OSCR

The perineurium integrates leptin with its sympathetic outflow to protect against obesity.

A correction to this paper has been published: the notice, 42581328, from Europe PMC.

Code ↔ Paper

15 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 15 matches · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Extended_data_Fig. 5a.R, the whole file · a weak match · score 0.84 · Adra2b, Adra1a, Adra1b, Adra2a, Adra2c, adrenergic receptors
  2. [2] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Extended_data_Fig. 10_umap.R, the whole file · a weak match · score 0.79 · Adra2b, Adra1a, Adra1b, Adra2a, Adra2c, adrenergic receptors
  3. [3] § Results › scRNA-seq of sympathetic ganglia reveals a cluster of endothelial cells highly expressing the leptin receptor ↔ scripts/Figure. 1b_UMAP.R, the whole file · a weak match · score 0.79 · superior cervical ganglia, scRNA, Schwann cells, stellate ganglia, UMAP, endothelial cells
  4. [4] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Extended_data_Fig. 10_umap.R, the whole file · a weak match · score 0.76 · Adra1a, Adra1b, Adra2a, Adra2c, adrenergic receptors, arcuate
  5. [5] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Extended_data_Fig.10_coexpression.R, the whole file · a weak match · score 0.74 · Adra1a, Adra1b, Adra2a, Adra2c, adrenergic receptors, Adrb3
  6. [6] § Results › scRNA-seq of sympathetic ganglia reveals a cluster of endothelial cells highly expressing the leptin receptor ↔ scripts/Figure. 1c_stacked_violin.R, lines 1–52 · score 0.72 · Cd11c, Schwann cells, Cd45, Cdh2, Mbp, Des
  7. [7] § Methods › Cell clustering and cell-type annotation ↔ scripts/Figure. 6a_b_f_seurat_analysis_hSG_hSCG_KZ.R, lines 46–89 · score 0.72 · FindVariableFeatures, ScaleData, barcodes, Seurat, resolution, UMAP
  8. [8] § Results › scRNA-seq of sympathetic ganglia reveals a cluster of endothelial cells highly expressing the leptin receptor ↔ scripts/Figure. 1b_UMAP.R, the whole file · a weak match · score 0.69 · mouse superior cervical, scRNA, sc nm, stellate ganglia, UMAP, cell myelinating
  9. [9] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Figure. 6a_b_f_seurat_analysis_hSG_hSCG_KZ.R, lines 91–131 · score 0.61 · double positive cells, immune cells, fibroblasts, muscle, nucleus, endothelial cell
  10. [10] § Results › SPCs highly co-express Lepr and Adrb2, unlike any other cell type ↔ scripts/Extended_data_Fig. 6_7.R, lines 1–44 · score 0.58 · Tabula Muris, Lepr Adrb2, droplet, organ, heart, lung
  11. [11] § Results › LEPR+ADRB2+ SPCs are present in humans, and common polymorphisms of LEPR and ADRB2 show male-specific synergistic interaction with obesity risk ↔ scripts/Figure. 6a_b_f_seurat_analysis_hSG_hSCG_KZ.R, lines 91–131 · score 0.57 · immune cell, ADRB2 expression, donors, female, Sex, nuclei
  12. [12] § Results › Obesity and high leptin drive apoptosis of SPCs, which is prevented by sympathomimetic ADRB2 agonism ↔ scripts/Figure. 5b_DEG_volcanoplot.R, the whole file · a weak match · score 0.56 · Tnfrsf1a, DEGs, expressed genes, apoptosis, obese
  13. [13] § Results › scRNA-seq of sympathetic ganglia reveals a cluster of endothelial cells highly expressing the leptin receptor ↔ scripts/Figure. 2b_2c_coexpression.R, lines 1–83 · score 0.54 · sc nm, cells co expressing, lean mice, cell myelinating, Correlogram, Box
  14. [14] § Results › scRNA-seq of sympathetic ganglia reveals a cluster of endothelial cells highly expressing the leptin receptor ↔ scripts/Extended_data_Fig. 1b_1c.R, the whole file · a weak match · score 0.53 · sample classification, sc nm, UMAP, cell myelinating, ECs, Endothelial cells
  15. [15] § Results › Obesity and high leptin drive apoptosis of SPCs, which is prevented by sympathomimetic ADRB2 agonism ↔ scripts/Extended_data_Fig.10_coexpression.R, the whole file · a weak match · score 0.52 · adrenergic receptors, expressed genes, ADRB3, ADRB1, cut, diet

