in FN`[gene_table$`Upregulated >2x in FN` %in% list_gene_names_ARM_sym]
  185. up1_fn <- gene_table$`Upregulated 1-2x in FN`[gene_table$`Upregulated 1-2x in FN` %in% list_gene_names_ARM_sym]
  186. up0_fn <- gene_table$`Upregulated 0-1x in FN`[gene_table$`Upregulated 0-1x in FN` %in% list_gene_names_ARM_sym]
  187. up2_lc <- gene_table$`Upregulated >2x in LC`[gene_table$`Upregulated >2x in LC` %in% list_gene_names_ARM_sym]
  188. up1_lc <- gene_table$`Upregulated 1-2x in LC`[gene_table$`Upregulated 1-2x in LC` %in% list_gene_names_ARM_sym]
  189. up0_lc <- gene_table$`Upregulated 0-1x in LC`[gene_table$`Upregulated 0-1x in LC` %in% list_gene_names_ARM_sym]
  191. # Combine results into a named list for clarity
  192. result <- list(
  193. "Upregulated >2x in FN" = up2_fn,
  194. "Upregulated 1-2x in FN" = up1_fn,
  195. "Upregulated 0-1x in FN" = up0_fn,
  196. "Upregulated >2x in LC" = up2_lc,
  197. "Upregulated 1-2x in LC" = up1_lc,
  198. "Upregulated 0-1x in LC" = up0_lc
  199. )
  201. # Print results
  202. print(result)
  203. ```
  205. ```{r Scatter plot LiveCells vs FixedNuclei, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  206. topT <- readRDS("ws/1DP_03_top_genes_ARM_file.rds")
  208. # Define gene groups with their specific colors
  209. gene_groups <- list(
  210. up2 = list(genes = Symbol_to_ENSEMBL(c("Fth1", "Eef1a1")), color = "#5B1F3D"),
  211. up1 = list(genes = Symbol_to_ENSEMBL(c("Cd52", "Apoe", "Ctsd", "Ctsz", "Ccl6", "Npc2", "Ctsl", "Ctsb", "Tpi1", "Cd63", "Cd9", "Tyrobp", "Aldoa","Pkm", "B2m", "Ldha", "Cst7", "Lyz2", "H2-D1")), color = "#A7586D"),
  212. up0 = list(genes = Symbol_to_ENSEMBL(c("Ccl3", "Pld3", "Cd83", "Lpl", "Plek", "Gusb", "Serpine2", "Lgals3bp", "H2-K1", "Clec7a")), color = "#8aa5bf")
  213. )
  215. # Add a `group` and `label_color` column to `topT`
  216. topT$group <- NA
  217. topT$label_color <- NA
  218. for (group in names(gene_groups)) {
  219. genes <- toupper(gene_groups[[group]]$genes)
  220. topT$group[toupper(rownames(topT)) %in% genes] <- group
  221. topT$label_color[toupper(rownames(topT)) %in% genes] <- gene_groups[[group]]$color
  222. }
  225. plots[["scatter_plotLCvsFN"]] <- ggplot(topT, aes(x = LiveCells, y = FixedNuclei)) +
  226. labs(
  227. x = "Log normalized mean expression - LiveCells",
  228. y = "Log normalized mean expression - FixedNuclei"
  229. ) +
  230. geom_abline(slope = 1, intercept = c(0, -2, -1, 1, 2), linetype = c(1, 3, 3, 3, 3)) +
  232. # Base points with color based on differences
  233. geom_point(
  234. aes(color = ifelse(
  235. LiveCells - FixedNuclei > 1, "#5F7048", # Strongly up in LiveCells
  236. ifelse(
  237. FixedNuclei - LiveCells > 1, "#d9741a", # Strongly up in FixedNuclei
  238. "#A1B8D4" # Similar expression
  239. )
  240. )),
  241. size = 0.5,
  242. na.rm = TRUE
  243. ) +
  245. # Highlight gene groups
  246. geom_point(data = subset(topT, !is.na(group)), aes(color = group), size = 0.5) +
  249. # Add gene labels only for up1 and up2
  250. geom_label_repel(
  251. data = subset(topT, group %in% c("up1", "up2")),
  252. aes(
  253. label = ENSEMBL_to_Symbol(rownames(subset(topT, group %in% c("up1", "up2")))),
  254. color = label_color
  255. ),
  256. box.padding = 0.22,
  257. label.padding = 0.1,
  258. point.padding = 0.1,
  259. size = 1.8,
  260. max.iter = 20000,
  261. min.segment.length = 0,
  262. max.overlaps = 20,
  263. show.legend = FALSE,
  264. label.size = 0,
  265. segment.size = 0.1
  266. )+
  268. # Update the manual color scale with more colors
  269. scale_color_manual(
  270. values = c(
  271. "black", # labels >2 LiveCells
  272. "#5F7048", # points >1 LiveCells
  273. # "#7286AA", # labels for genes <1
  274. "gray80", # points < 1
