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Molecular Disambiguation of Heart Rate Control by the Nucleus Ambiguus.

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  1. [1] § Materials and Methods › Single-nuclei RNA-sequencing ↔ Reference_Query_Mapping .Rmd, lines 184 to 223 · score 0.66 · log normalization, FindVariableFeatures, Seurat, mapping, PCA, resolution · Report a mismatch
  2. [2] § Materials and Methods › Single-nuclei RNA-sequencing ↔ Reference_Query_Mapping .Rmd, lines 184 to 223 · score 0.65 · FindAllMarkers, Slc17a6, Seurat, threshold, PCA, resolution · Report a mismatch
  3. [3] § Materials and Methods › Single-nuclei RNA-sequencing ↔ namb_data_analysis_Jalil_Maira.Rmd, lines 262 to 289 · score 0.61 · FindAllMarkers, BuildClusterTree, log2, threshold, relatedness, PCA · Report a mismatch
  4. [4] § Results › Molecular classification of nAmb neurons in the adult mouse ↔ Batch_elimination_noFemale.Rmd, lines 180 to 206 · score 0.55 · Slc32a1, Slc17a6, Slc18a3, batches, Isl1, clusters · Report a mismatch
  5. [5] § Results › Molecular classification of nAmb neurons in the adult mouse ↔ Reference_Query_Mapping .Rmd, lines 86 to 111 · score 0.54 · ACP neurons, ACV neurons, Bche, Calb1, Npy2r, heart · Report a mismatch

