OSCR

NodoMap: A single-cell and spatial transcriptomic atlas of the mouse nodose ganglion.

Code ↔ Paper

25 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 25 matches
  1. [1] § Results › Differences between left and right nodose neuronal gene expression ↔ paper_figures/IPA_update.R, lines 1–85 · score 0.91 · respiratory electron transport, oxidative phosphorylation, insulin secretion, serotonin receptor, Glutaminergic receptor, NGN18
  2. [2] § STAR★Methods › Quantification and statistical analysis › Integration of snRNASeq and spatial transcriptomics data ↔ ST_analysis/5_RCTD_improve.R, lines 43–97 · score 0.90 · fc cutoff reg, UMI min sigma, gene cutoff, pixel, query, RCTD
  3. [3] § STAR★Methods › Quantification and statistical analysis › Cell-cell communication ↔ paper_figures/4_Nodomap_cellchat_analysis.R, lines 232–295 · score 0.86 · HB_NE_Gcg, Secreted Signaling, protein Signaling, CellChat, Prlr, interactions
  4. [4] § Results › The effects of fasting on nodose neurons ↔ paper_figures/IPA_update.R, lines 88–172 · score 0.85 · eukaryotic translation initiation, amino acid deficiency, EIF2AK4, AMPK, regulating, metabolic
  5. [5] § STAR★Methods › Quantification and statistical analysis › Dataset quality control ↔ Pre-processing/Nodose_ganglia_dataset_processing.r, lines 239–275 · score 0.84 · remove low quality, scDblFinder, removing doublets, pre processing, mitochondrial, age
  6. [6] § Results › Exploring potential vagal to hindbrain pathways ↔ paper_figures/4_Nodomap_cellchat_analysis.R, lines 232–295 · score 0.83 · HB_NE_Gcg, ppg neurons, CellChat, Enho, TAC, Prlr
  7. [7] § Results › Classifying neuronal clusters ↔ paper_figures/FF_LR_marker_supptable.R, lines 2–75 · score 0.81 · Slc6a2, Slc18a3, Slc17a7, Hand2, Vip, Trpa1
  8. [8] § STAR★Methods › Quantification and statistical analysis › Batch correction and clustering ↔ Pre-processing/integrating-nodose-dataset.r, lines 1–46 · score 0.79 · SCTransform, FindClusters, FindNeighbors, pre processed, integrated nodose, clustered
  9. [9] § Results › Classifying neuronal clusters ↔ paper_figures/2_NP_NT_analysis.R, lines 18–101 · score 0.75 · Slc17a6, Slc18a3, Slc17a7, Cartpt, neurotransmitter, Dbh
  10. [10] § STAR★Methods › Quantification and statistical analysis › Batch correction and clustering ↔ ST_analysis/Integrate_nodose_st_20230921.R, lines 1–37 · score 0.75 · SCTransform, FindClusters, FindNeighbors, integrated nodose, clustered
  11. [11] § Results › Classifying neuronal clusters ↔ paper_figures/class_DEGs_sum.R, lines 1–65 · score 0.72 · Nav1.1, Nav1.8, sodium channel, neuronal clusters, NGN21, JGN3
  12. [12] § Results › Differences between left and right nodose neuronal gene expression ↔ paper_figures/LR_FF_gutgenes_Vln.R, lines 154–215 · score 0.71 · Slc5a3, Slc5a5, Slc5a7, Insr, Cnr1, nutrient
  13. [13] § STAR★Methods › Quantification and statistical analysis › Batch correction and clustering ↔ Quality Control/asw_QC2_Phox2b.R, lines 1–45 · score 0.68 · Phox2b expressed clusters, ASW, graph, nodose ganglia, model, PC
  14. [14] § Results › Classifying neuronal clusters ↔ paper_figures/neuron_classification_update.R, lines 498–579 · score 0.65 · sodium channel, lightly myelinated, Nefh, Cntn1, Cntnap1, Ncam1
  15. [15] § STAR★Methods › Quantification and statistical analysis › Batch correction and clustering ↔ Quality Control/ASW-integrated-nodose_230601.R, lines 1–42 · score 0.65 · Silhouette widths, ASW, distance, PCs, matrix, quality
  16. [16] § Results › Jugular neurons ↔ paper_figures/IPA_update.R, lines 1–85 · score 0.64 · glutaminergic receptor, amino acid deficiency, EIF2AK4, GCN2, pathways, clusters
  17. [17] § STAR★Methods › Quantification and statistical analysis › Differential gene expression analysis ↔ paper_figures/class_DEGs_sum.R, lines 157–242 · score 0.62 · ad libitum, overnight fasted, Sum, Seurat, downregulated, upregulated
  18. [18] § Results › The effects of fasting on nodose neurons ↔ paper_figures/class_DEGs_sum.R, lines 157–242 · score 0.62 · ad libitum, overnight fasted, MGC6, downregulated, upregulated, NGN20
  19. [19] § Results › Creating the NodoMap ↔ paper_figures/cluster_annotations_comparions.R, lines 63–143 · score 0.60 · clustering strategy, Phox2b, NodoMap, Kupari, score, neurons
  20. [20] § STAR★Methods › Quantification and statistical analysis › Neighborhood analysis to identify spatial patterns in cell distribution ↔ paper_figures/7_neighbourhood_analysis.R, lines 43–99 · score 0.58 · inverse distance, neighborhood, slice, weighted, doublet, singlet
  21. [21] § Results › Classifying neuronal clusters ↔ paper_figures/class_DEGs_sum.R, lines 68–155 · score 0.58 · organ projection, classed, ileum, jejunum, lungs, duodenum
  22. [22] § Results › Classifying neuronal clusters ↔ paper_figures/neuron_classification_update.R, lines 643–713 · score 0.58 · organ projection, classified, ileum, jejunum, lungs, duodenum
  23. [23] § STAR★Methods › Quantification and statistical analysis › Defining cell types ↔ Quality Control/Integrated_nodose_dataset.R, lines 66–142 · score 0.57 · Phox2b, cell clusters, Emcn, Ptprc, Prdm12, Ebf2
  24. [24] § STAR★Methods › Quantification and statistical analysis › Neurotransmitter/neuropeptide assignment ↔ paper_figures/2_NP_NT_analysis.R, lines 18–101 · score 0.57 · Bdnf, Calcb, neurotransmitters, Neuropeptides, Cartpt, Calca
  25. [25] § Results › Exploring potential vagal to hindbrain pathways ↔ paper_figures/4_Nodomap_cellchat_analysis.R, lines 143–189 · score 0.56 · protein signaling, signaling pathways, CellChat, interactions, enriched, NodoMap

