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TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice.

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  1. [1] § Methods › RNA-seq based on fluorescence-activated cell sorting (FACS) ↔ Bulk RNA-seq analysis workflow.R, lines 1–71 · score 0.56 · DEGseq, fold change, adj, log2, RNA

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The authors' code

R · 304 lines · 12 KB · CC-BY-4.0 · 1 match

  1. Sys.setenv(LANGUAGE = "en")
  2. options(stringsAsFactors = FALSE)
  4. #####Differential analysis
  5. BiocManager::install(c("DEGseq","qvalue"))
  6. library(qvalue)
  7. library(DEGseq)
  8. library(data.table)
  9. geneExpFile <-data.frame(fread("gene_count_matrix.csv",header=T))
  10. geneExpFile$name<-make.unique(geneExpFile$name)
  11. row.names(geneExpFile) <-geneExpFile$name
  12. geneExpFile1<-geneExpFile[3:14]
  13. geneExpFile1<-geneExpFile1+1
  14. write.csv(geneExpFile1,file="gene+count+matrix.csv")
  15. treatment_positive_df <-readGeneExp(file = "gene+count+matrix.csv",geneCol = 1,valCol = c(2,4,6),sep = ",")
  16. control_positive_df <-readGeneExp(file = "gene+count+matrix.csv",geneCol = 1,valCol = c(8,10,12),sep = ",")
  17. treatment_df <-readGeneExp(file = "gene+count+matrix.csv",geneCol = 1,valCol = c(3,5,7),sep = ",")
  18. control_df <-readGeneExp(file = "gene+count+matrix.csv",geneCol = 1,valCol = c(9,11,13),sep = ",")
  19. treatment_positive_depth <-c(48359508,52611832,50916518)
  20. control_positive_depth <-c(41714122,48820574,49144424)
  21. treatment_depth<-c(49698164,42923092,63185328)
  22. control_depth <-c(50013002,47967490,47360832)
  23. #options(digits = 22)
  24. #Comparison within the control group
  25. DEGexp(geneExpMatrix2 = control_positive_df, geneCol1 = 1, expCol1 = 2:4, depth1 = control_positive_depth, groupLabel1 = "Tre",
  26. geneExpMatrix1 = control_df, geneCol2 = 1, expCol2 = 2:4, depth2 = control_depth, groupLabel2 = "Con",
  27. method = "LRT", normalMethod = "median", outputDir = "tmp")
  28. #Comparison within the tFus group
  29. DEGexp(geneExpMatrix2 = treatment_positive_df, geneCol1 = 1, expCol1 = 2:4, depth1 = treatment_positive_depth, groupLabel1 = "Tre",
  30. geneExpMatrix1 = treatment_df, geneCol2 = 1, expCol2 = 2:4, depth2 = treatment_depth, groupLabel2 = "Con",
  31. method = "LRT", normalMethod = "median", outputDir = "tmp")
  32. #Comparison between the control and the tFus group
  33. DEGexp(geneExpMatrix2 = treatment_positive_df, geneCol1 = 1, expCol1 = 2:4, depth1 = treatment_positive_depth, groupLabel1 = "Tre",
  34. geneExpMatrix1 = control_positive_df, geneCol2 = 1, expCol2 = 2:4, depth2 = control_positive_depth, groupLabel2 = "Con",
  35. method = "LRT", normalMethod = "median", outputDir = "tmp")
  36. #Rename each output txt file and saved as csv format
  37. ########vocalno painting
  38. library(ggplot2)
  40. contr_compare<-data.frame(fread("control+G vs -G adj.csv",header=T))
  41. Tfus_compare<-data.frame(fread("tfus+G vs -G adj.csv",header=T))
  42. Tfus_contr_compare<-data.frame(fread("tfus+G vs control +G adj.csv",header=T))
  43. M<-contr_compare
  44. M<-Tfus_compare
  45. M<-Tfus_contr_compare
  46. logFC_cutoff <-2
  47. Pvalue_cutoff<-0.001
  48. #Let each comparison data equals to M and repeat the following codes
  49. M$change[M$log2.Fold_change.>=logFC_cutoff & M$q.value.Benjamini.et.al..1995.<Pvalue_cutoff] <-"Down"
