OSCR

Putative glioblastoma origin-like cells in the subventricular zone: isolation and characterization.

Code ↔ Paper

6 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 6 matches
  1. [1] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/preprocess_v1.R, lines 324–387 · score 0.81 · ElbowPlot, IntegrateLayers, FindClusters, FindNeighbors, resolution, Harmony
  2. [2] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/celltype_assign_v1.Rmd, lines 6–127 · score 0.71 · snRNA, CN11, CN10, CN9, GN15, GN16
  3. [3] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/preprocess_v1.R, lines 223–277 · score 0.70 · GN5, v1, CN11, CN10, CN9, GN1
  4. [4] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/preprocess_v2.R, lines 19–124 · score 0.65 · FindClusters, FindNeighbors, Seurat, classified, cell
  5. [5] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/preprocess_v2.R, lines 19–124 · score 0.64 · parameter sweep, Doublet Finder, pK, v2, cells
  6. [6] § Materials and methods › Processing human snRNA-seq data ↔ paper-scripts/snRNA_preprocess/preprocess_v1.R, lines 101–205 · score 0.61 · parameter sweep, Doublet Finder, pK, cells

Paper

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The authors' code

R · 391 lines · 16 KB · no license · 3 matches

  1. pkgs = c("Seurat", "sctransform", "glmGamPoi", "DoubletFinder",
  2. "dplyr", "plyr", "tibble", "fs", "ggplot2", "parallel", "future")
  3. lapply(pkgs, library, character.only = TRUE)
  5. # read_file = function(f){
  6. # dat.fl = readRDS(f)}
  8. # 필터 정의
  9. min_cells = 3
  10. min_features = 200
  11. max_percent_mt = 10
  12. min_nFeature_RNA = 500
  13. max_nFeature_RNA = 10000
  14. min_nCount_RNA = 1000
  15. max_nCount_RNA = 30000
  17. # 파일 경로 탐색
  18. get_files = function(root_path) {
  19. files = dir_ls(root_path, recurse = TRUE, regexp = "filtered_feature_bc_matrix.h5$")
  20. return(files)
  21. }
  23. # file_path = "/Volumes/WindySSD/YUGBM_OC/cellranger/run_cellranger_count/CN8_RawSVZ_Nuclei_control1/outs/filtered_feature_bc_matrix.h5"
  25. # 수행할 작업 정의
  26. make_seurat_obj = function(file_path) {
  27. message(paste("Start with sample: ", basename(dirname(dirname(file_path)))))
  28. seurat_obj = CreateSeuratObject(counts = Read10X_h5(file_path, use.names = TRUE),
  29. min.cells = min_cells, min.features = min_features,
  30. project = "YUGBM")
  31. seurat_obj[["percent.mt"]] = PercentageFeatureSet(seurat_obj, pattern="^MT-")
  33. p_before_filtering = VlnPlot(seurat_obj, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) +
  34. labs(caption = "Before filtering")
  36. seurat_obj <- subset(seurat_obj,
  37. subset =
  38. percent.mt < max_percent_mt &
  39. nFeature_RNA > min_nFeature_RNA &
  40. nFeature_RNA < max_nFeature_RNA &
  41. nCount_RNA > min_nCount_RNA &
  42. nCount_RNA < max_nCount_RNA )
  44. p_after_filtering = VlnPlot(seurat_obj, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) +
  45. labs(caption = "After filtering")
  47. # SC transform
  48. # seurat_obj = SCTransform(seurat_obj, vars.to.regress = "percent.mt",
  49. # return.only.var.genes = TRUE, verbose = FALSE)
  50. seurat_obj = SCTransform(seurat_obj, vars.to.regress = "percent.mt",
  51. return.only.var.genes = FALSE, verbose = FALSE)
  52. seurat_obj = RunPCA(seurat_obj, verbose = FALSE)
  54. PCpercentage = seurat_obj@reductions$pca@stdev / sum(seurat_obj@reductions$pca@stdev) * 100
  55. PCcumulative = cumsum(PCpercentage)
  56. pc1 = which(PCcumulative > 90 & PCpercentage < 5)[1]
  57. pc2 = sort(which((PCpercentage[1:length(PCpercentage)-1] - PCpercentage[2:length(PCpercentage)]) > 0.1), decreasing = T)[1] + 1
