OSCR

Cellular signatures of melanocortin pathway genes across the locus coeruleus.

Code ↔ Paper

27 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 27 matches
  1. [1] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ figures_main.Rmd, lines 338–409 · score 0.93 · MC3R, MC2R, MC5R, MC1R, melanocortin pathway, MC4R
  2. [2] § Materials and methods › Neuronal subtype analysis in the integrated snRNA-seq meta-dataset ↔ 02_meta_integration.Rmd, lines 227–277 · score 0.92 · avg_log2FC, discriminative genes, FindAllMarkers, ligand genes, low background, curated gene
  3. [3] § Materials and methods › Integration with published Weber LC Visium data and spatial cluster annotation ↔ figures_main.Rmd, lines 412–465 · score 0.90 · SCN4B, GABAergic, serotonergic neurons, glutamatergic neurons, CRH neurons, spatial cell
  4. [4] § Results › Visium spatial transcriptomics of the human LC ↔ figures_supplementary.Rmd, lines 337–383 · score 0.90 · SCN4B, GABAergic, PENK neurons, VGF neurons, serotonergic neurons, glutamatergic neurons
  5. [5] § Results › Visium spatial transcriptomics of the human LC ↔ figures_main.Rmd, lines 412–465 · score 0.90 · SCN4B, GABAergic, PENK neurons, VGF neurons, serotonergic neurons, glutamatergic neurons
  6. [6] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ figures_supplementary.Rmd, lines 227–284 · score 0.88 · MC3R, MC2R, MC5R, MC1R, MC4R, neuron subtypes
  7. [7] § Materials and methods › snRNA-seq analysis of human LC ↔ 01_basak_snRNAseq.Rmd, lines 116–153 · score 0.86 · kernel density, QC batches, Gaussian mixture, batch aware, nFeature_RNA, nCount_RNA
  8. [8] § Materials and methods › snRNA-seq analysis of human LC ↔ figures_main.Rmd, lines 168–221 · score 0.85 · C1QB, pan neuronal, SLC6A2, AIF1, GFAP, PDGFRA
  9. [9] § Results › Visium spatial transcriptomics of the human LC ↔ figures_main.Rmd, lines 338–409 · score 0.85 · MC3R, MC2R, MC5R, MC1R, MC4R, AD associated
  10. [10] § Materials and methods › Integration with published Weber LC Visium data and spatial cluster annotation ↔ figures_supplementary.Rmd, lines 385–437 · score 0.84 · SCN4B, DCN, HBA2, SLC6A2, CCDC153, MAG
  11. [11] § Materials and methods › snRNA-seq analysis of human LC ↔ 01_basak_snRNAseq.Rmd, lines 23–62 · score 0.82 · filtered feature bc, LC001A, LC002A, percent.mt, Seurat, matrices
  12. [12] § Results › Visium spatial transcriptomics of the human LC ↔ figures_supplementary.Rmd, lines 227–284 · score 0.82 · MC3R, MC2R, MC5R, MC1R, MC4R, SLC6A2
  13. [13] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ figures_main.Rmd, lines 270–335 · score 0.82 · HTR2C, BDNF, CARTPT, CHRNA3, DLK1, GPX3
  14. [14] § Materials and methods › Integration with published Weber LC Visium data and spatial cluster annotation ↔ 04_visium_integrated.Rmd, lines 95–126 · score 0.77 · SCTransform, FindNeighbors, k.param, PCA, resolutions, layers
  15. [15] § Materials and methods › snRNA-seq analysis of human LC ↔ 02_meta_integration.Rmd, lines 134–191 · score 0.76 · C1QB, pan neuronal, Broad cell, EGFR, GJA1, MBP
  16. [16] § Materials and methods › snRNA-seq analysis of human LC ↔ 01_basak_snRNAseq.Rmd, lines 65–85 · score 0.72 · FindClusters, FindNeighbors, k.param, PCA, variable, resolutions
  17. [17] § Materials and methods › snRNA-seq analysis of human LC ↔ 03_basak_visium.Rmd, lines 20–47 · score 0.69 · filtered feature bc, percent.mt, nFeature_RNA, nCount_RNA, matrices, UMI
  18. [18] § Materials and methods › snRNA-seq analysis of human LC ↔ R/qc_plots_by_sample.R, lines 77–118 · score 0.67 · Gaussian mixture, nFeature_RNA, nCount_RNA, log2, thresholds, QC
  19. [19] § Materials and methods › Visium spatial transcriptomics analysis of human LC ↔ 03_basak_visium.Rmd, lines 20–47 · score 0.66 · Load10X_Spatial, filtered feature, QC metrics, periphery, nCount_RNA, cropping
  20. [20] § Materials and methods › Neuronal subtype analysis in the integrated snRNA-seq meta-dataset ↔ 02_meta_integration.Rmd, lines 98–132 · score 0.66 · FindNeighbors, k.param, PCA, variable, resolutions, layers
  21. [21] § Results › Visium spatial transcriptomics of the human LC ↔ 03_basak_visium.Rmd, lines 115–121 · score 0.66 · Upper Caudal, Lower Caudal, Upper Rostral, Lower Rostral, cropping, Visium
  22. [22] § Materials and methods › snRNA-seq meta-analysis of human LC datasets ↔ 02_meta_integration.Rmd, lines 77–95 · score 0.62 · pontine reticular formation, PB, PN, pons, parabrachial, Siletti
  23. [23] § Materials and methods › snRNA-seq meta-analysis of human LC datasets ↔ figures_main.Rmd, lines 168–221 · score 0.61 · pan neuronal, SLC6A2, STMN2, SYT1, ependymal, vascular
  24. [24] § Materials and methods › Integration with published Weber LC Visium data and spatial cluster annotation ↔ 02_meta_integration.Rmd, lines 98–132 · score 0.61 · FindNeighbors, k.param, PCA, biological, resolutions, layers
  25. [25] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ 02_meta_integration.Rmd, lines 227–277 · score 0.58 · discriminative gene pair, transcriptionally distinct, ligands, GPCRs, curated, cluster
  26. [26] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ 02_meta_integration.Rmd, lines 134–191 · score 0.55 · QC metrics, Siletti, NE neurons, ependymal, vascular, OPCs
  27. [27] § Results › Single-nucleus transcriptomics and meta-analysis of the human LC ↔ 02_meta_integration.Rmd, lines 279–333 · score 0.51 · neuron subtypes, SLC6A2, MET, Siletti, Weber, Basak

