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Enhanced P-TEFb activity compromises dentate gyrus neurogenesis in mice.

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  1. [1] § Methods › Single-cell sequencing, RNA-seq and bioinformatic analyses ↔ scRNA-seq.r, lines 1 to 39 · score 0.54 · RNA seq, Seurat, pre, matrix, mice, cell · Report a mismatch

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The authors' code

R · 227 lines · 7.7 KB · no license · 1 match

  1. #mouse ID8_P53KO_NACT
  2. #创建seurat对象
  4. library(Seurat)
  5. library(dplyr)
  6. library(ggplot2)
  7. library(patchwork)
  8. library(homologene)
  11. after<-Read10X("./ID8_P53KO_NACT_after/filtered_feature_bc_matrix/")
  12. after <- CreateSeuratObject(counts =after , project = "ID8_P53KO_NACT_after",min.cells = 3, min.features = 200)
  14. #质量控制
  15. after[["percent.mt"]] <- PercentageFeatureSet(after,pattern = "^mt-")
  16. preQC_after <- VlnPlot(after, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
  17. ncol = 3,
  18. group.by = "orig.ident",
  19. pt.size = 0)
  20. ggsave("./after_preQC_1.pdf", preQC_after, width=7 ,height=6)
  22. #过滤低质量数据
  23. after <- subset(after, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 15)
  24. preQC_after <- VlnPlot(after, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
  25. ncol = 3,
  26. group.by = "orig.ident",
  27. pt.size = 0)
  29. after[["percent.rb"]] <- PercentageFeatureSet(after, pattern = "^RP[SL]")
  33. #去除线粒体基因
  34. mt.index <- grep(pattern = "^mt-", x = rownames(x = after@assays$RNA@counts), value = FALSE)
  35. after <- after[-mt.index,]
  38. #scale数据
  39. after <- NormalizeData(after,normalization.method = "LogNormalize", scale.factor = 10000)
  41. #特征选择
  42. after <- FindVariableFeatures(after, nfeatures = 4000, selected.method="vst")
  44. # 可视化高变基因
  45. var_plot <- VariableFeaturePlot(after)
  46. p3<-LabelPoints(plot = var_plot, points = head(VariableFeatures(after), repel = TRUE))
  47. ggsave("after_Variable_Genes.pdf", p3,width = 10)
  49. #缩放数据
  50. after <- ScaleData(after,verbose=FALSE)
  53. #细胞周期评分
  55. s.genes <- cc.genes$s.genes
  56. g2m.genes <- cc.genes$g2m.genes
  57. mouse.s.genes<-homologene(s.genes, inTax = 9606, outTax = 10090)
  58. mouse.g2m.genes<-homologene(g2m.genes, inTax = 9606, outTax = 10090)
  60. mouse.s.gene <- as.character(mouse.s.genes[[2]])
  61. mouse.g2m.gene <- as.character(mouse.g2m.genes[[2]])
  62. after <- CellCycleScoring(after,
  63. s.features = mouse.s.gene,
  64. g2m.features = mouse.g2m.gene,
  65. set.ident = TRUE)
  67. # PCA降维
  68. after <- RunPCA(
  69. after,
  70. features = VariableFeatures(object = after),
  71. npcs = 50 # 计算前50个主成分
  72. )
  74. # 可视化PCA结果
  75. p1<-DimPlot(after, reduction = "pca")
  76. ggsave("./after_pca_1.pdf",p1, width=7 ,height=6)
  77. p2<-ElbowPlot(after, ndims = 50) # 选择主成分数量（拐点位置）
  78. ggsave("./after_Eblowplot_1.pdf", p2, width=7 ,height=6)
  80. #聚类分析
  81. # 确定主成分数（通常10-30）
  82. pca_dims <- 1:30 # 根据ElbowPlot调整
  84. # 构建KNN图
  85. after <- FindNeighbors(after, dims = pca_dims)
  87. # 聚类（分辨率越高，簇越多）
  88. after <- FindClusters(
  89. after,
  90. resolution = 0.7 # 通常0.4-1.2（小样本用低值）
  91. )
  93. # 非线性降维 (UMAP/t-SNE)
  94. after <- RunUMAP(after, dims = pca_dims)
  95. after <- RunTSNE(after, dims = pca_dims)
  97. # 可视化聚类结果
  98. umap_plot <- DimPlot(after, reduction = "umap", label = TRUE)
  99. tsne_plot <- DimPlot(after, reduction = "tsne", label = TRUE)
  100. combined_plot <- umap_plot + tsne_plot
  101. ggsave("after_Clustering_UMAP_TSNE.pdf", combined_plot, width = 14)
  103. # ----------------------
  104. # 步骤7: 差异表达基因（标记基因）
  105. # ----------------------
  106. # 寻找每个簇的标记基因
  107. markers <- FindAllMarkers(
  108. after,
  109. only.pos = TRUE, # 只保留阳性标记
  110. min.pct = 0.25, # 基因在至少25%的簇细胞中表达
  111. logfc.threshold = 0.25
  112. )
  113. write.table(markers,file="./after_significant_marker_genes.txt",quote=F,sep="\t",col.names=FALSE)
  114. # 提取每个簇的前5标记基因
  115. top_markers <- markers %>%
  116. group_by(cluster) %>%
