OSCR

Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex.

Code ↔ Paper

20 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 20 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Spatial transcriptomics allow for the mapping of the transcriptional cell types to their histological counterparts ↔ Source/Z_Visualization/Z103_Fig3_MS.R, lines 151–204 · score 0.91 · Inh TAFA1, Inh PVALB, Inh SST, Inh LAMP5, Exc THEMIS, LIBD spatial
  2. [2] § Results › Spatial transcriptomics allow for the mapping of the transcriptional cell types to their histological counterparts ↔ Source/Z_Visualization/Z206_SupplFig_CellType_ModuleScore_NegControl.R, lines 89–157 · score 0.89 · Inh TAFA1, Inh PVALB, Inh SST, Inh LAMP5, Exc THEMIS, Exc FEZF2
  3. [3] § Methods › Cell clustering, dimensionality reduction and cell-type annotation ↔ Source/Z_Visualization/Z204_SupplFig_Hierarchical_CellType_Clustering.R, lines 262–324 · score 0.77 · BuildClusterTree, scVI, hierarchical clustering, Hierarchical cell, latent, dendrogram
  4. [4] § Results › Multi-modal ATAC-seq and RNA-seq in the same nucleus reveals an increased resolution of cell-type identity in the motor cortex ↔ Source/Z_Visualization/Z101_Fig1_MS.R, lines 358–439 · score 0.76 · P2RY12, SLC17A7, AQP4, ASC, GAD2, OPALIN
  5. [5] § Methods › Annotation and linkage of chromatin accessibility ↔ Source/D_Functional_Analysis/D2_Chromatin_Regions_Annotation_ChipSeekR.R, lines 57–133 · score 0.76 · ChIPseeker, peakAnno, TSS, UTR, Intergenic, Intron
  6. [6] § Methods › Functional analysis ↔ Source/D_Functional_Analysis/D22_Add_ALS_ALSFTD_Signatures_Module_Scores.R, lines 66–143 · score 0.70 · module scores, AddModuleScore, gene symbol, gene id, Seurat
  7. [7] § Methods › FANS RNA-seq data pre-processing ↔ Source/F_FANS/A6_ScDblFinder_Detect_Doublets.R, lines 1–54 · score 0.67 · artificial doublets, ScDblFinder, Seurat, FANS, RNA
  8. [8] § Results › Hierarchical annotation of cell-type identity and compositional analysis ↔ Source/Z_Visualization/Z203_SupplFig_Hierarchical_CellType_Tree.R, lines 135–192 · score 0.66 · Inh VIP, Inh LAMP5, hierarchical cell, tree, PAX6, PVALB
  9. [9] § Results › The ALS/ALS-FTD motor cortex is transcriptionally altered across hierarchical cell types ↔ Source/A_Data_Wrangling/A3_Hierarchical_Cell_Type_Annotation.R, lines 49–106 · score 0.64 · RORB CUX2, RORB LNX2, LINC00507 FREM3, L3, hierarchical, cell