  58. nPC = min(pc1, pc2)
  59. cat('selected PC value :', nPC, "\n")
  60. nPC = max(nPC, 10)
  62. seurat_obj = RunUMAP(seurat_obj, dims = 1:nPC, verbose = FALSE)
  63. seurat_obj = RunTSNE(seurat_obj, dims = 1:nPC, verbose = FALSE)
  65. seurat_obj = FindNeighbors(seurat_obj, dims = 1:nPC, verbose = FALSE)
  66. seurat_obj = FindClusters(seurat_obj, verbose = FALSE)
  68. sample = basename(dirname(dirname(file_path)))
  69. p_cluster_umap = DimPlot(seurat_obj, reduction="umap", pt.size = 0.3, label = TRUE) +
  70. labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  71. p_cluster_tsne = DimPlot(seurat_obj, reduction="tsne", pt.size = 0.3, label = TRUE) +
  72. labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  74. # save_dir = paste0(dirname(dirname(file_path)), "/seurat")
  75. save_dir = paste0(dirname(dirname(file_path)), "/sct")
  76. if(!dir.exists(save_dir)) { dir.create(save_dir, recursive = TRUE) }
  78. # output_path = file.path(save_dir, "seurat_sct.rds")
  79. output_path = file.path(save_dir, "seurat_sct_all_genes.rds")
  80. saveRDS(seurat_obj, output_path)
  82. # pdf(file.path(save_dir, "cluster.pdf"), width=7, height=6)
  83. # DimPlot(seurat_obj, reduction="umap", pt.size = 0.3, label = TRUE) +
  84. # labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  85. # DimPlot(seurat_obj, reduction="tsne", pt.size = 0.3, label = TRUE) +
  86. # labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  87. # dev.off()
  89. ggsave(filename = paste0(save_dir, "/cluster_umap.png"), plot = p_cluster_umap, width=6, height=5)
  90. ggsave(filename = paste0(save_dir, "/cluster_tsne.png"), plot = p_cluster_tsne, width=6, height=5)
  92. message(paste("Processed and saved: ", save_dir))
  93. }
  95. # 파일 경로 탐색
  96. get_seurat_sct = function(root_path) {
  97. files = dir_ls(root_path, recurse = TRUE, regexp = "seurat_sct_all_genes.rds")
  98. return(files)
  99. }
  101. # Doublet finder
  102. doublet_filtering = function(seurat_path) {
  104. message(paste("Start with sample: ", basename(dirname(dirname(seurat_path)))))
  105. seurat_obj = readRDS(seurat_path)
  106. PCpercentage = seurat_obj@reductions$pca@stdev / sum(seurat_obj@reductions$pca@stdev) * 100
  107. PCcumulative = cumsum(PCpercentage)
  108. pc1 = which(PCcumulative > 90 & PCpercentage < 5)[1]
  109. pc2 = sort(which((PCpercentage[1:length(PCpercentage)-1] - PCpercentage[2:length(PCpercentage)]) > 0.1), decreasing = T)[1] + 1
  110. nPC = min(pc1, pc2)
  111. nPC = max(nPC, 10)
  112. cat('selected PC value :', nPC, "\n")
  114. ## pK Identification (no ground-truth) ---------------------------------------------------------------------------------------
  115. sweep.res.list <- paramSweep(seurat_obj, PCs = 1:nPC, sct = TRUE)
  116. sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)
  117. bcmvn <- find.pK(sweep.stats)
  118. max_pK <- bcmvn[which.max(bcmvn$BCmetric)[1], ]$pK
  119. nPK <- as.numeric(levels( max_pK ))[ max_pK ]
  121. ## Homotypic Doublet Proportion Estimate -------------------------------------------------------------------------------------
  122. cells = ncol(seurat_obj)
  124. if(cells < 500 ){
  125. Doublet_percentage = 0.004
  126. } else if(500 <= cells & cells < 1000){
  127. Doublet_percentage = 0.008
  128. } else if(1000 <= cells & cells < 2000){
  129. Doublet_percentage = 0.016
  130. } else if(2000 <= cells & cells < 3000){
  131. Doublet_percentage = 0.024
  132. } else if(3000 <= cells & cells < 4000){
  133. Doublet_percentage = 0.032
  134. } else if(4000 <= cells & cells < 5000){
