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Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy.

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  1. [1] § Methods › Cell-Cell Communication Analysis ↔ 05_snRNA_cellchat.R, lines 182–253 · score 0.79 · groupSize, netVisual_aggregate, cell communication, Circle, vertex, CellChat
  2. [2] § Methods › SnATAC-Seq Data Processing and Analysis ↔ 07_snATAC_hclust_TF.R, the whole file · a weak match · score 0.55 · snapATAC, TF, barcodes, cut, Seurat, seq

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The authors' code

R · 258 lines · 13 KB · MIT · 1 match

  1. library(Seurat)
  2. library(dplyr)
  3. library(ggplot2)
  4. library(patchwork)
  5. library(CellChat)
  6. library(ggalluvial)
  7. library(ComplexHeatmap)
  8. library(NMF)
  9. library(circlize)
  10. options(stringsAsFactors = FALSE)
  11. options(future.globals.maxSize= 891289600)
  13. setwd("/mnt/alamo01/users/gbb/NDMG_snRNA/seurat/Myeloid/ELSA_out/")
  14. MM <- read.table("Myeloid_scale.data_top100")
  15. Microglia <- subset(MM, subset = MM$V4=="Microglia")
  16. Macrophage <- subset(MM, subset = MM$V4=="Macrophage")
  17. snRNA <- readRDS("../../snRNA_All.rds")
  18. for (i in 1:length([email hidden]$orig.ident)){
  19. if(names([email hidden][i]) %in% Microglia[,1])
  20. {[email hidden]$cellType[i] = "Microglia"}
  21. else if(names([email hidden][i]) %in% Macrophage[,1])
  22. {[email hidden]$cellType[i] = "Macrophage"}
  23. }
  25. ################################# Location ###############
  26. setwd("/mnt/alamo01/users/gbb/NDMG_snRNA/cellchat/")
  27. snRNA_OP <- snRNA[,snRNA$treatement == "ONC201" & snRNA$location == "Pons"]
  28. snRNA_OT <- snRNA[,snRNA$treatement == "ONC201" & snRNA$location == "Thalamus"]
  29. snRNA_SP <- snRNA[,snRNA$treatement == "Standard" & snRNA$location == "Pons"]
  30. snRNA_ST <- snRNA[,snRNA$treatement == "Standard" & snRNA$location == "Thalamus"]
  31. # snRNA_UP <- snRNA[,snRNA$treatement == "Untreated" & snRNA$location == "Pons"]
  32. # snRNA_UT <- snRNA[,snRNA$treatement == "Untreated" & snRNA$location == "Thalamus"]
  33. rm(snRNA)
  34. gc()
  35. #
  36. cellchat <- createCellChat(object = snRNA_OP, group.by = "cellType", assay = "RNA")
  37. # cellchat <- createCellChat(object = snRNA_OT, group.by = "cellType", assay = "RNA")
  38. # cellchat <- createCellChat(object = snRNA_SP, group.by = "cellType", assay = "RNA")
  39. # cellchat <- createCellChat(object = snRNA_ST, group.by = "cellType", assay = "RNA")
  40. CellChatDB <- CellChatDB.human
  41. CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling
  42. cellchat@DB <- CellChatDB.use
  43. cellchat <- subsetData(cellchat)
  44. future::plan("multisession", workers = 15)
  45. cellchat <- identifyOverExpressedGenes(cellchat)
  46. cellchat <- identifyOverExpressedInteractions(cellchat)
  47. cellchat <- projectData(cellchat, PPI.human)
  48. cellchat <- computeCommunProb(cellchat, raw.use = FALSE)
  49. cellchat <- computeCommunProbPathway(cellchat)
  50. cellchat <- aggregateNet(cellchat)
  51. cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
  52. cellchat@netP$pathways
  53. saveRDS(cellchat, file = "snRNA_ONC201_Pons_CellChat.rds")
  54. # saveRDS(cellchat, file = "snRNA_ONC201_Thalamus_CellChat.rds")
  55. # saveRDS(cellchat, file = "snRNA_Standard_Pons_CellChat.rds")
  56. # saveRDS(cellchat, file = "snRNA_Standard_Thalamus_CellChat.rds")
  58. setwd("/mnt/alamo01/users/gbb/NDMG_snRNA/cellchat/location/")