  10. [10] § Results › Hierarchical annotation of cell-type identity and compositional analysis ↔ Source/A_Data_Wrangling/A3_Hierarchical_Cell_Type_Annotation.R, lines 183–233 · score 0.64 · Inh VIP, Inh LAMP5, hierarchical cell, lymphocyte, PAX6, PVALB
  11. [11] § Methods › Cell clustering, dimensionality reduction and cell-type annotation ↔ Source/F_FANS/A15_Multiome_Label_Transfer.R, the whole file · a weak match · score 0.63 · FindTransferAnchors, TransferData, Seurat, FANS, WNN, RNA
  12. [12] § Methods › FANS RNA-seq data pre-processing ↔ Source/F_FANS/A3_Souporcell_Doublet_Detection.sh, the whole file · a weak match · score 0.63 · doublet detection, FANS seq, Cellranger
  13. [13] § Methods › Cryptic exon and alternative poly-adenylation analysis ↔ Source/N_APA_Analysis/N1_APA_FANS_DaPars.sh, the whole file · a weak match · score 0.62 · DaPars_main.py, python, Bedgraph
  14. [14] § Methods › Cryptic exon and alternative poly-adenylation analysis ↔ Source/N_APA_Analysis/N4_APA_M0_Case_WNN_L25_DaPars.sh, the whole file · a weak match · score 0.62 · DaPars_main.py, python, Bedgraph, cell
  15. [15] § Results › Hierarchical annotation of cell-type identity and compositional analysis ↔ Source/Z_Visualization/Z203_SupplFig_Hierarchical_CellType_Tree.R, lines 135–192 · score 0.55 · Inh neurons, Exc RORB, Exc LINC00507, LAMP5, microglia, astrocytes
  16. [16] § Methods › Nuclei filtering ↔ Source/A_Data_Wrangling/A1_Prepare_SeuratObject.R, lines 179–238 · score 0.54 · nucleosome free, mitochondrial, TSS, fragments, GEX, enrichment
  17. [17] § Methods › Functional analysis ↔ Source/I_Spatial_Analysis/I3_LIBD_Spatial_GeneModuleScores_CellTypes.R, lines 151–199 · score 0.54 · module scores, AddModuleScore, ENSEMBL, symbol, Seurat, Gene
  18. [18] § Results › Spatial transcriptomics allow for the mapping of the transcriptional cell types to their histological counterparts ↔ Source/Z_Visualization/Z103_Fig3_MS.R, lines 316–394 · score 0.54 · Exc FEZF2 NTNG1, Gene signature score, Boxplots, extratelencephalic, LIBD, Figure 3
  19. [19] § Results › Multi-modal ATAC-seq and RNA-seq in the same nucleus reveals an increased resolution of cell-type identity in the motor cortex ↔ Source/Z_Visualization/Z204_SupplFig_Hierarchical_CellType_Clustering.R, lines 262–324 · score 0.53 · latent space, scVI, hierarchically, clustering, ATAC, WNN
  20. [20] § Results › Hierarchical annotation of cell-type identity and compositional analysis ↔ Source/Z_Visualization/Z209_SupplFig_C9orf72.R, lines 2669–2743 · score 0.52 · Exc RORB, Exc LINC00507, MASC, compositional, LAMP5, microglia