  117. slice_max(n = 5, order_by = avg_log2FC)
  119. # 可视化标记基因表达
  120. heatmap_plot <- DoHeatmap(
  121. after,
  122. features = top_markers$gene
  123. )
  124. ggsave("after_Marker_Heatmap.pdf", heatmap_plot, width = 12)
  126. saveRDS(after, file = "./01_ID8_p53KO_NACT_after_del_mt.RDS")
  129. #merge
  130. sampleList <- list(before,after)
  133. merge <- FindIntegrationAnchors(object.list = sampleList, dims = 1:50)
  134. merge <- IntegrateData(anchorset = merge, dims = 1:50)
  135. saveRDS(merge, file = "./merge/01_merge_mt_del.RDS")
  137. DefaultAssay(merge) <- "integrated"
  140. merge <- SCTransform(merge, variable.features.n=4000)
  141. merge <- RunPCA(merge, npcs = 50, verbose = FALSE)
  142. merge <- RunHarmony(merge,group.by.vars='orig.ident',dims=1:50,assay='SCT')
  143. merge <- FindNeighbors(merge, reduction = "harmony", dims = 1:30)
  144. merge <- FindClusters(merge, resolution = 0.5,algorithm=1)
  145. merge <- RunUMAP(merge, reduction = "harmony", dims = 1:30,assay='SCT')
  147. p1 <- DimPlot(merge, reduction = "umap", label = TRUE, repel = TRUE)
  148. p2 <- DimPlot(merge, reduction = "umap", group.by = "orig.ident",label=TRUE)
  149. write.table(merge$seurat_clusters,file="./all_merge_umapCluster.txt",quote=F,sep="\t",col.names=FALSE)
  150. ggsave("./01_merge_umap.pdf", p1, width=12 ,height=10)
  151. ggsave("./01_merge_umap_group.pdf", p2, width=12 ,height=10)
  152. saveRDS(merge, file = "./01_merge_mt_del_processed.RDS")
  154. #Cell Annotation
  155. marker_genes <- list(
  156. Astrocytes = c("Sox9", "Etv4","Sall3","Grin2c","Kcng4"),
  157. RG_like=c("Tfap2c", "Hopx","Rhcg","Vnn1","Wnt8b"),
  158. nIPC=c("Mxd3","Ascl1","Neurog2","Rfc4","Lockd"),
  159. Neuroblast1=c("Eomes","Neurod4","Slc17a6","Mpped1","Calb2"),
  160. Neuroblast2=c("Sox11","Bhlhe22","Neurod2","Gal","Igfbpl1"))
  161. merge <- AddModuleScore(merge, features = marker_genes, name = "ModuleScore")
  162. cluster_annotations <- [email hidden] %>%
  163. group_by(seurat_clusters) %>%
  164. summarize(across(starts_with("ModuleScore"), mean))
  166. predicted_cell_types <- cluster_annotations %>%
  167. rowwise() %>%
  168. mutate(predicted_type = names(marker_genes)[which.max(c_across(starts_with("ModuleScore")))])
  170. # 将预测的类型加入到Seurat对象的元数据中
  171. [email hidden]$predicted_type <- factor([email hidden]$seurat_clusters,
  172. levels = cluster_annotations$seurat_clusters,
  173. labels = predicted_cell_types$predicted_type)
  175. p1 <- DimPlot(merge, reduction = "umap", label = TRUE, repel = TRUE)
  176. p2 <- DimPlot(merge, reduction = "umap", group.by = "predicted_type",label=TRUE)
  177. DimPlot(merge, group.by = "predicted_type", label = TRUE, repel = TRUE) + NoLegend()
  178. write.table(merge$seurat_clusters,file="merge_umapCluster.txt",quote=F,sep="\t",col.names=FALSE)
  179. ggsave("merge_umap_predict_celltype.pdf", p1, width=12 ,height=10)
  180. ggsave("merge_umap_predict_celltype_group.pdf", p2, width=12 ,height=10)
  183. #执行差异表达分析
  185. # 获取所有细胞群体
  186. clusters <- unique(merge$manual_annotation)
  188. # 定义组别
  189. comparisons <- list(
  190. c("p7_l7_nestin", "p7_l7_wt"),
  191. c("p7_l7_nestin", "p7_l7_emxt"),
  192. c( "p7_l7_emxt", "p7_l7_wt")
  193. )
  195. # 存储结果
  196. results_list <- list()
  198. for (comparison in comparisons) {
  199. group1 <- comparison[1]
  200. group2 <- comparison[2]
  201. for (cluster in clusters) {
  202. # 选择当前群体的细胞
  203. cluster_cells <- WhichCells(merge, expression = manual_annotation == cluster)
  205. # 进行差异表达分析
  206. degs <- FindMarkers(
  207. object = merge,
  208. ident.1 = group1,
  209. ident.2 = group2,
  210. cells = cluster_cells,
  211. group.by = "orig.ident", min.pct=0.1,logfc.threshold=0.25, only.pos=F
  212. )
  214. # 存储结果，命名格式为: "群体_比较组1_vs_比较组2"
  215. results_list[[paste(cluster, group1, "vs", group2, sep = "_")]] <- degs
  216. }
  217. }
  218. 保存结果为CSV文件
  220. 将每个比较的结果保存为CSV文件。
  222. for (name in names(results_list)) {
  223. output_file <- paste0(name, "_DEGs.csv")
  224. write.csv(results_list[[name]], file = output_file, row.names = TRUE)
  225. }