  64. #################################Treatment ###############
  65. setwd("/mnt/alamo01/users/gbb/NDMG_snRNA/cellchat/")
  66. snRNA_O <- snRNA[,snRNA$treatement == "ONC201"]
  67. snRNA_S <- snRNA[,snRNA$treatement == "Standard"]
  68. rm(snRNA)
  69. gc()
  71. cellchat <- createCellChat(object = snRNA_O, group.by = "cellType", assay = "RNA")
  72. # cellchat <- createCellChat(object = snRNA_S, group.by = "cellType", assay = "RNA")
  73. CellChatDB <- CellChatDB.human
  74. CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling
  75. cellchat@DB <- CellChatDB.use
  76. cellchat <- subsetData(cellchat)
  77. future::plan("multisession", workers = 4)
  78. cellchat <- identifyOverExpressedGenes(cellchat)
  79. cellchat <- identifyOverExpressedInteractions(cellchat)
  80. cellchat <- projectData(cellchat, PPI.human)
  81. cellchat <- computeCommunProb(cellchat, raw.use = FALSE)
  82. cellchat <- computeCommunProbPathway(cellchat)
  83. cellchat <- aggregateNet(cellchat)
  84. cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")
  85. cellchat@netP$pathways
  86. saveRDS(cellchat, file = "snRNA_ONC201_CellChat.rds")
  87. # saveRDS(cellchat, file = "snRNA_Untreated_CellChat.rds")
  88. # saveRDS(cellchat, file = "snRNA_Standard_CellChat.rds")
  89. }
  91. ############################ Compare
  92. ##################### ONC201_VS_Standard ################################
  93. cellchat.ONC201 <- readRDS("../snRNA_ONC201_CellChat.rds")
  94. cellchat.Standard <- readRDS("../snRNA_Standard_CellChat.rds")
  95. group.new = levels(cellchat.Standard@idents)
  96. cellchat.ONC201 <- liftCellChat(cellchat.ONC201, group.new)
  97. object.list <- list(ONC201 = cellchat.ONC201, Standard = cellchat.Standard)
  98. cellchat <- mergeCellChat(object.list, add.names = names(object.list), cell.prefix = TRUE)
  99. cellchat
  101. # Part I: Compare the total number of interactions and interaction strength
  102. pdf("ONC201_Standard_1.pdf")
  103. gg1 <- compareInteractions(cellchat, show.legend = F, group = c(1,2))
  104. gg2 <- compareInteractions(cellchat, show.legend = F, group = c(1,2), measure = "weight")
  105. gg1 + gg2
  106. #
  107. netVisual_diffInteraction(cellchat, weight.scale = T)
  108. netVisual_diffInteraction(cellchat, weight.scale = T, measure = "weight")
  109. #
  110. netVisual_heatmap(cellchat)
  111. netVisual_heatmap(cellchat, measure = "weight")
  112. # Compare the major sources and targets in 2D space
  113. num.link <- sapply(object.list, function(x) {rowSums(x@net$count) + colSums(x@net$count)-diag(x@net$count)})
  114. weight.MinMax <- c(min(num.link), max(num.link)) # control the dot size in the different datasets
  115. gg <- list()
  116. for (i in 1:length(object.list)) {
  117. gg[[i]] <- netAnalysis_signalingRole_scatter(object.list[[i]], title = names(object.list)[i],
  118. weight.MinMax = weight.MinMax)
  119. }
  120. patchwork::wrap_plots(plots = gg)
  121. # Subset T-cell and Tumor
  122. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Tumor")
  123. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "T-cells") #, signaling.exclude = c("MIF"))
  124. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "M0")
  125. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "M1")
  126. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "M2")
  127. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Neuron")
  128. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Astrocytes")