Paper

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The authors' code

R · 397 lines · 11 KB · no license · 2 matches

  3. ### 0.0 Load libraries -------------------------------------------------------
  5. source("~/ALS_Brain_Multiome.Rcfg")
  7. library(qs)
  8. library(tidyverse)
  9. library(data.table)
  10. library(spatialLIBD)
  11. library(Seurat)
  12. library(EnsDb.Hsapiens.v86)
  15. qs::set_trust_promises(TRUE)
  16. qs::set_trust_promises(TRUE)
  21. ### 1.0 Load data ------------------------------------------------------------
  23. M0_WNN_L25_HC_ModuleScores <- qread(
  24. paste0(
  25. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  26. "M0_WNN_L25_HC_ModuleScores",
  27. ".qrds"
  28. ),
  29. nthr=nthr
  30. )
  32. M0_WNN_L4_HC_ModuleScores <- qread(
  33. paste0(
  34. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  35. "M0_WNN_L4_HC_ModuleScores",
  36. ".qrds"
  37. ),
  38. nthr=nthr
  39. )
  41. M0_ETNC_HC_ModuleScores <- qread(
  42. paste0(
  43. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  44. "M0_ETNC_HC_ModuleScores",
  45. ".qrds"
  46. ),
  47. nthr=nthr
  48. )
  51. LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores <- qread(
  52. paste0(
  53. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  54. "LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores",
  55. ".qrds"
  56. ),
  57. nthr=nthr
  58. )
  60. LIBD_Spatial_Seurat_WNN_L4_HC_ModuleScores <- qread(
  61. paste0(
  62. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  63. "LIBD_Spatial_Seurat_WNN_L4_HC_ModuleScores",
  64. ".qrds"
  65. ),
  66. nthr=nthr
  67. )
  69. LIBD_Spatial_Seurat_ETNC_HC_ModuleScores <- qread(
  70. paste0(
  71. "../Data/Annotations/Module_Scores/RNA/HC_Derived/",
  72. "LIBD_Spatial_Seurat_ETNC_HC_ModuleScores",
  73. ".qrds"
  74. ),
  75. nthr=nthr
  76. )
  79. spe <- qread(
  80. paste0(
  81. "../Data/SpatialData/",
  82. "LIBD_Spatial_Spe",
  83. ".qrds"
  84. ),
  85. nthr = nthr
  86. )
  88. all(colnames(spe)==LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores$Original_Barcode)
  89. all(spe$key==LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores$key)
  91. all(colnames(spe)==LIBD_Spatial_Seurat_ETNC_HC_ModuleScores$Original_Barcode)
  92. all(spe$key==LIBD_Spatial_Seurat_ETNC_HC_ModuleScores$key)
  94. Files = setNames(
  95. read.csv("../Data/cfg/Files_List.txt", header=FALSE, sep="\t")[,2],
  96. nm=read.csv("../Data/cfg/Files_List.txt", header=FALSE, sep="\t")[,1]
  97. )
  100. CARD <- qread(
  101. Files["CARD_WNN_L25"],
  102. nthr=nthr
  103. )
  106. all(CARD$key==spe$key)
  108. ColDict_WNN_L25 <- setNames(
  109. object = readxl::read_xlsx(paste0(
  110. "../Data/Visualization/",
  111. "ALS_Brain_Multiome_ColDicts",
  112. ".xlsx"
  113. ),
  114. sheet = "WNN_L25"
  115. )$Color,
  116. nm = readxl::read_xlsx(paste0(
  117. "../Data/Visualization/",
  118. "ALS_Brain_Multiome_ColDicts",
  119. ".xlsx"
  120. ),
  121. sheet = "WNN_L25"
  122. )$WNN_L25
  123. )
  125. ColDict_WNN_L4 <- setNames(
  126. object = readxl::read_xlsx(paste0(
