OSCR

Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methodology › Weighted Co‐Expression Network Analysis ↔ src/03-wgcna.R, lines 127–167 · score 0.96 · cutreeDynamic, deepSplit, distM, dynamicMods, minClusterSize, pamRespectsDendro
  2. [2] § Methodology › Pathway Analyses ↔ src/07-oxi-vs-veh.R, lines 94–132 · score 0.88 · biological pathways, select_fun, enrichGO, clusterProfiler, p.adjust, GO terms
  3. [3] § Methodology › Pathway Analyses ↔ src/03-pathways.R, lines 53–110 · score 0.86 · select_fun, enrichGO, clusterProfiler, p.adjust, GO terms, slimmed
  4. [4] § Methodology › Neuronal Protein Enrichments ↔ src/04-sex.R, lines 148–224 · score 0.81 · lmFit, eBayes, adj.P.Val, logFC, distinct gene, limma
  5. [5] § Methodology › Weighted Co‐Expression Network Analysis ↔ src/03-wgcna.R, lines 87–125 · score 0.77 · pickSoftThreshold, scale free topology, model fit, WGCNA, power, network
  6. [6] § Methodology › Immune Cell Enrichment ↔ src/02-turbo-enrichment-tables.R, lines 101–162 · score 0.75 · infiltration score, immune cells, ImmuCellAI, abundance, bulk, symbols
  7. [7] § Results › TurboID Proteomics Enables Deeper Insights Than Bulk Tissue Proteomes ↔ src/02-turbo-enrichment-tables.R, lines 101–162 · score 0.65 · infiltration score, ImmuCellAI, immune cell, bulk, enrichment, tissue
  8. [8] § Methodology › Synaptosome Comparisons ↔ src/04-externalvalidation.R, lines 285–337 · score 0.63 · clusterProfiler, junction, signalling, membrane, synaptosome, CC