Paper

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The authors' code

R · 200 lines · 9.4 KB · MIT · 3 matches

  1. #load required libraries
  2. library(Seurat)
  3. library(SeuratWrappers)
  4. library(Azimuth)
  5. library(ggplot2)
  6. library(patchwork)
  7. library(SCpubr)
  8. library(dplyr)
  9. library(stringr)
  10. library(ggnewscale)
  12. # 1. Load and preprocess superior cervical ganglion (SCG) data
  13. data.healthy <- '231208_snSeq_clean_up_hSCG_healthy_CBend3_threshold_150_set20-50k_filtered_seurat.h5'
  14. data.data_healthy <- Read10X_h5(filename = data.healthy, use.names = TRUE)
  15. SCG_health <- CreateSeuratObject(counts = data.data_healthy)
  16. SCG_health
  17. SCG_health$stim <- "Health"
  18. SCG_health$orig <- "SCG"
  19. SCG_health <- PercentageFeatureSet(object = SCG_health, pattern = "^MT-", col.name = "percent.mt")
  20. VlnPlot(object = SCG_health, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3,pt.size = 0.1)
  21. SCG_health <- subset(x= SCG_health, subset = nFeature_RNA > 500 & nFeature_RNA < 2000 & percent.mt < 1)
  22. SCG_health <- RunAzimuth(SCG_health, reference = "humancortexref")
  24. # 2. Load and preprocess stellate ganglion (SG) data
  25. data.healthy2 <- '231207_snSeq_clean_up_hSG_CoV_healthy_CBend3_exp20_ttl60_lr_510_5_threshould_60_filtered_seurat.h5'
  26. data.data_healthy2 <- Read10X_h5(filename = data.healthy2, use.names = TRUE)
  27. SG_health <- CreateSeuratObject(counts = data.data_healthy2)
  28. SG_health
  29. SG_health$stim <- "Health2"
  30. SG_health$orig <- "Stellate"
  31. SG_health <- PercentageFeatureSet(object = SG_health, pattern = "^MT-", col.name = "percent.mt")
  32. VlnPlot(object = SG_health, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3,pt.size = 0.1)
  33. SG_health <- subset(x= SG_health, subset = nFeature_RNA > 500 & nFeature_RNA < 2000 & percent.mt < 1)
  34. SG_health <- RunAzimuth(SG_health, reference = "humancortexref")
  36. # 3. Adjust and assign donor IDs
  37. donor_id <- read.csv(file = "donor_ids_SCG.tsv", sep = "\t", header = T)
  38. donor_id_pp <- read.csv(file = "donor_id_SG.tsv", sep = "\t", header = T)
  40. # Adjust donor IDs to ensure consistency across samples.
  41. # Both files originally contain donors labeled "0" and "1", but they refer to different individuals.
  42. # To avoid confusion, reassign donor IDs in the second sample: donor 0 → donor 2, and donor 1 → donor 3
  43. donor_id_pp <- donor_id_pp%>%mutate(donor_id = ifelse(donor_id == "donor0", "donor2",
  44. ifelse(donor_id == "donor1", "donor3", donor_id)))
  46. # Assign donor IDs to SCG Seurat object (SCG_health)
  47. donor_id <- data.frame(barcode = donor_id$cell, donor_id = donor_id$donor_id)
  48. meta <- [email hidden]
  49. meta$barcode <- rownames(meta)
  50. meta <- meta%>%left_join(donor_id, by = c("barcode"))
  51. rownames(meta) <- meta$barcode
  52. [email hidden] <- meta
  54. # Assign donor IDs to SG Seurat object (SG_health)
  55. donor_id <- data.frame(barcode = donor_id_pp$cell, donor_id = donor_id_pp$donor_id)
  56. meta <- [email hidden]
  57. meta$barcode <- rownames(meta)
  58. meta <- meta%>%left_join(donor_id, by = c("barcode"))
  59. rownames(meta) <- meta$barcode
  60. [email hidden] <- meta
  62. # 4. Merge and process combined object
  63. SGs.combined <- merge(SCG_health, y = SG_health, add.cell.ids = c("Health", "Health2"), project = "Ganglia")
  64. SGs.combined
  65. head(colnames(SGs.combined))
  66. table(SGs.combined$orig.ident)