scRNA-seq.r at commit 4a63c0d, no license · at the source

Overview

Authors: Yin Fang1, Tong Qiu1, Ping Wang2, Shujun Bai1, Min Wang1, Chao Yang1, Yan Wang1, Peixuan Zhang1, He Wang2, Shanling Liu2, Xue Xiao1, Qintong Li1
ORCID iDs: Yin Fang, Tong Qiu, Min Wang, Yan Wang, Shanling Liu, Xue Xiao, Qintong Li
  1. Departments of Obstetrics & Gynecology and Pediatrics, Cancer Center, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Development and Related Diseases of Women and Children Key Laboratory of Sichuan Province, Sichuan University,Chengdu, China
  2. Department of Medical Genetics, Prenatal Diagnostic Center, West China Second University Hospital, Sichuan University,Chengdu, China
Institutions: Sichuan University (China)
Journal: The EMBO journal, volume 45, issue 9, pages 3102-3123
Dates: received 1 August 2025; accepted 25 February 2026; published online 23 March 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44318-026-00752-w · PMID 41872527 · PMCID PMC13144724 · OpenAlex W7140124583
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Graphs
Keywords: Alazami Syndrome, LARP7, Transcription Regulation, P-TEFb, Stem Cell, Chromatin, Transcription & Genomics, Development, Neuroscience
MeSH: Dentate Gyrus*, Neurogenesis*, Positive Transcriptional Elongation Factor B*, Ribonucleoproteins*, Animals, Cell Proliferation, Male, Mice, Mice, Knockout, RNA-Binding Proteins (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (32271348); Department of Science and Technology of Sichuan Province (2024NSFSC0731); West Women and Children Medical Research Development Center (XBFY-YJXM2025004)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Enhanced P-TEFb activity is thought to promote cell proliferation by increasing the transcriptional output of RNA polymerase II. The 7SK snRNP complex, which contains LARP7 and HEXIM1, sequesters and inhibits most cellular P-TEFb to prevent premature transcription elongation. Paradoxically, instead of exerting overgrowth effects, biallelic inactivation of LARP7 is linked to Alazami syndrome, a human neurodevelopmental disorder characterized by growth restriction and cognitive impairment. Here, we report that conditional ablation of either Larp7 or Hexim1 in the murine brain reduces the size and impairs the function of the hippocampal dentate gyrus during the neonatal period. Functional analyses reveal that increased P-TEFb activity enhances self-renewal transcriptional programs in transit-amplifying neuronal progenitor cells to limit neurogenesis in developing dentate gyri. These results demonstrate that dysregulated subtissular stem cell dynamics can reconcile increased P-TEFb activity with reduced organ growth, and suggest a translational opportunity for repurposing P-TEFb inhibitors to treat medical conditions affecting dentate gyrus size and function.