  275. "black", # labels >1 LiveCells
  276. "#d9741a", # points >1 FixedNuclei
  277. "black", # points for genes <1
  278. "black", # points for genes >1 LiveCells
  279. "black" # points for genes >2 LiveCells
  280. )
  281. ) +
  282. theme_mk +
  283. remove_grid +
  284. theme(
  285. legend.position = "none"
  286. )
  288. plots[["scatter_plotLCvsFN"]]
  289. ```
  291. #Scatter plot LiveNuclei vs FixedNuclei in ARM
  292. ```{r Table for ScatterPlot LiveNuclei vs FixedNuclei, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  293. #gene_table - helper for faster dividing of ARM genes into slots for ScatterPlot
  294. gene_table <- readRDS("ws/1DP_03_gene_table_ScatterPlot_LNvsFN.rds")
  296. #filtering for ARM genes
  298. list_gene_names_ARM_sym <- c(
  299. "Apoe", "Cst7", "Lpl", "Clec7a", "Lyz2", "Ccl3", "Lgals3bp", "Cd63", "H2-D1",
  300. "Ctsb", "Cd9", "Ctsl", "Fth1", "Ctsz", "Cd52", "Ctsd", "B2m", "Eef1a1", "Cd83",
  301. "Ccl6", "Hif1a", "Tyrobp", "Serpine2", "Cadm1", "H2-K1", "Npc2", "Pkm",
  302. "Aldoa", "Tpi1", "Pld3", "Gusb", "Plek", "Ldha"
  303. )
  305. # Assuming `gene_table` is your data frame
  306. # Initialize vectors for each category
  307. up2_fn <- gene_table$`Upregulated >2x in FN`[gene_table$`Upregulated >2x in FN` %in% list_gene_names_ARM_sym]
  308. up1_fn <- gene_table$`Upregulated 1-2x in FN`[gene_table$`Upregulated 1-2x in FN` %in% list_gene_names_ARM_sym]
  309. up0_fn <- gene_table$`Upregulated 0-1x in FN`[gene_table$`Upregulated 0-1x in FN` %in% list_gene_names_ARM_sym]
  310. up2_ln <- gene_table$`Upregulated >2x in LN`[gene_table$`Upregulated >2x in LN` %in% list_gene_names_ARM_sym]
  311. up1_ln <- gene_table$`Upregulated 1-2x in LN`[gene_table$`Upregulated 1-2x in LN` %in% list_gene_names_ARM_sym]
  312. up0_ln <- gene_table$`Upregulated 0-1x in LN`[gene_table$`Upregulated 0-1x in LN` %in% list_gene_names_ARM_sym]
  314. # Combine results into a named list for clarity
  315. result <- list(
  316. "Upregulated >2x in FN" = up2_fn,
  317. "Upregulated 1-2x in FN" = up1_fn,
  318. "Upregulated 0-1x in FN" = up0_fn,
  319. "Upregulated >2x in LN" = up2_ln,
  320. "Upregulated 1-2x in LN" = up1_ln,
  321. "Upregulated 0-1x in LN" = up0_ln
  322. )
  324. # Print results
  325. print(result)
  326. ```
  328. ```{r Scatter plot LiveNuclei vs FixedNuclei, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  329. # Load gene expression table for LN vs FN
  330. topT_LNvsFN <- readRDS("ws/1DP_03_top_genes_ARM_file.rds")
  332. # Define gene groups with their specific colors
  333. gene_groups_LNvsFN <- list(
  334. up1 = list(genes = Symbol_to_ENSEMBL(c("Lyz2")), color = "#A7586D"),
  335. up0 = list(genes = Symbol_to_ENSEMBL(c(
  336. "Cd83", "Ctsz", "Ccl6", "Hif1a", "Ctsl", "Serpine2", "Cd9", "Lgals3bp",
  337. "B2m", "H2-K1", "Cst7", "H2-D1", "Clec7a", "Cd52", "Ccl3", "Apoe",
  338. "Pld3", "Ctsd", "Lpl", "Plek", "Npc2", "Ctsb", "Tpi1", "Fth1",
  339. "Cd63", "Gusb", "Tyrobp", "Aldoa", "Cadm1", "Pkm", "Eef1a1", "Ldha"
  340. )), color = "#8aa5bf")
  341. )
  343. # Add `group` and `label_color` to a copy of topT
  344. topT_LNvsFN$group <- NA
  345. topT_LNvsFN$label_color <- NA
  346. for (group in names(gene_groups_LNvsFN)) {
  347. genes <- toupper(gene_groups_LNvsFN[[group]]$genes)
  348. topT_LNvsFN$group[toupper(rownames(topT_LNvsFN)) %in% genes] <- group
  349. topT_LNvsFN$label_color[toupper(rownames(topT_LNvsFN)) %in% genes] <- gene_groups_LNvsFN[[group]]$color
  350. }
  352. # Create the plot
  353. plots[["scatter_plotLNvsFN"]] <- ggplot(topT_LNvsFN, aes(x = LiveNuclei, y = FixedNuclei)) +
  354. labs(
  355. x = "Log normalized mean expression - LiveNuclei",
  356. y = "Log normalized mean expression - FixedNuclei"
  357. ) +