  127. "../Data/Visualization/",
  128. "ALS_Brain_Multiome_ColDicts",
  129. ".xlsx"
  130. ),
  131. sheet = "WNN_L4"
  132. )$Color,
  133. nm = readxl::read_xlsx(paste0(
  134. "../Data/Visualization/",
  135. "ALS_Brain_Multiome_ColDicts",
  136. ".xlsx"
  137. ),
  138. sheet = "WNN_L4"
  139. )$WNN_L4
  140. )
  143. libd_layer_colors2 <- libd_layer_colors
  144. libd_layer_colors2["WM"] <- "#A4A4A4"
  145. libd_layer_colors3 <- libd_layer_colors2
  146. libd_layer_colors3["WM"] <- "#515151"
  151. ### 2.0 Plot combined spatial and single-cell data per WNN_L25 CellType ------
  153. for (i in grep("500fts", colnames(LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores))){
  155. name=colnames(LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores)[i]
  156. spe@colData[[name]] <- base::scale(LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores[[name]])[,1]
  159. df = M0_WNN_L25_HC_ModuleScores
  160. CellType=str_split(str_split(name, "WNN_L25_", simplify=TRUE)[,2], "_ModuleScore_", simplify=TRUE)[,1]
  162. spe@colData[[paste0("CARD_", CellType)]] <- CARD[[CellType]]
  165. if(CellType %in% c("Astrocytes", "Oligodendrocytes", "OPC", "Microglia")){
  166. df$tmp <- df$WNN_L25
  167. df$tmp[df$tmp==CellType] <- str_replace_all(CellType, "_", " ")
  168. df$tmp[df$WNN_L15=="Glia" & df$tmp!=str_replace_all(CellType, "_", " ") ] <- "Other Glia"
  169. df$tmp[df$WNN_L15=="Inh_Neurons"] <- "Inh Neurons"
  170. df$tmp[df$WNN_L15=="Exc_Neurons"] <- "Exc Neurons"
  171. df$tmp <- factor(df$tmp, levels=c(str_replace_all(CellType, "_", " ") , "Other Glia", "Exc Neurons", "Inh Neurons"))
  172. }
  174. if(CellType %in% c("Exc_RORB", "Exc_THEMIS", "Exc_FEZF2", "Exc_LINC00507")){
  175. df$tmp <- df$WNN_L25
  176. df$tmp[df$tmp==CellType] <- str_replace_all(CellType, "_", " ")
  177. df$tmp[df$WNN_L15=="Exc_Neurons" & df$tmp!=str_replace_all(CellType, "_", " ")] <- "Other Exc Neurons"
  178. df$tmp[df$WNN_L15=="Inh_Neurons"] <- "Inh Neurons"
  179. df$tmp[df$WNN_L15=="Glia"] <- "Glia"
  180. df$tmp <- factor(df$tmp, levels=c(str_replace_all(CellType, "_", " "), "Other Exc Neurons", "Inh Neurons", "Glia"))
  181. }
  185. if(CellType %in% c("Inh_TAFA1_VIP", "Inh_PVALB", "Inh_SST", "Inh_LAMP5_PAX6")){
  186. df$tmp <- df$WNN_L25
  187. df$tmp[df$tmp==CellType] <- str_replace_all(CellType, "_", " ")
  188. df$tmp[df$WNN_L15=="Inh_Neurons" & df$tmp!=str_replace_all(CellType, "_", " ")] <- "Other Inh Neurons"
  189. df$tmp[df$WNN_L15=="Exc_Neurons"] <- "Exc Neurons"
  190. df$tmp[df$WNN_L15=="Glia"] <- "Glia"
  191. df$tmp <- factor(df$tmp, levels=c(str_replace_all(CellType, "_", " "), "Other Inh Neurons", "Exc Neurons", "Glia"))
  192. }
  195. df$Score <- base::scale(M0_WNN_L25_HC_ModuleScores[[name]])[,1]
  196. fill_cols <- c(
  197. setNames(ColDict_WNN_L25, nm=str_replace_all(names(ColDict_WNN_L25), "_", " ")),
  198. "Exc Neurons"="#AA0000",
  199. "Other Exc Neurons"="#AA0000",
  200. "Inh Neurons"="#0000AA",
  201. "Other Inh Neurons"="#0000AA",
  202. "Glia"="#00AA00",
  203. "Other Glia"="#00AA00"
  204. )