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The authors' code

R Markdown · 487 lines · 16 KB · MIT · 8 matches

  1. ---
  2. title: "02 — snRNA-seq Meta-integration: Three-dataset RPCA, Neuronal Subtypes, and DE"
  3. ---
  5. This script integrates the Basak snRNA-seq data with the published Weber et al.
  6. (2024) and Siletti et al. (2023) LC/pons datasets, annotates the integrated
  7. object, performs neuron-level subtype analysis, and runs differential expression
  8. between the two dominant DBH+ NE neuron subtypes.
  10. Inputs:
  11. - `output/01_basak/basak_snRNA_seurat.rds`
  12. - `data/external/Weber/weber_snRNA_seurat.rds`
  13. - `data/external/Siletti/Pons_PnRF_PB_seurat.rds`
  15. Outputs (Supplementary Table 2 documents nuclei counts per dataset):
  16. - `output/02_meta/meta_snRNA_allcells_seurat.rds` (46,417 nuclei)
  17. - `output/02_meta/meta_snRNA_neurons_seurat.rds` (33,375 neuronal nuclei)
  18. - `output/02_meta/DE/markers_MAST.csv`
  20. ```{r setup}
  21. library(Seurat)
  22. library(SeuratWrappers)
  23. library(dplyr)
  24. library(ggplot2)
  25. library(ggrepel)
  27. dir.create("output/02_meta/DE", recursive = TRUE, showWarnings = FALSE)
  29. options(future.globals.maxSize = 40 * 1024^3) # 40 GB for large integrations
  30. ```
  33. # 1. Prepare datasets for integration
  35. ## 1.1 Basak
  37. ```{r basak-prep}
  38. basak <- readRDS("output/01_basak/basak_snRNA_seurat.rds") # 2,071 nuclei
  40. basak <- RenameCells(basak, add.cell.id = "basak")
  42. basak$Dataset <- "Basak"
  43. basak$Sample <- basak$Section
  44. basak$Donor <- "Donor_1"
  45. basak$orig.ident <- basak$Section
  47. [email hidden] <- [email hidden] %>%
  48. dplyr::select(Dataset, orig.ident, Donor, Sample,
  49. nCount_RNA, nFeature_RNA, percent.mt,
  50. SectionLevel, CellType, CellType_AllCells)
  51. ```
  53. ## 1.2 Weber et al. 2024
  55. The Weber dataset is available from the original publication.
  56. See `data/README.md` for the expected file path.
  58. ```{r weber-prep}
  59. weber <- readRDS("data/external/Weber/weber_snRNA_seurat.rds") # 20,191 nuclei
  61. weber <- DietSeurat(weber, assay = "RNA")
  62. weber@assays$RNA$scale.data <- NULL
  63. weber@assays$RNA$data <- NULL
  65. weber$Dataset <- "Weber"
  66. weber$Donor <- weber$orig.ident
  67. weber$nCount_RNA <- weber$nCount_originalexp
  68. weber$nFeature_RNA <- weber$nFeature_originalexp
  69. weber[["percent.mt"]] <- PercentageFeatureSet(weber, pattern = "^MT-")
  71. [email hidden] <- [email hidden] %>%
  72. dplyr::select(Dataset, orig.ident, Donor, Sample,
  73. nCount_RNA, nFeature_RNA, percent.mt,
  74. label_merged, supervised_NE, unsupervised_NE)
  75. ```
  77. ## 1.3 Siletti et al. 2023
  79. Only the parabrachial / pontine reticular formation section (PnRF_PB) containing
  80. DBH+ NE neurons is used. See `data/README.md` for the expected file path.
  82. ```{r siletti-prep}
  83. siletti <- readRDS("data/external/Siletti/Pons_PnRF_PB_seurat.rds") # 24,155 nuclei
  85. siletti$Dataset <- "Siletti"
  86. siletti$Sample <- as.character(siletti$sample_id)
  87. siletti$Donor <- as.character(siletti$donor_id)
  88. siletti[["percent.mt"]] <- PercentageFeatureSet(siletti, pattern = "^MT-")
  90. [email hidden] <- [email hidden] %>%
  91. dplyr::select(Dataset, orig.ident, Donor, Sample,
  92. nCount_RNA, nFeature_RNA, percent.mt,
  93. supercluster_term, cluster_id, subcluster_id,
  94. dissection, tissue, sex, development_stage)
  95. ```