  358. geom_abline(slope = 1, intercept = c(0, -2, -1, 1, 2), linetype = c(1, 3, 3, 3, 3)) +
  360. # Base points
  361. geom_point(
  362. aes(color = ifelse(
  363. LiveNuclei - FixedNuclei > 1, "#8a5c1a",
  364. ifelse(FixedNuclei - LiveNuclei > 1, "#d9741a", "#A1B8D4")
  365. )),
  366. size = 0.5,
  367. na.rm = TRUE
  368. ) +
  370. # Highlight gene groups
  371. geom_point(data = subset(topT_LNvsFN, !is.na(group)), aes(color = group), size = 0.5) +
  373. # Add labels only for up1
  374. geom_label_repel(
  375. data = subset(topT_LNvsFN, group %in% c("up1")),
  376. aes(
  377. label = ENSEMBL_to_Symbol(rownames(subset(topT_LNvsFN, group %in% c("up1")))),
  378. color = label_color
  379. ),
  380. box.padding = 0.22,
  381. label.padding = 0.1,
  382. point.padding = 0.1,
  383. size = 1.8,
  384. max.iter = 20000,
  385. min.segment.length = 0,
  386. max.overlaps = 20,
  387. show.legend = FALSE,
  388. label.size = 0,
  389. segment.size = 0.1
  390. ) +
  392. # Update the manual color scale with more colors
  393. scale_color_manual(
  394. values = c(
  395. "#8a5c1a", # points >1 LiveNuclei
  396. #"#7286AA", # labels <1
  397. "gray80", # points < 1
  398. "black", # labels > LiveNuclei
  399. "#d9741a", #points >1 FixedNuclei
  400. "black", # points for genes <1
  401. "black" # points >1 LiveNuclei SF
  402. ),
  403. guide = "none"
  404. ) +
  405. theme_mk +
  406. remove_grid +
  407. theme(
  408. legend.position = "none", # Show legend for color mapping
  409. )
  411. plots[["scatter_plotLNvsFN"]]
  412. ```
  414. # Fig 03 C
  415. ```{r Fig03_1stline, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  416. layout_1stline <- "
  417. AB"
  419. plots[["1st_line"]] <- plots[["scatter_plotLCvsFN"]] + plots[["scatter_plotLNvsFN"]]+
  420. plot_layout(design = layout_1stline)
  422. plots[["1st_line"]]
  424. ggsave(
  425. filename = "results/2V_Fig03_1stline_ScatterPlotLCvsFNt.svg", # Name of the file
  426. plot = plots[["1st_line"]], # The plot object to save
  427. device = "svg", # Specify the output format (png, pdf, svg)
  428. width = 12, # Width of the image
  429. height = 6, # Height of the image
  430. units = "cm" # Units for width and height
  431. )
  432. ```
  435. #Venn diagram with ARM activation - extraction only values
  436. ```{r Table with names from DEG, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  437. # Create an empty list to store the filtered results
  438. filtered_de_results <- list()
  440. # Create an empty list to store filtered genes for each condition
  441. filtered_genes <- list()
  443. # Iterate over the differential expression results stored in all_de_results
  444. for (name in names(de_results_all)) {
  445. # Get the DE results for the current iteration
  446. de_results <- de_results_all[[name]]
  448. # Filter the results based on the condition: avg_log2FC > 1
  449. filtered_results <- subset(de_results, p_val_adj < 0.05 & avg_log2FC > 1)
  451. # Save the filtered results into the new list
  452. filtered_de_results[[name]] <- filtered_results
  454. # Store only the gene names that meet the condition avg_log2FC > 1
  455. filtered_genes[[name]] <- rownames(filtered_results)
  457. # Print a message for tracking progress
  458. print(paste("Filtered DE results for:", name))
  459. }
  461. # Create the combined table with gene presence/absence
  462. gene_overlap_table <- data.frame(
  463. Gene = unique(unlist(filtered_genes)),
  464. LiveCells = unique(unlist(filtered_genes)) %in% filtered_genes$LiveCells,
  465. LiveNuclei = unique(unlist(filtered_genes)) %in% filtered_genes$LiveNuclei,
  466. FixedNuclei = unique(unlist(filtered_genes)) %in% filtered_genes$FixedNuclei
  467. )