  207. p1 <- ggplot(df) +
  208. aes(tmp, Score, fill=tmp) +
  209. geom_hline(yintercept = 0, size=0.8, col="#00000066", linetype=2) +
  210. geom_boxplot(fatten=2, size=0.8, col="#000000", outlier.shape = NA, ) +
  211. scale_fill_manual(values = fill_cols) +
  212. ylab("Gene signature score\n") +
  213. theme_classic() +
  214. theme(
  215. axis.title.x = element_blank(),
  216. axis.title.y = element_text(color="#000000", face = "bold.italic", size=12),
  217. axis.text.x = element_text(color="#000000", angle=45, hjust=1, face="bold.italic", size = 12 ),
  218. axis.text.y = element_text(color="#000000", face = "bold.italic", size=10),
  219. title = element_blank(),
  220. legend.position = "Null",
  221. axis.line = element_line(color="#000000", linewidth=0.6)
  222. )
  224. p2 <- vis_clus(
  225. spe = spe,
  226. clustervar = "layer_guess_reordered",
  227. sampleid = "151674",
  228. colors = libd_layer_colors3,
  229. spatial = TRUE,
  230. point_size = 1.5,
  232. ... = " LIBD Layers"
  233. ) +
  234. theme(
  235. title = element_blank(),
  236. legend.position = "bottom"
  237. )
  239. p2$layers[[1]]$aes_params$colour="#000000"
  241. p3 <- vis_gene(
  242. spe = spe[,!is.na(spe$layer_guess_reordered)],
  243. sampleid = "151674",
  244. geneid = name,
  245. spatial = TRUE,
  246. point_size = 1.6
  247. ) + theme(
  248. title = element_blank(),
  249. legend.position = "bottom"
  250. )
  252. p4 <- LIBD_Spatial_Seurat_WNN_L25_HC_ModuleScores %>%
  253. mutate(Score=base::scale(.data[[name]])) %>%
  254. group_by(layer_guess_reordered, sample_id) %>%
  255. dplyr::filter(!is.na(layer_guess_reordered)) %>%
  256. summarize(Score=mean(Score)) %>%
  257. ggplot() +
  258. aes(layer_guess_reordered, Score, fill=layer_guess_reordered) +
  259. geom_hline(yintercept = 0, size=0.8, col="#00000066", linetype=2) +
  260. geom_boxplot(fatten=1.6, size=0.6, color="#000000CC", outlier.shape=NA) +
  261. geom_jitter(fill="#00000088", col="#000000FF", shape=21, size=2.4, width=0.15) +
  262. scale_fill_manual(values=libd_layer_colors2) +
  263. ggtitle(name) +
  264. ylab("Gene signature score\n") +
  265. theme_classic() +
  266. theme(
  267. axis.title.x = element_blank(),
  268. axis.title.y = element_text(color="#000000", face = "bold.italic", size=12),
  269. axis.text.x = element_text(color="#000000", angle=45, hjust=1, face="bold.italic", size=12),
  270. axis.text.y = element_text(color="#000000", face = "bold.italic", size=10),
  271. title = element_blank(),
  272. legend.position = "Null",
  273. axis.line = element_line(color="#000000", linewidth=0.6)
  274. )
  277. p5 <- vis_gene(
  278. spe = spe[,!is.na(spe$layer_guess_reordered)],
  279. sampleid = "151674",
  280. geneid = paste0("CARD_", CellType),
  281. spatial = FALSE,
  282. point_size = 1.6,
  283. viridis = FALSE
  284. ) +
  285. scale_fill_gradientn(colours = c("lightblue", "lightyellow", "red"), na.value = "#00000000") +
  286. theme(
  287. title = element_blank(),
  288. legend.position = "bottom",
  289. legend.text = element_text(color="#000000", angle=45, hjust=1)