  67. SGs.combined$Method <- "snSeq"
  68. SGs.combined <- NormalizeData(SGs.combined, verbose = FALSE)
  69. SGs.combined <- FindVariableFeatures(SGs.combined)
  70. SGs.combined <- ScaleData(SGs.combined)
  71. SGs.combined <- RunPCA(SGs.combined)
  72. ElbowPlot(SGs.combined)
  73. SGs.combined <- FindNeighbors(SGs.combined, dims = 1:20, reduction = "pca")
  74. SGs.combined <- FindClusters(SGs.combined, resolution = 0.1, cluster.name = "unintegrated_clusters")
  75. SGs.combined <- RunUMAP(SGs.combined, dims = 1:20, reduction = "pca", reduction.name = "umap.unintegrated")
  76. # visualize by batch and cell type annotation
  77. # cell type annotations were previously added by Azimuth
  78. DimPlot(SGs.combined, reduction = "umap.unintegrated", split.by = "orig.ident")
  79. DimPlot(SGs.combined, reduction = "umap.unintegrated", group.by = c("predicted.subclass", "orig.ident"))
  81. # 5. Perform integration and clustering
  82. DefaultAssay(SGs.combined) <- "RNA"
  83. SGs.combined <- IntegrateLayers(object = SGs.combined, method = HarmonyIntegration,
  84. orig.reduction = "pca", new.reduction = "harmony", verbose = FALSE)
  86. SGs.combined <- FindNeighbors(SGs.combined, reduction = "harmony", dims = 1:20)
  87. SGs.combined <- FindClusters(SGs.combined, resolution = 0.075, cluster.name = "harmony_clusters")
  88. SGs.combined <- RunUMAP(SGs.combined, reduction = "harmony", dims = 1:20, reduction.name = "umap.harmony")
  89. p1 <- DimPlot(SGs.combined, reduction = "umap.harmony", group.by = c("orig.ident", "harmony_clusters"), combine = FALSE)
  91. # 6. Visualize integration results
  92. DimPlot(SGs.combined, reduction = "umap.harmony", group.by = c("orig.ident", "harmony_clusters"))
  93. do_DimPlot(SGs.join, reduction = "umap.harmony", label = T)
  95. # 7. Join layers and verify metadata
  96. SGs.join <- JoinLayers(SGs.combined)
  97. do_DimPlot(SGs.join, reduction = "umap.harmony", split.by = "donor_id")
  99. # 8. Identify marker genes
  100. All_markers_default_values_SGs_join_Res_0_1 <- FindAllMarkers(SGs.join)
  101. write.csv(All_markers_default_values_SGs_join_Res_0_1, file="All_markers_default_values_SGs_join_Res_0_1.csv")
  103. # 9. Visualize gene expression
  104. do_NebulosaPlot(SGs.join, c("LEPR", "ADRB2"), joint = TRUE, reduction = "umap.harmony", sequential.palette = "OrRd")
  106. # 10. Assign sex based on donor IDs
  107. FeaturePlot(SGs.join, features=c("XIST", "UTY"), split.by = "donor_id", reduction = "umap.harmony")
  109. meta <- [email hidden]
  110. meta <- meta%>%mutate(sex=ifelse(donor_id=="donor1"|donor_id=="donor2", "male",
  111. ifelse(donor_id=="donor0"|donor_id== "donor3", "female","unknown")))
  112. [email hidden] <- meta
  113. do_DimPlot(SGs.join, reduction = "umap.harmony", split.by = "sex")
  115. # 11. Rename clusters
  116. SGs_rename <- RenameIdents(SGs.join, `0`= "Glia_cells", `1` ="Fibroblasts", `2` = "Endothelial_cells",
  117. `3` = "Smooth_muscle_cells", `4`= "Glia_cells_2", `5` = "Immune_cells",
  118. `6` = "Sympathetic_neurons")
  119. do_DimPlot(SGs_rename, reduction = "umap.harmony", label = T, label.box = T)
  121. # 12. Filter for nuclei with valid sex assignment
  122. SGs_rename_filtered <- subset(SGs_rename, subset = sex %in% c("male", "female"))
  124. ### Dotplot of ADRB2 * LEPR coexpression
  125. # 13. Fetch expression data for LEPR, ADRB2, and cell type metadata
  126. # - Retrieves gene expression data and cell type annotations from the Seurat object