  469. # Add overlap and status information
  470. gene_overlap_table <- gene_overlap_table %>%
  471. rowwise() %>%
  472. mutate(
  473. OverlapCount = sum(c_across(LiveCells:FixedNuclei)),
  474. Status = case_when(
  475. OverlapCount == 3 ~ "Overlap All",
  476. OverlapCount == 2 & LiveCells & LiveNuclei ~ "LiveCells and LiveNuclei",
  477. OverlapCount == 2 & LiveCells & FixedNuclei ~ "LiveCells and FixedNuclei",
  478. OverlapCount == 2 & LiveNuclei & FixedNuclei ~ "LiveNuclei and FixedNuclei",
  479. OverlapCount == 1 & LiveCells ~ "Unique LiveCells",
  480. OverlapCount == 1 & LiveNuclei ~ "Unique LiveNuclei",
  481. OverlapCount == 1 & FixedNuclei ~ "Unique FixedNuclei",
  482. TRUE ~ "Other"
  483. )
  484. )
  486. # Split genes by category
  487. genes_by_category <- gene_overlap_table %>%
  488. select(Gene, Status) %>%
  489. group_by(Status) %>%
  490. mutate(GeneIndex = row_number()) %>% # Create an index for each gene in a category
  491. pivot_wider(names_from = Status, values_from = Gene, values_fill = NA)
  494. # Save the horizontal table to an Excel file
  495. wb <- createWorkbook()
  496. addWorksheet(wb, "Gene Overlap Analysis")
  497. writeData(wb, "Gene Overlap Analysis", genes_by_category)
  498. saveWorkbook(wb, "ws/2V_04_DEG_gene_overlap.xlsx", overwrite = TRUE)
  499. ```
  501. Gene numbers for DEG Venn diagram
  502. ```{r Venn, eval = TRUE, echo = FALSE, message = FALSE, warning=FALSE}
  503. # Create an empty list to store the filtered results
  504. filtered_de_results <- list()
  506. # Create an empty list to store filtered genes for each condition
  507. filtered_genes <- list()
  509. # Iterate over the differential expression results stored in all_de_results
  510. for (name in names(de_results_all)) {
  511. # Get the DE results for the current iteration
  512. de_results <- de_results_all[[name]]
  514. # Filter the results based on the condition: avg_log2FC > 1
  515. filtered_results <- subset(de_results, p_val_adj < 0.05 & avg_log2FC > 1)
  517. # Save the filtered results into the new list
  518. filtered_de_results[[name]] <- filtered_results
  520. # Store only the gene names that meet the condition avg_log2FC > 1
  521. filtered_genes[[name]] <- rownames(filtered_results)
  523. # Print a message for tracking progress
  524. print(paste("Filtered DE results for:", name))
  525. }
  528. # Create a combined table with presence/absence of genes in each condition
  529. gene_overlap_table <- data.frame(
  530. Gene = unique(unlist(filtered_genes)),
  531. LiveCells = unique(unlist(filtered_genes)) %in% filtered_genes$LiveCells,
  532. LiveNuclei = unique(unlist(filtered_genes)) %in% filtered_genes$LiveNuclei,
  533. FixedNuclei = unique(unlist(filtered_genes)) %in% filtered_genes$FixedNuclei
  534. )
  536. # Add overlap and status information
  537. gene_overlap_table <- gene_overlap_table %>%
  538. rowwise() %>%
  539. mutate(
  540. OverlapCount = sum(c_across(LiveCells:FixedNuclei)),
  541. Status = case_when(
  542. OverlapCount == 3 ~ "Overlap All",
  543. OverlapCount == 2 & LiveCells & LiveNuclei ~ "LiveCells and LiveNuclei",
  544. OverlapCount == 2 & LiveCells & FixedNuclei ~ "LiveCells and FixedNuclei",
  545. OverlapCount == 2 & LiveNuclei & FixedNuclei ~ "LiveNuclei and FixedNuclei",
  546. OverlapCount == 1 & LiveCells ~ "Unique LiveCells",
  547. OverlapCount == 1 & LiveNuclei ~ "Unique LiveNuclei",
  548. OverlapCount == 1 & FixedNuclei ~ "Unique FixedNuclei",
  549. TRUE ~ "Other"
  550. )
  551. )
  553. # Group and count genes by status
  554. summary_table <- gene_overlap_table %>%
  555. group_by(Status) %>%
  556. summarize(UniqueGenes = n(), .groups = "drop")
  558. # Display the summary table
  559. print(summary_table)
  561. ```