  98. # 2. Three-dataset RPCA integration (all cell types)
  100. Four integration methods were evaluated (RPCA, CCA, Harmony, FastMNN);
  101. RPCA produced the best mixing of datasets while preserving biological structure
  102. and was used for all analyses.
  104. ```{r integrate-allcells}
  105. seurat_meta <- merge(basak, y = c(weber, siletti)) # 46,417 nuclei
  106. rm(basak, weber, siletti)
  108. seurat_meta[["RNA"]] <- JoinLayers(seurat_meta[["RNA"]])
  110. seurat_meta <- DietSeurat(seurat_meta, assays = "RNA")
  111. seurat_meta@assays$RNA$scale.data <- NULL
  113. seurat_meta[["RNA"]] <- split(seurat_meta[["RNA"]],
  114. f = seurat_meta$Sample)
  116. seurat_meta <- NormalizeData(seurat_meta, verbose = FALSE)
  117. seurat_meta <- FindVariableFeatures(seurat_meta, verbose = FALSE)
  118. seurat_meta <- ScaleData(seurat_meta, verbose = FALSE)
  119. seurat_meta <- RunPCA(seurat_meta, verbose = FALSE)
  120. seurat_meta <- IntegrateLayers(seurat_meta, method = RPCAIntegration,
  121. new.reduction = "integrated.rpca",
  122. verbose = FALSE, k.weight = 100)
  123. seurat_meta <- FindNeighbors(seurat_meta, reduction = "integrated.rpca",
  124. dims = 1:30, k.param = 20, verbose = FALSE)
  125. seurat_meta <- FindClusters(seurat_meta,
  126. resolution = seq(0.1, 2.0, by = 0.1),
  127. verbose = FALSE)
  128. seurat_meta <- RunUMAP(seurat_meta, reduction = "integrated.rpca",
  129. dims = 1:30, reduction.name = "umap.rpca",
  130. verbose = FALSE)
  131. seurat_meta[["RNA"]] <- JoinLayers(seurat_meta[["RNA"]])
  132. ```
  134. ## 2.1 Annotate broad cell types
  136. Annotation at res 0.6 based on canonical marker expression.
  137. NE neurons: DBH, TH, SLC6A2; pan-neuronal: STMN2, SYT1;
  138. oligodendrocytes: MAG, MBP, MOG, PLP1; OPCs: PDGFRA, EGFR;
  139. astrocytes: GFAP, GJA1, AQP4; ependymal: FOXJ1, CCDC153, CAPS;
  140. microglia: C1QB, AIF1; vascular: ACTA2, TAGLN, COL1A1.
  142. ```{r annotate-allcells}
  143. Idents(seurat_meta) <- seurat_meta$RNA_snn_res.0.6
  145. seurat_meta <- RenameIdents(seurat_meta,
  146. "21" = "Neurons_NE",
  147. "24" = "Neurons",
  148. "0" = "Neurons", "1" = "Neurons", "2" = "Neurons",
  149. "4" = "Neurons", "7" = "Neurons", "8" = "Neurons",
  150. "10" = "Neurons", "11" = "Neurons", "12" = "Neurons",
  151. "14" = "Neurons", "15" = "Neurons", "16" = "Neurons",
  152. "18" = "Neurons", "19" = "Neurons", "20" = "Neurons",
  153. "23" = "Neurons", "26" = "Neurons", "27" = "Neurons",
  154. "28" = "Neurons", "29" = "Neurons",
  155. "3" = "Oligo", "5" = "Oligo", "9" = "Oligo",
  156. "13" = "OPCs",
  157. "6" = "Astro",
  158. "17" = "Microglia",
  159. "25" = "Ependymo",
  160. "22" = "Vascular"
  161. )
  163. seurat_meta$CellType_Integrated <- Idents(seurat_meta)
  165. table(seurat_meta$CellType_Integrated, seurat_meta$Dataset, useNA = "ifany")
  166. # Expected (Supplementary Table 2):
  167. # Neurons_NE : Basak=346, Weber=306, Siletti=113
  168. # Neurons : Basak=1012, Weber=16201, Siletti=15397
  170. seurat_meta@assays$RNA$scale.data.1 <- NULL
  171. seurat_meta@assays$RNA$scale.data <- NULL
  173. [email hidden] <- [email hidden] %>%
  174. dplyr::select(
  175. # Core identity
  176. Dataset, orig.ident, Donor, Sample,
  177. # Final annotation
  178. RNA_snn_res.0.6, CellType_Integrated,
  179. # QC metrics
  180. nCount_RNA, nFeature_RNA, percent.mt,
  181. # Basak-specific
  182. SectionLevel,
  183. # Weber-specific (useful for cross-dataset comparison)
  184. label_merged, supervised_NE, unsupervised_NE,