Paper

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The authors' code

R Markdown · 312 lines · 14 KB · CC-BY-4.0 · 3 matches

  1. ---
  2. author: "Maira Jalil"
  3. date: "5/4/2023"
  4. output: html_document
  5. editor_options:
  6. chunk_output_type: console
  7. ---
  8. #Load libraries
  9. ```{r}
  10. #can't remember if you need all of these libraries for this particular figure, but these are the ones included in my script
  11. library(amap)
  12. library(FactoMineR)
  13. library(scatterD3)
  14. library(gplots)
  15. library(ggplot2)
  16. library(cowplot)
  17. library(ClassDiscovery)
  18. library(pheatmap)
  19. library(dplyr)
  20. library(Seurat)
  21. library(pheatmap)
  22. library(Matrix)
  23. library(latticeExtra)
  24. library(tools)
  25. library(matrixStats)
  26. ```
  27. #Load Jalil et al dataset
  28. ```{r}
  29. #load data
  30. namb_all.int <-readRDS("~/Desktop/Lab/Projects/Namb data/Sequencing/nAmb_Jalil.rds")
  31. DimPlot(namb_all.int, reduction = "umap", label = TRUE, pt.size = 1) + NoLegend()
  32. ```
  33. #Load Veerakumar dataset
  34. ```{r}
  35. # perform label transfer
  36. Karasnow.data = read.csv(file = "~/Desktop/Lab/Projects/Namb data/Sequencing/Karasnow data/GSE198709_AMB_raw_counts.csv.gz", header = TRUE, row.names = 1)
  37. #specify the gene names are the rownames
  38. gene_List <- rownames(Karasnow.data)
  39. Karasnow1 <- CreateSeuratObject(counts = Karasnow.data, project = "Namb", min.cells = 3, min.features = 200)
  40. #this resembles our snucseq data
  41. VlnPlot(Karasnow1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0.001)
  42. length(WhichCells(Karasnow1))
  43. plot1 <- FeatureScatter(Karasnow1, feature1 = "nCount_RNA", feature2 = "percent.mt")
  44. plot2 <- FeatureScatter(Karasnow1, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
  45. plot1+plot2
  46. ###Now choosing the dataset
  47. Karasnow1 <- subset(Karasnow1, subset = nFeature_RNA > 100)
  48. #Data Normalization
  49. Karasnow1 <- NormalizeData(Karasnow1)
  50. ##### Find and visualize highly variable genes
  51. Karasnow1 <- FindVariableFeatures(Karasnow1, selection.method = "vst", nfeatures = 2000)
  52. # Identify the 10 most highly variable genes
  53. top10 <- head(VariableFeatures(Karasnow1), 10)
  54. # plot variable features with and without labels
  55. plot1 <- VariableFeaturePlot(Karasnow1)
  56. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  57. LabelPoints(plot = plot1, points = top10, repel = TRUE, xnudge = 0, ynudge = 0)
  58. all.genes <- rownames(Karasnow1)
  59. Karasnow1 <- ScaleData(Karasnow1, features = all.genes)
  60. Karasnow1 <- RunPCA(Karasnow1, features = VariableFeatures(object = Karasnow1))
  61. # Examine and visualize PCA results a few different ways
  62. print(Karasnow1[["pca"]], dims = 1:5, nfeatures = 5)
  63. VizDimLoadings(Karasnow1, dims = 1:2, reduction = "pca")
  64. DimPlot(Karasnow1, reduction = "pca")
  65. DimHeatmap(Karasnow1, dims = 1, cells = 203, balanced = TRUE)
  66. DimHeatmap(Karasnow1, dims = 1:12, cells = 203, balanced = TRUE)
  67. ElbowPlot(Karasnow1)
  68. ```
  70. ```{r}
  71. ##Perform clustering
  72. x <- 12
  73. Karasnow1 <- FindNeighbors(Karasnow1, dims = 1:x)
  74. Karasnow1 <- FindClusters(Karasnow1, resolution = 0.7)
  75. head(Idents(Karasnow1), 5)
  76. length(WhichCells(Karasnow1))
  77. ##### Visualize clustering
  78. Karasnow1 <- RunUMAP(Karasnow1, dims = 1:x)
  79. ##### Reorder clusters by relatedness
  80. Karasnow1=BuildClusterTree(Karasnow1,reorder=TRUE, reorder.numeric = TRUE)
  81. PlotClusterTree(Karasnow1)
  82. DimPlot(Karasnow1, reduction = "umap", label = T, pt.size= 2)
  83. Karasnow1.markers <- FindAllMarkers(Karasnow1, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  84. ```
  86. ```{r}
  87. #Acp neurons cluster 3
  88. #not use dotplot because scaling that data will lead to misleading results
  89. markers.plot <- c("Trhr", "Sema3d", "Pappa2", "Mc4r", "Calb1", "Gabre")
  90. VlnPlot (object = Karasnow1, features= markers.plot, stack = T, sort = F, flip = TRUE)
  91. #ACV neurons cluster1
  92. markers.plot <- c("Hoxa5", "Ghsr", "Bche", "Htr3b", "Sstr2", "P2ry1")