  290. )
  294. p_all <- plot(p1 + p2 + p3 + p4 + p5) +
  295. patchwork::plot_layout(ncol=5, nrow=1, , widths=c(1,2,2,2,2), heights = c(1,1,1,1,1))
  297. ggsave(
  298. plot = p_all,
  299. filename = paste0(
  300. "../Data/Visualization/Figures/Fig3/Combined_Spatial_",
  301. CellType,
  302. ".pdf"
  303. ),
  304. dpi=300,
  305. width = 5400,
  306. height = 1800,
  307. units = "px"
  308. )
  310. rm(df)
  311. }
  316. ### 3.0 Plot extraterencephalic neuron markers -------------------------------
  318. spe@colData["ETNC_Score_500fts"] <- base::scale(LIBD_Spatial_Seurat_WNN_L4_HC_ModuleScores$WNN_L4_Exc_FEZF2_NTNG1_ModuleScore_500fts_1)[,1]
  321. df = M0_ETNC_HC_ModuleScores
  323. df$tmp <- df$WNN_L25
  324. df$tmp[df$WNN_L4=="Exc_FEZF2_NTNG1"] <- "Exc_FEZF2_NTNG1"
  325. df$tmp[df$WNN_L25=="Exc_FEZF2" & df$WNN_L4!="Exc_FEZF2_NTNG1"] <- "Other Exc FEZF2"
  326. df$tmp[df$WNN_L15=="Exc_Neurons" & df$WNN_L25!="Exc_FEZF2"] <- "Other Exc"
  327. df$tmp[df$WNN_L15=="Inh_Neurons"] <- "Inh"
  328. df$tmp[df$WNN_L15=="Glia"] <- "Glia"
  329. df$tmp <- factor(df$tmp, levels=c("Exc_FEZF2_NTNG1", "Other Exc FEZF2", "Other Exc", "Inh", "Glia"))
  331. fill_cols <- c(
  332. setNames(ColDict_WNN_L25, nm=str_replace_all(names(ColDict_WNN_L25), "_", " ")),
  334. "Exc_FEZF2_NTNG1" = as.character(ColDict_WNN_L4["Exc_FEZF2_NTNG1"]),
  335. "Other Exc FEZF2" = as.character(ColDict_WNN_L25["Exc_FEZF2"]),
  336. "Other Exc"="#AA0000",
  337. "Inh"="#0000AA",
  338. "Glia"="#00AA00"
  339. )
  342. p1 <- ggplot(df) +
  343. aes(tmp, ETNC_ModuleScore_500fts_1, fill=tmp) +
  344. geom_hline(yintercept = 0, size=0.8, col="#00000066", linetype=2) +
  345. geom_boxplot(fatten=2, size=0.8, col="#000000", outlier.shape = NA, ) +
  346. scale_fill_manual(values = fill_cols) +
  347. ylab("Gene signature score\n") +
  348. theme_classic() +
  349. theme(
  350. axis.title.x = element_blank(),
  351. axis.title.y = element_text(color="#000000", face = "bold.italic", size=12),
  352. axis.text.x = element_text(color="#000000", angle=45, hjust=1, face="bold.italic", size = 12 ),
  353. axis.text.y = element_text(color="#000000", face = "bold.italic", size=10),
  354. title = element_blank(),
  355. legend.position = "Null",
  356. axis.line = element_line(color="#000000", linewidth=0.6)
  357. )
  359. ggsave(
  360. plot = p1,
  361. filename = paste0(
  362. "../Data/Visualization/Figures/Fig3/",
  363. "Exc_FEZF2_M0_RNA_Score",
  364. ".pdf"
  365. ),
  366. dpi=300,
  367. width = 900,
  368. height = 1800,
  369. units = "px"
  370. )
  373. p3 <- vis_gene(
  374. spe = spe[,!is.na(spe$layer_guess_reordered)],
  375. sampleid = "151674",
  376. geneid = "ETNC_Score_500fts",
  377. spatial = TRUE,
  378. point_size = 1.6
  379. ) + theme(
  380. title = element_blank(),
  381. legend.position = "bottom"
  382. )
  384. ggsave(
  385. plot = p3,
  386. filename = paste0(
  387. "../Data/Visualization/Figures/Fig3/Exc_FEZF2_Spatial",
  388. ".pdf"
  389. ),
  390. dpi=300,
  391. width = 900,
  392. height = 1800,
  393. units = "px"
  394. )