  50. M$change[(M$log2.Fold_change.<logFC_cutoff &
  51. M$log2.Fold_change.>-logFC_cutoff)
  52. | M$q.value.Benjamini.et.al..1995.>Pvalue_cutoff] <-"No"
  53. M$change[M$log2.Fold_change.<=-logFC_cutoff & M$q.value.Benjamini.et.al..1995.<Pvalue_cutoff] <-"Up"
  54. table(M$change)
  55. M$change <-as.factor(M$change)
  56. p<-ggplot(M,aes(x=log2.Fold_change.,y=-log10(q.value.Benjamini.et.al..1995.),color=change))+
  57. geom_point(alpha=0.4,size=1)+
  58. scale_color_manual(values = c("Down"='#006699',"No"='#bebebe',"Up"='#ffad21'))+
  59. geom_vline(xintercept = c(-logFC_cutoff,logFC_cutoff),linetype="dashed",color="black",linewidth=1)+
  60. geom_hline(yintercept=-log10(Pvalue_cutoff),linetype="dashed",color="black",linewidth=1)+
  61. labs(x="log2(Fold Change)",y="-log10(P Value)")+
  62. theme_bw()+
  63. theme(legend.position = "right")
  64. p1<-p+scale_y_continuous(limits = c(0,20))
  65. ggsave('vocalno.pdf',plot = p1,width = 8,height = 6)
  66. contr_compare<-merge(M,geneExpFile1[,7:12],by.x = 1,by.y = 0)
  67. Tfus_compare<-merge(M,geneExpFile1[,1:6],by.x = 1,by.y = 0)
  68. Tfus_contr_compare<-merge(M,geneExpFile1[,c(1,3,5,7,9,11)],by.x = 1,by.y = 0)
  69. write.csv(contr_compare,file="contr_compare.csv")
  70. write.csv(Tfus_compare,file="Tfus_compare.csv")
  71. write.csv(Tfus_contr_compare,file="Tfus_contr_compare.csv")
  73. ############Venn painting
  74. install.packages("vctrs")
  75. install.packages("devtools")
  76. devtools::install_github("yanlinlin82/ggvenn")
  78. library(grid)
  79. library(vctrs)
  80. library(ggvenn)
  81. conUp<-data.frame(fread("conUp.csv",header=T))
  82. tfusUp<-data.frame(fread("tfusUp.csv",header=T))
  83. tvcUp<-data.frame(fread("tvcUp.csv",header=T))
  85. x <-list('Control'=conUp$GeneNames,
  86. 'Tfus vs Control'=tvcUp$GeneNames,
  87. 'Tfus'=tfusUp$GeneNames)
  88. Up<-ggvenn(x,
  89. show_percentage = F,
  90. stroke_color = "white",
  91. fill_color = c("#b2e7cb","#b2d4ec","#ffb2b2"),
  92. set_name_color = c("#4a9b83","#1d6295","#ff0000"))
  93. ggsave('venn_Up.pdf',plot = Up,width = 8,height = 6)
  95. conDown<-data.frame(fread("conDown.csv",header=T))
  96. tfusDown<-data.frame(fread("tfusDown.csv",header=T))
  97. tvcDown<-data.frame(fread("tvcDown.csv",header=T))
  98. x <-list('Control'=conDown$GeneNames,
  99. 'Tfus vs Control'=tvcDown$GeneNames,
  100. 'Tfus'=tfusDown$GeneNames)
  101. Down<-ggvenn(x,
  102. show_percentage = F,
  103. stroke_color = "white",
  104. fill_color = c("#b2e7cb","#b2d4ec","#ffb2b2"),
  105. set_name_color = c("#4a9b83","#1d6295","#ff0000"))
  106. ggsave('venn_Down.pdf',plot = Down,width = 8,height = 6)
  108. #########intersection filtering
  109. df1<-tvcUp$GeneNames
  110. df2<-conUp$GeneNames
  111. df3<-tfusUp$GeneNames
  112. tvcup_re<-setdiff(df1,df2)
  113. tfusup_re<-setdiff(df3,df2)
  114. tvc_plus_tfus<-intersect(tvcup_re,tfusup_re)
  115. write.csv(tvc_plus_tfus, 'tvc_plus_tfus.csv')
  118. #############GO analysis
  119. BiocManager::install("org.Mm.eg.db")
  120. library(org.Mm.eg.db)
  121. install.packages("Rcpp")
  122. library(Rcpp)
  123. library(clusterProfiler)
  124. gene1<-tvc_plus_tfus
  125. gene_ENTREZID <- unlist(na.omit(mapIds(x = org.Mm.eg.db,
  126. keys = gene1,
  127. keytype = "SYMBOL",
  128. column = "ENTREZID",
  129. multiVals = "first")))