  185. # Siletti-specific
  186. supercluster_term, dissection
  187. )
  189. saveRDS(seurat_meta, file = "output/02_meta/meta_snRNA_allcells_seurat.rds")
  190. rm(seurat_meta)
  191. ```
  194. # 3. Neuron-only integration and subtype annotation
  196. ```{r neurons-subset}
  197. seurat_meta <- readRDS("output/02_meta/meta_snRNA_allcells_seurat.rds")
  199. neurons <- subset(seurat_meta,
  200. idents = c("Neurons_NE", "Neurons")) # 33,375 nuclei
  201. rm(seurat_meta)
  203. neurons <- DietSeurat(neurons, assays = "RNA")
  204. ```
  206. ```{r neurons-integrate}
  207. neurons[["RNA"]] <- split(neurons[["RNA"]], f = neurons$Sample)
  209. neurons <- NormalizeData(neurons, verbose = FALSE)
  210. neurons <- FindVariableFeatures(neurons, verbose = FALSE)
  211. neurons <- ScaleData(neurons, verbose = FALSE)
  212. neurons <- RunPCA(neurons, verbose = FALSE)
  213. neurons <- IntegrateLayers(neurons, method = RPCAIntegration,
  214. new.reduction = "integrated.rpca",
  215. verbose = FALSE, k.weight = 60)
  216. neurons <- FindNeighbors(neurons, reduction = "integrated.rpca",
  217. dims = 1:30, k.param = 10, verbose = FALSE)
  218. neurons <- FindClusters(neurons,
  219. resolution = seq(0.1, 1.5, by = 0.1),
  220. verbose = FALSE)
  221. neurons <- RunUMAP(neurons, reduction = "integrated.rpca",
  222. dims = 1:30, reduction.name = "umap.rpca",
  223. verbose = FALSE)
  224. neurons[["RNA"]] <- JoinLayers(neurons[["RNA"]])
  225. ```
  227. ## 3.1 Identify cluster markers using curated GPCR/ligand gene list
  229. Cluster labels are derived by selecting the top-2 discriminative genes from the
  230. IUPHAR curated list per cluster, concatenated into a gene-pair name.
  232. ```{r neurons-markers}
  233. Idents(neurons) <- neurons$RNA_snn_res.1.5
  234. length(levels(neurons)) # 68 transcriptionally distinct clusters
  236. # Load IUPHAR GPCR/ligand gene list
  237. gpcr_df <- read.csv("resources/IUPHAR/GtP_to_HGNC_mapping.csv", skip = 1)
  238. gene_vector <- unique(gpcr_df$HGNC.Symbol)
  240. # Cluster markers restricted to curated gene list
  241. marker_genes <- FindAllMarkers(
  242. object = neurons,
  243. features = intersect(gene_vector, rownames(neurons)),
  244. verbose = FALSE
  245. )
  246. write.csv(marker_genes,
  247. file = "output/02_meta/DEGs_neurons_res1.5_curated.csv")
  249. # Select top 2 discriminative genes per cluster (high FC, low background)
  250. markers_filtered <- marker_genes %>%
  251. dplyr::filter(avg_log2FC > 0.5 & pct.1 > 0.1 &
  252. pct.2 < 0.05 & p_val_adj < 0.05) %>%
  253. group_by(cluster) %>%
  254. slice_head(n = 2) %>%
  255. ungroup() %>%
  256. mutate(cluster = factor(cluster, levels = levels(neurons))) %>%
  257. arrange(cluster)
  259. # Relax criteria for clusters with no marker meeting strict filters
  260. missing_clusters <- setdiff(levels(neurons),
  261. unique(markers_filtered$cluster))
  263. if (length(missing_clusters) > 0) {
  264. missing_markers <- marker_genes %>%
  265. dplyr::filter(cluster %in% missing_clusters,
  266. avg_log2FC > 0.5 & pct.1 > 0.05 &
  267. pct.2 < 0.2 & p_val_adj < 0.05) %>%
  268. group_by(cluster) %>%
  269. slice_head(n = 2) %>%
  270. ungroup() %>%
  271. mutate(cluster = factor(cluster, levels = levels(neurons))) %>%
  272. arrange(cluster)
  273. }
  275. marker_df <- rbind(markers_filtered, missing_markers) %>%
  276. mutate(cluster = factor(cluster, levels = levels(neurons)))
  277. ```
  279. ## 3.2 Build cluster annotation labels
  281. ```{r neurons-annotate}
