OSCR

Semaglutide attenuates neuroinflammation in male mice.

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
  1. [1] § Methods › Single-nucleus RNA-seq data analysis › Analysis of SEA-AD snRNA-seq dataset ↔ analysis/integration_with_published_atlases.Rmd, lines 283–306 · score 0.80 · dimensionality reduction, SCTransform, FindNeighbors, FindClusters, RunPCA, Seurat
  2. [2] § Methods › Bulk RNA-seq data analysis › WGCNA ↔ analysis/WGCNA.Rmd, lines 107–120 · score 0.78 · deep split, cutreeDynamic, pam stage, WGCNA, modules, clustering
  3. [3] § Methods › Single-nucleus RNA-seq data analysis › DVC cell population annotation ↔ analysis/clustering_DVC_neurons.Rmd, lines 147–184 · score 0.71 · Slc17a6, Slc32a1, neuronal populations, Gad2, Tph2, Chat
  4. [4] § Methods › Single-nucleus RNA-seq data analysis › Initial processing ↔ analysis/integration_with_published_atlases.Rmd, lines 283–306 · score 0.69 · SCTransform, FindNeighbors, FindClusters, RunPCA, Seurat, clustering
  5. [5] § Methods › Single-nucleus RNA-seq data analysis › Comparing treatment response between the hippocampus and DVC ↔ analysis/figure5.Rmd, lines 355–443 · score 0.65 · fold changes, semaglutide induced, LPS induced, Spearman, correlate, rho
  6. [6] § Methods › Single-nucleus RNA-seq data analysis › Cell-cell communication analysis ↔ analysis/figure3.Rmd, lines 158–192 · score 0.65 · log fold change, CellChat, ligand, veh PBS, sema LPS, veh LPS
  7. [7] § Methods › Bulk RNA-seq data analysis › WGCNA ↔ analysis/figure1.Rmd, lines 308–342 · score 0.63 · likelihood ratio, logistic regression, eigengene, Modules, treatment
  8. [8] § Methods › Statistics and reproducibility › Statistical analyses ↔ analysis/figure1.Rmd, lines 308–342 · score 0.62 · likelihood ratio, logistic regression, module, FDR, matrices, treatment
  9. [9] § Results › Semaglutide attenuates LPS-induced neuroinflammation ↔ analysis/figure1.Rmd, lines 201–247 · score 0.59 · fractional area, entire brain, sema PBS, veh PBS, sema LPS, veh LPS
  10. [10] § Methods › Single-nucleus RNA-seq data analysis › Hippocampal cell population annotation ↔ analysis/integration_with_published_atlases.Rmd, lines 237–266 · score 0.57 · TransferData, endothelial cells, pericytes2, Bcas1, oligodendrocytes, VLMCs
  11. [11] § Methods › Single-nucleus RNA-seq data analysis › Milo analysis ↔ analysis/figure2.Rmd, lines 151–269 · score 0.56 · testNhoods, abundant neighborhoods, veh PBS, sema LPS, veh LPS, Mixed
  12. [12] § Methods › Single-nucleus RNA-seq data analysis › Milo analysis ↔ analysis/figure5.Rmd, lines 105–223 · score 0.56 · testNhoods, abundant neighborhoods, veh PBS, sema LPS, veh LPS, Mixed
  13. [13] § Methods › Bulk RNA-seq data analysis › Functional gene set enrichment analysis (WGCNA) ↔ analysis/figure1.Rmd, lines 466–510 · score 0.55 · KEGG, gost, kME, intersecting, query, Module
  14. [14] § Results › Semaglutide activates neurons in the dorsal vagal complex that express inflammation-attenuating neurotransmitters ↔ analysis/merge_DVC_glia_and_neurons.Rmd, lines 15–54 · score 0.55 · cholinergic neurons, ependymal cells, tanycytes, DVC
  15. [15] § Methods › Single-nucleus RNA-seq data analysis › Hippocampal cell population annotation ↔ analysis/integration_with_published_atlases.Rmd, lines 156–203 · score 0.54 · silhouette score, FindClusters, optimal, resolution, clustering, hippocampal
  16. [16] § Methods › Single-nucleus RNA-seq data analysis › Integration with AD genome-wide association study data ↔ analysis/figure4.Rmd, lines 30–43 · score 0.53 · AD GWAS, scDRS, enrichment, score, cell
  17. [17] § Results › Semaglutide activates neurons in the dorsal vagal complex that express inflammation-attenuating neurotransmitters ↔ analysis/figure5.Rmd, lines 105–223 · score 0.52 · abundant neighborhoods, DA neighborhoods, cell population, neurons, Sema, PBS
  18. [18] § Methods › Statistics and reproducibility › Statistical analyses ↔ analysis/SCENIC_analysis.Rmd, lines 325–424 · score 0.51 · linear mixed, regression, SCENIC, lsmeans, regulon, FDR
  19. [19] § Methods › Single-nucleus RNA-seq data analysis › Integration with AD genome-wide association study data ↔ analysis/figure4.Rmd, lines 30–43 · score 0.51 · enrichment score, scDRS, AD, cell
  20. [20] § Results › Semaglutide-induced attenuation of neuroinflammation is accompanied by changes in microglia, endothelial cells and pericytes ↔ analysis/figure5.Rmd, lines 355–443 · score 0.50 · log2 fold changes, semaglutide induced, Spearman, correlation, genes, treatment