  131. go_enrich_results_ALL <- enrichGO(gene = gene_ENTREZID,
  132. OrgDb = "org.Mm.eg.db",
  133. ont = "ALL" ,
  134. pvalueCutoff = 0.05,
  135. qvalueCutoff = 0.05,
  136. readable = TRUE)
  137. write.csv(go_enrich_results_ALL@result, 'GO_gene_ALL_enrichresults.csv')
  140. #####KEGG analysis
  141. kegg_enrich_results <- enrichKEGG(gene = gene_ENTREZID,
  142. organism = "mmu",
  143. keyType = "kegg",
  144. pAdjustMethod = "BH",
  145. pvalueCutoff = 0.05,
  146. qvalueCutoff = 0.05)
  147. kk_read <- DOSE::setReadable(kegg_enrich_results,
  148. OrgDb="org.Mm.eg.db",
  149. keyType='ENTREZID')#ENTREZID to gene Symbol
  150. write.csv(kk_read@result,'KEGG_gene_enrichresults.csv')
  153. ########GO terms painting
  154. library(enrichplot)
  155. library(ggplot2)
  156. bp <-data.frame(fread("GO_gene_ALL_enrichresults_tfusplustvcBP.csv",header = T))
  157. mf <-data.frame(fread("GO_gene_ALL_enrichresults_tfusplustvcMF.csv",header = T))
  158. cc <-data.frame(fread("GO_gene_ALL_enrichresults_tfusplustvcCC.csv",header = T))
  160. d<-bp
  161. d<-mf
  162. d<-cc
  163. #Let each GO ontology equals to d and repeat the following codes
  164. d<-d[order(-d$Count),]
  165. d <-d[1:13,]
  166. d$Description <- factor(d$Description,levels=d$Description)
  168. mytheme <- theme(axis.title=element_text(face="bold", size=14,colour = 'black'),
  169. axis.text.y =element_text(face="bold", size=14,colour = 'black'),
  170. axis.text.x=element_text(size=8),
  171. axis.line = element_line(linewidth=0.5, colour = 'black'),
  172. panel.background = element_rect(color='black'),
  173. legend.key = element_blank()
  174. )
  175. p <- ggplot(d,aes(x=Count,y=Description,colour=-1*log10(p.adjust),size=Count))+
  176. geom_point()+
  177. scale_size(range=c(2, 8))+
  178. scale_colour_gradient(low = "blue",high = "red")+
  179. theme_bw()+
  180. ylab("GO_BP Pathway Terms")+
  181. xlab("Gene numbers")+
  182. labs(color=expression(-log[10](PValue)))+mytheme
  183. ggsave('GO_BP.pdf',plot = p,width = 10,height = 6)
  185. #########KEGG terms painting
  186. kegg_up <-data.frame(fread("KEGG_gene_enrichresults_tfusplustvc.csv",header = T))
  187. kegg_down <-data.frame(fread("KEGG_gene_enrichresults_tfusdowntvc.csv",header = T))
  188. d<-kegg_up
  189. d<-d[order(-d$Count),]
  190. d <-d[1:10,]
  191. d$Description <- factor(d$Description,levels=d$Description)
  192. kegg_up<-d
  193. d<-kegg_down
  194. d<-d[order(-d$Count),]
  195. d <-d[1:10,]
  196. d$Description <- factor(d$Description,levels=d$Description)
  197. kegg_down<-d
  198. kegg<-rbind(kegg_up,kegg_down)
  199. kegg$number <- factor(rev(1:nrow(kegg)))
  200. kegg$type<-factor(c(rep("Up", 10),rep("Down", 10)),levels=c("Up", "Down"))
  202. p <- ggplot(data=kegg, aes(x=number, y=Count, fill=type)) +
  203. geom_bar(stat="identity", width=0.8) + coord_flip() +
  204. scale_fill_manual(values = c('#FD8D62',"#8DA1CB")) + theme_test() +
  205. scale_x_discrete(labels=kegg$Description) +
  206. xlab("KEGG term") +
  207. theme(axis.text=element_text(face = "bold", color="gray50")) +
  208. labs(title = "The Most Enriched KEGG Terms")
  209. ggsave('kegg_all.pdf',plot = p,width = 10,height = 8)
  212. ################preparation file for cytoscape and Upset of GO terms
  213. library(UpSetR)
  215. go_results<- data.frame(fread("pathway_select.csv",header = T))