  282. cluster_annotations <- marker_df %>%
  283. group_by(cluster) %>%
  284. summarise(Annotation = paste(gene, collapse = "_"), .groups = "drop") %>%
  285. mutate(row_id = row_number()) %>%
  286. arrange(Annotation, row_id) %>%
  287. group_by(Annotation) %>%
  288. mutate(
  289. Annotation_unique = ifelse(
  290. row_number() == 1,
  291. Annotation,
  292. paste0(Annotation, "_", row_number())
  293. )
  294. ) %>%
  295. ungroup() %>%
  296. arrange(row_id) %>%
  297. select(-row_id)
  299. write.csv(cluster_annotations,
  300. file = "output/02_meta/cluster_annotations_neurons.csv")
  302. # Transfer annotations to metadata
  303. seurat_meta_df <- as.data.frame([email hidden]) %>%
  304. dplyr::select(RNA_snn_res.1.5)
  306. meta_df <- seurat_meta_df %>%
  307. tibble::rownames_to_column("row_id") %>%
  308. mutate(Cluster_Join = as.character(RNA_snn_res.1.5)) %>%
  309. left_join(cluster_annotations, by = c("Cluster_Join" = "cluster")) %>%
  310. rename(CellType = Annotation_unique) %>%
  311. select(-Cluster_Join) %>%
  312. tibble::column_to_rownames("row_id") %>%
  313. dplyr::select(CellType)
  315. neurons <- AddMetaData(neurons, metadata = meta_df,
  316. col.name = "Neuron_Subtype")
  318. # Prioritise DBH subtypes at the top of the level ordering
  319. dbh_clusters <- c("SLC6A2_DBH", "DBH_SLC6A2", "SLC6A2_MET")
  320. cluster_levels <- c(dbh_clusters,
  321. setdiff(sort(unique(neurons$Neuron_Subtype)),
  322. dbh_clusters))
  323. neurons$Neuron_Subtype <- factor(neurons$Neuron_Subtype,
  324. levels = cluster_levels)
  325. Idents(neurons) <- neurons$Neuron_Subtype
  327. table(neurons$Neuron_Subtype[neurons$Neuron_Subtype %in% dbh_clusters],
  328. neurons$Dataset[neurons$Neuron_Subtype %in% dbh_clusters],
  329. useNA = "ifany")
  330. # SLC6A2/DBH: Basak=256, Weber=285, Siletti=65
  331. # DBH/SLC6A2: Basak=112, Weber=177, Siletti=5
  332. # SLC6A2/MET: Basak=3, Weber=43, Siletti=43
  333. ```
  335. ```{r neurons-save}
  336. [email hidden] <- [email hidden] %>%
  337. dplyr::select(
  338. # Core identity
  339. Dataset, orig.ident, Donor, Sample,
  340. # Annotations
  341. Neuron_Subtype, CellType_Integrated,
  342. # QC metrics
  343. nCount_RNA, nFeature_RNA, percent.mt,
  344. # Basak-specific
  345. SectionLevel,
  346. # Weber-specific (useful for cross-dataset comparison)
  347. label_merged, supervised_NE, unsupervised_NE,
  348. # Siletti-specific
  349. supercluster_term, dissection
  350. )
  352. saveRDS(neurons, file = "output/02_meta/meta_snRNA_neurons_seurat.rds")
  353. rm(neurons)
  354. ```
  357. # 4. Differential expression: SLC6A2_DBH vs DBH_SLC6A2 (MAST)
  359. MAST single-cell DE comparing the two most abundant DBH+ NE neuron subtypes,
  360. adjusting for donor effect, library size, and mitochondrial read fraction.
  362. ## 4.1 Data preparation
  364. ```{r de-prep}
  365. library(MAST)
  367. neurons <- readRDS("output/02_meta/meta_snRNA_neurons_seurat.rds")
  368. Idents(neurons) <- neurons$Neuron_Subtype
  370. dbh_subset <- subset(neurons,
  371. idents = c("SLC6A2_DBH", "DBH_SLC6A2")) # 900 cells
  372. rm(neurons)
  374. # Remove donors with < 20 cells in either subtype (statistical stability)
  375. dbh_subset$Donor <- factor(dbh_subset$Donor)
  376. dbh_subset$Neuron_Subtype <- droplevels(dbh_subset$Neuron_Subtype)
  378. meta_df <- [email hidden] %>%
  379. tibble::rownames_to_column("cell_id") %>%
  380. group_by(Neuron_Subtype, Donor) %>%
  381. mutate(n_cells = n()) %>%
  382. ungroup()