Paper

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The authors' code

R Markdown · 451 lines · 19 KB · no license · 4 matches

  1. ---
  2. title: "integration_with_published_atlases"
  3. author: "Mette Ludwig"
  4. date: "2022-10-14"
  5. output: workflowr::wflow_html
  6. editor_options:
  7. chunk_output_type: inline
  8. ---
  11. ```{r}
  12. library(anndata)
  13. library(Seurat)
  14. library(ggplot2)
  15. library(ggpubr)
  16. library(cluster)
  17. library(parallelDist)
  18. library(doMC)
  19. library(Seurat)
  20. source("/projects/mludwig/Ludwig-2021/Ludwig-2021/code/compute_sil.R")
  21. ```
  23. # Load Seurat objects
  24. ```{r}
  25. hippo.glia <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs/hippo_glia_Seurat_obj.rds")
  26. hippo.neurons <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs/hippo_neurons_Seurat_obj.rds")
  27. ```
  29. # Preprocess Duan et al. (Neuron, 2018)
  30. ```{r}
  31. # Load Seurat obj
  32. duan <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs_publications/Duan_Seurat_obj.rds")
  34. # Normalize
  35. duan <- SCTransform(duan, verbose = FALSE)
  37. # Define major cell type identities
  38. duan$cell.type <- "Unknown"
  39. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(21))] <- "Pericytes"
  40. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(23))] <- "Pericytes"
  41. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(17))] <- "VLMCs"
  42. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(13))] <- "Microglia"
  43. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(3))] <- "Endothelial_cells"
  44. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(5))] <- "Endothelial_cells"
  45. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(0))] <- "Astrocytes"
  46. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(24))] <- "Astrocytes"
  47. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(18))] <- "Neurons"
  48. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(1))] <- "Neurons"
  49. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(4))] <- "Neurons"
  50. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(8))] <- "Neurons"
  51. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(10))] <- "Neurons"
  52. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(11))] <- "Neurons"
  53. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(20))] <- "Neurons"
  54. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(14))] <- "Neurons"
  55. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(15))] <- "Astrocytes"
  56. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(7))] <- "Oligodendrocytes"
  57. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(22))] <- "Oligodendrocytes"
  58. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(29))] <- "Oligodendrocytes"
  59. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(19))] <- "Oligodendrocytes"
  60. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(6))] <- "Oligodendrocytes"
  61. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(25))] <- "Oligodendrocytes"
  62. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(9))] <- "Oligodendrocytes"
  63. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(26))] <- "Ependymal_cells"
  64. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(2))] <- "Neurons"
  65. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(12))] <- "Neurons"
  66. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(16))] <- "Neurons"
  67. duan$cell.type[which(duan$RNA_snn_res.1 %in% c(28))] <- "Neurons"
  69. # Define minor cell type identities
  70. duan$cell.type2 <- "Unknown"
  71. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(21))] <- "Pericytes1"
  72. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(23))] <- "Pericytes2"
  73. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(17))] <- "VLMCs"
  74. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(13))] <- "Microglia"
  75. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(3))] <- "Endothelial_cells1"
  76. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(5))] <- "Endothelial_cells2"