  135. Doublet_percentage = 0.040
  136. } else if(5000 <= cells & cells < 6000){
  137. Doublet_percentage = 0.048
  138. } else if(6000 <= cells & cells < 7000){
  139. Doublet_percentage = 0.056
  140. } else if(7000 <= cells & cells < 8000){
  141. Doublet_percentage = 0.064
  142. } else if(8000 <= cells & cells < 9000){
  143. Doublet_percentage = 0.072
  144. } else {
  145. Doublet_percentage = 0.080
  146. }
  148. # doublet rate : https://kb.10xgenomics.com/hc/en-us/articles/360001378811-What-is-the-maximum-number-of-cells-that-can-be-profiled
  150. homotypic.prop <- modelHomotypic([email hidden]$seurat_clusters) ## ex: annotations <- [email hidden]$ClusteringResults
  151. nExp_poi <- round(Doublet_percentage*nrow([email hidden])) ## Assuming 7.5% doublet formation rate - tailor for your dataset
  152. nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))
  154. seurat_obj <- doubletFinder(seurat_obj, PCs = 1:nPC, pN = 0.25, pK = nPK, nExp = nExp_poi, reuse.pANN = FALSE, sct = TRUE)
  155. cat('Total', cells, 'cells, Expected doublet percentage :', Doublet_percentage)
  157. n <-grepl('^pANN', colnames([email hidden]))
  158. stopifnot(sum(n)==1)
  159. nPANN = colnames([email hidden])[n]
  160. nClass = paste0("DF.classifications_", substr(nPANN, 6, nchar(nPANN)))
  162. ## Run DoubletFinder with varying classification stringencies ----------------------------------------------------------------
  163. seurat_obj <- doubletFinder(seurat_obj, PCs = 1:nPC, pN = 0.25, pK = nPK, nExp = nExp_poi.adj, reuse.pANN = nPANN, sct = TRUE)
  165. p <-grepl(nClass, colnames([email hidden]))
  166. stopifnot(sum(p)==1)
  167. colnames([email hidden])[p] <-'DFclass'
  169. # pdf(sprintf("%s-seurat-DimPlot-doubletFinder-%s.pdf", output_barcode, as.character(nPC)))
  170. # DimPlot(seurat_obj, group.by="DFclass", cols=c("red", "lightgray"), raster = TRUE) + NoAxes()
  171. # dev.off()
  172. n_singlet = length([email hidden]$DFclass[[email hidden]$DFclass=="Singlet"])
  173. n_doublet = length([email hidden]$DFclass[[email hidden]$DFclass=="Doublet"])
  175. save_dir = paste0(dirname(dirname(seurat_path)), "/sct")
  176. if(!dir.exists(save_dir)) { dir.create(save_dir, recursive = TRUE) }
  178. sample = basename(dirname(dirname(seurat_path)))
  179. p_cluster_umap = DimPlot(seurat_obj, reduction="umap", pt.size = 0.3, label = TRUE) +
  180. labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  182. p_doublet = DimPlot(seurat_obj, group.by="DFclass", cols=c("red", "lightgray"), pt.size = 0.3) + NoAxes() +
  183. labs(title = sample,
  184. subtitle = paste0("Doublet proportion: ", Doublet_percentage, "\n",
  185. "Doublet: ", n_doublet, "\n",
  186. "Singlet: ", n_singlet ),
  187. caption = paste0("selected PC: ", nPC, "\n",
  188. "selected pK: ", nPK) )
  189. ggsave(filename = paste0(save_dir, "/doublet.png"), plot = p_doublet, width=10, height=5)
  191. seurat_singlet = subset(seurat_obj, subset=DFclass=="Singlet")
  192. output_path = file.path(save_dir, "seurat_sct_singlet.rds")
  193. saveRDS(seurat_singlet, output_path)
  195. sample = basename(dirname(dirname(seurat_path)))
  196. p_cluster_umap = DimPlot(seurat_singlet, reduction="umap", pt.size = 0.3, label = TRUE) +
  197. labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  198. p_cluster_tsne = DimPlot(seurat_singlet, reduction="tsne", pt.size = 0.3, label = TRUE) +
  199. labs(title = sample, subtitle = paste0("selected PC: ", nPC)) + theme_classic()
  201. ggsave(filename = paste0(save_dir, "/cluster_umap_singlet.png"), plot = p_cluster_umap, width=6, height=5)