Paper

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The authors' code

R · 417 lines · 13 KB · no license · 2 matches

  1. library(dplyr)
  2. library(tidyr)
  3. library(ggplot2)
  4. library(viridis)
  5. library(ComplexHeatmap)
  7. library(WGCNA)
  8. library(clusterProfiler)
  9. library(enrichplot)
  10. library(org.Mm.eg.db)
  12. #dir.create("./output/WGCNA/")
  13. PATH_results = "./output/WGCNA/"
  15. df <- read.csv("./data/matrix-for-limma.csv", header = TRUE)
  16. colData <- read.csv("./data/colData-for-limma.csv", header = TRUE)
  17. enrichments <- read.csv("./output/enrichments_75filt.csv",
  18. check.names = FALSE, header = TRUE, row.names = 1)
  19. #-------------------------------------------------------------------------------
  21. # test_df <- enrichments$Gene[enrichments$Tissue == "paw"]
  22. test_df <- enrichments$Gene[!duplicated(enrichments$Gene)]
  24. head(test_df)
  26. df_filt <- df[df$genes %in% test_df, ]
  27. df_filt <- df_filt[!duplicated(df_filt$genes), ]
  29. rownames(df_filt) <- df_filt$genes
  31. df_filt$genes <- NULL
  32. df_filt$proteins <- NULL
  34. df_filt <- df_filt[colnames(df_filt) %in% colData$sampleID[colData$Turbo == "T"]]
  35. colData <- colData[match(colnames(df_filt), colData$sampleID), ]
  36. rownames(colData) <- colData$sampleID
  38. # #keep genes in more than one tissue only
  39. sample_tissue_map <- setNames(colData$Tissue, colData$sampleID)
  40. sample_tissues <- sample_tissue_map[colnames(df_filt)]
  42. filter_genes <- function(expression_values) {
  43. expressed_tissues <- unique(na.omit(sample_tissues[!is.na(expression_values)]))
  44. length(expressed_tissues) > 1
  45. }
  47. # Apply the filter function to rows (genes)
  48. df_filt <- df_filt[apply(df_filt, 1, filter_genes), ]
  50. # min_value <- min(df_filt, na.rm = TRUE)
  51. # median_value <- median(as.matrix(df_filt), na.rm = TRUE)
  52. #
  53. # impute_median_per_group <- function(df, colData, group_col) {
  54. #
  55. # colData <- colData[colnames(df), , drop = FALSE]
  56. # group_ids <- colData[[group_col]]
  57. #
  58. # # Apply function to each row (gene)
  59. # df <- t(apply(df, 1, function(gene_expr) {
  60. #
  61. # group_medians <- tapply(gene_expr, group_ids, median, na.rm = TRUE)
  62. # overall_median <- median(gene_expr, na.rm = TRUE)
  63. # gene_expr[is.na(gene_expr)] <- ifelse(is.na(group_medians[group_ids[is.na(gene_expr)]]),
  64. # overall_median, group_medians[group_ids[is.na(gene_expr)]])
  65. # return(gene_expr)
  66. # }))
  67. #
  68. # return(df)
  69. # }
  70. #
  71. # df_filt <- impute_median_per_group(df_filt, colData, "Tissue")
  73. df_filt <- as.data.frame(df_filt)
  75. table(is.na(df_filt))
  77. #filter for more variable genes
  78. gene_variances <- apply(df_filt, 1, var, na.rm = TRUE)
  79. hist(gene_variances)
  80. range(gene_variances)
  82. df_filt <- df_filt[gene_variances > 0, ]
  83. dim(df_filt)
  85. #-------------------------------------------------------------------------------
  87. # options(stringsAsFactors = FALSE)
  88. # enableWGCNAThreads(nThreads = 8)
  90. datExpr <- as.data.frame(t(df_filt))
  92. powers <- c(1:50) # Range of powers to test
  93. sft <- pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, networkType = "signed")
  95. plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], type="o",
  96. xlab="Soft Threshold (power)", ylab="Scale Free Topology Model Fit",
  97. main="Soft Threshold Selection")
  98. abline(h=0.8, col="red") # Choose power where fit is ~0.9
  100. power <- 14 # Adjust based on previous step
  101. adjacency <- adjacency(datExpr, power = power, type = "signed")
  103. # Convert adjacency into a Topological Overlap Matrix (TOM)
  104. TOM <- TOMsimilarity(adjacency, TOMType = "signed")
  105. dissTOM <- 1 - TOM
  107. #-------------------------------------------------------------------------------
  109. set.seed(52)
  110. selectedGenes <- sample(ncol(datExpr), 400)
  111. TOMsubset <- TOM[selectedGenes, selectedGenes]
  113. # Visualize with a heatmap
  114. heatmap(TOMsubset, col = viridis::viridis(100), symm = TRUE)
  116. pheatmap(TOMsubset,
  117. color = viridis::viridis(100),
  118. clustering_method = "average",
  119. main = "TOM Heatmap (Subset of Genes)")
  121. #-------------------------------------------------------------------------------
  124. # Hierarchical clustering of genes
  125. geneTree <- hclust(as.dist(dissTOM), method = "average")
  127. # Plot the dendrogram
  128. plot(geneTree, main = "Gene Clustering Dendrogram", sub = "", xlab = "")
  130. pdf(file = paste(PATH_results, "dendrogram_raw.pdf", sep=""), width = 8, height = 8)
  131. plot(geneTree, main = "Gene Clustering Dendrogram", sub = "", xlab = "")
  132. dev.off()
  134. # Dynamic tree cut to identify modules
  135. minModuleSize <- 30 # Minimum module size