Z103_Fig3_MS.R at commit 2669a36, no license · at the source

Overview

Authors: Wolfgang P. Ruf1,2, Julia K. Kühlwein1, Laura Meier1, Sarah J. Brockmann1, Jaehyun LeeBae2, Ghazaleh Sadri-Vakili3, Deniz Yilmazer-Hanke4, Susanne Petri5, Dietmar R. Thal6,7,8, Veselin Grozdanov1, Karin M. Danzer1,2
ORCID iDs: Jaehyun LeeBae, Deniz Yilmazer-Hanke, Dietmar R. Thal, Veselin Grozdanov, Karin M. Danzer
  1. Department of Neurology, University Clinic, University of Ulm,Ulm, Germany
  2. German Center for Neurodegenerative Diseases (DZNE),Ulm, Germany
  3. Sean M. Healey & AMG Center for ALS at Mass General, Massachusetts General Hospital,Boston, MA USA
  4. Clinical Neuroanatomy, Department of Neurology, University Clinic, University of Ulm,Ulm, Germany
  5. Department of Neurology, Hannover Medical School, 1, Carl-Neuberg-Strasse,Hannover, Germany
  6. Laboratory for Neuropathology, Department of Imaging and Pathology and Leuven Brain Institute (LBI), KU-Leuven, Herestraat 49,Leuven, Belgium
  7. Department of Pathology, University Hospitals Leuven,Leuven, Belgium
  8. Laboratory of Neuropathology, Institute of Pathology, Ulm University,Ulm, Germany
Institutions: Universität Ulm (Germany); German Center for Neurodegenerative Diseases (Germany); Massachusetts General Hospital (United States); Medizinische Hochschule Hannover (Germany); KU Leuven (Belgium); Universitair Ziekenhuis Leuven (Belgium)
Journal: Nature communications, volume 17, issue 1, article 2406
Dates: received 10 December 2024; accepted 13 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-69944-6 · PMID 41803120 · PMCID PMC12982666 · OpenAlex W7134824833
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Amyotrophic lateral sclerosis, Gene expression profiling
MeSH: Amyotrophic Lateral Sclerosis*, DNA-Binding Proteins*, Motor Cortex*, Neurons*, Flow Cytometry, Humans, Spatial Transcriptomics (* major topic)
Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex
Funding: Target ALS Grant; KU-Leuven internal funding
Citations: cited by 3 papers (Europe PMC); 64 references in the paper

Abstract

Cytoplasmic TDP-43 pathology is a pathological sign of ALS/ALS-FTD and a converging disease event across different genotypes, phenotypes and CNS areas. To understand this process and target it therapeutically, we need to define which cell types are affected and which cell-type specific effects make them particularly vulnerable. We coupled flow-cytometry nuclear sorting and sequencing with single-nucleus multi-omic ATAC-seq and RNA-seq and spatial transcriptomics to define the transcriptional cell type of affected neurons in the post-mortem ALS/ALS-FTD motor cortex (30 ALS, 20 ALS-FTD & 32 control samples). Here, we show that mainly excitatory cortical neurons are affected by TDP-43 pathology and define the cell types that are affected the most: intratelencephalic L2-L3-LINC00507-FREM3, L3-L5-RORB-LNX2, L3-L5-RORB-ADGRL4 & L6-THEMIS-LINC00343 neurons and extratelencephalic L5-FEZF2-NTNG1 neurons. Transcriptional aberrations by TDP-43 pathology, like cryptic exon inclusion, are cell-type specific and affect distinct gene sets in each cell type, highlighting the need to address TDP-43 pathology in a cell-type specific manner.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

timoast/sinto

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 37b7db17de64c6ddd3a8512356b966adf9ecd9a8, 14 May 2024
Languages: Python (19)
Size: 42 files, 19 scripts
Software Heritage: archived
Found in: the text, “Cryptic exon and alternative poly-adenylation an”
Holds: README, license file, environment (pyproject.toml, setup.cfg), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: pysam (8 files), NumPy (1 file), SAMtools (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/timoast/sinto

DanzerLab/Ruf_et_al_2026

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2669a363973d23da0bfcd15d8149953e87db0dca, 29 January 2026
Languages: R (135), Shell (57), Python (2)
Size: 195 files, 194 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (82 files), DESeq2 (75 files), Seurat (56 files), data.table (36 files), clusterProfiler (7 files), SAMtools (6 files), patchwork (4 files), BEDTools (3 files), circlize (3 files), deepTools (3 files), ArviZ (2 files), ComplexHeatmap (2 files), igraph (2 files), lme4 (2 files), Matplotlib (2 files), pandas (2 files), reshape2 (2 files), Scanpy (2 files), easystats (1 file), ggplot2 (1 file), ggpubr (1 file), Monocle 3 (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
195 files
At the source: github.com/DanzerLab/Ruf_et_al_2026

Code availability

The code generated for the analysis and the code used to generate the figures is available on GitHub: https://github.com/DanzerLab/Ruf_et_al_2026.