  127. expr <- FetchData(SGs_rename_filtered, vars = c("LEPR", "ADRB2", "celltype")) %>%
  128. rename(cluster = celltype)
  130. # 14. Identify double-positive cells (double-positive if both LEPR and ADRB2 expression are greater than 0)
  131. expr <- expr %>% mutate(double_pos = LEPR > 0 & ADRB2 > 0)
  133. # 15. Calculate percentage of double-positive cells per cluster (% of cluster cells)
  134. percent_df <- expr %>%
  135. group_by(cluster) %>%
  136. summarise(pct_double = mean(double_pos) * 100)
  138. # 16. Calculate distribution of double-positive cells across clusters
  139. # - Determines the percentage of all double-positive cells belonging to each cluster
  140. double_pos_per_cluster <- expr %>%
  141. filter(double_pos) %>%
  142. group_by(cluster) %>%
  143. summarise(n_double_pos = n()) %>%
  144. mutate(pct_of_all_double = n_double_pos / sum(n_double_pos) * 100)
  146. # 17. Aggregate expression data
  147. # - Computes mean expression of LEPR and ADRB2 per cluster
  148. agg_expr <- expr %>%
  149. group_by(cluster) %>%
  150. summarise(
  151. LEPR = mean(LEPR),
  152. ADRB2 = mean(ADRB2)
  153. ) %>%
  154. # Join with percent_df and double_pos_per_cluster to include pct_double and pct_of_all_double
  155. left_join(percent_df, by = "cluster") %>%
  156. left_join(double_pos_per_cluster, by = "cluster")
  158. # 18. Compute co-expression score
  159. # - Creates a co-expression score as the product of mean LEPR and ADRB2 expression
  160. agg_expr$coexpr_LEPR_ADRB2 <- agg_expr$LEPR * agg_expr$ADRB2
  162. # 19. Reorder clusters for visualization
  163. # - Extracts base cluster names and sex suffixes
  164. # - Orders clusters: 'end' clusters first, then others alphabetically by base name and sex (male, then female)
  165. agg_expr <- agg_expr %>%
  166. mutate(
  167. base_cluster = str_replace(cluster, "_male|_female", ""),
  168. sex = if_else(str_detect(cluster, "_male$"), "male", "female"),
  169. is_end_group = str_detect(cluster, "end_male$|end_female$")
  170. ) %>%
  171. arrange(is_end_group, base_cluster, factor(sex, levels = c("male", "female")))
  173. # Convert cluster to factor to preserve order in plot
  174. agg_expr$cluster <- factor(agg_expr$cluster, levels = agg_expr$cluster)
  176. # 20. Visualize double-positive cell distribution
  177. # - Creates a scatter plot showing the percentage of all double-positive cells per cluster
  178. # - Points are sized by percentage and colored by co-expression score, with separate color scales for male and female
  179. ggplot() +
  180. # Male points
  181. geom_point(data = filter(agg_expr, sex == "male"),
  182. aes(x = cluster, y = pct_of_all_double, size = pct_of_all_double, color = coexpr_LEPR_ADRB2),
  183. alpha = 0.8) +
  184. scale_color_gradientn(colors = c("grey90", "lightblue", "blue2", "darkblue")) +
  185. new_scale_color() +
  186. # Female points
  187. geom_point(data = filter(agg_expr, sex == "female"),
  188. aes(x = cluster, y = pct_of_all_double, size = pct_of_all_double, color = coexpr_LEPR_ADRB2),
  189. alpha = 0.8) +
  190. scale_color_gradientn(colors = c("lightpink", "red4")) +
  191. scale_size(range = c(3, 10)) +
  192. labs(
  193. x = "Cluster",
  194. y = "% of All Double-Positive Cells",
  195. size = "% of All Double-Positive",
  196. color = "LEPR × ADRB2 Coexpression",
  197. title = "Double-Positive Cells as % of Total Across Clusters"
  198. ) +
  199. theme_minimal() +
  200. theme(axis.text.x = element_text(angle = 45, hjust = 1))