  77. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(0))] <- "Astrocytes1"
  78. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(24))] <- "Astrocytes2"
  79. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(18))] <- "Spock1_neurons"
  80. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(1))] <- "Nrgn_neurons1"
  81. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(4))] <- "Nrgn_neurons2"
  82. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(8))] <- "Nrgn_neurons3"
  83. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(10))] <- "Nrgn_neurons4"
  84. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(11))] <- "Nrgn_neurons5"
  85. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(20))] <- "Nrgn_neurons6"
  86. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(14))] <- "Reln_neurons"
  87. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(15))] <- "Tfap2c_cells"
  88. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(7))] <- "Mobp_oligodendrocytes1"
  89. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(22))] <- "Mobp_oligodendrocytes2"
  90. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(29))] <- "Mobp_oligodendrocytes3"
  91. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(19))] <- "Bcas1_oligodendrocytes"
  92. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(6))] <- "C1ql1_oligodendrocytes1"
  93. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(25))] <- "C1ql1_oligodendrocytes2"
  94. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(9))] <- "Enpp2_oligodendrocytes"
  95. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(26))] <- "Ependymal_cells"
  96. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(2))] <- "Unknown_neurons1"
  97. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(12))] <- "Unknown_neurons2"
  98. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(16))] <- "Unknown_neurons3"
  99. duan$cell.type2[which(duan$RNA_snn_res.1 %in% c(28))] <- "Unknown_neurons4"
  101. # Save
  102. # saveRDS(duan, file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs_publications/Duan_Seurat_obj_labels.rds")
  103. ```
  105. # Transfer labels from Duan atlas
  106. ```{r}
  107. duan <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs_publications/Duan_Seurat_obj_labels.rds")
  109. # Subset Duan atlas to glial cells
  110. duan.glia <- subset(duan, cell.type != "Neurons")
  112. # Normalize
  113. duan.glia <- SCTransform(duan.glia, verbose = F, method = "qpoisson")
  115. # Run dimensionality reduction
  116. duan.glia <- RunPCA(duan.glia, verbose = F)
  117. ElbowPlot(duan.glia, ndims = 50)
  118. duan.glia <- RunUMAP(duan.glia, dims = 1:30, n.neighbors = 50)
  120. # Transfer labels to hippocampal glial atlas
  121. anchors <- FindTransferAnchors(reference = duan.glia, query = hippo.glia,
  122. dims = 1:30, normalization.method = "SCT")
  123. predictions <- TransferData(anchorset = anchors, refdata = duan.glia$cell.type2,
  124. dims = 1:30)
  126. hippo.glia$duan.predictions <- predictions$predicted.id
  127. hippo.glia$duan.score <- predictions$prediction.score.max
  129. DimPlot(hippo.glia, group.by = "duan.predictions", label = T)
  130. ```
  131. # Label cell type identities
  132. ```{r}
  133. hippo.glia$cell.type <- ""
  134. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(0, 2, 8))] <- "Mobp_oligodendrocytes"
  135. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(1))] <- "Astrocytes"
  136. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(3))] <- "Microglia"
  137. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 == 7)] <- "Unknown_glia"
  138. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.1 == 21)] <- "Pericytes2"
  139. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.1 == 25)] <- "VLMCs"
  140. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(5) &
  141. hippo.glia$SCT_snn_res.1 == "18")] <- "Pericytes1"
  142. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(5) &
  143. hippo.glia$SCT_snn_res.1 != "18")] <- "Endothelial_cells"
  144. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(4) &