  216. colnames(go_results)[colnames(go_results)=="Type"]<-"Ontology"
  218. nodes_list <- list()
  219. edges_list <- list()
  220. for (i in 1:nrow(go_results)) {
  221. pathway <- go_results[i, ]
  222. genes <- unlist(strsplit(pathway$geneID, "/"))
  223. pathway_node <- data.frame(
  224. ID = pathway$ID,
  225. name = pathway$Description,
  226. Type = "Pathway",
  227. Ontology = pathway$Ontology,
  228. stringsAsFactors = FALSE
  229. )
  230. nodes_list[[i]] <- pathway_node
  232. gene_nodes <- data.frame(
  233. ID = genes,
  234. name = genes,
  235. Type = "Gene",
  236. Ontology = NA,
  237. stringsAsFactors = FALSE
  238. )
  239. nodes_list[[i]] <- rbind(nodes_list[[i]], gene_nodes)
  241. pathway_edges <- data.frame(
  242. fromNode_ID = rep(pathway$ID, length(genes)),
  243. fromNode_name = rep(pathway$Description, length(genes)),
  244. toNode_ID = genes,
  245. toNode_name = genes,
  246. Relations_Type = rep("Path-gene", length(genes)),
  247. Ontology = rep(pathway$Ontology, length(genes)),
  248. stringsAsFactors = FALSE
  249. )
  250. edges_list[[i]] <- pathway_edges
  251. }
  253. nodes <- do.call(rbind, nodes_list)
  254. edges <- do.call(rbind, edges_list)
  256. gene_freq<-table(edges$toNode_name)
  257. gene_freq_df <- as.data.frame(gene_freq)
  258. colnames(gene_freq_df) <- c("Gene", "Frequency")
  259. gene_freq_df <- gene_freq_df[order(-gene_freq_df$Frequency), ]
  260. select<-gene_freq_df[gene_freq_df$Frequency>=5,]
  261. p<-ggplot(select, aes(x = Gene, y = Frequency)) +
  262. geom_bar(stat = "identity") +
  263. theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  264. labs(title = "Gene Frequency in GO Pathways", x = "Gene", y = "Frequency")
  265. ggsave('gene_frequency_GO.pdf',plot =p,width = 10,height=6 )
  266. write.table(nodes, "nodes.txt", sep = "\t", row.names = FALSE)
  267. write.table(edges, "edges.txt", sep = "\t", row.names = FALSE)
  269. x <-list('GO:0042391'=edges_list[[1]]$toNode_ID,
  270. 'GO:1990351'=edges_list[[2]]$toNode_ID,
  271. 'GO:1902495'=edges_list[[3]]$toNode_ID,
  272. 'GO:0022804'=edges_list[[4]]$toNode_ID,
  273. 'GO:0046873'=edges_list[[5]]$toNode_ID,
  274. 'GO:0005216'=edges_list[[6]]$toNode_ID)
  275. p<-upset(fromList(x),
  276. nsets = 6,
  277. order.by = "freq",
  278. mainbar.y.label = "Intersection size",
  279. sets.x.label = "Set size",
  280. sets.bar.color = c("#b2e7cb","#ff0000","#ff0000","#b2d4ec","#b2d4ec","#ff0000"
  281. ),
  282. matrix.color = "black",
  283. main.bar.color = "black",
  284. text.scale = c(1.5, 1.5, 1.5, 1.5, 1.5, 1),
  285. shade.color = "gray88")
  287. #####heatmap painting
  288. library(ggplot2)
  289. library(reshape2)
  290. select <- data.frame(fread("filtered_expr.csv",header = T))
  291. row.names(select)<-select$GeneNames
  292. select<-select[,11:16]
  293. select<-log2(select+0.01)
  295. scaled_matrix <- scale(t(select))
  296. row.names(scaled_matrix)<-c('tFUS-1','tFUS-2','tFUS-3','Ctrl-1','Ctrl-2','Ctrl-3')
  297. melted_matrix <- melt(scaled_matrix)
  299. p<-ggplot(melted_matrix, aes(x = Var2, y = Var1, fill = value)) +
  300. geom_tile() +
  301. scale_fill_gradient2(low = "#008B8B", high = "red", mid = "white", midpoint = 0) +
  302. theme_minimal() +
  303. theme(axis.text.x =element_text(angle = 60, vjust = 0.5, hjust = 0.3))
  304. ggsave('heat_map_select.pdf',plot = p,width = 12,height = 3)