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

Tracing map

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  • 213 scripts, each with its path and the digest of its content;
  • 20 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability

The raw sequencing data that was generated in this study is available on EGA under EGAD50000002240 (https://ega-archive.org/datasets/EGAD50000002240) (https://ega-archive.org/studies/EGAS50000001562) (multi-omic dataset) and EGAD50000002243 (https://ega-archive.org/datasets/EGAD50000002243) (FANS-seq dataset) and is subject to controlled access due to data privacy regulations (identifiable genomic sequence data in combination with phenotype). Requests for accessing the raw data are to be addressed to the Data Access Committee (EGAC50000000856) (https://ega-archive.org/dacs/EGAC50000000856) through the EGA platform. Requests will be reviewed by the Data Access Committee (expected timeframe: 25 working days), approved for use (in five years timeframe) if the application requires access to the raw genomic sequences and requires the signing of a data transfer agreement to guarantee compliance with the European General Data Protection Regulation (GDPR). Further requests for custom analysis without access to the raw data (i.e., collaboration) can be made to the corresponding authors per e-mail. The processed single-nucleus sequencing data with associated metadata is available on Zenodo under 10.5281/zenodo.18370645 (https://zenodo.org/records/18370646) (multi-omic dataset) and 10.5281/zenodo.18371192 (https://zenodo.org/records/18371193) (FANS-seq dataset). The publicly available data was accessed from the provided resources in Maynard et al.22,59, Pineda et al.20, Wang et al.17, Li et al.21, Limone et al.29, Liu et al.10 and Gittings et al18. Source data are provided in this paper.

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

Versions

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

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 7 MeSH terms, 2 funders, 63 references.

Cite

This paper

Ruf, W. P., Kühlwein, J. K., Meier, L., Brockmann, S. J., LeeBae, J., Sadri-Vakili, G., Yilmazer-Hanke, D., Petri, S., Thal, D. R., Grozdanov, V., & Danzer, K. M. (2026). Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex. Nature communications, 17(1), 2406. https://doi.org/10.1038/s41467-026-69944-6

BibTeX

@article{ruf2026multi,
author = {Ruf, Wolfgang P. and Kühlwein, Julia K. and Meier, Laura and Brockmann, Sarah J. and LeeBae, Jaehyun and Sadri-Vakili, Ghazaleh and Yilmazer-Hanke, Deniz and Petri, Susanne and Thal, Dietmar R. and Grozdanov, Veselin and Danzer, Karin M.},
title = {{Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {2406},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-69944-6},
url = {https://doi.org/10.1038/s41467-026-69944-6},
pmid = {41803120},
pmcid = {PMC12982666}
}

RIS

TY - JOUR
AU - Ruf, Wolfgang P.
AU - Kühlwein, Julia K.
AU - Meier, Laura
AU - Brockmann, Sarah J.
AU - LeeBae, Jaehyun
AU - Sadri-Vakili, Ghazaleh
AU - Yilmazer-Hanke, Deniz
AU - Petri, Susanne
AU - Thal, Dietmar R.
AU - Grozdanov, Veselin
AU - Danzer, Karin M.
TI - Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/09
VL - 17
IS - 1
SP - 2406
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-69944-6
UR - https://doi.org/10.1038/s41467-026-69944-6
LA - en
ER -

CSL-JSON

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