  129. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Oligodendrocytes")
  130. netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Endothelial-cells")
  132. # Identify and visualize the conserved and context-specific signaling pathways
  133. gg1 <- rankNet(cellchat, mode = "comparison", stacked = T, do.stat = TRUE, font.size = 6)
  134. gg2 <- rankNet(cellchat, mode = "comparison", stacked = F, do.stat = TRUE, font.size = 6)
  135. gg1 + gg2
  136. # Outgoing
  137. i = 1
  138. pathway.union <- union(object.list[[i]]@netP$pathways, object.list[[i+1]]@netP$pathways)
  139. ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "outgoing", signaling = pathway.union,
  140. title = names(object.list)[i], width = 5, height = 6,
  141. color.heatmap = "OrRd", font.size = 4)
  142. ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "outgoing", signaling = pathway.union,
  143. title = names(object.list)[i+1], width = 5, height = 6,
  144. color.heatmap = "OrRd", font.size = 4)
  145. draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))
  146. # Incoming
  147. ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "incoming", signaling = pathway.union,
  148. title = names(object.list)[i], width = 5, height = 6,
  149. color.heatmap = "OrRd", font.size = 4)
  150. ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "incoming", signaling = pathway.union,
  151. title = names(object.list)[i+1], width = 5, height = 6,
  152. color.heatmap = "OrRd", font.size = 4)
  153. draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))
  154. # Overall
  155. ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "all", signaling = pathway.union,
  156. title = names(object.list)[i], width = 5, height = 6,
  157. color.heatmap = "OrRd", font.size = 4)
  158. ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "all", signaling = pathway.union,
  159. title = names(object.list)[i+1], width = 5, height = 6,
  160. color.heatmap = "OrRd", font.size = 4)
  161. draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))
  162. dev.off()
  164. pdf("ONC201_Standard_2.pdf")
  165. for(i in 1:9){
  166. p <- netVisual_bubble(cellchat, sources.use = i, targets.use = c(1:9), comparison = c(1, 2),
  167. angle.x = 45, font.size = 4)
  168. plot(p)
  169. }
  170. #
  171. for(i in 1:9){
  172. p <- netVisual_bubble(cellchat, sources.use = i, targets.use = c(1:9), comparison = c(1, 2),
  173. max.dataset = 1, title.name = "Increased signaling in ONC201", angle.x = 45,
  174. remove.isolate = F, font.size = 4)
  175. plot(p)
  176. p <- netVisual_bubble(cellchat, sources.use = i, targets.use = c(1:9), comparison = c(1, 2),
  177. max.dataset = 2, title.name = "Decreased signaling in ONC201", angle.x = 45,
  178. remove.isolate = F, font.size = 4)
  179. plot(p)
  180. }
  182. # dysfunctional signaling by using DEGs
  183. pos.dataset = "ONC201"
  184. features.name = pos.dataset
  185. cellchat <- identifyOverExpressedGenes(cellchat, group.dataset = "datasets", pos.dataset = pos.dataset,
  186. features.name = features.name, only.pos = FALSE, thresh.pc = 0.1,
  187. thresh.fc = 0.1, thresh.p = 1)
  188. net <- netMappingDEG(cellchat, features.name = features.name)
  189. net.up <- subsetCommunication(cellchat, net = net, datasets = "ONC201",ligand.logFC = 0.2, receptor.logFC = 0.2) #???
  190. net.down <- subsetCommunication(cellchat, net = net, datasets = "Standard",ligand.logFC = -0.1, receptor.logFC = -0.1) #???
  191. gene.up <- extractGeneSubsetFromPair(net.up, cellchat)