  384. cells_to_keep <- meta_df %>%
  385. filter(n_cells >= 20) %>%
  386. pull(cell_id)
  388. dbh_filtered <- subset(dbh_subset, cells = cells_to_keep) # 890 cells
  389. rm(dbh_subset)
  391. table(dbh_filtered$Neuron_Subtype, dbh_filtered$Donor, useNA = "ifany")
  392. # SLC6A2_DBH: Br2701=157, Br8079=124, Donor_1=256, H18.30.002=41, H19.30.001=24
  393. # DBH_SLC6A2: Br2701=80, Br8079=96, Donor_1=112
  394. ```
  396. ## 4.2 MAST
  398. ```{r mast}
  399. Idents(dbh_filtered) <- dbh_filtered$Neuron_Subtype
  400. log2FC_limit <- 2
  402. markers_mast <- FindMarkers(
  403. object = dbh_filtered,
  404. ident.1 = "SLC6A2_DBH",
  405. ident.2 = "DBH_SLC6A2",
  406. test.use = "MAST",
  407. logfc.threshold = 0.1,
  408. min.pct = 0.05,
  409. only.pos = FALSE,
  410. latent.vars = c("nCount_RNA", "percent.mt", "Donor")
  411. )
  413. write.csv(markers_mast,
  414. file = "output/02_meta/DE/markers_MAST.csv")
  416. # Construct volcano plot dataframe (used in figures code)
  417. markers_filtered <- markers_mast %>%
  418. as.data.frame() %>%
  419. tibble::rownames_to_column("gene") %>%
  420. mutate(
  421. neg_log10_p = -log10(p_val_adj),
  422. sig = case_when(
  423. p_val_adj < 0.05 & avg_log2FC > log2FC_limit ~ "Up_in_SLC6A2_DBH",
  424. p_val_adj < 0.05 & avg_log2FC < -log2FC_limit ~ "Up_in_DBH_SLC6A2",
  425. TRUE ~ "Not_significant"
  426. )
  427. ) %>%
  428. dplyr::filter(sig %in% c("Up_in_SLC6A2_DBH", "Up_in_DBH_SLC6A2"))
  430. write.csv(markers_filtered,
  431. file = "output/02_meta/DE/markers_MAST_filtered.csv")
  433. table(markers_filtered$sig) # Up_in_DBH_SLC6A2 = 20, Up_in_SLC6A2_DBH = 5
  434. rm(dbh_filtered)
  435. ```
  437. > HTR2C, SNTG1 : Up_in_DBH_SLC6A2
  440. # 5. FindAllMarkers: DBH subtype markers (used in Fig 1f heatmap)
  442. Seurat FindAllMarkers identifies genes with specificity for each DBH subtype
  443. (including SLC6A2_MET). Genes from this analysis supplement the literature-
  444. curated gene set displayed in the heatmap of Fig 1f.
  446. ```{r findallmarkers}
  447. mito_ribo_genes <- readRDS("resources/gene_lists/mito_ribo_genes.rds")
  449. neurons <- readRDS("output/02_meta/meta_snRNA_neurons_seurat.rds")
  450. Idents(neurons) <- neurons$Neuron_Subtype
  452. dbh_subset <- subset(neurons,
  453. idents = c("SLC6A2_DBH", "DBH_SLC6A2", "SLC6A2_MET")) # 989 cells
  454. rm(neurons)
  456. # Remove uninformative gene classes
  457. gene_vector <- setdiff(rownames(dbh_subset), mito_ribo_genes)
  458. gene_vector <- setdiff(gene_vector,
  459. grep("^ENSG", gene_vector, value = TRUE))
  460. gene_vector <- setdiff(gene_vector,
  461. grep("^LINC", gene_vector, value = TRUE))
  463. all_markers <- FindAllMarkers(
  464. object = dbh_subset,
  465. features = gene_vector,
  466. logfc.threshold = 0.5,
  467. min.pct = 0.05,
  468. verbose = FALSE
  469. )
  470. write.csv(all_markers,
  471. file = "output/02_meta/DE/markers_FindAllMarkers_DBH.csv")
  473. # Filtered marker set used in Fig 1f
  474. markers_filtered <- all_markers %>%
  475. dplyr::filter(avg_log2FC > 0.5 & pct.1 > 0.2 &
  476. pct.2 < 0.2 & p_val_adj < 0.05) %>%
  477. mutate(cluster = factor(cluster,
  478. levels = c("SLC6A2_DBH", "DBH_SLC6A2", "SLC6A2_MET"))) %>%
  479. arrange(cluster)
  481. write.csv(markers_filtered,
  482. file = "output/02_meta/DE/markers_FindAllMarkers_DBH_filtered.csv")
  484. rm(dbh_subset, all_markers, markers_filtered)
  485. ```
  486. > CHRNA3 : Up in SLC6A2_DBH
  487. > LEPR, BDNF, TMC3, INVS : Up in SLC6A2_MET