Paper

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The authors' code

R · 313 lines · 9.4 KB · MIT · 4 matches

  1. library(ggplot2)
  2. library(reshape2)
  3. library(dplyr)
  4. library(scCustomize)
  5. library(patchwork)
  6. library(RColorBrewer)
  7. library(viridis)
  8. library(stringr)
  9. library(pheatmap)
  11. # Extract the neuronal cluster annotations------------------------------------------
  12. neurons <- c("JGN1",
  13. "JGN2",
  14. "JGN3",
  15. "JGN4",
  16. "JGN5",
  17. "NGN1",
  18. "NGN2",
  19. "NGN3",
  20. "NGN4",
  21. "NGN5",
  22. "NGN6",
  23. "NGN7",
  24. "NGN8",
  25. "NGN9",
  26. "NGN10",
  27. "NGN11",
  28. "NGN12",
  29. "NGN13",
  30. "NGN14",
  31. "NGN15",
  32. "NGN16",
  33. "NGN17",
  34. "NGN18",
  35. "NGN19",
  36. "NGN20",
  37. "NGN21")
  40. sodium_channel <- c("Nav1.8",
  41. "Nav1.8",
  42. "Nav1.1/Nav1.8",
  43. "Nav1.8",
  44. "Nav1.1",
  45. "Nav1.8",
  46. "Nav1.8",
  47. "Nav1.8",
  48. "Nav1.8",
  49. "Nav1.8",
  50. "Nav1.1",
  51. "Nav1.1",
  52. "Nav1.8",
  53. "Nav1.8",
  54. "Nav1.8",
  55. "Nav1.8",
  56. "Nav1.1",
  57. "Nav1.8",
  58. "Nav1.1",
  59. "Nav1.1",
  60. "Nav1.1",
  61. "Nav1.1",
  62. "Nav1.1/Nav1.8",
  63. "Nav1.1",
  64. "Nav1.8",
  65. "Nav1.1")
  68. fibre_type <- c("Unmyelinated",
  69. "Unmyelinated",
  70. "Myelinated",
  71. "Lightly myelinated",
  72. "Lightly myelinated",
  73. "Lightly myelinated",
  74. "Lightly myelinated",
  75. "Unmyelinated",
  76. "Unmyelinated",
  77. "Unmyelinated",
  78. "Lightly myelinated",
  79. "Lightly myelinated",
  80. "Unmyelinated",
  81. "Unmyelinated",
  82. "Myelinated",
  83. "Unmyelinated",
  84. "Myelinated",
  85. "Unmyelinated",
  86. "Myelinated",
  87. "Lightly myelinated",
  88. "Lightly myelinated",
  89. "Lightly myelinated",
  90. "Lightly myelinated",
  91. "Unmyelinated",
  92. "Unmyelinated",
  93. "Myelinated")
  96. sensor_type <- c("Mechanosensor/Nocisensor",
  97. "Nocisensor",
  98. "Mechanosensor/Nocisensor",
  99. "Mechanosensor",
  100. "Nocisensor",
  101. "Nocisensor",
  102. "Nocisensor",
  103. "Mechanosensor",
  104. "Nocisensor",
  105. "Mechanosensor/Nocisensor",
  106. "Mechanosensor",
  107. "Mechanosensor",
  108. "Mechanosensor/Nocisensor",
  109. "Nocisensor",
  110. "Mechanosensor",
  111. "Nocisensor",
  112. "Mechanosensor/Nocisensor",
  113. "Nocisensor",
  114. "Mechanosensor",
  115. "Mechanosensor",
  116. "Mechanosensor",
  117. "Mechanosensor",
  118. "Mechanosensor/Nocisensor",
  119. "Nocisensor",
  120. "Nocisensor",
  121. "Mechanosensor")
  124. organ_innervated <- c("Broad projection",
  125. "Broad projection",
  126. "Gut",
  127. "Broad projection",
  128. "Lung",