Figure. 6a_b_f_seurat_analysis_hSG_hSCG_KZ.R at commit 7d55375, under MIT · at the source

Overview

Authors: Gitalee Sarker1, Emma Haberman1, Anandhakumar Chandran2, Thomas Monfeuga2, Cyrielle Maroteau2, Sofia Lundh3, David S Oliveira1, Andrea Raimondi4, Karin Ziegler5,6, Tiago Gomes7, Tiago N Cordeiro7, Karishma Parekh1, Emilia Schmid1, Paola Fernández-Sanmartín8, Alina Griebel5, Noelia Martinez-Sanchez9, Bernardo A Arús10,11,12,13, Samson W Cheung1, Yitao Zhu1, Michal Shaked2
and 7 other authorsSarah C L Knowles14, Stefan Hanns Engelhardt5, Matteo Iannacone15,16, Philipp E Scherer17, Miguel López8, Enrique M Toledo2, Ana I Domingos1
ORCID iDs: Gitalee Sarker, Thomas Monfeuga, Sofia Lundh, David S Oliveira, Karin Ziegler, Tiago Gomes, Emilia Schmid, Paola Fernández-Sanmartín, Noelia Martinez-Sanchez, Bernardo A Arús, Stefan Hanns Engelhardt, Matteo Iannacone, Philipp E Scherer, Miguel López, Enrique M Toledo, Ana I Domingos
17 affiliations
  1. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
  2. Research and Development, Novo Nordisk Research Centre Oxford, Oxford, UK
  3. Department of Pathology and Imaging, Global Drug Discovery, Novo Nordisk, Måløv, Denmark
  4. Experimental Imaging Centre, IRCCS San Raffaele Scientific Institute, Milan, Italy
  5. Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
  6. DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
  7. Instituto de Tecnologia Química e Biológica António Xavier, ITQB NOVA, Lisbon, Portugal
  8. Neurobesity Group, Department of Physiology, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela, Santiago, Spain
  9. Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, UK
  10. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
  11. Medizinische Fakultät and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
  12. Department of Functional Imaging in Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
  13. German Cancer Research Center (DKFZ), Heidelberg, Germany
  14. Department of Biology, University of Oxford, Oxford, UK
  15. Vita-Salute San Raffaele University, Milan, Italy
  16. Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
  17. Touchstone Diabetes Center, The University of Texas Southwestern Medical Center, Dallas, TX USA
Institutions: University of Oxford (United Kingdom); Novo Nordisk (Denmark) (Denmark); IRCCS Ospedale San Raffaele (Italy); German Centre for Cardiovascular Research (Germany); Technical University of Munich (Germany); Instituto de Tecnologia Química e Biológica António Xavier (Portugal); Universidade de Santiago de Compostela (Spain); Center for Research in Molecular Medicine and Chronic Diseases (Spain); Oxford Centre for Diabetes, Endocrinology and Metabolism (United Kingdom); German Cancer Research Center (Germany); Helmholtz-Zentrum Dresden-Rossendorf (Germany); National Center for Tumor Diseases (Germany); University Hospital Carl Gustav Carus (Germany); Nationales Centrum für Tumorerkrankungen Dresden (Germany); Technische Universität Dresden (Germany); Vita-Salute San Raffaele University (Italy); The University of Texas Southwestern Medical Center (United States)
Journal: Nature metabolism, volume 8, issue 7, pages 1563-1582
Dates: received 20 February 2026; accepted 2 June 2026; published online 13 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42255-026-01555-3 · PMID 42443582 · PMCID PMC13400315 · OpenAlex W7168153812
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging
Keywords: Molecular biology, Neuroscience, Metabolism
MeSH: Leptin*, Obesity*, Peripheral Nerves*, Sympathetic Nervous System*, Animals, Energy Metabolism, Ganglia, Sympathetic, Humans, Male, Mice, Mice, Knockout, Receptors, Adrenergic, beta-2, Receptors, Leptin, Thermogenesis (* major topic)
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: European Research Council (771431); Ministerio de Ciencia e Innovación co-funded by the FEDER Program of the EU; Novo Nordisk Postdoctoral Fellowship; Wellcome Trust (208576/Z/17/Z); Biotechnology and Biological Sciences Research Council (BB/Y006488/1); RCUK | Biotechnology and Biological Sciences Research Council
Citations: not cited yet (Europe PMC); 80 references in the paper
Notices: A correction to this paper has been published (42581328, from Europe PMC)