  145. hippo.glia$SCT_snn_res.1 != 19)] <- "C1ql1_oligodendrocytes"
  146. hippo.glia$cell.type[which(hippo.glia$SCT_snn_res.0.1 %in% c(4) &
  147. hippo.glia$SCT_snn_res.1 == 19)] <- "Bcas1_oligodendrocytes"
  149. hippo.glia$cell.type <- factor(hippo.glia$cell.type, levels = c("Astrocytes", "Bcas1_oligodendrocytes",
  150. "C1ql1_oligodendrocytes", "Endothelial_cells",
  151. "Microglia", "Mobp_oligodendrocytes",
  152. "Pericytes1", "Pericytes2",
  153. "Unknown_glia", "VLMCs"))
  154. ```
  156. # Compute silhouette scores to identify optimal clustering resolution
  157. ```{r}
  158. resolution <- seq(0.1, 1, 0.1)
  160. pc <- hippo.glia@reductions$[email hidden][, 1:30]
  161. distance <- parDist(pc, method = "euclidean")
  163. cluster.id <- as.character([email hidden][, "cell.type"])
  164. silhouette.cell.type <- compute.sil(x = cluster.id, dist = distance)
  166. print(mean(silhouette.cell.tyoe))
  168. stability <- data.frame(matrix(NA, nrow = ncol(hippo.glia), ncol = length(resolution)))
  169. colnames(stability) <- resolution
  170. rownames(stability) <- colnames(hippo.glia)
  172. for(i in 1:length(resolution)) {
  173. print(i)
  175. hippo.glia <- FindClusters(object = hippo.glia, resolution = resolution[i],
  176. verbose = F)
  177. cluster.id <- as.numeric(as.character([email hidden][, paste0("SCT_snn_res.", resolution[i])]))
  179. silhouette <- compute.sil(x = cluster.id, dist = distance)
  181. print(mean(silhouette))
  183. stability[,i] <- silhouette
  185. }
  186. stability <- cbind(cell.type = silhouette.cell.type, stability)
  188. # Save
  189. # saveRDS(stability, file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs/silhouette_glia.rds")
  190. stability <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs/silhouette_glia.rds")
  192. # Plot
  193. stability$cell <- rownames(stability)
  194. stability.melt <- reshape2::melt(stability, id.vars = "cell")
  195. stability.melt$variable <- gsub("cell.type", "Duan labels", stability.melt$variable)
  197. ggplot(stability.melt) +
  198. geom_boxplot(aes(x = variable, y = value), outlier.shape = NA, fill = "darkcyan") +
  199. theme_pubr() +
  200. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  201. xlab("Resolution") + ylab("Silhouette score") +
  202. scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.25))
  203. ```
  205. # Preprocess Posner et al. (Current Opinion in Immunology, 2022)
  206. ```{r}
  207. # # Load anndata obj
  208. # posner.h5ad <- read_h5ad("/scratch/mlf210/CNSbordercellatlas_Dec22.h5ad")
  209. #
  210. # # Convert anndata obj to Seurat obj
  211. # posner.counts <- rbind(as.matrix(posner.h5ad[["X"]][1:65000, ]),
  212. # as.matrix(posner.h5ad[["X"]][65001:nrow(posner.h5ad[["X"]]), ]))
  213. #
  214. # posner.counts <- t(posner.counts)
  215. #
  216. # posner <- CreateSeuratObject(posner.counts, meta.data = posner.h5ad[["obs"]])
  217. # rm(posner.counts)
  218. # rm(posner.h5ad)
  219. #
  220. # # Normalize
  221. # posner <- SCTransform(posner, verbose = F, method = "qpoisson")
  222. #
  223. # # Run dimensionality reduction
  224. # posner <- RunPCA(posner, verbose = F)
  225. # ElbowPlot(posner, ndims = 50)
  226. # posner <- RunUMAP(posner, dims = 1:30, n.neighbors = 50)
  227. #
  228. # # Run clustering
  229. # posner <- FindNeighbors(posner, dims = 1:30, k.param = 50)
  230. # posner <- FindClusters(posner, resolution = 0.1, verbose = F)
  231. #
  232. # # Save
  233. # saveRDS(posner, file = "projects/mludwig/hippo_GLP1/output/Seurat_objs_publications/Posner_Seurat_obj.rds")
  234. ```
  237. # Transfer labels from Posner atlas
  238. ```{r}
  239. posner <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs_publications/Posner_Seurat_obj.rds")
  241. anchors <- FindTransferAnchors(reference = posner, query = hippo.glia,
  242. dims = 1:30, normalization.method = "SCT")
  243. predictions <- TransferData(anchorset = anchors, refdata = posner$celltype,
  244. dims = 1:30)
  245. saveRDS(predictions, file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs/Posner_predictions.rds")
