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GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat.

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  1. [1] § Methods › Single cell RNA-seq ↔ Figure7/Script.R, lines 1–39 · score 0.91 · FindVariableFeatures, NormalizeData, RunPCA, ScaleData, Seurat, matrix

Paper

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The authors' code

R · 171 lines · 6.8 KB · no license · 1 match

  1. #安装包
  2. install.packages('Seurat')
  3. install.packages(c('dplyr','patchwork'))
  4. install.packages('BiocManager')
  5. BiocManager::install('scater')
  6. install.packages('ggplot2')
  8. #加载包
  9. library(Seurat)
  10. library(dplyr)
  11. library(patchwork)
  12. library(cowplot)
  13. library(tidyverse)
  14. library(ggplot2)
  16. #导入数据
  17. pbmc.data <- Read10X(data.dir = "F:/MeA 10X/outs/filtered_feature_bc_matrix")
  18. #min.cells = 3
  19. #min.features = 200
  20. pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
  21. pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
  22. VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
  23. pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
  24. pbmc <-NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
  25. pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
  26. head(pbmc)
  28. #提取表达量变化最显著的10个基因，用于PCA
  29. top10 <- head(VariableFeatures(pbmc), 10)
  30. #可视化高变基因
  31. plot1 <- VariableFeaturePlot(pbmc)
  32. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  33. plot1 + plot2
  34. #归一化处理
  35. all.genes <- rownames(pbmc)
  36. pbmc <- ScaleData(pbmc, features = all.genes)
  37. #PCA降维聚类
  38. pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
  39. ElbowPlot(pbmc)
  41. #可视化umap
  42. pbmc <- FindNeighbors(pbmc, dims = 1:20)
  43. pbmc <- FindClusters(pbmc, resolution = 0.3)
  44. pbmc <- RunUMAP(pbmc, dims = 1:20)
  45. p1 <-DimPlot(pbmc, reduction = "umap")
  46. p1
  48. #可视化T-SNE
  49. pbmc <- RunTSNE(pbmc, dims = 1:20)
  50. p2 <- DimPlot(pbmc, reduction = "tsne")
  51. p2
  53. #找marker基因
  54. markers <- FindAllMarkers(pbmc, logfc.threshold = 0.25, min.pct = 0.1,
  55. only.pos = TRUE, pbmc = "wilcox")
  56. pbmc.markers<-markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
  59. #自动区分聚类
  61. library(celldex)
  62. library(SingleR)
  63. library(scater)
  64. library(SummarizedExperiment)
  66. #存储对比用小鼠数据库
  67. Mouse.brain <- MouseRNAseqData()
  68. save(Mouse.brain,file="F:/hip 10X mice+egfp/R/Mouse.brain.RData")
  69. yes
  70. test.seu<-pbmc
  71. test.count=as.data.frame(test.seu[["RNA"]]@counts)
  74. load(file="Mouse.brain.RData")
  75. common_mouse <- intersect(rownames(test.count), rownames(Mouse.brain))
  76. Mouse.brain <- Mouse.brain[common_mouse,]
  77. test.count_forhpca <- test.count[common_mouse,]
  78. test.count_forhpca.se <- SummarizedExperiment(assays=list(counts=test.count_forhpca))
  79. test.count_forhpca.se <- logNormCounts(test.count_forhpca.se)
  81. pred.main.mouse <- SingleR(test = test.count_forhpca.se, ref = Mouse.brain, labels = Mouse.brain$label.main)
  82. result_main_mouse <- as.data.frame(pred.main.mouse$labels)
  83. result_main_mouse$CB <- rownames(pred.main.mouse)
  84. colnames(result_main_mouse) <- c('MOUSE_Main', 'CB')
  86. write.table(result_main_mouse, file = "MOUSE_Main.txt", sep = '\t', row.names = FALSE, col.names = TRUE, quote = FALSE)
  87. head(result_main_mouse)