  129. "Broad projection",
  130. "Broad projection",
  131. "Gut",
  132. "Broad projection",
  133. "Broad projection",
  134. "Broad projection",
  135. "Pancreas",
  136. "Broad projection",
  137. "Gut",
  138. "Gut",
  139. "Gut",
  140. "Duodenum",
  141. "Broad projection",
  142. "Gut",
  143. "Heart",
  144. "Gut",
  145. "Broad projection",
  146. "Gut",
  147. "Gut",
  148. "Broad projection",
  149. "Jejunum/Ileum")
  151. dt <- data.frame(Clusters = neurons,
  152. Sodium_channel = sodium_channel,
  153. Fibre_type = fibre_type,
  154. Sensor_type = sensor_type,
  155. Organ_projection = organ_innervated)
  157. # Extract the DEGs info ---------------------------------------------------
  158. ###Overnight fasting vs Ad libitum
  159. seurat <- list.files(path = ".", pattern = "^fedfasted\\d+.txt")
  160. seurat <- do.call(rbind, Map("cbind", lapply(seurat, read.delim), cluster=seurat))
  161. seurat$cluster <- gsub("\\D","",seurat$cluster)
  162. colnames(seurat)[1] <- "gene"
  164. seurat <- subset(seurat, subset = p_val < 0.05)
  166. degs <- seurat %>%
  167. group_by(cluster) %>%
  168. dplyr::count(cluster)
  169. up <- seurat %>%
  170. group_by(cluster) %>%
  171. summarise(upregulated = sum(avg_log2FC>0))
  172. down <- seurat %>%
  173. group_by(cluster) %>%
  174. summarise(downregulated = sum(avg_log2FC<0))
  175. degs$Fast_Up <- up$upregulated[match(degs$cluster, up$cluster)]
  176. degs$Fast_Down <- down$downregulated[match(degs$cluster, down$cluster)]
  177. degs$cluster <- as.numeric(degs$cluster)
  178. degs <- degs %>%
  179. arrange(cluster)
  181. temp <- degs[,c(1,3,4)]
  182. temp2 <- data.frame(cluster = c(7,8,18,19,30,31,33,34,39,42,45,46,48,49,50,51,52), Fast_Up = 0, Fast_Down = 0)
  183. temp <- rbind(as.data.frame(temp), temp2)
  184. temp<-temp[order(temp$cluster),]
  186. clustermarkers <- c("EC1",
  187. "SGC1",
  188. "SGC2",
  189. "MGC1",
  190. "SGC3",
  191. "NGN1",
  192. "HC1",
  193. "SGC4",
  194. "SGC5",
  195. "FB1",
  196. "SGC6",
  197. "MGC2",
  198. "FB2",
  199. "NGN2",
  200. "GC1",
  201. "NGN3",
  202. "MGC3",
  203. "NGN4",
  204. "SGC7",
  205. "MGC4",
  206. "NGN5",
  207. "SGC8",
  208. "JGN1",
  209. "NGN6",
  210. "GC2",
  211. "JGN2",
  212. "FB3",
  213. "NGN7",
  214. "NGN8",
  215. "EC2",
  216. "NGN9",
  217. "NGN10",
  218. "NGN11",
  219. "NGN12",
  220. "EC3",
  221. "NGN13",
  222. "NGN14",
  223. "JGN3",
  224. "HC2",
  225. "NGN15",
  226. "JGN4",
  227. "NGN16",
  228. "NGN17",
  229. "FB4",
  230. "GC3",
  231. "NGN18",
  232. "JGN5",
  233. "MGC5",
  234. "MGC6",
  235. "NGN19",
  236. "NGN20",
  237. "NGN21")
  239. temp$clustermarkers <- clustermarkers
  240. temp$Fast_Down <- temp$Fast_Down*-1