Bulk RNA-seq analysis workflow.R, under CC-BY-4.0 · at the source

Overview

Authors: Cheng Wu1,2,3, Jie You1,2,3, Tao Sheng1,2,3, Guo-Feng Li4, Can Zhang1,2,3, Li Liu5, Li-Zhen Xu6, Wei Xiong7, Fan Yang6, Wei Yang1,2,3,6, Wei-Bao Qiu8, Hai-Rong Zheng8, Xiang-Yao Li1,2,3
ORCID iDs: Jie You, Li-Zhen Xu, Wei Xiong, Wei Yang, Wei-Bao Qiu, Hai-Rong Zheng, Xiang-Yao Li
  1. Department of Psychiatry, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University,Yiwu, China
  2. Center for Membrane Receptors and Brain Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University,Yiwu, China
  3. NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain, Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University,Hangzhou, China
  4. School of Biomedical Engineering, Guangdong Medical University,Dongguan, China
  5. Core Facilities of the School of Medicine, Zhejiang University,Hangzhou, China
  6. Department of Biophysics, Institute of Neuroscience, Zhejiang University School of Medicine,Hangzhou, Zhejiang China
  7. Chinese Institute for Brain Research,Beijing, China
  8. Shenzhen Key Laboratory of Ultrasound Imaging and Therapy, Paul C. Lauterbur Research Center for Biomedical Imaging, State Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Shenzhen, China
Institutions: Zhejiang University (China); Guangdong Medical College (China); Chinese Institute for Brain Research (China); Shenzhen Institutes of Advanced Technology (China)
Journal: Nature communications, volume 17, issue 1, article 8129
Dates: received 13 March 2025; accepted 16 June 2026; published online 30 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74779-2 · PMID 42373633 · PMCID PMC13458073 · OpenAlex W7166572037
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Ion channels in the nervous system, Neurophysiology, Biomedical engineering
MeSH: Cerebral Cortex*, Neurons*, TRPC Cation Channels*, Animals, Early Growth Response Protein 1, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Ion Channels and Receptors (Sensory Systems, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82101250)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

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awnsjjj/TRPC4-TRPC5-are-critical-for-neuronal-modulation-by-tFUS-in-retrosplenial-cortex-in-male-mice

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At the source: github.com/awnsjjj/TRPC4-TRPC5-are-critical-for-neuronal-modulation-by-tFUS-in-retrosplenial-cortex-in-male-mice

Zenodo 15128201

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codeocean:1809197

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Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 8 MeSH terms, 1 funder, 69 references.

Cite

This paper

Wu, C., You, J., Sheng, T., Li, G.-F., Zhang, C., Liu, L., Xu, L.-Z., Xiong, W., Yang, F., Yang, W., Qiu, W.-B., Zheng, H.-R., & Li, X.-Y. (2026). TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice. Nature communications, 17(1), 8129. https://doi.org/10.1038/s41467-026-74779-2

BibTeX

@article{wu2026trpc4,
author = {Wu, Cheng and You, Jie and Sheng, Tao and Li, Guo-Feng and Zhang, Can and Liu, Li and Xu, Li-Zhen and Xiong, Wei and Yang, Fan and Yang, Wei and Qiu, Wei-Bao and Zheng, Hai-Rong and Li, Xiang-Yao},
title = {{TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8129},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74779-2},
url = {https://doi.org/10.1038/s41467-026-74779-2},
pmid = {42373633},
pmcid = {PMC13458073}
}

RIS

TY - JOUR
AU - Wu, Cheng
AU - You, Jie
AU - Sheng, Tao
AU - Li, Guo-Feng
AU - Zhang, Can
AU - Liu, Li
AU - Xu, Li-Zhen
AU - Xiong, Wei
AU - Yang, Fan
AU - Yang, Wei
AU - Qiu, Wei-Bao
AU - Zheng, Hai-Rong
AU - Li, Xiang-Yao
TI - TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/30
VL - 17
IS - 1
SP - 8129
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74779-2
UR - https://doi.org/10.1038/s41467-026-74779-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74779-2",
"type": "article-journal",
"title": "TRPC4/TRPC5 are critical for neuronal modulation by transcranial focused ultrasound in retrosplenial cortex in male mice",
"container-title": "Nature communications",
"author": [
{
"family": "Wu",
"given": "Cheng"
},
{
"family": "You",
"given": "Jie"
},
{
"family": "Sheng",
"given": "Tao"
},
{
"family": "Li",
"given": "Guo-Feng"
},
{
"family": "Zhang",
"given": "Can"
},
{
"family": "Liu",
"given": "Li"
},
{
"family": "Xu",
"given": "Li-Zhen"
},
{
"family": "Xiong",
"given": "Wei"
},
{
"family": "Yang",
"given": "Fan"
},
{
"family": "Yang",
"given": "Wei"
},
{
"family": "Qiu",
"given": "Wei-Bao"
},
{
"family": "Zheng",
"given": "Hai-Rong"
},
{
"family": "Li",
"given": "Xiang-Yao"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8129",
"DOI": "10.1038/s41467-026-74779-2",
"PMID": "42373633",
"PMCID": "PMC13458073",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74779-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}

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