  247. hippo.glia$posner.predictions <- predictions$predicted.id
  248. hippo.glia$posner.score <- predictions$prediction.score.max
  250. DimPlot(hippo.glia, group.by = "posner.predictions", label = T)
  252. # Update the labels
  253. hippo.glia$cell.type2 <- hippo.glia$cell.type
  254. hippo.glia$cell.type2 <- gsub("Unknown_glia", "Neutrophils", hippo.glia$cell.type2)
  256. hippo.glia$cell.type2 <- factor(hippo.glia$cell.type2, levels = c("Astrocytes",
  257. "Bcas1_oligodendrocytes",
  258. "C1ql1_oligodendrocytes",
  259. "Endothelial_cells",
  260. "Microglia",
  261. "Mobp_oligodendrocytes",
  262. "Neutrophils",
  263. "Pericytes1",
  264. "Pericytes2",
  265. "VLMCs"))
  266. ```
  269. # Save
  270. ```{r}
  271. [email hidden] <- [email hidden][, c("nCount_RNA", "nFeature_RNA",
  272. "nCount_SCT", "nFeature_SCT",
  273. "percent.mt", "pool", "hash.ID",
  274. "treatment", "drug", "time", "run",
  275. "SCT_snn_res.0.1", "SCT_snn_res.1",
  276. "duan.predictions", "duan.score",
  277. "cell.type", "cell.type2")]
  278. # saveRDS(hippo.glia,
  279. # file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs/hippo_glia_Seurat_obj_labels.rds")
  280. ```
  283. # Preprocess Smart-seq data Yao et al. (Cell, 2021)
  284. ```{r}
  285. # Load Seurat object
  286. load("/projects/mludwig/hippo_GLP1/data/2021_Yao/Seurat.ss.rda")
  288. # Subset yao atlas to hippocampal neurons
  289. yao <- subset(ss.seurat, region_label == "HIP" & class_label != "Non-Neuronal")
  291. # Normalize
  292. yao <- SCTransform(yao, verbose = F, method = "qpoisson")
  294. # Run dimensionality reduction
  295. yao <- RunPCA(yao, verbose = F)
  296. ElbowPlot(yao, ndims = 50)
  297. yao <- RunUMAP(yao, dims = 1:30, n.neighbors = 50)
  299. # Run clustering
  300. yao <- FindNeighbors(yao, dims = 1:30, k.param = 50)
  301. yao <- FindClusters(yao, resolution = 0.1, verbose = F)
  303. DimPlot(yao, group.by = "class_label")
  304. DimPlot(yao, group.by = "subclass_label", label = T)
  305. DimPlot(yao, group.by = "region_label", label = T)
  306. ```
  308. # Transfer labels from Yao data
  309. ```{r}
  310. anchors <- FindTransferAnchors(reference = yao, query = hippo.neurons,
  311. dims = 1:30, normalization.method = "SCT")
  312. predictions <- TransferData(anchorset = anchors, refdata = yao$subclass_label,
  313. dims = 1:30)
  315. hippo.neurons$yao.predictions <- predictions$predicted.id
  316. hippo.neurons$yao.score <- predictions$predicted.id
  318. DimPlot(hippo.neurons, group.by = "yao.predictions", label = T)
  319. DimPlot(hippo.neurons, group.by = "cell.type", label = T)
  320. DimPlot(hippo.neurons, group.by = "SCT_snn_res.1", label = T)
  321. ```
  323. # Label cell type identities
  324. ```{r}
  325. hippo.neurons$cell.type <- paste0("Neurons", hippo.neurons$SCT_snn_res.0.1)
  326. hippo.neurons$cell.type <- factor(hippo.neurons$cell.type,
  327. levels = paste0("Neurons", sort(unique(hippo.neurons$SCT_snn_res.0.1))))
  330. hippo.neurons$cell.type2 <- ""
  331. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 17)] <- "Vip"
  332. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 15)] <- "Sncg"
  333. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 %in% c(14, 22))] <- "Lamp5"
  334. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 12)] <- "Sst"
  335. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 16)] <- "Pvalb"
  336. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 %in% c(0, 1, 2, 4, 5,
  337. 9, 13, 21, 24))] <- "DG"
  338. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 %in% c(3, 6, 8))] <- "CA1"
  339. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 %in% c(7, 11, 18, 23))] <- "CA3"
  340. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 19)] <- "SUB"
  341. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 20)] <- "ProS"
  342. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.1 == 10)] <- "CA2/CA3"
  344. DimPlot(hippo.neurons, group.by = "cell.type2", label = T)
  345. ```
  348. # Compute silhouette scores to identify optimal clustering resolution
  349. ```{r}
  350. resolution <- seq(0.1, 1, 0.1)