Abstract

The regulatory mechanism of leptin’s afferent action in the brain is contingent upon the efferent sympathetic innervation of white and brown adipose tissues. Nonetheless, the peripheral regulation governing the afferent–efferent balance remains ambiguous. Here we show the enriched expression of both leptin receptor (Lepr) and β2-adrenergic receptor (Adrb2) in perineurial cells that form a barrier around sympathetic ganglia and nerve bundles in adipose tissues, using single-cell RNA sequencing on mouse sympathetic ganglia. Lepr+ sympathetic perineurial cells (SPCs) are molecularly similar to endothelial cells. Conditional knockout of Adrb2 in Lepr+ cells, including SPCs, predisposes male mice to obesity by lowering energy expenditure and thermogenesis without affecting food intake. Notably, obesity-associated hyperleptinaemia causes apoptosis in SPCs, disrupting the perineurial barrier and concomitant adipose sympathetic neuropathy. This deleterious effect can be reversed by partial reduction of leptin or sympathomimetic β2-adrenergic receptor agonism. Clinically, we observed a male-specific synergistic effect of LEPR and ADRB2 polymorphisms on increased body mass index risk in a large European population. We propose that SPCs coordinate the afferent and efferent arms of the neuroendocrine loop of leptin action to regulate energy expenditure and body weight.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

automet26/Sympathetic_Perineurium_Leptin_Obesity

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7d55375af11054caec5863f15ec1cc193d72bce3, 29 June 2026
Languages: R (21)
Size: 26 files, 21 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (21 files), tidyverse (20 files), ggplot2 (17 files), patchwork (11 files), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/automet26/Sympathetic_Perineurium_Leptin_Obesity

Code availability

No custom code or mathematical algorithm was created in this work. The code used to create figures from scRNA-seq dataset and all additional input data files can be found on GitHub via https://github.com/automet26/Sympathetic_Perineurium_Leptin_Obesity. Please direct any other bioinformatic inquiries to G.S.

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

Datasets cited

Data Availability Statement

The raw single-cell sequencing data of sympathetic ganglia supporting the findings in this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE233163 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233163). The raw data of the public dataset reanalysed in this study can be found in the GEO repository under the following accession codes: Tabula Muris (GSE109774 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109774) and GSE93374 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93374)), mouse sympathetic ganglia (GSE231767 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE231767)), dorsal root ganglia (GSE175421 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175421)), nodose ganglia (GSE124312 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124312)), sciatic nerve (GSE137870 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137870)), MCA (GSE108097 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108097)), Tabula Muris Senis (GSE132042 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132042)), WAT (mouse, GSE17617 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17617); human, GSE155960 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE155960)), BAT (mouse, GSE207707 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207707); human, E-MTAB-9199 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9199/)), human sympathetic ganglia (GSE241386 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE241386)) and Mouse Organogenesis Cell Atlas (GSE119945 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119945)). The Seurat object containing the HypoMap dataset is available via 10.17863/CAM.87955. Source data are provided with this paper.