  93. VlnPlot (object = Karasnow1, features= markers.plot, stack = T, sort = F, flip = TRUE)
  94. #heart markers
  95. markers.plot <- c("Celf6", "Kcna5", "Tbx3", "Dgkb", "Npy2r")
  96. DotPlot (object = Karasnow1, features= markers.plot)
  97. VlnPlot (object = Karasnow1, features= markers.plot, stack = T, sort = F, flip = TRUE)
  98. #larynx cluster cluster 2
  99. markers.plot <- c("Calca", "Pnoc", "Uts2", "Lhx4", "Dlk1")
  100. VlnPlot (object = Karasnow1, features= markers.plot, stack = T, sort = F, flip = TRUE)
  101. #all namb marker
  102. markers.plot <- c("Slc18a3", "Isl1")
  103. VlnPlot (object = Karasnow1, features= markers.plot)
  104. VlnPlot(Karasnow1, features = c("Vipr2", "Adcyap1", "Crhr2"))
  105. #cluster 2
  106. VlnPlot(Karasnow1, features = c("Calca", "Dlk1"))
  107. #cluster1
  108. VlnPlot(Karasnow1, features = c("Ghsr", "Bche", "Htr3b", "Npy2r"))
  109. #cluster 3
  110. VlnPlot(Karasnow1, features = c("Trhr", "Sema3d", "Pappa2", "Pcdh18"))
  111. ```
  113. ```{r}
  114. new.cluster.ids_numeric <- c("01","02","03")
  115. names(new.cluster.ids_numeric) <- levels(Karasnow1)
  116. Karasnow1 <- RenameIdents(Karasnow1, new.cluster.ids_numeric)
  117. Karasnow1$annotationsnumeric=Idents(Karasnow1)
  118. new.cluster.ids <- c("n01.ACV","n02.larynx","n03.ACP")
  119. names(new.cluster.ids) <- levels(Karasnow1)
  120. Karasnow1 <- RenameIdents(Karasnow1, new.cluster.ids)
  121. Karasnow1$annotationsnumeric=Idents(Karasnow1)
  122. Karasnow1$annotations=Idents(Karasnow1)
  123. DimPlot(Karasnow1, reduction = "umap", label = TRUE, pt.size = 2) + NoLegend()
  124. ```
  126. ```{r}
  127. PlotClusterTree(Karasnow1)
  128. VlnPlot(Karasnow1, features=c("Adcyap1", "Crhr2", "Uts2", "Vipr2", "Calca"), pt.size=0.1)
  129. markers.plot <- c("Adcyap1", "Calca")
  130. VlnPlot (object = Karasnow1, features= markers.plot, stack = T, sort = F, flip = TRUE)
  131. #remotes::install_github("satijalab/seurat", ref = "release/4.0.0")
  132. DefaultAssay(namb_all.int)<- "integrated"
  133. DefaultAssay(Karasnow1)<- "RNA"
  134. anchors <- FindTransferAnchors(
  135. reference = namb_all.int,
  136. query = Karasnow1,reference.assay = "integrated",
  137. query.assay = "RNA", dims = 1:50)
  138. predictions_Veerakumar <- TransferData(anchorset = anchors, refdata = namb_all.int$annotationsnumeric, dims = 1:30)
  139. #query dataset goes here
  140. Karasnow1 <- AddMetaData(Karasnow1, metadata = predictions_Veerakumar)
  141. ```
  142. #Create Figure:
  143. ```{r}
  144. pids = as.integer(as.matrix(Karasnow1$predicted.id))
  145. ones = as.integer(pids==1)
  146. Karasnow1$ones = ones
  147. twos = as.integer(pids==2)
  148. Karasnow1$twos = twos
  149. threes = as.integer(pids==3)
  150. Karasnow1$threes = threes
  151. fours = as.integer(pids==4)
  152. Karasnow1$fours = fours
  153. fives = as.integer(pids==5)
  154. Karasnow1$fives = fives
  155. sixes = as.integer(pids==6)
  156. Karasnow1$sixes = sixes
  157. ```
  159. ```{r}
  160. ct_factor = factor(Karasnow1$tree.ident)
  161. ct_factor = factor(ct_factor,levels(ct_factor)[rev(c(6,5,4,3,2,1))])
  162. Karasnow1$tree.ident <- ct_factor
  163. #mapping on the UMAP
  164. library(Seurat)
  165. library(SeuratDisk)
  166. library(ggplot2)
  167. library(patchwork)
  168. Karasnow1 <- MapQuery(
  169. anchorset = anchors,
  170. query = Karasnow1,
  171. reference = namb_all.int,
  172. refdata = list(
  173. Karasnow1 = "Karasnow",
  174. namb_all.int = "namb"
  175. ),
  176. reference.reduction = "spca",
  177. reduction.model = "wnn.umap"
  178. )
  179. markers.plot <- c("ones","twos","threes","fours","fives","sixes")
  180. VlnPlot(Karasnow1,features=c("ones","twos","threes","fours","fives","sixes"),group.by="tree.ident", stack = F)
  181. DotPlot(Karasnow1,features=c("ones","twos","threes","fours","fives","sixes"),group.by="tree.ident", scale=F) + scale_colour_gradient2(low = "#00FFFF", mid = "#FFFFFF", high = "#FF00FF")
  182. DotPlot(Karasnow1,features=c("ones","twos","threes","fours","fives","sixes"),group.by="tree.ident",scale.by="size") + scale_colour_gradient2(low = "#00FFFF", mid = "#FFFFFF", high = "#FF00FF")