  202. ggsave(filename = paste0(save_dir, "/cluster_tsne_singlet.png"), plot = p_cluster_tsne, width=6, height=5)
  204. message(paste("Processed and saved: ", save_dir))
  205. }
  207. # 메인 함수 1
  208. main_process = function(root_path) {
  209. files = get_files(root_path)
  210. for (file in files) {
  211. make_seurat_obj(file)
  212. }
  213. }
  215. # 메인 함수 2
  216. doublet_finder_process = function(root_path) {
  217. files = get_seurat_sct(root_path)
  218. for (file in files) {
  219. doublet_filtering(file)
  220. }
  221. }
  223. # 실행
  224. root_path = "/Volumes/WindySSD/YUGBM_OC/cellranger/run_cellranger_count/"
  225. main_process(root_path)
  226. doublet_finder_process(root_path)
  228. # 메인 함수 2 실행
  229. #root_path = "/Volumes/WindySSD/YUGBM_OC/cellranger/run_cellranger_count_newsample/CN11-C"
  236. # 합친거
  246. #
  252. # read_file = function(f){
  253. # dat.fl = readRDS(f)}
  254. #
  255. # list = list.files(path="/Volumes/WindySSD/YUGBM_OC/cellranger/run_cellranger_count/", recursive=T, pattern="seurat_sct_singlet.rds", full.names=T)
  256. #
  257. # data = sapply(list, read_file)
  258. #
  259. # sample_code = c("CN10-V", "CN9-V",
  260. # "GC14-C", "GC14-T",
  261. # "GN1-V", "GN1-T",
  262. # "GN15-T1", "GN15-T2", "GN15-V",
  263. # "GN16-T1", "GN16-T2", "GN16-V",
  264. # "GN17-T1", "GN17-T2", "GN17-V",
  265. # "GN2-V", "GN2-T",
  266. # "GN3-T", "GN3-V",
  267. # "GN5-V", "GN5-T",
  268. # "GN6-V", "GN6-T",
  269. # "GN7-C", "GN7-V", "GN7-T",
  270. # "GN8-T", "GN8-V")
  271. #
  272. # data_merged = merge(x = data[[1]],
  273. # y = data[2:length(data)],
  274. # add.cell.ids = sample_code)
  275. # saveRDS(data_merged, "/Users/home/Desktop/project/gbmoc/data/merge/sct_merged_v1.RDS")
  276. #
  277. #
  279. # # Version 2 test (ver2.1에서 GN6 sample 제외)
  280. # list = list.files(path="/Volumes/WindySSD/YUGBM_OC/cellranger/ver2", recursive=T, pattern="seurat_sct_singlet.rds", full.names=T)
  281. # read_file = function(f){
  282. # dat.fl = readRDS(f)}
  283. # data = sapply(list, read_file)
  284. #
  285. # sample_code = c("CN10-V", "CN9-V",
  286. # "GC14-C", "GC14-T",
  287. # "GN1-V", "GN1-T",
  288. # "GN15-T1", "GN15-V",
  289. # "GN16-T1", "GN16-V",
  290. # "GN17-T1", "GN17-V",
  291. # "GN2-V", "GN2-T",
  292. # "GN3-T", "GN3-V",
  293. # "GN7-C", "GN7-V", "GN7-T",
  294. # "GN8-T", "GN8-V")
  295. #
  296. # merged = merge(x = data[[1]],
  297. # y = data[2:length(data)],
  298. # add.cell.ids = sample_code)
  299. # saveRDS(merged, "/Users/home/Desktop/project/gbmoc/data/merge/sct_merged_v2.RDS")
  300. # merged = readRDS("/Users/home/Desktop/project/gbmoc/data/merge/sct_merged_v2.RDS")
  301. #
  302. # # merged[["RNA"]] = split(merged[["RNA"]], f=merged$sample_barcode)
  303. # merged = readRDS("/Users/home/Desktop/project/gbmoc/data/merge/sct_merged_v2.RDS")
  304. # merged = SCTransform(merged, vst.flavor="v2")
  305. # merged = RunPCA(merged)
  306. # saveRDS(merged, "/Users/home/Desktop/project/gbmoc/data/merge/sct_merged_pca_v2.RDS")
  307. #
  308. # # Integrate (Harmony)
  309. # integrated = IntegrateLayers(
  310. # object = merged, method = HarmonyIntegration,
  311. # orig.reduction = "pca", new.reduction = "harmony",
  312. # normalization.method = "SCT",
  313. # assay = "SCT", verbose = TRUE)
  315. # Version 2.3 : 23 samples
  316. # Version 2.4 : 24 samples
  317. pkgs = c("Seurat", "sctransform", "glmGamPoi",
  318. "dplyr", "plyr", "tibble", "fs", "ggplot2", "parallel", "future")
  319. lapply(pkgs, library, character.only = TRUE)
  320. options(future.globals.maxSize = 50*1024^3)
  322. read_file = function(f){
  323. dat.fl = readRDS(f)}