  241. temp<-temp[order(temp$clustermarkers),]
  242. temp <- temp[c(13:17,24:45),]
  244. #Tag fasting DEGs to annotations
  245. dt$Upregulated_in_Fasting <- temp$Fast_Up[match(dt$Clusters,temp$clustermarkers)]
  246. dt$Downregulated_in_Fasting <- temp$Fast_Down[match(dt$Clusters,temp$clustermarkers)]
  248. ###Left and right nodose ganglia
  249. seurat <- list.files(path = ".", pattern = "^leftright\\d+.txt")
  250. seurat <- do.call(rbind, Map("cbind", lapply(seurat, read.delim), cluster=seurat))
  251. seurat$cluster <- gsub("\\D","",seurat$cluster)
  252. colnames(seurat)[1] <- "gene"
  254. seurat <- subset(seurat, subset = p_val < 0.05)
  256. degs <- seurat %>%
  257. group_by(cluster) %>%
  258. dplyr::count(cluster)
  259. up <- seurat %>%
  260. group_by(cluster) %>%
  261. summarise(upregulated = sum(avg_log2FC>0))
  262. down <- seurat %>%
  263. group_by(cluster) %>%
  264. summarise(downregulated = sum(avg_log2FC<0))
  265. degs$Right_Up <- up$upregulated[match(degs$cluster, up$cluster)]
  266. degs$Right_Down <- down$downregulated[match(degs$cluster, down$cluster)]
  268. degs$cluster <- as.numeric(degs$cluster)
  269. degs <- degs %>%
  270. arrange(cluster)
  272. degs$clustermarkers <- clustermarkers
  273. degs$Right_Down <- degs$Right_Down*-1
  274. degs <- degs[order(degs$clustermarkers),]
  275. degs <- degs[c(13:17,24:45),]
  277. #Tag position DEGs to annotations
  278. dt$Enriched_in_Right <- degs$Right_Up[match(dt$Clusters,degs$clustermarkers)]
  279. dt$Enriched_in_Left <- degs$Right_Down[match(dt$Clusters,degs$clustermarkers)]
  281. #Heatmap
  282. rownames(dt) <- dt$Clusters
  283. dt$Clusters <- NULL
  285. degs <- dt[,c(5:8)]
  286. classification <- dt[,c(1:4)]
  288. col_class <- list(Sodium_channel = brewer.pal(8, "Set2")[2:4],
  289. Fibre_type = brewer.pal(8, "Set1")[1:3],
  290. Sensor_type = brewer.pal(8, "Dark2")[1:3],
  291. Organ_projection = brewer.pal(8, "Accent")[1:7])
  293. names(col_class$Sodium_channel) <- unique(classification$Sodium_channel)
  294. names(col_class$Fibre_type) <- unique(classification$Fibre_type)
  295. names(col_class$Sensor_type) <- unique(classification$Sensor_type)
  296. names(col_class$Organ_projection) <- unique(classification$Organ_projection)
  298. pdf("deg_anno_heatmap.pdf", width = 10, height = 15)
  299. pheatmap(degs, color = colorRampPalette(rev(brewer.pal(n=9, name = "RdYlBu")))(100),
  300. cluster_rows = T,
  301. cluster_cols = F,
  302. angle_col = 315,
  303. cellwidth = 25,
  304. cellheight = 25,
  305. annotation_row = classification,
  306. annotation_colors = col_class,
  307. fontfamily = "Helvetica",
  308. fontsize = 20)
  309. dev.off()