  183. ```
  184. #Load Coverdell dataset
  185. ```{r}
  186. #load snucseq data
  187. ChatNamb.data=read.table(file = "~/Desktop/Lab/Projects/Namb data/Sequencing/ChatNAmb_dge.tsv", header = TRUE, row.names = 1)
  188. ChatNAmb <- CreateSeuratObject(counts = ChatNamb.data, min.cells = 3, min.features = 2000, project = "ChatNAmb")
  189. ChatNAmb <- NormalizeData(object = ChatNAmb, normalization.method = "LogNormalize")
  190. # Visualize QC metrics as a violin plot
  191. VlnPlot(ChatNAmb, features = c("nFeature_RNA", "nCount_RNA"), ncol = 3)
  192. # Identify the 10 most highly variable genes
  193. top10 <- head(VariableFeatures(ChatNAmb), 10)
  194. # plot variable features with and without labels
  195. ChatNAmb <- FindVariableFeatures(ChatNAmb, selection.method = "vst", nfeatures = 2000)
  196. plot1 <- VariableFeaturePlot(ChatNAmb)
  197. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  198. CombinePlots(plots = list(plot1, plot2))
  199. all.genes <- rownames(ChatNAmb)
  200. ChatNAmb <- ScaleData(ChatNAmb, features = all.genes)
  201. ChatNAmb <- RunPCA(ChatNAmb, features = VariableFeatures(object = ChatNAmb))
  202. # Examine and visualize PCA results a few different ways
  203. print(ChatNAmb[["pca"]], dims = 1:5, nfeatures = 5)
  204. VizDimLoadings(ChatNAmb, dims = 1:2, reduction = "pca")
  205. DimPlot(ChatNAmb, reduction = "pca")
  206. DimHeatmap(ChatNAmb, dims = 1, cells = 500, balanced = TRUE)
  207. DimHeatmap(ChatNAmb, dims = 1:15, cells = 500, balanced = TRUE)
  208. ElbowPlot(ChatNAmb)
  209. ChatNAmb <- FindNeighbors(ChatNAmb, dims = 1:8)
  210. ChatNAmb <- FindClusters(ChatNAmb, resolution = 0.5)
  211. head(Idents(ChatNAmb), 5)
  212. ChatNAmb <- RunUMAP(ChatNAmb, dims = 1:10)
  213. DimPlot(ChatNAmb, reduction = "umap")
  214. ChatNAmb.markers <- FindAllMarkers(ChatNAmb, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  215. ChatNAmb.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)
  216. # you can plot raw counts as well
  217. VlnPlot(ChatNAmb, features = c("Vipr2","Slc17a6" , "Crhr2", "Slc6a5", "Adcyap1"), slot = "counts", log = TRUE)
  218. VlnPlot(ChatNAmb, features = c("Vipr2", "Tbx3", "Crhr2", "Col1a2", "Adcyap1", "Calca", "Sox5", "Uts2", "Chat", "Phox2b", "Nos1", "Htr3a", "Npy2r", "Trhr", "Kcnj14", "Edn1", "Edn3", "Oprm1"), slot = "counts")
  219. VlnPlot(ChatNAmb, features = c("Vipr2", "Tbx3"), slot = "counts", log = TRUE)
  220. FeaturePlot(ChatNAmb, features = c("Vipr2", "Tbx3", "Crhr2", "Col1a2", "Adcyap1", "Calca", "Sox5", "Uts2", "Chat", "Phox2b", "Nos1", "Htr3a", "Npy2r", "Trhr", "Kcnj14", "Edn1", "Edn3", "Oprm1"))
  221. top10 <- ChatNAmb.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
  222. DoHeatmap(ChatNAmb, features = top10$gene) + NoLegend()
  223. ```
  224. #Chatnamb neurons
  225. ```{r}
  226. VlnPlot(ChatNAmb, features = c("Vipr2","Slc17a6" , "Crhr2", "Slc6a5", "Adcyap1"), slot = "counts", log = TRUE)
  227. new.cluster.ids <- c("n01.Vipr2","n02.Slc17a6/Chat","n03.Crhr2","n04.Slc6a5/Chat","n05.Adcyap1")
  228. names(new.cluster.ids) <- levels(ChatNAmb)
  229. ChatNAmb <- RenameIdents(ChatNAmb, new.cluster.ids)
  230. DimPlot(ChatNAmb, reduction = "umap", label = TRUE, pt.size = 2) + NoLegend()
  231. Idents(ChatNamb)=ChatNAmb$celltype
  232. # subset relevant cell types from snucseq data data object
  233. Chat_Nambonly_SeuratObject = subset(ChatNAmb,idents=c("n01.Vipr2","n03.Crhr2","n05.Adcyap1"))
  234. Chat_Nambonly_SeuratObject <- NormalizeData(Chat_Nambonly_SeuratObject, normalization.method = "LogNormalize", scale.factor = 10000)
  235. Chat_Nambonly_SeuratObject <- FindVariableFeatures(Chat_Nambonly_SeuratObject, selection.method = "vst", nfeatures = 2000)
  236. top10 <- head(VariableFeatures(Chat_Nambonly_SeuratObject), 10)
  237. # plot variable features with and without labels
  238. plot1 <- VariableFeaturePlot(Chat_Nambonly_SeuratObject)
  239. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  240. LabelPoints(plot = plot1, points = top10, repel = TRUE, xnudge = 0, ynudge = 0)
  241. Chat_Nambonly_SeuratObject <- ScaleData(Chat_Nambonly_SeuratObject, features = gene_List)