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 1 match between paragraphs and lines of code.

ketone08/Larp7_KO_mouse

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4a63c0da8eafd9ca97d365fe26c2539ce3a871a3, 23 June 2025
Languages: R (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), Seurat (4 files), tidyverse (4 files), cowplot (1 file), patchwork (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
5 files
At the source: github.com/ketone08/Larp7_KO_mouse

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Other data links

Data availability

The sequencing data produced in this study have been deposited in Gene Expression Omnibus (GEO) repository under the accession number GSE300207 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300207). The code used to perform analyses in this paper is available on GitHub at: https://github.com/ketone08/Larp7_KO_mouse.

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44318-026-00752-w (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44318-026-00752-w).

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 8 keywords, 10 MeSH terms, 3 funders, 80 references.

Cite

This paper

Fang, Y., Qiu, T., Wang, P., Bai, S., Wang, M., Yang, C., Wang, Y., Zhang, P., Wang, H., Liu, S., Xiao, X., & Li, Q. (2026). Enhanced P-TEFb activity compromises dentate gyrus neurogenesis in mice. The EMBO journal, 45(9), 3102-3123. https://doi.org/10.1038/s44318-026-00752-w

BibTeX

@article{fang2026enhanced,
author = {Fang, Yin and Qiu, Tong and Wang, Ping and Bai, Shujun and Wang, Min and Yang, Chao and Wang, Yan and Zhang, Peixuan and Wang, He and Liu, Shanling and Xiao, Xue and Li, Qintong},
title = {{Enhanced P-TEFb activity compromises dentate gyrus neurogenesis in mice}},
journal = {The EMBO journal},
year = {2026},
month = mar,
volume = {45},
number = {9},
pages = {3102--3123},
publisher = {Nature Publishing Group},
issn = {0261-4189},
doi = {10.1038/s44318-026-00752-w},
url = {https://doi.org/10.1038/s44318-026-00752-w},
pmid = {41872527},
pmcid = {PMC13144724}
}

RIS

TY - JOUR
AU - Fang, Yin
AU - Qiu, Tong
AU - Wang, Ping
AU - Bai, Shujun
AU - Wang, Min
AU - Yang, Chao
AU - Wang, Yan
AU - Zhang, Peixuan
AU - Wang, He
AU - Liu, Shanling
AU - Xiao, Xue
AU - Li, Qintong
TI - Enhanced P-TEFb activity compromises dentate gyrus neurogenesis in mice
T2 - The EMBO journal
J2 - EMBO J
PY - 2026
DA - 2026/03/23
VL - 45
IS - 9
SP - 3102
EP - 3123
SN - 0261-4189
PB - Nature Publishing Group
DO - 10.1038/s44318-026-00752-w
UR - https://doi.org/10.1038/s44318-026-00752-w
LA - en
ER -

CSL-JSON

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"author": [
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{
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"DOI": "10.1038/s44318-026-00752-w",
"PMID": "41872527",
"PMCID": "PMC13144724",
"ISSN": "0261-4189",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

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Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.