2V_04_DEG.Rmd at commit 2e9446e, no license · at the source

Overview

Authors: Dominika Dostalova1,2, Pavel Abaffy1,3, Eva Rohlova3, Jan Kriska4, Tomas Knotek4,5, Jana Tureckova4, Denisa Kirdajova1, Miroslava Anderova4, Lukas Valihrach1,3
ORCID iDs: Dominika Dostalova, Pavel Abaffy, Eva Rohlova, Jan Kriska, Tomas Knotek, Jana Tureckova, Denisa Kirdajova, Miroslava Anderova, Lukas Valihrach
  1. Laboratory of Glial Biology and Omics Technologies, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
  2. Faculty of Science, Charles University, Prague, Czech Republic
  3. GeneCore Facility, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
  4. Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
  5. Second Faculty of Medicine, Charles University, Prague, Czech Republic
Institutions: Charles University (Czechia); Czech Academy of Sciences, Institute of Biotechnology (Czechia); Czech Academy of Sciences (Czechia); Czech Academy of Sciences, Institute of Experimental Medicine (Czechia)
Journal: Cellular and molecular neurobiology, volume 46, issue 1, article 113
Dates: received 11 December 2025; accepted 4 May 2026; published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s10571-026-01743-5 · PMID 42133140 · PMCID PMC13357468 · OpenAlex W7161125067
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), stroke (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions
Keywords: Enrichment, Frozen tissue, Microglia, PU.1, Single-nucleus RNA - sequencing, Stroke
MeSH: Brain*, Cryopreservation*, Freezing*, Gene Expression Profiling*, Microglia*, Transcriptome*, Animals, Male, Mice, Mice, Inbred C57BL, Sequence Analysis, RNA, Single-Cell Gene Expression Analysis (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Grantová Agentura, Univerzita Karlova (No. 2224); Grantová Agentura České Republiky (23-05327S); Ministerstvo Školství, Mládeže a Tělovýchovy (Financed by EU-Next Generation EU, LX22NPO5107, Financed by EU—Next Generation EU, MULTIOMICS_CZ CZ.02.01.01/00/23_020/0008540)
Citations: not cited yet (Europe PMC); 45 references in the paper
Research resources: RRID:AB_2149209, ACSA-2 RRID:AB_2727978, PU.1 RRID:AB_2799984, RRID:AB_3718051

Abstract

Single-cell transcriptomics has revealed the central role of microglia in brain development, homeostasis, and disease, particularly in the context of neuroinflammation. While single-cell RNA-sequencing enables targeted microglial analysis from fresh tissue, studying these cells in cryopreserved or archival samples remains challenging due to the lack of a protocol for their specific enrichment and RNA-sequencing. In this study, we developed a method for profiling microglial nuclei from fresh-frozen tissue using Smart-seq3xpress. This approach relies on PU.1-based enrichment, made possible by a brief formaldehyde fixation step to preserve the antigen. To ensure compatibility with Smart-seq3xpress, we utilized a Thermolabile Proteinase K treatment to reverse cross-links, thereby maintaining high transcriptomic sensitivity. We benchmarked the method in a mouse model of ischemic stroke, evaluating both technical performance and its ability to capture biologically meaningful microglial states. Compared to standard single-nucleus protocols, our approach yielded higher gene and UMI counts and a greater proportion of coding reads. Transcriptomic profiles closely matched those from whole-cell RNA-sequencing, including the detection of activation markers and diverse microglial subpopulations. This approach enables high-resolution transcriptomic analysis of microglia from fresh-frozen or archival brain samples, overcoming major limitations of single-nucleus RNA sequencing. It broadens access to cellular insights from biobanked material, supporting both basic research and translational studies of neuroinflammatory disease.