  242. Chat_Nambonly_SeuratObject <- RunPCA(Chat_Nambonly_SeuratObject, features = VariableFeatures(object = Chat_Nambonly_SeuratObject))
  243. # Examine and visualize PCA results a few different ways
  244. print(Chat_Nambonly_SeuratObject[["pca"]], dims = 1:5, nfeatures = 5)
  245. VizDimLoadings(Chat_Nambonly_SeuratObject, dims = 1:2, reduction = "pca")
  246. DimPlot(Chat_Nambonly_SeuratObject, reduction = "pca")
  247. DimHeatmap(Chat_Nambonly_SeuratObject, dims = 1, cells = 400, balanced = TRUE)
  248. DimHeatmap(Chat_Nambonly_SeuratObject, dims = 1:12, cells = 400, balanced = TRUE)
  249. ElbowPlot(Chat_Nambonly_SeuratObject)
  250. ```
  252. ```{r}
  253. x <- 10
  254. Chat_Nambonly_SeuratObject <- FindNeighbors(Chat_Nambonly_SeuratObject, dims = 1:x)
  255. Chat_Nambonly_SeuratObject <- FindClusters(Chat_Nambonly_SeuratObject, resolution = 0.25)
  256. head(Idents(Chat_Nambonly_SeuratObject), 5)
  257. length(WhichCells(Chat_Nambonly_SeuratObject))
  258. ##### Visualize clustering
  259. Chat_Nambonly_SeuratObject <- RunUMAP(Chat_Nambonly_SeuratObject, dims = 1:x)
  260. ##### Reorder clusters by relatedness
  261. Chat_Nambonly_SeuratObject=BuildClusterTree(Chat_Nambonly_SeuratObject,reorder=TRUE, reorder.numeric = TRUE)
  262. PlotClusterTree(Chat_Nambonly_SeuratObject)
  263. DimPlot(Chat_Nambonly_SeuratObject, reduction = "umap", label = T)
  264. VlnPlot(Chat_Nambonly_SeuratObject, features = c("Vipr2", "Crhr2","Adcyap1"), slot = "counts", log = TRUE)
  265. ```
  266. # perform label transfer
  267. ```{r}
  268. DefaultAssay(namb_all.int)<- "integrated"
  269. DefaultAssay(Chat_Nambonly_SeuratObject)<- "RNA"
  270. anchors_namb <- FindTransferAnchors(reference = namb_all.int, query = Chat_Nambonly_SeuratObject,dims = 1:30)
  271. predictions <- TransferData(anchorset = anchors_namb, refdata = namb_all.int$annotationsnumeric, dims = 1:30)
  272. Chat_Nambonly_SeuratObject <- AddMetaData(Chat_Nambonly_SeuratObject, metadata = predictions)
  273. ```
  275. ```{r}
  276. DotPlot(Chat_Nambonly_SeuratObject, features = c("Vipr2", "Adcyap1", "Crhr2"))
  277. new.cluster.ids <- c("n01.Vipr2","n02.Crhr2","n03.Adcyap1")
  278. names(new.cluster.ids) <- levels(Chat_Nambonly_SeuratObject)
  279. Chat_Nambonly_SeuratObject <- RenameIdents(Chat_Nambonly_SeuratObject, new.cluster.ids)
  280. DimPlot(Chat_Nambonly_SeuratObject, reduction = "umap", label = TRUE, pt.size = 2) + NoLegend()
  281. Chat_Nambonly_SeuratObject$annotations=Idents(Chat_Nambonly_SeuratObject)
  282. new.cluster.ids_numeric <- c("01","02","03")
  283. names(new.cluster.ids_numeric) <- levels(Chat_Nambonly_SeuratObject)
  284. Chat_Nambonly_SeuratObject <- RenameIdents(Chat_Nambonly_SeuratObject, new.cluster.ids_numeric)
  285. Chat_Nambonly_SeuratObject$annotationsnumeric=Idents(Chat_Nambonly_SeuratObject)
  286. DimPlot(Chat_Nambonly_SeuratObject, reduction = "umap", label = TRUE, pt.size = 2) + NoLegend()
  287. DotPlot(Chat_Nambonly_SeuratObject, features = c("Vipr2", "Adcyap1", "Crhr2"))
  288. ```
  290. #Create Figure:
  291. ```{r}
  292. pids = as.integer(as.matrix(Chat_Nambonly_SeuratObject$predicted.id))
  293. ones = as.integer(pids==1)
  294. Chat_Nambonly_SeuratObject$ones = ones
  295. twos = as.integer(pids==2)
  296. Chat_Nambonly_SeuratObject$twos = twos
  297. threes = as.integer(pids==3)
  298. Chat_Nambonly_SeuratObject$threes = threes
  299. fours = as.integer(pids==4)
  300. Chat_Nambonly_SeuratObject$fours = fours
  301. fives = as.integer(pids==5)
  302. Chat_Nambonly_SeuratObject$fives = fives
  303. sixes = as.integer(pids==6)
  304. Chat_Nambonly_SeuratObject$sixes = sixes
  305. ```
  307. ```{r}
  308. ct_factor1_chat = factor(Chat_Nambonly_SeuratObject$tree.ident)
  309. ct_factor1_chat = factor(ct_factor1_chat,levels(ct_factor1_chat)[rev(c(6,5,4,3,2,1))])
  310. Chat_Nambonly_SeuratObject$tree.ident <- ct_factor1_chat
  311. DotPlot(Chat_Nambonly_SeuratObject,features=c("ones","twos","threes","fours","fives","sixes"),group.by="tree.ident",scale.by="size") + scale_colour_gradient2(low = "#00FFFF", mid = "#FFFFFF", high = "#FF00FF")
  312. ```