  136. dynamicMods <- cutreeDynamic(dendro = geneTree, distM = dissTOM,
  137. deepSplit = 2, pamRespectsDendro = FALSE,
  138. minClusterSize = minModuleSize)
  140. # Convert module labels to colors
  141. moduleColors <- labels2colors(dynamicMods)
  142. table(moduleColors)
  144. # Plot dendrogram with module colors
  145. plotDendroAndColors(geneTree, moduleColors, "Dynamic Tree Cut",
  146. dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05)
  148. pdf(file = paste(PATH_results, "dendrogram_colours.pdf", sep=""), width = 8, height = 5)
  149. plotDendroAndColors(geneTree, moduleColors, "Dynamic Tree Cut",
  150. dendroLabels = FALSE, hang = 0.03, addGuide = FALSE, guideHang = 0.05)
  152. dev.off()
  154. #-------------------------
  156. traits <- colData
  157. rownames(traits) <- traits$sampleID
  159. traits$Turbo <- NULL
  160. traits$Sample <- NULL
  161. traits$ID <- NULL
  162. traits$sampleID <- NULL
  163. traits$Tissue <- as.integer(as.factor(traits$Tissue))
  164. traits$Sex <- as.integer(as.factor(traits$Sex))
  166. # Check and match samples
  167. traits <- traits[match(rownames(datExpr), rownames(traits)),]
  169. # Correlate module eigengenes with traits
  170. MEs <- moduleEigengenes(datExpr, colors = moduleColors)$eigengenes
  171. MEs <- MEs[, !colnames(MEs) %in% "MEgrey"]
  173. moduleTraitCor <- cor(MEs, traits, use = "p", method = "spearman")
  174. moduleTraitP <- corPvalueStudent(moduleTraitCor, nrow(datExpr))
  176. # extrat significant correlations
  177. p_values <- moduleTraitP[, 1]
  178. adjusted_p_values <- p.adjust(p_values, method = "BH")
  179. significant_names <- rownames(moduleTraitP)[adjusted_p_values < 0.05]
  181. significant_names
  183. # Plot heatmap of module-trait relationships
  184. labeledHeatmap(Matrix = moduleTraitCor, xLabels = colnames(traits),
  185. yLabels = names(MEs), ySymbols = names(MEs), colorLabels = FALSE,
  186. textMatrix = signif(moduleTraitCor, 2),
  187. main = "Module-Trait Relationships")
  189. tissue_values <- moduleTraitCor[, "Tissue"]
  190. ordered_rows <- order(tissue_values, decreasing = TRUE)
  191. moduleTraitCor_ordered <- moduleTraitCor[ordered_rows, , drop = FALSE]
  192. moduleTraitP_ordered <- moduleTraitP[ordered_rows, , drop = FALSE]
  194. pdf(file = paste(PATH_results, "modules-heatmap.pdf", sep=""), width = 6, height = 7)
  195. labeledHeatmap(Matrix = moduleTraitCor_ordered,
  196. xLabels = colnames(traits),
  197. yLabels = rownames(moduleTraitCor_ordered),
  198. ySymbols = rownames(moduleTraitCor_ordered),
  199. colorLabels = FALSE,
  200. textMatrix = signif(moduleTraitCor_ordered, 2),
  201. main = "Module-Trait Relationships",
  202. colors = viridis(100)) # Use the viridis color scale with 100 shades
  203. dev.off()
  205. str(moduleTraitP)
  207. #---------------------------
  209. traits
  210. tissue_names <- c("DRG","LSC", "paw", "SCN") #adjust order as needed, check names
  211. traits$Tissue_name <- tissue_names[traits$Tissue]
  213. MEs_matrix <- t(MEs) # Transpose MEs, so rows are modules and columns are samples
  215. match_index <- match(colnames(MEs_matrix), rownames(traits))
  216. traits <- traits[match_index, ]
  218. rownames(MEs_matrix) <- names(MEs) # Rows are module names
  220. tail(MEs_matrix)
  221. tail(traits)
  223. MEs_matrix_sig <- MEs_matrix[rownames(MEs_matrix) %in% significant_names,]
  225. match_index <- match(colnames(MEs_matrix_sig), rownames(traits))
  226. traits <- traits[match_index, ]
  228. tissue_list <- as.factor(traits$Tissue_name)
  230. # Create a color mapping for metadata
  231. tissue_colors <- c("DRG" = "#f55c3a",
  232. "LSC" = "#edb127",
  233. "SCN" = "#29c99d",
  234. "paw" = "#95459b"
  235. )
  237. # Assign colors to metadata levels
  238. col_fun <- tissue_colors[tissue_list]
  240. Heatmap(MEs_matrix_sig,
  241. name = "Module Eigengenes",
  242. column_title = "Tissue",
  243. row_title = "Module",
  244. show_row_names = TRUE,
  245. show_column_names = TRUE, # Show tissue names on top
  246. cluster_rows = TRUE, # Cluster the rows (modules)
  247. cluster_columns = TRUE, # Cluster the columns (samples)
  248. top_annotation = HeatmapAnnotation(tissue = tissue_list, col = list(tissue = col_fun)),
  249. heatmap_legend_param = list(title = "Eigengene Value")
  250. )
  252. pdf(file = paste(PATH_results, "modules-by-tissue-heatmap.pdf", sep=""), width = 7, height = 6)
  253. Heatmap(MEs_matrix_sig,
  254. name = "Module Eigengenes",
  255. column_title = "Tissue",
  256. row_title = "Module",
  257. show_row_names = TRUE,
  258. show_column_names = FALSE, # Show tissue names on top
  259. cluster_rows = TRUE, # Cluster the rows (modules)
  260. cluster_columns = TRUE, # Cluster the columns (samples)
  261. top_annotation = HeatmapAnnotation(tissue = tissue_list, col = list(tissue = col_fun)),