02_meta_integration.Rmd at commit a0838e9, under MIT · at the source

Overview

Authors: Alisha Basak1,2, Fahrünisa Meryem Betül Erol1,2, Maria Caterina De Rosa1,2, Zhangji Dong2,3, Victor Ogbolu4, Hannah J. Glover1,2, Rick Rausch1,2, Gunnar Hargus5, Jordi Creus-Muncunill6, Heather Buchanan5, Yu Bai6, Qi Su6, Betty Chang6, Christina Adler6, Delaney Flaherty5,7, Benjamin Ciener5,7, Harrison Xiao5,7, Hasini Reddy5,7, Pascaline Aime-Wilson6, Christiane Reitz8
and 6 other authorsMark W. Sleeman6, Judith Y. Altarejos6, Rudolph L. Leibel1,2, Liang Oscar Qiang4, Andrew F. Teich5,7, Claudia A. Doege2,5
ORCID iDs: Fahrünisa Meryem Betül Erol, Claudia A. Doege
  1. Department of Pediatrics, Division of Molecular Genetics, Columbia University Irving Medical Center,New York, NY USA
  2. Naomi Berrie Diabetes Center, Columbia University Irving Medical Center,New York, NY USA
  3. Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-Innovation Center of Neuroregeneration, Nantong University,Nantong, 226001 Jiangsu China
  4. Department of Neurobiology and Anatomy, Drexel University College of Medicine,Philadelphia, PA USA
  5. Department of Pathology and Cell Biology, Columbia University Irving Medical Center,New York, NY USA
  6. Regeneron Genetics Center, Regeneron Pharmaceuticals Inc.,Tarrytown, NY 10591 USA
  7. Taub Institute, Columbia University Irving Medical Center,New York, NY USA
  8. Department of Neurology, Columbia University Irving Medical Center,New York, NY USA
Institutions: Columbia University Irving Medical Center (United States); Nantong University (China); Drexel University (United States); Regeneron (United States) (United States)
Journal: Acta neuropathologica communications, volume 14, issue 1, article 96
Dates: received 26 August 2025; accepted 23 March 2026; published online 4 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s40478-026-02287-x · PMID 41935248 · PMCID PMC13097939 · OpenAlex W7149404857
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Locus coeruleus, Melanocortin pathway, Alzheimer’s disease, Food intake, Obesity, Energy balance
MeSH: Locus Coeruleus*, Melanocortins*, Neurons*, Signal Transduction*, Adaptor Proteins, Signal Transducing, Alzheimer Disease, Animals, Female, Humans, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Receptor Activity-Modifying Proteins (* major topic)
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Please see manuscript
Citations: not cited yet (Europe PMC); 145 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 27 matches between paragraphs and lines of code.