  352. pc <- hippo.neurons@reductions$[email hidden][, 1:30]
  353. distance <- parDist(pc, method = "euclidean")
  355. cluster.id <- as.character([email hidden][, "cell.type2"])
  356. silhouette.cell.type <- compute.sil(x = cluster.id, dist = distance)
  359. stability <- data.frame(matrix(NA, nrow = ncol(hippo.neurons), ncol = length(resolution)))
  360. colnames(stability) <- resolution
  361. rownames(stability) <- colnames(hippo.neurons)
  363. for(i in 1:length(resolution)) {
  364. print(i)
  366. hippo.neurons <- FindClusters(object = hippo.neurons, resolution = resolution[i],
  367. verbose = F)
  368. cluster.id <- as.numeric(as.character([email hidden][, paste0("SCT_snn_res.", resolution[i])]))
  370. silhouette <- compute.sil(x = cluster.id, dist = distance)
  372. print(mean(silhouette))
  374. stability[,i] <- silhouette
  376. }
  378. stability <- cbind(cell.type = silhouette.cell.type, stability)
  380. # saveRDS(stability, file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs/silhouette_neurons.rds")
  381. stability <- readRDS("/projects/mludwig/hippo_GLP1/output/Seurat_objs/silhouette_neurons.rds")
  383. stability$cell <- rownames(stability)
  384. stability.melt <- reshape2::melt(stability, id.vars = "cell")
  385. stability.melt$variable <- gsub("cell.type", "Yao labels", stability.melt$variable)
  387. ggplot(stability.melt) +
  388. geom_boxplot(aes(x = variable, y = value), outlier.shape = NA, fill = "darkcyan") +
  389. theme_pubr() +
  390. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  391. xlab("Resolution") + ylab("Silhouette score") +
  392. scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.25))
  394. ```
  396. # Update cell type labels at optimal clustering resolution
  397. ```{r}
  398. cellex <- read.csv(gzfile("/projects/mludwig/hippo_GLP1/output/cellex/output/hippo_neurons_mm.esmu.csv.gz"), row.names = 1)
  400. neuron.markers <- data.frame(matrix(NA, nrow = length(levels(hippo.neurons$cell.type)), ncol = 4))
  401. colnames(neuron.markers) <- c("cell.type", "marker1", "marker2", "marker3")
  402. neuron.markers$cell.type <- levels(hippo.neurons$cell.type)
  404. # Plot marker gene expression
  405. for (i in 1:nrow(neuron.markers)) {
  407. neuron.cell.type <- neuron.markers$cell.type[i]
  410. idx <- which(hippo.neurons$cell.type == neuron.cell.type)
  412. genes.ordered <- rownames(cellex)[order(cellex[, neuron.cell.type], decreasing = T)]
  413. genes.ordered <- genes.ordered[!(grepl("^Gm[0-9]|^ENSMUS|[0-9]Rik$", genes.ordered))]
  415. z <- 2
  416. for (j in genes.ordered) {
  418. nonzero.counts <- which(hippo.neurons@assays$RNA@counts[j, idx] != 0)
  419. nonzero.pct <- length(nonzero.counts) / length(idx) * 100
  421. if (nonzero.pct >= 25) {
  422. neuron.markers[i, z] <- j
  423. z <- z + 1
  424. }
  425. if (z > 4) {
  426. break
  427. }
  428. }
  429. }
  431. neuron.markers
  433. hippo.neurons$cell.type2 <- ""
  434. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 0)] <- "Glis3_DG_neurons"
  435. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 1)] <- "Satb2_CA1_neurons"
  436. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 2)] <- "Cd109_CA3_neurons"
  437. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 3)] <- "Crhbp_MGE_neurons"
  438. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 4)] <- "Lhx1_CA2/CA3_neurons"
  439. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 5)] <- "Htr3a_CGE_neurons"
  440. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 6)] <- "Igfbpl1_DG_neurons"
  441. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 7)] <- "Sfta3-ps_CGE_neurons"
  442. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 8)] <- "Csf2rb2_CA3_neurons"
  443. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 9)] <- "Abca12_Subiculum_neurons"
  444. hippo.neurons$cell.type2[which(hippo.neurons$SCT_snn_res.0.1 == 10)] <- "Nts_Prosubiculum_neurons"
  445. ```
  447. # Save
  448. ```{r}
  449. # saveRDS(hippo.neurons,
  450. # file = "/projects/mludwig/hippo_GLP1/output/Seurat_objs/hippo_neurons_Seurat_obj_labels.rds")
  451. ```