  192. gene.down <- extractGeneSubsetFromPair(net.down, cellchat)
  193. pairLR.use.up = net.up[, "interaction_name", drop = F]
  194. pairLR.use.down = net.down[, "interaction_name", drop = F]
  195. for(i in c(1:3,6,7,9)){
  196. gg1 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.up, sources.use = i, targets.use = c(1:9),
  197. comparison = c(1, 2), angle.x = 45, remove.isolate = T,
  198. title.name = paste0("Up-regulated signaling in ", names(object.list)[1]))
  199. plot(gg1)
  200. gg2 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.down, sources.use = i, targets.use = c(1:9),
  201. comparison = c(1, 2), angle.x = 45, remove.isolate = T,
  202. title.name = paste0("Down-regulated signaling in ", names(object.list)[1]))
  203. plot(gg2)
  204. }
  206. for(i in c(1,2,6,9)){
  207. netVisual_chord_gene(object.list[[1]], sources.use = i, targets.use = c(1:9), slot.name = 'net',
  208. net = net.up, lab.cex = 0.8, small.gap = 3.5,
  209. title.name = paste0("Up-regulated signaling in ", names(object.list)[1]))
  210. } #
  211. for(i in c(1,2,4:9)){
  212. netVisual_chord_gene(object.list[[2]], sources.use = i, targets.use = c(1:9), slot.name = 'net',
  213. net = net.down, lab.cex = 0.8, small.gap = 3.5,
  214. title.name = paste0("Down-regulated signaling in ", names(object.list)[1]))
  215. } #
  216. dev.off()
  218. # Part IV: Visually compare cell-cell communication using Hierarchy plot, Circle plot or Chord diagram
  219. pathways_ONC201 <- object.list[[1]]@netP$pathways
  220. pathways_Standard <- object.list[[2]]@netP$pathways
  221. pathways.show <- intersect(pathways_Standard, pathways_ONC201)
  222. groupSize.ONC201 <- as.numeric(table(cellchat.ONC201@idents))
  223. groupSize.Standard <- as.numeric(table(cellchat.Standard@idents))
  224. # Circle
  225. pdf("ONC201_Standard_3.pdf")
  226. par(mfrow = c(1,2), xpd=TRUE)
  227. for(i in 1:length(pathways.show)){
  228. weight.max <- getMaxWeight(object.list, slot.name = c("netP"), attribute = pathways.show[i])
  229. netVisual_aggregate(object.list[[1]], signaling = pathways.show[i], layout = "circle", edge.weight.max = weight.max[1],
  230. vertex.weight = groupSize.ONC201, signaling.name = paste(pathways.show[i], names(object.list)[1]))
  231. netVisual_aggregate(object.list[[2]], signaling = pathways.show[i], layout = "circle", edge.weight.max = weight.max[1],
  232. vertex.weight = groupSize.Standard, signaling.name = paste(pathways.show[i], names(object.list)[2]))
  233. }
  234. # Heatmap
  235. par(mfrow = c(1,2), xpd=TRUE)
  236. for(i in 1:length(pathways.show)){
  237. ht <- list()
  238. ht[[1]] <- netVisual_heatmap(object.list[[1]], signaling = pathways.show[i], color.heatmap = "Reds",
  239. title.name = paste(pathways.show[i], "signaling ",names(object.list)[1]))
  240. ht[[2]] <- netVisual_heatmap(object.list[[2]], signaling = pathways.show[i], color.heatmap = "Reds",
  241. title.name = paste(pathways.show[i], "signaling ",names(object.list)[2]))
  242. ComplexHeatmap::draw(ht[[1]] + ht[[2]], ht_gap = unit(0.5, "cm"))
  243. }
  244. # Chord diagram
  245. par(mfrow = c(2,2), xpd=TRUE)
  246. for(i in 1:length(pathways.show)){
  247. netVisual_aggregate(object.list[[1]], signaling = pathways.show[i], layout = "chord",
  248. signaling.name = paste(pathways.show[i], names(object.list)[1]))
  249. netVisual_aggregate(object.list[[2]], signaling = pathways.show[i], layout = "chord",
  250. signaling.name = paste(pathways.show[i], names(object.list)[2]))
  251. }
  253. dev.off()