fmbetul/LC-melanocortin-analysis

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a0838e9ac6d49db720a1daa218fae7a478e9bbab, 9 April 2026
Languages: R (9)
Size: 20 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 6 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (7 files), Seurat (7 files), tidyverse (7 files), patchwork (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
11 files
At the source: github.com/fmbetul/LC-melanocortin-analysis

Code availability statement

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Tracing map

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  • 27 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 26 authors, 6 keywords, 14 MeSH terms, 1 funder, 143 references.

Cite

This paper

Basak, A., Erol, F. M. B., De Rosa, M. C., Dong, Z., Ogbolu, V., Glover, H. J., Rausch, R., Hargus, G., Creus-Muncunill, J., Buchanan, H., Bai, Y., Su, Q., Chang, B., Adler, C., Flaherty, D., Ciener, B., Xiao, H., Reddy, H., Aime-Wilson, P., . . . Doege, C. A. (2026). Cellular signatures of melanocortin pathway genes across the locus coeruleus. Acta neuropathologica communications, 14(1), 96. https://doi.org/10.1186/s40478-026-02287-x

BibTeX

@article{basak2026cellular,
author = {Basak, Alisha and Erol, Fahrünisa Meryem Betül and De Rosa, Maria Caterina and Dong, Zhangji and Ogbolu, Victor and Glover, Hannah J. and Rausch, Rick and Hargus, Gunnar and Creus-Muncunill, Jordi and Buchanan, Heather and Bai, Yu and Su, Qi and Chang, Betty and Adler, Christina and Flaherty, Delaney and Ciener, Benjamin and Xiao, Harrison and Reddy, Hasini and Aime-Wilson, Pascaline and Reitz, Christiane and Sleeman, Mark W. and Altarejos, Judith Y. and Leibel, Rudolph L. and Qiang, Liang Oscar and Teich, Andrew F. and Doege, Claudia A.},
title = {{Cellular signatures of melanocortin pathway genes across the locus coeruleus}},
journal = {Acta neuropathologica communications},
year = {2026},
month = apr,
volume = {14},
number = {1},
pages = {96},
publisher = {BMC},
issn = {2051-5960},
doi = {10.1186/s40478-026-02287-x},
url = {https://doi.org/10.1186/s40478-026-02287-x},
pmid = {41935248},
pmcid = {PMC13097939}
}

RIS

TY - JOUR
AU - Basak, Alisha
AU - Erol, Fahrünisa Meryem Betül
AU - De Rosa, Maria Caterina
AU - Dong, Zhangji
AU - Ogbolu, Victor
AU - Glover, Hannah J.
AU - Rausch, Rick
AU - Hargus, Gunnar
AU - Creus-Muncunill, Jordi
AU - Buchanan, Heather
AU - Bai, Yu
AU - Su, Qi
AU - Chang, Betty
AU - Adler, Christina
AU - Flaherty, Delaney
AU - Ciener, Benjamin
AU - Xiao, Harrison
AU - Reddy, Hasini
AU - Aime-Wilson, Pascaline
AU - Reitz, Christiane
AU - Sleeman, Mark W.
AU - Altarejos, Judith Y.
AU - Leibel, Rudolph L.
AU - Qiang, Liang Oscar
AU - Teich, Andrew F.
AU - Doege, Claudia A.
TI - Cellular signatures of melanocortin pathway genes across the locus coeruleus
T2 - Acta neuropathologica communications
J2 - Acta Neuropathol Commun
PY - 2026
DA - 2026/04/04
VL - 14
IS - 1
SP - 96
SN - 2051-5960
PB - BMC
DO - 10.1186/s40478-026-02287-x
UR - https://doi.org/10.1186/s40478-026-02287-x
LA - en
ER -

CSL-JSON

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