OSCR

Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression.

Code ↔ Paper

4 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 4 matches
  1. [1] § Materials and methods › Single-nucleus RNA sequencing annotation ↔ celltype_analysis.Rmd, lines 265–283 · score 0.93 · L6 CT, Sst Chodl, Glutamatergic Neurons, GABAergic, median, Car3
  2. [2] § Materials and methods › snRNA-seq raw data processing, integration, clustering analysis ↔ celltype_analysis.Rmd, lines 224–249 · score 0.78 · finding variable, module scoring, log normalization, neighbors, resolution, regression
  3. [3] § Materials and methods › Inferred CNV analysis ↔ infercnv.Rmd, lines 6–31 · score 0.67 · infer CNV, HMM, denoise, Genomics, cutoff, raw
  4. [4] § Materials and methods › Cell cycle position analysis with TRICYCLE ↔ tricycle.Rmd, lines 120–184 · score 0.61 · project_cycle_space, cycle position, TRICYCLE, cell, Tumor

Paper

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The authors' code

R Markdown · 321 lines · 13 KB · no license · 2 matches

  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. editor_options:
  5. chunk_output_type: console
  6. ---
  9. ```{r}
  10. library(Seurat)
  11. library(ggplot2)
  12. library(dplyr)
  13. library(RColorBrewer)
  14. ```
  16. ```{r}
  17. setwd("C:/Users/denni/OneDrive/Desktop/Frontiers Resubmission/bioinformatics work/")
  19. nBrain <- Read10X(data.dir = "filtered_cellranger_output//10X_5KadultMouse/5k_mouse_brain_CNIK_3pv3_raw_feature_bc_matrix/")
  20. nBrain_seurat <- CreateSeuratObject(counts = nBrain, project = "normal Brain", min.features = 150)
  21. nBrain_seurat[["percent.mt"]] <- PercentageFeatureSet(nBrain_seurat, pattern = "^mt-")
  22. nBrain_subset <- subset(nBrain_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000 & percent.mt < 10)
  23. nBrain_subset <- subset(nBrain_subset, cells = sample(names(nBrain_subset$orig.ident), size = 5000))
  25. Early_L2 <- Read10X(data.dir = "filtered_cellranger_output/Early-stage L2/")
  26. Early_L2_seurat <- CreateSeuratObject(counts = Early_L2, project = "E1", min.features = 150)
  27. Early_L2_seurat[["percent.mt"]] <- PercentageFeatureSet(Early_L2_seurat, pattern = "^mt-")
  28. Early_L2_subset <- subset(Early_L2_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  30. Early_R2L3 <- Read10X(data.dir = "filtered_cellranger_output/Early-stage R2L3/")
  31. Early_R2L3_seurat <- CreateSeuratObject(counts = Early_R2L3, project = "E2", min.features = 150)
  32. Early_R2L3_seurat[["percent.mt"]] <- PercentageFeatureSet(Early_R2L3_seurat, pattern = "^mt-")
  33. Early_R2L3_subset <- subset(Early_R2L3_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  35. Early_R2L5 <- Read10X(data.dir = "filtered_cellranger_output/Early-stage R2L5/")
  36. Early_R2L5_seurat <- CreateSeuratObject(counts = Early_R2L5, project = "E3", min.features = 150)
  37. Early_R2L5_seurat[["percent.mt"]] <- PercentageFeatureSet(Early_R2L5_seurat, pattern = "^mt-")
  38. Early_R2L5_subset <- subset(Early_R2L5_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  40. Late_L3 <- Read10X(data.dir = "filtered_cellranger_output/Late-stage L3/")
  41. Late_L3_seurat <- CreateSeuratObject(counts = Late_L3, project = "L1", min.features = 150)
  42. Late_L3_seurat[["percent.mt"]] <- PercentageFeatureSet(Late_L3_seurat, pattern = "^mt-")
  43. Late_L3_subset <- subset(Late_L3_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  45. Late_L5 <- Read10X(data.dir = "filtered_cellranger_output/Late-stage L5/")