integration_with_published_atlases.Rmd at commit 4901ebb, no license · at the source

Overview

Authors: Dylan M Belmont-Rausch1,2,3, Mette Q Ludwig1,3, Marie A Bentsen4, Stine N Hansen3, Anna Secher3, Dorte Holst3, Jaime Moreno3, Vivek Das4, Kristoffer L Egerod1, Anne-Mette Bjerregaard4, Kristoffer Niss3, Sarah Bau3, Charles Pyke3, Kevin Dalgaard3, Myrte Merkestein3, Franziska Wichern5, Charlotte Thim Hansen4, Joseph Polex-Wolf4, Lotte Bjerre Knudsen3, Tune H Pers1,2
ORCID iDs: Dylan M Belmont-Rausch, Mette Q Ludwig, Anna Secher, Jaime Moreno, Vivek Das, Kristoffer L Egerod, Anne-Mette Bjerregaard, Sarah Bau, Lotte Bjerre Knudsen, Tune H Pers
  1. Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
  2. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA USA
  3. Research and Development, Novo Nordisk A/S, Måløv, Denmark
  4. Research and Development, Novo Nordisk A/S, Søborg, Denmark
  5. Gubra A/S, Hørsholm, Denmark
Institutions: Broad Institute (United States); University of Copenhagen (Denmark); Novo Nordisk (Denmark) (Denmark); Novo Nordisk Foundation Center for Basic Metabolic Research; Gubra (Denmark) (Denmark)
Journal: Nature communications, volume 17, issue 1, article 7328
Dates: received 25 April 2025; accepted 27 May 2026; published online 9 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74038-4 · PMID 42265098 · PMCID PMC13402617 · OpenAlex W7164043252
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Spectral & time-frequency
Keywords: Neurodegeneration, Alzheimer's disease, Diseases of the nervous system
MeSH: Glucagon-Like Peptides*, Neuroinflammatory Diseases*, Animals, Brain, Cytokines, Disease Models, Animal, Endothelial Cells, Glucagon-Like Peptide-1 Receptor, Glucagon-Like Peptide-1 Receptor Agonists, Humans, Lipopolysaccharides, Male, Mice, Mice, Inbred C57BL, Microglia, Neurons, Neutrophils, Semaglutide, Signal Transduction (* major topic)
Topic: Diabetes Treatment and Management (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: Det Frie Forskningsråd (8045-00091B); Novo Nordisk Fonden (NNF18CC0034900); Lundbeckfonden (Lundbeck Foundation) (R190-2014-3904); NIDDK NIH HHS (R01 DK124238); Foundation for the National Institutes of Health (R01 DK124238)
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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perslab/Rausch-Ludwig-Bentsen-2026