05_snRNA_cellchat.R at commit 6d5efbb, under MIT · at the source

Overview

Authors: Masahiro Okada1, Bavani Subramaniam2, Baobao Geng3, Jangham Jung1, Bohyeon Yu1, Felicia Lin1, Yu Fu3, Yishu Wang3, Weiqiang Yu3, Sandra Laternser4, Nalin Gupta1, Sabine Müller1,5, Lin Wang3, Aaron Diaz1, Javad Nazarian4
ORCID iDs: Bavani Subramaniam, Jangham Jung, Bohyeon Yu, Yu Fu, Weiqiang Yu, Nalin Gupta, Aaron Diaz, Javad Nazarian
  1. Department of Neurological Surgery, University of California, San Francisco, California, USA
  2. Center for Cancer and Immunology Research, Children’s National Hospital, Washington, District of Columbia, USA
  3. State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China
  4. Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
  5. Department of Neurology, University of California, San Francisco, California, USA
Institutions: University of California, San Francisco (United States); Children's National (United States); Chinese Academy of Medical Sciences & Peking Union Medical College (China); Suzhou Institute of Systems Medicine (China); University of Zurich (Switzerland); University Children's Hospital Zurich (Switzerland)
Journal: Neuro-oncology, volume 28, issue 9, pages 2248-2262
Dates: received 14 May 2025; accepted 6 May 2026; published online 24 May 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/neuonc/noag119 · PMID 42178382 · PMCID PMC13550678 · OpenAlex W7162292892
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: biomarkers, diffuse intrinsic pontine glioma, diffuse midline glioma, DIPG, DMG, Dordaviprone, imipridone, metabolism, Modeyso, ONC201, ONC206, scATAC-seq, scRNA-seq, single-cell genomics, TME, tumor microenvironment
MeSH: Antineoplastic Agents*, Brain Neoplasms*, Cell Nucleus*, Drug Resistance, Neoplasm*, Glioma*, Mitochondria*, Biomarkers, Tumor, Child, Child, Preschool, Female, Humans, Male (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: CAMS Innovation Fund for Medical Sciences (2023-I2M-2-005, 2022-I2M-1-024, 2022-I2M-2-004); NCI NIH HHS (R01CA246722, R01 CA246722); NLM NIH HHS (R01LM013897, R01 LM013897); Rising Tide Foundation (CCR-20-500); DIPG/DMG Research Funding Alliance (30008097); Basic Research Program of Jiangsu (BK20240025); Gusu Innovation and Entrepreneurship Leading Talents Program (ZXL2024381)
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

Background: Imipridone ONC201 is the first FDA-approved therapy for H3K27-altered diffuse midline glioma; however, clinical responses remain limited. Defining tumor-intrinsic determinants and microenvironmental, extrinsic factors that shape sensitivity or resistance to imipridones will identify actionable therapeutic opportunities and inform improved clinical strategies.

Methods: To identify mechanisms of imipridone resistance, we obtained postmortem brain tissue from DMG patients who had received imipridones and/or standard care. Single-nucleus RNA and open-chromatin sequencing were performed on N = 22 cases. Immunofluorescence-based myeloid phenotyping was performed on N = 46 cases. Mitochondrial copy-number analysis was performed on N = 19 cases. Validation of imipridone sensitivity, its effect on mitochondrial density, and its synergy with inhibition of mitochondrial biogenesis were assessed in DMG primary cells.

Results: We established a single-cell RNA/open-chromatin atlas from postmortem DMG cases and found imipridone treatment resulting in regressed mesenchymal transition, reduced myeloid-derived suppressive cells, and reversed aberrant H3K27-altered enhancer activity. Resistant tumors showed increased mitochondrial density, turnover, and membrane potential. Mitochondrial biogenesis and PPARGC1A emerged as resistance biomarkers and actionable targets.

Conclusions: These studies implicate mitochondrial biogenesis as a biomarker of imipridone resistance and a focus for the development of combinatorial strategies to provide effective therapeutic options for a challenging pediatric brain tumor.

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 2 matches between paragraphs and lines of code.