class_DEGs_sum.R at commit 5585c67, under MIT · at the source

Overview

Authors: Sijing Cheng1, Georgina KC Dowsett2, Kara Rainbow2, Mariana Norton1, Anna G Roberts1, Phyllis Phuah1, Gavin A Bewick3, Brian YH Lam2, Giles SH Yeo2, Kevin G Murphy1
ORCID iDs: Kevin G Murphy
  1. Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Hammersmith Hospital, 6th Floor Commonwealth Building, London W12 0NN, UK
  2. MRC Metabolic Diseases Unit, Institute of Metabolic Science Metabolic Research Laboratories, University of Cambridge, Cambridge CB2 0QQ, UK
  3. Diabetes and Obesity Theme, School of Cardiovascular and Metabolic Medicine and Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 1UL, UK
Institutions: Hammersmith Hospital (United Kingdom); University of Cambridge (United Kingdom); Wellcome/MRC Institute of Metabolic Science (United Kingdom); MRC Metabolic Diseases Unit; King's College London (United Kingdom)
Journal: Cell press blue, volume 1, issue 4, article None
Dates: received 11 May 2025; accepted 12 June 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.cpblue.2026.100072 · PMID 42483274 · PMCID PMC13385471 · OpenAlex W7169804554
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: vagus, nodose ganglia, gut-brain axis, dorsal-vagal complex
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/X017273/1, BB/W001497/1, BB/S017593/1); Wellcome Trust (226800/Z/22/Z, 310835/Z/24/Z, 208363/Z/17/Z); Diabetes UK (18/0005886, 20/0006295)
Citations: not cited yet (Europe PMC); 110 references in the paper
Research resources: C57BL/6J mice RRID:IMSR_JAX:000664

Abstract

The vagus nerve is a central component of the parasympathetic nervous system, innervating multiple abdominal organs to monitor and regulate their function. Vagal sensory neuron cell bodies reside in the nodose ganglia, but their molecular diversity and spatial organization remain incompletely defined. Here, we generated and integrated single-nucleus RNA sequencing (snRNA-seq) data with multiple published datasets to construct a unified atlas of 106,436 cells and combined this with spatial transcriptomics to create a single-cell and spatial map of the mouse nodose ganglion (NodoMap). We identify 21 neuronal subtypes and multiple non-neuronal populations, revealing subtype-specific gene expression differences between left and right ganglia and transcriptional responses to fasting. Spatial analysis shows intermingled neuronal populations with distinct cellular neighborhoods. Together, NodoMap provides a high-resolution resource for dissecting vagal sensory circuits and identifying molecular targets involved in energy homeostasis and metabolic disease.

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 25 matches between paragraphs and lines of code.

sc2470/NodoMap

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5585c67a93e31d934428717ca46cf8dd00c6e0f4, 11 January 2026
Languages: R (34)
Size: 36 files, 34 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (27 files), ggplot2 (26 files), tidyverse (17 files), patchwork (12 files), pheatmap (4 files), cowplot (2 files), reshape2 (2 files), igraph (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/sc2470/NodoMap

Zenodo 20329018

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (27 files), ggplot2 (26 files), tidyverse (17 files), patchwork (12 files), pheatmap (4 files), cowplot (2 files), reshape2 (2 files), igraph (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
36 files
At the source: doi.org/10.5281/zenodo.20329018

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 68 scripts, each with its path and the digest of its content;
  • 25 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

Data and code availability

Raw sequencing files have been deposited at GEO: GSE296454 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296454). The four existing published datasets24,30,37,40 analyzed are deposited at GEO: GSE124312 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124312), GSE138651 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138651), GSE185173 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185173), and GSE192987 (https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE192987). The NodoMap full dataset is available to explore on CellXgene here: https://cellxgene.cziscience.com/collections/982f9f44-031c-4c8c-91ee-dcaa53b10151. The RDS objects are available to download from the University of Cambridge APOLLO repository here: https://doi.org/10.17863/CAM.125125.