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Commit: 4901ebba0474462ba663b6b1f81635f99886ffb3, 8 May 2026
Languages: R (37)
Size: 48 files, 37 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation, 36 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: Seurat (28 files), ggplot2 (23 files), ggpubr (21 files), tidyverse (14 files), cowplot (13 files), reshape2 (8 files), emmeans (6 files), patchwork (5 files), lme4 (4 files), SingleCellExperiment (4 files), DESeq2 (3 files), WGCNA (2 files), anndata (1 file), data.table (1 file), mgcv (1 file), NumPy (1 file), pandas (1 file), Scanpy (1 file)
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At the source: github.com/perslab/Rausch-Ludwig-Bentsen-2026

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Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 3 keywords, 19 MeSH terms, 5 funders, 76 references.

Cite

This paper

Belmont-Rausch, D. M., Ludwig, M. Q., Bentsen, M. A., Hansen, S. N., Secher, A., Holst, D., Moreno, J., Das, V., Egerod, K. L., Bjerregaard, A.-M., Niss, K., Bau, S., Pyke, C., Dalgaard, K., Merkestein, M., Wichern, F., Hansen, C. T., Polex-Wolf, J., Knudsen, L. B., & Pers, T. H. (2026). Semaglutide attenuates neuroinflammation in male mice. Nature communications, 17(1), 7328. https://doi.org/10.1038/s41467-026-74038-4

BibTeX

@article{belmontrausch2026semaglutide,
author = {Belmont-Rausch, Dylan M and Ludwig, Mette Q and Bentsen, Marie A and Hansen, Stine N and Secher, Anna and Holst, Dorte and Moreno, Jaime and Das, Vivek and Egerod, Kristoffer L and Bjerregaard, Anne-Mette and Niss, Kristoffer and Bau, Sarah and Pyke, Charles and Dalgaard, Kevin and Merkestein, Myrte and Wichern, Franziska and Hansen, Charlotte Thim and Polex-Wolf, Joseph and Knudsen, Lotte Bjerre and Pers, Tune H},
title = {{Semaglutide attenuates neuroinflammation in male mice}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7328},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74038-4},
url = {https://doi.org/10.1038/s41467-026-74038-4},
pmid = {42265098},
pmcid = {PMC13402617}
}

RIS

TY - JOUR
AU - Belmont-Rausch, Dylan M
AU - Ludwig, Mette Q
AU - Bentsen, Marie A
AU - Hansen, Stine N
AU - Secher, Anna
AU - Holst, Dorte
AU - Moreno, Jaime
AU - Das, Vivek
AU - Egerod, Kristoffer L
AU - Bjerregaard, Anne-Mette
AU - Niss, Kristoffer
AU - Bau, Sarah
AU - Pyke, Charles
AU - Dalgaard, Kevin
AU - Merkestein, Myrte
AU - Wichern, Franziska
AU - Hansen, Charlotte Thim
AU - Polex-Wolf, Joseph
AU - Knudsen, Lotte Bjerre
AU - Pers, Tune H
TI - Semaglutide attenuates neuroinflammation in male mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/09
VL - 17
IS - 1
SP - 7328
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74038-4
UR - https://doi.org/10.1038/s41467-026-74038-4
LA - en
ER -

CSL-JSON

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