  324. version_num = "v2.4"
  325. list = list.files(path=sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s",version_num), recursive=T, pattern="seurat_sct_singlet.rds", full.names=T)
  326. data = sapply(list, read_file)
  328. sample_code = c("CN10-V", "CN10-C", "CN11-C", "CN9-V",
  329. "GC14-C",
  330. "GN1-V", "GN1-T",
  331. "GN15-T1", "GN15-V",
  332. "GN16-T1", "GN16-V",
  333. "GN17-T1", "GN17-V",
  334. "GN2-V", "GN2-T",
  335. "GN3-T", "GN3-V",
  336. "GN6-V", "GN6-T",
  337. "GN7-C", "GN7-V", "GN7-T",
  338. "GN8-T", "GN8-V")
  340. merged = merge(x = data[[1]],
  341. y = data[2:length(data)],
  342. add.cell.ids = sample_code)
  344. [email hidden]$sample_barcode = sapply(strsplit(rownames([email hidden]), "_"), `[`, 1)
  345. [email hidden]$patient_barcode = sapply(strsplit([email hidden]$sample_barcode, "-"), `[`, 1)
  346. print( sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_merged_%s.RDS", version_num, version_num) )
  347. saveRDS(merged, sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_merged_%s.RDS", version_num, version_num))
  349. merged = readRDS(sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_merged_%s.RDS", version_num, version_num))
  350. merged = SCTransform(merged, vst.flavor="v2")
  351. merged = RunPCA(merged)
  352. saveRDS(merged, sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_merged_%s.RDS", version_num, version_num))
  355. integrated = IntegrateLayers(
  356. object = merged, method = HarmonyIntegration,
  357. orig.reduction = "pca", new.reduction = "harmony",
  358. normalization.method = "SCT",
  359. assay = "SCT", verbose = TRUE)
  360. # saveRDS(integrated, sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_integrated_%s.RDS", version_num, version_num))
  362. # Select PC value
  363. ElbowPlot(integrated)
  364. PCpercentage = integrated@reductions$pca@stdev / sum(integrated@reductions$pca@stdev) * 100
  365. PCcumulative = cumsum(PCpercentage)
  366. pc1 = which(PCcumulative > 90 & PCpercentage < 5)[1]
  367. pc2 = sort(which((PCpercentage[1:length(PCpercentage)-1] - PCpercentage[2:length(PCpercentage)]) > 0.1), decreasing = T)[1] + 1
  368. nPC = min(pc1, pc2)
  369. cat('selected PC value :', nPC)
  371. integrated = FindNeighbors(integrated, reduction = "harmony", dims=1:nPC)
  372. integrated = FindClusters(integrated, resolution = 0.5, cluster.name = "harmony_clusters")
  373. integrated = RunUMAP(integrated, reduction = "harmony", dims=1:nPC, resolution=0.5, reduction.name = "umap.harmony")
  374. integrated = RunTSNE(integrated, reduction = "harmony", dims=1:nPC, resolution=0.5, reduction.name = "tsne.harmony")
  376. [email hidden]$sample_barcode = sapply(strsplit(rownames([email hidden]), "_"), `[`, 1)
  377. [email hidden]$patient_barcode = sapply(strsplit([email hidden]$sample_barcode, "-"), `[`, 1)
  378. [email hidden]$sample_type1 = sapply(strsplit([email hidden]$sample_barcode, "-"), `[`, 2)
  380. DimPlot(integrated, reduction="tsne.harmony")
  381. DimPlot(integrated, reduction="umap.harmony")
  382. DimPlot(integrated, reduction="tsne.harmony", group.by="sample_barcode")
  383. DimPlot(integrated, reduction="umap.harmony", group.by="sample_barcode")
  384. DimPlot(integrated, reduction="tsne.harmony", group.by="patient_barcode")
  385. DimPlot(integrated, reduction="umap.harmony", group.by="patient_barcode")
  386. cat(sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_integrated_%s.RDS", version_num, version_num))
  387. saveRDS(integrated, sprintf("/Volumes/WindySSD/YUGBM_OC/cellranger/%s/sct_integrated_%s.RDS", version_num, version_num))