Graphical Abstract: A modified Smart-seq3xpress protocol was developed for microglial profiling by combining formaldehyde fixation and PU.1-based nuclei enrichment from fresh-frozen brain tissue. The method yields high gene and UMI counts alongside strong coding coverage, enabling high-resolution characterization of diverse microglial activation states following stroke.

Supplementary Information: The online version contains supplementary material available at 10.1007/s10571-026-01743-5.

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

Repository

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GliaOmicsLab/microglia-sc-snRNAseq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2e9446edb410619736e4773edbeeab4e5d34d5fc, 12 May 2026
Languages: R (17)
Size: 19 files, 17 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (renv.lock), 15 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (14 files), tidyverse (14 files), patchwork (13 files), Seurat (13 files), reshape2 (9 files), ComplexHeatmap (8 files), pheatmap (4 files), clusterProfiler (3 files), data.table (2 files), ggpubr (1 file), reticulate (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
18 files
At the source: github.com/GliaOmicsLab/microglia-sc-snRNAseq

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

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Data

Datasets cited

Data Availability

The data that support the findings of this study are publicly available on Zenodo (https://zenodo.org/records/16737457), and the code for analysis is available on GitHub (https://github.com/GliaOmicsLab/microglia-sc-snRNAseq). Data from the main experiment (all cells (https://scarfweb.nygen.io/eu-central-1/public/q2acwcw8) and the subset of microglia (https://scarfweb.nygen.io/eu-central-1/public/wxvqmskh)) are available for online analysis via the Nygen portal. A detailed version of the optimized protocol is available at Protocols.io (https://www.protocols.io/private/E618BD14711011F08A690A58A9FEAC02).

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

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 12 MeSH terms, 3 funders, 43 references, 4 RRIDs.

Cite

This paper

Dostalova, D., Abaffy, P., Rohlova, E., Kriska, J., Knotek, T., Tureckova, J., Kirdajova, D., Anderova, M., & Valihrach, L. (2026). Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue. Cellular and molecular neurobiology, 46(1), 113. https://doi.org/10.1007/s10571-026-01743-5

BibTeX

@article{dostalova2026adapted,
author = {Dostalova, Dominika and Abaffy, Pavel and Rohlova, Eva and Kriska, Jan and Knotek, Tomas and Tureckova, Jana and Kirdajova, Denisa and Anderova, Miroslava and Valihrach, Lukas},
title = {{Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue}},
journal = {Cellular and molecular neurobiology},
year = {2026},
month = may,
volume = {46},
number = {1},
pages = {113},
publisher = {Springer},
issn = {0272-4340},
doi = {10.1007/s10571-026-01743-5},
url = {https://doi.org/10.1007/s10571-026-01743-5},
pmid = {42133140},
pmcid = {PMC13357468}
}

RIS

TY - JOUR
AU - Dostalova, Dominika
AU - Abaffy, Pavel
AU - Rohlova, Eva
AU - Kriska, Jan
AU - Knotek, Tomas
AU - Tureckova, Jana
AU - Kirdajova, Denisa
AU - Anderova, Miroslava
AU - Valihrach, Lukas
TI - Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue
T2 - Cellular and molecular neurobiology
J2 - Cell Mol Neurobiol
PY - 2026
DA - 2026/05/14
VL - 46
IS - 1
SP - 113
SN - 0272-4340
PB - Springer
DO - 10.1007/s10571-026-01743-5
UR - https://doi.org/10.1007/s10571-026-01743-5
LA - en
ER -

CSL-JSON

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"id": "10.1007/s10571-026-01743-5",
"type": "article-journal",
"title": "Adapted Smart-seq3xpress Facilitates Selective Microglial Transcriptomic Profiling From Frozen Brain Tissue",
"container-title": "Cellular and molecular neurobiology",
"author": [
{
"family": "Dostalova",
"given": "Dominika"
},
{
"family": "Abaffy",
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{
"family": "Rohlova",
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{
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"family": "Anderova",
"given": "Miroslava"
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"container-title-short": "Cell Mol Neurobiol",
"volume": "46",
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"PMCID": "PMC13357468",
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"language": "en",
"issued": {
"date-parts": [
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}
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