  89. [email hidden]$CB=rownames([email hidden])
  90. [email hidden]=merge([email hidden],result_main_mouse,by="CB")
  91. rownames([email hidden])=[email hidden]$CB
  93. p5 <- DimPlot(test.seu, reduction = "tsne", group.by = "MOUSE_Main", pt.size=0.5)+theme(
  94. axis.line = element_blank(),
  95. axis.ticks = element_blank(),axis.text = element_blank()
  96. )
  97. p6 <- DimPlot(test.seu, reduction = "tsne", group.by = "ident", pt.size=0.5, label = TRUE,repel = TRUE)+theme(
  98. axis.line = element_blank(),
  99. axis.ticks = element_blank(),axis.text = element_blank()
  100. )
  101. fig_tsne <- plot_grid(p6, p5, labels = c('ident','MOUSE_Main'),rel_widths = c(2,3))
  102. ggsave(filename = "tsne4.pdf", plot = fig_tsne, device = 'pdf', width = 36, height = 12, units = 'cm')
  104. #作图
  105. Oligodendrocyte_progenitor_cells=c(0,1,2)
  106. Oligodendrocyte_cells=c(3)
  107. Endothelial_cells=c(4)
  108. Fibroblasts=c(5)
  109. Macrophages=c(6)
  110. Neutrophils=c(7)
  111. Astrocytes=c(8)
  112. Oligodendrocyte_Astrocytes=c(9)
  113. Pericytes=c(10)
  115. current.cluster.ids <- c(Oligodendrocyte_progenitor_cells,
  116. Oligodendrocyte_cells,
  117. Endothelial_cells,
  118. Fibroblasts,
  119. Macrophages,
  120. Neutrophils,
  121. Astrocytes,
  122. Oligodendrocyte_Astrocytes,
  123. Pericytes)
  125. new.cluster.ids <- c(rep("Oligodendrocyte progenitor cells",length(Oligodendrocyte_progenitor_cells)),
  126. rep("Oligodendrocyte cells",length(Oligodendrocyte_cells)),
  127. rep("Endothelial cells",length(Endothelial_cells)),
  128. rep("Fibroblasts",length(Fibroblasts)),
  129. rep("Macrophages",length(Macrophages)),
  130. rep("Neutrophils",length(Neutrophils)),
  131. rep("Astrocytes",length(Astrocytes)),
  132. rep("Oligodendrocyte/Astrocytes",length(Oligodendrocyte_Astrocytes)),
  133. rep("Pericytes",length(Pericytes))
  134. )
  136. [email hidden]$celltype <- plyr::mapvalues(x = as.integer(as.character([email hidden]$seurat_clusters)), from = current.cluster.ids, to = new.cluster.ids)
  138. head([email hidden])
  139. table([email hidden]$celltype)
  141. [email hidden]$celltype<-factor([email hidden]$celltype,level=c("Oligodendrocyte progenitor cells",
  142. "Oligodendrocyte cells",
  143. "Oligodendrocyte/Astrocytes",
  144. "Astrocytes",
  145. "Endothelial cells",
  146. "Pericytes",
  147. "Fibroblasts",
  148. "Macrophages",
  149. "Neutrophils"))
  151. plotCB=as.data.frame([email hidden]%>%filter(seurat_clusters!="5" & seurat_clusters!="6"& seurat_clusters!="7"))[,"CB"]
  152. DimPlot(test.seu, reduction = "tsne",group.by = "celltype", pt.size=0.3,cells = plotCB,)
  153. ggsave("tense.png",units = "cm",width = 18,height = 10,dpi=1000,bg = "white")
  155. saveRDS(test.seu,file = "test.seu.rds") #保存test.seu
  157. gene <-read.table("F:/hip 10X mice+egfp/R/gene.txt")
  159. gene <- c("Pdgfra","Pebp1","Oxt","Gpr17","Uchl1","Agrp","Cpt1a","Stat3","Lepr","Cck","Nr4a3")
  160. gene_jyj <- c("Kcnj13","Kcnj10","Kcnj8","Kcnj3","Kcnj9","Kcnj16")
  161. test.seu1<-test.seu
  163. [email hidden]<-filter([email hidden],seurat_clusters!="5" & seurat_clusters!="6"& seurat_clusters!="7")
  165. DotPlot(test.seu,features = gene_jyj,group.by = "celltype")+
  166. scale_size()
  167. ggsave("dotplot.png",units = "cm",width = 25,height = 10,dpi=1000,bg = "white")
  170. FeaturePlot(test.seu,features = "Pdgfra",reduction = "tsne",pt.size = 1,cells = plotCB)+
  171. scale_