No custom code or mathematical algorithm was created in this work. The code used to create figures from scRNA-seq dataset and all additional input data files can be found on GitHub via https://github.com/automet26/Sympathetic_Perineurium_Leptin_Obesity. Please direct any other bioinformatic inquiries to G.S.

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 → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 27 authors, 3 keywords, 14 MeSH terms, 6 funders, 78 references, 1 integrity notice.

Cite

This paper

Sarker, G., Haberman, E., Chandran, A., Monfeuga, T., Maroteau, C., Lundh, S., Oliveira, D. S., Raimondi, A., Ziegler, K., Gomes, T., Cordeiro, T. N., Parekh, K., Schmid, E., Fernández-Sanmartín, P., Griebel, A., Martinez-Sanchez, N., Arús, B. A., Cheung, S. W., Zhu, Y., . . . Domingos, A. I. (2026). The perineurium integrates leptin with its sympathetic outflow to protect against obesity. Nature metabolism, 8(7), 1563-1582. https://doi.org/10.1038/s42255-026-01555-3

BibTeX

@article{sarker2026perineurium,
author = {Sarker, Gitalee and Haberman, Emma and Chandran, Anandhakumar and Monfeuga, Thomas and Maroteau, Cyrielle and Lundh, Sofia and Oliveira, David S and Raimondi, Andrea and Ziegler, Karin and Gomes, Tiago and Cordeiro, Tiago N and Parekh, Karishma and Schmid, Emilia and Fernández-Sanmartín, Paola and Griebel, Alina and Martinez-Sanchez, Noelia and Arús, Bernardo A and Cheung, Samson W and Zhu, Yitao and Shaked, Michal and Knowles, Sarah C L and Engelhardt, Stefan Hanns and Iannacone, Matteo and Scherer, Philipp E and López, Miguel and Toledo, Enrique M and Domingos, Ana I},
title = {{The perineurium integrates leptin with its sympathetic outflow to protect against obesity}},
journal = {Nature metabolism},
year = {2026},
month = jul,
volume = {8},
number = {7},
pages = {1563--1582},
publisher = {Nature Portfolio},
issn = {2522-5812},
doi = {10.1038/s42255-026-01555-3},
url = {https://doi.org/10.1038/s42255-026-01555-3},
pmid = {42443582},
pmcid = {PMC13400315}
}

RIS

TY - JOUR
AU - Sarker, Gitalee
AU - Haberman, Emma
AU - Chandran, Anandhakumar
AU - Monfeuga, Thomas
AU - Maroteau, Cyrielle
AU - Lundh, Sofia
AU - Oliveira, David S
AU - Raimondi, Andrea
AU - Ziegler, Karin
AU - Gomes, Tiago
AU - Cordeiro, Tiago N
AU - Parekh, Karishma
AU - Schmid, Emilia
AU - Fernández-Sanmartín, Paola
AU - Griebel, Alina
AU - Martinez-Sanchez, Noelia
AU - Arús, Bernardo A
AU - Cheung, Samson W
AU - Zhu, Yitao
AU - Shaked, Michal
AU - Knowles, Sarah C L
AU - Engelhardt, Stefan Hanns
AU - Iannacone, Matteo
AU - Scherer, Philipp E
AU - López, Miguel
AU - Toledo, Enrique M
AU - Domingos, Ana I
TI - The perineurium integrates leptin with its sympathetic outflow to protect against obesity
T2 - Nature metabolism
J2 - Nat Metab
PY - 2026
DA - 2026/07/13
VL - 8
IS - 7
SP - 1563
EP - 1582
SN - 2522-5812
PB - Nature Portfolio
DO - 10.1038/s42255-026-01555-3
UR - https://doi.org/10.1038/s42255-026-01555-3
LA - en
ER -

CSL-JSON

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"author": [
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}

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