  262. heatmap_legend_param = list(title = "Eigengene Value")
  263. )
  265. dev.off()
  267. #-------------------------------------------------------------------------------
  269. #Check MEs by tissue
  270. MEs_long <- as.data.frame(MEs_matrix_sig) %>%
  271. mutate(Module = rownames(MEs_matrix_sig)) %>%
  272. pivot_longer(cols = -Module, names_to = "Sample", values_to = "ME_value")
  274. MEs_long <- MEs_long %>%
  275. left_join(colData, by = c("Sample" = "sampleID"))
  277. MEs_long$Tissue <- factor(MEs_long$Tissue, levels = c("paw", "SCN", "DRG", "LSC"))
  279. g <- ggplot(MEs_long, aes(x = Tissue, y = ME_value, fill = Tissue))
  280. g <- g + geom_boxplot()
  281. g <- g + theme_bw() + facet_wrap(~ Module, scales = "fixed")
  282. g <- g + # Facet by module, with independent y-scales
  283. theme(axis.text.x = element_text(angle = 45, hjust = 1)) + # Rotate x-axis labels
  284. labs(x = " ", y = "ME Value", title = "MEs by Tissue and Module")
  286. print(g)
  288. pdf(file = paste(PATH_results, "modules-by-tissue.pdf", sep=""), width = 8, height = 7)
  289. print(g)
  290. dev.off()
  292. #-------------------------------------------------------------------------------
  294. module <- "green"
  295. moduleGenes <- colnames(datExpr)[moduleColors == module]
  297. # Calculate module membership (MM) scores
  298. MM <- cor(datExpr[, moduleGenes], MEs[, paste0("ME",module)], use = "p")
  299. names(MM) <- row.names(MM)
  301. topHubGenes <- names(sort(MM, decreasing = TRUE)[1:10])
  302. print(topHubGenes)
  304. MM <- as.data.frame(MM)
  306. write.csv(MM, paste0(PATH_results,"module_green.csv"))
  308. #-------------------------------------------------------------------------------
  310. # run GO analysis on each module
  311. background <- test_df
  312. # background <- rownames(df_filt)
  314. module <- "green"
  315. moduleGenes <- colnames(datExpr)[moduleColors == module]
  317. rm(ego2, ego)
  318. ego <- enrichGO(gene = moduleGenes,
  319. universe = background,
  320. OrgDb = org.Mm.eg.db,
  321. keyType = "SYMBOL",
  322. ont = "BP",
  323. pAdjustMethod = "BH",
  324. pvalueCutoff = 0.05,
  325. qvalueCutoff = 0.05,
  326. readable = TRUE)
  328. # remove redundancy in the GO terms
  329. ego2 <- clusterProfiler::simplify(ego, cutoff=0.7, by="p.adjust", select_fun=min, measure = 'Wang')
  331. pdf(file = paste(PATH_results, "BP_green-network.pdf", sep=""), width = 8, height = 8)
  332. goplot(ego2)
  333. dev.off()
  335. pdf(file = paste(PATH_results, "BP_green-barplot.pdf", sep=""), width = 6, height = 3)
  336. mutate(ego2, qscore = -log(p.adjust, base=10)) %>%
  337. barplot(x="qscore")
  338. dev.off()
  340. pdf(file = paste(PATH_results, "BP_green-upset.pdf", sep=""), width = 7, height = 4)
  341. upsetplot(ego2)
  342. dev.off()
  344. write.csv(ego2, paste(PATH_results, "BP_green.csv"))
  346. #-----
  348. ego <- enrichGO(gene = moduleGenes,
  349. universe = background,
  350. OrgDb = org.Mm.eg.db,
  351. keyType = "SYMBOL",
  352. ont = "CC",
  353. pAdjustMethod = "BH",
  354. pvalueCutoff = 0.05,
  355. qvalueCutoff = 0.05,
  356. readable = TRUE)
  358. # remove redundancy in the GO terms
  359. ego2 <- clusterProfiler::simplify(ego, cutoff=0.7, by="p.adjust", select_fun=min, measure = 'Wang')
  361. pdf(file = paste(PATH_results, "CC_green-network.pdf", sep=""), width = 8, height = 8)
  362. goplot(ego2)
  363. dev.off()
  365. pdf(file = paste(PATH_results, "CC_green-barplot.pdf", sep=""), width = 6, height = 3)
  366. mutate(ego2, qscore = -log(p.adjust, base=10)) %>%
  367. barplot(x="qscore")
  368. dev.off()
  370. pdf(file = paste(PATH_results, "CC_green-upset.pdf", sep=""), width = 7, height = 4)
  371. upsetplot(ego2)
  372. dev.off()
  374. write.csv(ego2, paste(PATH_results, "CC_green.csv"))
  376. #---
  378. ego <- enrichGO(gene = moduleGenes,
  379. universe = background,
  380. OrgDb = org.Mm.eg.db,
  381. keyType = "SYMBOL",
  382. ont = "MF",
  383. pAdjustMethod = "BH",
  384. pvalueCutoff = 0.05,
  385. qvalueCutoff = 0.05,
  386. readable = TRUE)
  388. # # remove redundancy in the GO terms
  389. # ego2 <- clusterProfiler::simplify(ego, cutoff=0.7, by="p.adjust", select_fun=min, measure = 'Wang')
  391. pdf(file = paste(PATH_results, "MF_green-network.pdf", sep=""), width = 8, height = 8)
  392. goplot(ego)
  393. dev.off()
  395. pdf(file = paste(PATH_results, "MF_green-barplot.pdf", sep=""), width = 6, height = 3)
  396. mutate(ego, qscore = -log(p.adjust, base=10)) %>%
  397. barplot(x="qscore")
  398. dev.off()
  400. pdf(file = paste(PATH_results, "MF_green-upset.pdf", sep=""), width = 7, height = 4)
  401. upsetplot(ego)
  402. dev.off()
  404. write.csv(ego, paste(PATH_results, "MF_green.csv"))
  406. #---
  410. print(g)
  412. #-------------------------------------------------------------------------------
  414. #SCRATCH