preprocess_v1.R at commit 1687e38, no license · at the source

Overview

Authors: Hyeong-Cheol Oh1,2,3, Ran Joo Choi1,2,4, Se-Young Jo5,6, Eunchae Yeo7, Euna Jo1,2,4,5, Jin-Kyoung Shim1,2,4, Kibyeong Kim1,2,4, Seo Jin Kim1,2,4, Hye Joung Cho1,2,4, Hyun Jung Kim8, Joo Ho Lee9, Seon-Jin Yoon1,2, Ryong Nam Kim1,2, Jeongsoo Won5, Jiho Park5, Seunghyun Kang5,10, Jihwan Yoo11, Ju-Hyung Moon1,2,4, Tae Hoon Roh1,2,4, Eui-Hyun Kim1,2,4
and 8 other authorsSe Hoon Kim12, Jong Hee Chang1,2,4, Albert H. Kim13,14, Hyun Seok Kim15, Jeong Ho Lee16, Hoon Kim7,10, Sangwoo Kim6,17, Seok-Gu Kang1,2,4,18
ORCID iDs: Hyeong-Cheol Oh, Se-Young Jo, Jeongsoo Won, Tae Hoon Roh, Se Hoon Kim, Hyun Seok Kim, Jeong Ho Lee, Sangwoo Kim, Seok-Gu Kang
18 affiliations
  1. Department of Neurosurgery, Brain Tumor Center, Severance Hospital, Yonsei University College of Medicine,Seoul, Republic of Korea
  2. Brain Tumor Translational Research Laboratory, Department of Biomedical Sciences, Yonsei University College of Medicine,Seoul, Republic of Korea
  3. Department of Pharmacology, Yonsei University College of Medicine,Seoul, Republic of Korea
  4. Brain Research Institute, Yonsei University College of Medicine,Seoul, Republic of Korea
  5. Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine,Seoul, Republic of Korea
  6. Department of Biomedical Systems Informatics, Yonsei University College of Medicine,Seoul, Republic of Korea
  7. Department of Biohealth Regulatory Science, School of Pharmacy, Sungkyunkwan University,Suwon, Republic of Korea
  8. Department of Anatomy, Korea University College of Medicine,Seoul, Republic of Korea
  9. Department of Radiation Oncology, Seoul National University Hospital, Seoul National University College of Medicine,Seoul, Republic of Korea
  10. Department of Biopharmaceutical Convergence, School of Pharmacy, Sungkyunkwan University,Suwon, Republic of Korea
  11. Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine,Seoul, Republic of Korea
  12. Department of Pathology, Severance Hospital, Yonsei University College of Medicine,Seoul, Republic of Korea
  13. Department of Neurosurgery, Washington University School of Medicine,St. Louis, MO USA
  14. The Brain Tumor Center, Siteman Cancer Center, Washington University School of Medicine St. Louis,St. Louis, MO USA
  15. Department of Biomedical Sciences, Yonsei University College of Medicine,Seoul, Republic of Korea
  16. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST),Daejeon, Republic of Korea
  17. Postech Biotech Center, Pohang University of Science and Technology (POSTECH),Pohang, Republic of Korea
  18. Department of Neurosurgery, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine,Seoul, Republic of Korea
Institutions: Yonsei University (South Korea); Sungkyunkwan University (South Korea); Korea University (South Korea); Seoul National University Hospital (South Korea); Washington University in St. Louis (United States); Korea Advanced Institute of Science and Technology (South Korea); Pohang University of Science and Technology (South Korea)
Journal: Experimental & molecular medicine, volume 58, issue 8, pages 2719-2732