Reference_Query_Mapping .Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Maira Jalil1, Mary Katherine MacMillan2, Tatiana C. Coverdell3, Veronica A. Gutierrez1, Maisie E. Crook1, Travis J. Heinrichs1, Ashlyn Dever1, Katie Shouse Cox1, Daniel S. Stornetta3, Yoko B. Wang4,5, Dana C. Schwalbe1, Adriana Singh1, Lane VanderVoort1, Carie R. Boychuk4,5, Stephen B. G. Abbott2,3, John N. Campbell1,2
ORCID iDs: Daniel S. Stornetta, Stephen B. G. Abbott, John N. Campbell
  1. Department of Biology, University of Virginia, Charlottesville, Virginia 22904
  2. Neuroscience Graduate Program, University of Virginia, Charlottesville, Virginia 22903
  3. Department of Pharmacology, University of Virginia, Charlottesville, Virginia 22903
  4. Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211
  5. Department of Pathobiology and Integrative Biomedical Sciences, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, 65211
Institutions: University of Virginia (United States); Missouri College (United States); University of Missouri (United States)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 42, article e0398262026
Dates: received 5 March 2026; accepted 8 September 2026; published online 21 October 2026; in print 21 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.0398-26.2026 · PMID 42736025 · PMCID PMC13689988 · OpenAlex W7212851585
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2023.12.16.571991
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials
Keywords: cardiovagal neurons, diving reflex, heart rate regulation, nucleus ambiguus, parasympathetic nervous system, single-cell RNA-sequencing, vagus nerve
MeSH: Heart Rate*, Medulla Oblongata*, Neurons*, Animals, Female, Male, Mice, Mice, Inbred C57BL, Receptors, Neuropeptide Y, Vagus Nerve (* major topic)
Journal subjects: Systems/Circuits
Topic: Heart Rate Variability and Autonomic Control (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) (HL148004, HL158187, HL153916); American Diabetes Association (1-18-INI-14); UV | Office of Vice President for Research, University of Virginia (VPR) (3 Cavaliers award); UVA (iPRIME fellowship); HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (DK147947)
Citations: cited by 2 papers (Europe PMC); 83 references in the paper
Research resources: AddGene plasmid #28306 RRID:Addgene_28306, the Genome Analysis and Technology Core RRID:SCR_018883