03-wgcna.R at commit 5b0f821, no license · at the source

Overview

Authors: Julia R Sondermann1, Allison M Barry1, Feng Xian1, Thomas Haberl1, David Gomez‐Varela1, Fritz Benseler2, Dietmar Schreiner3, Nils Brose2, Noa Lipstein2,4, Manuela Schmidt1
ORCID iDs: Julia R Sondermann, Allison M Barry, Feng Xian, Fritz Benseler, Manuela Schmidt
  1. Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
  2. Department of Molecular Neurobiology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
  3. Biozentrum, University of Basel, Basel, Switzerland
  4. Leibniz‐Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin, Germany
Institutions: University of Vienna (Austria); Max Planck Institute for Multidisciplinary Sciences (Germany); University of Basel (Switzerland); Leibniz-Forschungsinstitut für Molekulare Pharmakologie (Germany)
Journal: European journal of pain (London, England), volume 30, issue 5, article e70277
Dates: received 24 October 2025; accepted 8 April 2026; published online 26 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/ejp.70277 · PMID 42036936 · PMCID PMC13111903 · OpenAlex W4411324889
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.13.659475
Status: code verified
Categories: genetics / omics (modality), mouse (organism), pain (population), cellular / molecular (subfield)
Methods: Statistics, Spectral & time-frequency
Keywords: bulk proteomics, CIPN, DIA‐MS, DRG, proximity labelling, sensory neuron, TurboID
MeSH: Ganglia, Spinal*, Proteome*, Sensory Receptor Cells*, Animals, Mice, Mice, Transgenic, Proteomics, Spinal Cord (* major topic)
Topic: Biotin and Related Studies (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (390688087, CRC 1286, A11, CRC 1286, A09); Austrian Science Fund (P36554); Max-Planck-Gesellschaft
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Introduction: Understanding the molecular architecture of peripheral sensory neurons is critical as we pursue novel drug targets against pain and neuropathy. Sensory neurons in the dorsal root ganglion (DRG) show extensive compartmentalization; thus, understanding each compartment—from the peripheral to central terminals—is key to this effort.

Methods: To systematically profile this spatial complexity, we generated a TurboIDfl/fl transgenic mouse line (ROSA26em1(TurboID)Bros), enabling targeted proximity labelling and deep proteomic profiling of DRG neuron compartments via Tg(Advillin‐Cre)+.

Results: Our data reveal distinct proteomic signatures across neuronal compartments that reflect specialized neuronal functions. We provide proteomic insights into previously inaccessible nerve terminals both in the periphery (innervating the skin) and in the spinal cord. Further, using a DRG explant model of chemotherapy‐induced peripheral neuropathy, we uncover novel and discrete proteome changes, highlighting neuronal vulnerability.