CancerGenomicsLab/DMG_ONC201

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 6d5efbb554bb8428b07628dd5bd5cdd870ef23d6, 20 August 2025
Languages: R (6), Python (1)
Size: 16 files, 7 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (6 files), tidyverse (6 files), ggplot2 (4 files), patchwork (4 files), ggpubr (3 files), pheatmap (3 files), UMAP (3 files), anndata (1 file), circlize (1 file), ComplexHeatmap (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
9 files
At the source: github.com/CancerGenomicsLab/DMG_ONC201

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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;
  • 7 scripts, each with its path and the digest of its content;
  • 2 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 and Code Availability

The study data are available and can be visualized on the UCSC Cell browser (https://cells-test.gi.ucsc.edu/? ds=brain-dmg-multiomics+snrna-seq-data (https://cells-test.gi.ucsc.edu/?ds=brain-dmg-multiomics+snrna-seq-data)). All the custom code used is available from GitHub at https://github.com/CancerGenomicsLab/DMG_ONC201. The raw sequenced reads are available from the European Genome-Phenome Archive (EGAS50000001447).

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 16 keywords, 12 MeSH terms, 7 funders, 45 references.

Cite

This paper

Okada, M., Subramaniam, B., Geng, B., Jung, J., Yu, B., Lin, F., Fu, Y., Wang, Y., Yu, W., Laternser, S., Gupta, N., Müller, S., Wang, L., Diaz, A., & Nazarian, J. (2026). Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy. Neuro-oncology, 28(9), 2248-2262. https://doi.org/10.1093/neuonc/noag119

BibTeX

@article{okada2026single,
author = {Okada, Masahiro and Subramaniam, Bavani and Geng, Baobao and Jung, Jangham and Yu, Bohyeon and Lin, Felicia and Fu, Yu and Wang, Yishu and Yu, Weiqiang and Laternser, Sandra and Gupta, Nalin and Müller, Sabine and Wang, Lin and Diaz, Aaron and Nazarian, Javad},
title = {{Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy}},
journal = {Neuro-oncology},
year = {2026},
month = sep,
volume = {28},
number = {9},
pages = {2248--2262},
publisher = {Oxford University Press},
issn = {1522-8517},
doi = {10.1093/neuonc/noag119},
url = {https://doi.org/10.1093/neuonc/noag119},
pmid = {42178382},
pmcid = {PMC13550678}
}

RIS

TY - JOUR
AU - Okada, Masahiro
AU - Subramaniam, Bavani
AU - Geng, Baobao
AU - Jung, Jangham
AU - Yu, Bohyeon
AU - Lin, Felicia
AU - Fu, Yu
AU - Wang, Yishu
AU - Yu, Weiqiang
AU - Laternser, Sandra
AU - Gupta, Nalin
AU - Müller, Sabine
AU - Wang, Lin
AU - Diaz, Aaron
AU - Nazarian, Javad
TI - Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy
T2 - Neuro-oncology
J2 - Neuro Oncol
PY - 2026
DA - 2026/09/01
VL - 28
IS - 9
SP - 2248
EP - 2262
SN - 1522-8517
PB - Oxford University Press
DO - 10.1093/neuonc/noag119
UR - https://doi.org/10.1093/neuonc/noag119
LA - en
ER -

CSL-JSON

{
"id": "10.1093/neuonc/noag119",
"type": "article-journal",
"title": "Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy",
"container-title": "Neuro-oncology",
"author": [
{
"family": "Okada",
"given": "Masahiro"
},
{
"family": "Subramaniam",
"given": "Bavani"
},
{
"family": "Geng",
"given": "Baobao"
},
{
"family": "Jung",
"given": "Jangham"
},
{
"family": "Yu",
"given": "Bohyeon"
},
{
"family": "Lin",
"given": "Felicia"
},
{
"family": "Fu",
"given": "Yu"
},
{
"family": "Wang",
"given": "Yishu"
},
{
"family": "Yu",
"given": "Weiqiang"
},
{
"family": "Laternser",
"given": "Sandra"
},
{
"family": "Gupta",
"given": "Nalin"
},
{
"family": "Müller",
"given": "Sabine"
},
{
"family": "Wang",
"given": "Lin"
},
{
"family": "Diaz",
"given": "Aaron"
},
{
"family": "Nazarian",
"given": "Javad"
}
],
"container-title-short": "Neuro Oncol",
"volume": "28",
"issue": "9",
"page": "2248-2262",
"DOI": "10.1093/neuonc/noag119",
"PMID": "42178382",
"PMCID": "PMC13550678",
"ISSN": "1522-8517",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/neuonc/noag119",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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