Dates: received 3 February 2026; accepted 12 May 2026; published online 6 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s12276-026-01801-4 · PMID 42562888 · PMCID PMC13538383 · OpenAlex W7197047354
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: CNS cancer, Cancer genomics, Cancer stem cells, Stem-cell research, Cancer in the nervous system
MeSH: Brain Neoplasms*, Glioblastoma*, Lateral Ventricles*, Neoplastic Stem Cells*, Animals, Humans, Mice, Neural Stem Cells (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2024-00408191); Ministry of Health and Welfare (RS-2024-00438443, RS-2025-25467624); National Research Foundation of Korea(NRF) RS-2025-00523374; National Research Foundation of Korea(NRF) RS-2022-NR067592; National Research Foundation of Korea(NRF) RS-2024-00437820; National Research Foundation of Korea(NRF) RS-2023-00261820; Korean Ministry of Science and ICT NRF-2023R1A2C2005023; National Research Foundation of Korea(NRF) RS-2022-NR067592; National Research Foundation of Korea (NRF) RS-2023-00278314; National Research Foundation of Korea (NRF) RS-2023-00272621
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Glioblastoma (GBM) remains lethal despite maximal therapy. The adult subventricular zone (SVZ), a neural stem-cell niche, has been implicated as a potential site of origin, yet the identity and functional properties of putative GBM origin-like cells (GBM-OCs) within the SVZ remain unclear. An SVZ-restricted somatic mutation mouse model (Cre-induced EGFRvIII expression with Trp53 and Pten disruption) was established and mouse SVZ-derived cells were prospectively isolated for functional and molecular profiling. Self-renewal, multipotency, invasive potential and tumour-initiating capacity were assessed relative to control SVZ cells and matched tumour-derived tumourspheres. Whole-genome and RNA sequencing defined genomic and transcriptional alterations during early progression. Mouse GBM-OCs exhibited self-renewal and multilineage differentiation and initiated tumours only after re-implantation into the SVZ (11/29, 38%), whereas direct striatal implantation failed (0/25, 0%), indicating context-dependent tumorigenic potential associated with the SVZ microenvironment. In contrast, tumour-derived tumourspheres retained tumorigenic capacity upon implantation into both the SVZ and the striatum. During progression from mouse GBM-OCs to tumours, whole-chromosome and arm-level aneuploidies accumulated. In patients with GBM, multi-region single-nucleus RNA sequencing of tumour-free SVZ, matched tumours and tumour-free cortex identified rare neural stem cell-like, astrocyte-like and oligodendrocyte precursor-like SVZ populations transcriptionally aligned with GBM programmes. These cells showed single-nucleus RNA-inferred chromosome 7 gain and/or chromosome 10 loss signals, with concordant low-frequency copy-number alterations in the SVZ detected by exome sequencing and enriched in matched tumours. Together, these findings support the presence of SVZ-resident stem or progenitor-like populations with early GBM-associated features, consistent with putative GBM-OCs, and highlight the SVZ niche as a potential target for early detection and niche-informed therapeutic strategies.