Conclusion: Together, our findings provide a unique proteome atlas of the sensory neuron proteome across anatomical domains and demonstrate the utility of proximity labelling proteomics for detecting compartment‐specific molecular alterations in a disease model.

Significance: This study highlights the potential of TurboID‐based proteomics to uncover cell type‐specific differences in the peripheral nervous system, serving as a valuable resource for mechanistic studies of sensory neuron function and pathology.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

aliibarry/turboid

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5b0f821f7e647f6043e2e8264b1a6d9b215c571a, 9 May 2026
Languages: R (22)
Size: 500 files, 22 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (19 files), tidyverse (19 files), clusterProfiler (12 files), ComplexHeatmap (9 files), limma (8 files), reshape2 (6 files), circlize (4 files), cowplot (1 file), patchwork (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/aliibarry/turboID/data/

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

No dataset and no data link were found in the paper.

Data Availability Statement

The MS proteomics data, including metadata, fasta, and gene group matrices for each dataset have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez‐Riverol et al. 2022) partner repository with the dataset identifier PXD062198. Processed data are also available on github (https://github.com/aliibarry/turboID/data/). For queries regarding the TurboID mouse line please contact Nils Brose () or Noa Lipstein ().

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 2, 28 September 2026

  • Publisher: n/a → Wiley
  • Funding: added Deutsche Forschungsgemeinschaft: 390688087, CRC 1286, A11, CRC 1286, A09; Austrian Science Fund: P36554; Max-Planck-Gesellschaft

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 7 keywords, 8 MeSH terms, 77 references.

Cite

This paper

Sondermann, J. R., Barry, A. M., Xian, F., Haberl, T., Gomez‐Varela, D., Benseler, F., Schreiner, D., Brose, N., Lipstein, N., & Schmidt, M. (2026). Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons. European journal of pain (London, England), 30(5), e70277. https://doi.org/10.1002/ejp.70277

BibTeX

@article{sondermann2026proximity,
author = {Sondermann, Julia R and Barry, Allison M and Xian, Feng and Haberl, Thomas and Gomez‐Varela, David and Benseler, Fritz and Schreiner, Dietmar and Brose, Nils and Lipstein, Noa and Schmidt, Manuela},
title = {{Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons}},
journal = {European journal of pain (London, England)},
year = {2026},
month = may,
volume = {30},
number = {5},
pages = {e70277},
publisher = {Wiley},
issn = {1090-3801},
doi = {10.1002/ejp.70277},
url = {https://doi.org/10.1002/ejp.70277},
pmid = {42036936},
pmcid = {PMC13111903}
}

RIS

TY - JOUR
AU - Sondermann, Julia R
AU - Barry, Allison M
AU - Xian, Feng
AU - Haberl, Thomas
AU - Gomez‐Varela, David
AU - Benseler, Fritz
AU - Schreiner, Dietmar
AU - Brose, Nils
AU - Lipstein, Noa
AU - Schmidt, Manuela
TI - Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons
T2 - European journal of pain (London, England)
J2 - Eur J Pain
PY - 2026
DA - 2026/05/01
VL - 30
IS - 5
SP - e70277
SN - 1090-3801
PB - Wiley
DO - 10.1002/ejp.70277
UR - https://doi.org/10.1002/ejp.70277
LA - en
ER -

CSL-JSON

{
"id": "10.1002/ejp.70277",
"type": "article-journal",
"title": "Proximity Labelling Reveals the Compartmental Proteome of Murine Sensory Neurons",
"container-title": "European journal of pain (London, England)",
"author": [
{
"family": "Sondermann",
"given": "Julia R"
},
{
"family": "Barry",
"given": "Allison M"
},
{
"family": "Xian",
"given": "Feng"
},
{
"family": "Haberl",
"given": "Thomas"
},
{
"family": "Gomez‐Varela",
"given": "David"
},
{
"family": "Benseler",
"given": "Fritz"
},
{
"family": "Schreiner",
"given": "Dietmar"
},
{
"family": "Brose",
"given": "Nils"
},
{
"family": "Lipstein",
"given": "Noa"
},
{
"family": "Schmidt",
"given": "Manuela"
}
],
"container-title-short": "Eur J Pain",
"volume": "30",
"issue": "5",
"page": "e70277",
"DOI": "10.1002/ejp.70277",
"PMID": "42036936",
"PMCID": "PMC13111903",
"ISSN": "1090-3801",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/ejp.70277",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

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