Script.R at commit 552b82d, no license · at the source

Overview

Authors: Yujin Jian1,2, Shengyu Jin1,2, Peng Liu3, Xiaoli Zheng1,2, Xiaoqi Hong1,2, Yong Han4, Alexey Semyanov5, Shumin Duan6, Xiaoping Tong1,2,7
ORCID iDs: Yujin Jian, Shengyu Jin, Yong Han, Alexey Semyanov, Shumin Duan, Xiaoping Tong
  1. Department of Obstetrics and Gynecology, Songjiang Research Institute, Shanghai Key Laboratory of Emotions and Affective Disorders, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
  2. Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  3. Institute of Neuroscience, Key Laboratory of Molecular Neurobiology of Ministry of Education and the Collaborative Innovation Center for Brain Science, SMMU, Shanghai, China
  4. Songjiang Research Institute, Shanghai Key Laboratory of Emotions and Affective Disorders, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  5. Department of Physiology, Jiaxing University College of Medicine, Jiaxing, Zhejiang China
  6. Liangzhu Laboratory, MOE Frontier Science Center for Brain Science & Brain-Machine Integration, State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang China
  7. Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, China
Institutions: Shanghai Jiao Tong University (China); Second Military Medical University (China); Jiaxing University (China); Zhejiang University (China)
Journal: Nature communications, volume 17, issue 1, article 6812
Dates: received 9 June 2025; accepted 12 May 2026; published online 25 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73488-0 · PMID 42185310 · PMCID PMC13385396 · OpenAlex W7162291161
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Glial biology, Neurological disorders
MeSH: Empathy*, gamma-Aminobutyric Acid*, Neuroglia*, Proteoglycans*, Social Defeat*, Amygdala, Animals, Behavior, Animal, Chondroitin Sulfate Proteoglycan 4, Corticosterone, GABAergic Neurons, Glutamate Decarboxylase, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Signal Transduction, Social Behavior (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 66 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

aedasc/25-MeA-RNAseq-Analysis

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 552b82d86f6d2afd77aeb4a87a3f938cb8aecf6e, 8 February 2026
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), tidyverse (2 files), cowplot (1 file), patchwork (1 file), rstatix (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/aedasc/25-MeA-RNAseq-Analysis

Zenodo 19363737

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), tidyverse (2 files), cowplot (1 file), patchwork (1 file), rstatix (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-73488-0.

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  • 4 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-73488-0.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 18 MeSH terms, 63 references.

Cite

This paper

Jian, Y., Jin, S., Liu, P., Zheng, X., Hong, X., Han, Y., Semyanov, A., Duan, S., & Tong, X. (2026). GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat. Nature communications, 17(1), 6812. https://doi.org/10.1038/s41467-026-73488-0

BibTeX

@article{jian2026gaba,
author = {Jian, Yujin and Jin, Shengyu and Liu, Peng and Zheng, Xiaoli and Hong, Xiaoqi and Han, Yong and Semyanov, Alexey and Duan, Shumin and Tong, Xiaoping},
title = {{GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6812},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73488-0},
url = {https://doi.org/10.1038/s41467-026-73488-0},
pmid = {42185310},
pmcid = {PMC13385396}
}

RIS

TY - JOUR
AU - Jian, Yujin
AU - Jin, Shengyu
AU - Liu, Peng
AU - Zheng, Xiaoli
AU - Hong, Xiaoqi
AU - Han, Yong
AU - Semyanov, Alexey
AU - Duan, Shumin
AU - Tong, Xiaoping
TI - GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/25
VL - 17
IS - 1
SP - 6812
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73488-0
UR - https://doi.org/10.1038/s41467-026-73488-0
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73488-0",
"type": "article-journal",
"title": "GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat",
"container-title": "Nature communications",
"author": [
{
"family": "Jian",
"given": "Yujin"
},
{
"family": "Jin",
"given": "Shengyu"
},
{
"family": "Liu",
"given": "Peng"
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{
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}
],
"container-title-short": "Nat Commun",
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"PMID": "42185310",
"PMCID": "PMC13385396",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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25
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]
}
}

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