The droplet-based 10× single-cell sequence reads of mouse nodose ganglia were downloaded using FASTQ-dump from the Sequence Read Archive prefetch toolkit (NCBI SRA v.3.0.0).

Data used to construct figures are provided in Tables S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, and S19.

All original code for the pre-processing, integration, and plotting of data has been deposited at GitHub and is publicly available at https://github.com/sc2470/NodoMap and at Zenodo at https://doi.org/10.5281/zenodo.20329018.

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 → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 3 funders, 108 references, 1 RRID.

Cite

This paper

Cheng, S., Dowsett, G. K., Rainbow, K., Norton, M., Roberts, A. G., Phuah, P., Bewick, G. A., Lam, B. Y., Yeo, G. S., & Murphy, K. G. (2026). NodoMap: A single-cell and spatial transcriptomic atlas of the mouse nodose ganglion. Cell press blue, 1(4), None. https://doi.org/10.1016/j.cpblue.2026.100072

BibTeX

@article{cheng2026nodomap,
author = {Cheng, Sijing and Dowsett, Georgina KC and Rainbow, Kara and Norton, Mariana and Roberts, Anna G and Phuah, Phyllis and Bewick, Gavin A and Lam, Brian YH and Yeo, Giles SH and Murphy, Kevin G},
title = {{NodoMap: A single-cell and spatial transcriptomic atlas of the mouse nodose ganglion}},
journal = {Cell press blue},
year = {2026},
month = jul,
volume = {1},
number = {4},
pages = {None},
publisher = {Elsevier BV},
issn = {3051-3839},
doi = {10.1016/j.cpblue.2026.100072},
url = {https://doi.org/10.1016/j.cpblue.2026.100072},
pmid = {42483274},
pmcid = {PMC13385471}
}

RIS

TY - JOUR
AU - Cheng, Sijing
AU - Dowsett, Georgina KC
AU - Rainbow, Kara
AU - Norton, Mariana
AU - Roberts, Anna G
AU - Phuah, Phyllis
AU - Bewick, Gavin A
AU - Lam, Brian YH
AU - Yeo, Giles SH
AU - Murphy, Kevin G
TI - NodoMap: A single-cell and spatial transcriptomic atlas of the mouse nodose ganglion
T2 - Cell press blue
J2 - Cell Press Blue
PY - 2026
DA - 2026/07/20
VL - 1
IS - 4
SP - None
SN - 3051-3839
PB - Elsevier BV
DO - 10.1016/j.cpblue.2026.100072
UR - https://doi.org/10.1016/j.cpblue.2026.100072
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.cpblue.2026.100072",
"type": "article-journal",
"title": "NodoMap: A single-cell and spatial transcriptomic atlas of the mouse nodose ganglion",
"container-title": "Cell press blue",
"author": [
{
"family": "Cheng",
"given": "Sijing"
},
{
"family": "Dowsett",
"given": "Georgina KC"
},
{
"family": "Rainbow",
"given": "Kara"
},
{
"family": "Norton",
"given": "Mariana"
},
{
"family": "Roberts",
"given": "Anna G"
},
{
"family": "Phuah",
"given": "Phyllis"
},
{
"family": "Bewick",
"given": "Gavin A"
},
{
"family": "Lam",
"given": "Brian YH"
},
{
"family": "Yeo",
"given": "Giles SH"
},
{
"family": "Murphy",
"given": "Kevin G"
}
],
"container-title-short": "Cell Press Blue",
"volume": "1",
"issue": "4",
"page": "None",
"DOI": "10.1016/j.cpblue.2026.100072",
"PMID": "42483274",
"PMCID": "PMC13385471",
"ISSN": "3051-3839",
"publisher": "Elsevier BV",
"URL": "https://doi.org/10.1016/j.cpblue.2026.100072",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
20
]
]
}
}

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