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Repository

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hoonbiolab/yugbmoc_paper

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1687e383202ab474578ab20c0be50b11f7fb3cb7, 2 April 2026
Languages: R (6)
Size: 14 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggplot2 (5 files), data.table (3 files), pheatmap (3 files), Seurat (3 files), ComplexHeatmap (2 files), reshape2 (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/hoonbiolab/yugbmoc_paper

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

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

RNA sequencing data from mouse models and human whole-exome sequencing data have been deposited in the Sequence Read Archive (SRA) and are publicly available under accession numbers PRJNA1281712 and PRJNA470641, respectively. Single-nucleus RNA sequencing and bulk RNA sequencing data from human samples have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and are accessible through GEO Series accession numbers GSE301788 and GSE301596, respectively. Patient information and datasets used in this study are provided in Supplementary Tables 1–8. This study used custom Python scripts and open-source R packages (R version 4.3.2). The code used for the analysis has been deposited at: https://github.com/hoonbiolab/yugbmoc_paper.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 28 authors, 5 keywords, 8 MeSH terms, 5 funders, 54 references.

Cite

This paper

Oh, H.-C., Choi, R. J., Jo, S.-Y., Yeo, E., Jo, E., Shim, J.-K., Kim, K., Kim, S. J., Cho, H. J., Kim, H. J., Lee, J. H., Yoon, S.-J., Kim, R. N., Won, J., Park, J., Kang, S., Yoo, J., Moon, J.-H., Roh, T. H., . . . Kang, S.-G. (2026). Putative glioblastoma origin-like cells in the subventricular zone: isolation and characterization. Experimental & molecular medicine, 58(8), 2719-2732. https://doi.org/10.1038/s12276-026-01801-4

BibTeX

@article{oh2026putative,
author = {Oh, Hyeong-Cheol and Choi, Ran Joo and Jo, Se-Young and Yeo, Eunchae and Jo, Euna and Shim, Jin-Kyoung and Kim, Kibyeong and Kim, Seo Jin and Cho, Hye Joung and Kim, Hyun Jung and Lee, Joo Ho and Yoon, Seon-Jin and Kim, Ryong Nam and Won, Jeongsoo and Park, Jiho and Kang, Seunghyun and Yoo, Jihwan and Moon, Ju-Hyung and Roh, Tae Hoon and Kim, Eui-Hyun and Kim, Se Hoon and Chang, Jong Hee and Kim, Albert H. and Kim, Hyun Seok and Lee, Jeong Ho and Kim, Hoon and Kim, Sangwoo and Kang, Seok-Gu},
title = {{Putative glioblastoma origin-like cells in the subventricular zone: isolation and characterization}},
journal = {Experimental \& molecular medicine},
year = {2026},
month = aug,
volume = {58},
number = {8},
pages = {2719--2732},
publisher = {Korean Society for Biochemistry and Molecular Biology},
issn = {1226-3613},
doi = {10.1038/s12276-026-01801-4},
url = {https://doi.org/10.1038/s12276-026-01801-4},
pmid = {42562888},
pmcid = {PMC13538383}
}

RIS

TY - JOUR
AU - Oh, Hyeong-Cheol
AU - Choi, Ran Joo
AU - Jo, Se-Young
AU - Yeo, Eunchae
AU - Jo, Euna
AU - Shim, Jin-Kyoung
AU - Kim, Kibyeong
AU - Kim, Seo Jin
AU - Cho, Hye Joung
AU - Kim, Hyun Jung
AU - Lee, Joo Ho
AU - Yoon, Seon-Jin
AU - Kim, Ryong Nam
AU - Won, Jeongsoo
AU - Park, Jiho
AU - Kang, Seunghyun
AU - Yoo, Jihwan
AU - Moon, Ju-Hyung
AU - Roh, Tae Hoon
AU - Kim, Eui-Hyun
AU - Kim, Se Hoon
AU - Chang, Jong Hee
AU - Kim, Albert H.
AU - Kim, Hyun Seok
AU - Lee, Jeong Ho
AU - Kim, Hoon
AU - Kim, Sangwoo
AU - Kang, Seok-Gu
TI - Putative glioblastoma origin-like cells in the subventricular zone: isolation and characterization
T2 - Experimental & molecular medicine
J2 - Exp Mol Med
PY - 2026
DA - 2026/08/06
VL - 58
IS - 8
SP - 2719
EP - 2732
SN - 1226-3613
PB - Korean Society for Biochemistry and Molecular Biology
DO - 10.1038/s12276-026-01801-4
UR - https://doi.org/10.1038/s12276-026-01801-4
LA - en
ER -

CSL-JSON

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