Abstract

The nucleus ambiguus (nAmb) provides parasympathetic control of cardiorespiratory functions as well as motor control of the upper airways and esophagus. A subset of nAmb neurons innervates the heart through the vagus nerve to control cardiac function at rest and during key autonomic reflexes such as the mammalian diving reflex. Yet, how these cardiovagal nAmb neurons differ from other nAmb neurons in the adult brain remains unclear. We therefore classified adult male and female mouse nAmb neurons molecularly, anatomically, and functionally. First, our integrated analysis of single-nucleus RNA-sequencing data predicted multiple molecular subtypes of nAmb neurons. Mapping the axon projections of one nAmb neuron subtype, Npy2r-expressing nAmb neurons, showed that they innervate cardiac ganglia but not the upper airways or esophagus. Chemogenetically stimulating Npy2r + nAmb neurons robustly decreased heart rate through peripheral muscarinic acetylcholine receptors. Finally, voluntary underwater diving decreases heart rate and activates Npy2r + nAmb neurons, consistent with a cardiovagal function for this nAmb subtype. These results together reveal the molecular organization of nAmb neurons and its control of the heart rate.

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Zenodo 22864368

License: CC-BY-4.0
State: the link answers, verified on 24 October 2026
Evidence: files inventoried
Languages: R (5)
Size: 9 files, 5 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: 5 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), cowplot (4 files), ggplot2 (4 files), patchwork (4 files), Seurat (4 files), pheatmap (2 files)
Availability: 1 check, the latest on 24 October 2026: the link answers (HTTP 200)
  • 24 October 2026: the link answers (HTTP 200)
5 files
At the source: zenodo.org/records/22864368

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Data

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

Data Availability

The raw and processed snRNA-seq data and metadata reported in this paper are available at GEO accession number GSE348348. A user-friendly interface for visualizing and exploring the fully processed snRNA-seq data is available at the Broad Institute Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP3861/molecular-disambiguation-of-heart-rate-control-by-the-nucleus-ambiguus. The code used for processing, clustering, and visualizing the clustered snRNA-seq data and the clustering data files are available through Zenodo at https://zenodo.org/records/22864368. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon reasonable request.

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Version 1, 24 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 7 keywords, 10 MeSH terms, 5 funders, 81 references, 2 RRIDs.

Cite

This paper

Jalil, M., MacMillan, M. K., Coverdell, T. C., Gutierrez, V. A., Crook, M. E., Heinrichs, T. J., Dever, A., Cox, K. S., Stornetta, D. S., Wang, Y. B., Schwalbe, D. C., Singh, A., VanderVoort, L., Boychuk, C. R., Abbott, S. B. G., & Campbell, J. N. (2026). Molecular Disambiguation of Heart Rate Control by the Nucleus Ambiguus. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(42), e0398262026. https://doi.org/10.1523/jneurosci.0398-26.2026

BibTeX

@article{jalil2026molecular,
author = {Jalil, Maira and MacMillan, Mary Katherine and Coverdell, Tatiana C. and Gutierrez, Veronica A. and Crook, Maisie E. and Heinrichs, Travis J. and Dever, Ashlyn and Cox, Katie Shouse and Stornetta, Daniel S. and Wang, Yoko B. and Schwalbe, Dana C. and Singh, Adriana and VanderVoort, Lane and Boychuk, Carie R. and Abbott, Stephen B. G. and Campbell, John N.},
title = {{Molecular Disambiguation of Heart Rate Control by the Nucleus Ambiguus}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = oct,
volume = {46},
number = {42},
pages = {e0398262026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.0398-26.2026},
url = {https://doi.org/10.1523/jneurosci.0398-26.2026},
pmid = {42736025},
pmcid = {PMC13689988}
}

RIS

TY - JOUR
AU - Jalil, Maira
AU - MacMillan, Mary Katherine
AU - Coverdell, Tatiana C.
AU - Gutierrez, Veronica A.
AU - Crook, Maisie E.
AU - Heinrichs, Travis J.
AU - Dever, Ashlyn
AU - Cox, Katie Shouse
AU - Stornetta, Daniel S.
AU - Wang, Yoko B.
AU - Schwalbe, Dana C.
AU - Singh, Adriana
AU - VanderVoort, Lane
AU - Boychuk, Carie R.
AU - Abbott, Stephen B. G.
AU - Campbell, John N.
TI - Molecular Disambiguation of Heart Rate Control by the Nucleus Ambiguus
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/10/21
VL - 46
IS - 42
SP - e0398262026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.0398-26.2026
UR - https://doi.org/10.1523/jneurosci.0398-26.2026
LA - en
ER -

CSL-JSON

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