  46. Late_L5_seurat <- CreateSeuratObject(counts = Late_L5, project = "L2", min.features = 150)
  47. Late_L5_seurat[["percent.mt"]] <- PercentageFeatureSet(Late_L5_seurat, pattern = "^mt-")
  48. Late_L5_subset <- subset(Late_L5_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  50. Late_R2L2 <- Read10X(data.dir = "filtered_cellranger_output/Late-stage R2L2/")
  51. Late_R2L2_seurat <- CreateSeuratObject(counts = Late_R2L2, project = "L3", min.features = 150)
  52. Late_R2L2_seurat[["percent.mt"]] <- PercentageFeatureSet(Late_R2L2_seurat, pattern = "^mt-")
  53. Late_R2L2_subset <- subset(Late_R2L2_seurat, subset = nFeature_RNA >= 600 & nFeature_RNA <= 6000)
  54. ```
  56. ```{r}
  57. remove(Early_L2)
  58. remove(Early_L2_seurat)
  59. remove(Early_R2L3)
  60. remove(Early_R2L3_seurat)
  61. remove(Early_R2L5)
  62. remove(Early_R2L5_seurat)
  64. remove(nBrain)
  65. remove(nBrain_seurat)
  67. remove(Late_L3)
  68. remove(Late_L3_seurat)
  69. remove(Late_L5)
  70. remove(Late_L5_seurat)
  71. remove(Late_R2L2)
  72. remove(Late_R2L2_seurat)
  73. ```
  76. ```{r}
  77. Tumors.merged <- merge(x = Early_R2L3_subset,
  78. y = c(Early_R2L5_subset, Early_L2_subset,
  79. Late_L3_subset, Late_L5_subset, Late_R2L2_subset),
  80. add.cell.ids = c("Early_R2L3", "Early_R2L5", "Early_L2",
  81. "Late_L3", "Late_L5", "Late_R2L2"),
  82. project = "BBp53n Tumors")
  84. [email hidden]$tissue <- "NA"
  86. [email hidden][[email hidden]$orig.ident %in% "E1", ]$tissue <- "Early-Tumors"
  87. [email hidden][[email hidden]$orig.ident %in% "E2", ]$tissue <- "Early-Tumors"
  88. [email hidden][[email hidden]$orig.ident %in% "E3", ]$tissue <- "Early-Tumors"
  89. [email hidden][[email hidden]$orig.ident %in% "L1", ]$tissue <- "Late-Tumors"
  90. [email hidden][[email hidden]$orig.ident %in% "L2", ]$tissue <- "Late-Tumors"
  91. [email hidden][[email hidden]$orig.ident %in% "L3", ]$tissue <- "Late-Tumors"
  92. #[email hidden][[email hidden]$orig.ident %in% "normal Brain", ]$tissue <- "normal-tissue"
  96. #Late_L2.merged[["RNA"]] <- split(Late_L2.merged[["RNA"]], f = Late_L2.merged$tissue)
  97. #Late_L2.merged
  98. table(Tumors.merged$orig.ident)
  99. ```
  101. ```{r}
  102. res <- 1.0
  103. ndims <- 20
  104. vars.reg <- c('nCount_RNA')
  106. Tumors.merged <- NormalizeData(Tumors.merged, normalization.method = "LogNormalize", scale.factor = 1000)
  107. Tumors.merged <- FindVariableFeatures(Tumors.merged, selection.method = "vst", nfeatures = 3000)
  109. hvg <- VariableFeatures(Tumors.merged)
  110. var_regex = 'Rik$|^mt|^Gm' # remove pseudo, predicted, mitochondria genes from clustering analysis
  111. hvg = grep(var_regex, hvg, invert = T, value=T)
  112. ```
  116. ```{r}
  117. Tumors.merged %<>%
  118. ScaleData(vars.to.regress = vars.reg) %>%
  119. RunPCA(features = hvg, npcs = 50) %>%
  120. FindNeighbors(dims = 1:ndims) %>%
  121. FindClusters(resolution = res) %>%
  122. RunUMAP(dims = 1:ndims, reduction = "pca", n.neighbors = 200)
  124. DimHeatmap(Tumors.merged, dims = 1:9, cells = 500, balanced = T)
  126. DimPlot(Tumors.merged, reduction = "umap", group.by = "orig.ident", label = TRUE, label.box = FALSE, raster = FALSE) +
  127. ggtitle("Tumors merged") +
  128. theme_bw(base_size = 10)
  130. Tumors.merged <- IntegrateLayers(object = Tumors.merged,
  131. method = RPCAIntegration,
  132. orig.reduction = "pca",
  133. new.reduction = "rpca",
  134. verbose = TRUE)
  136. Tumors.merged <- FindNeighbors(Tumors.merged, reduction = "rpca", dims = 1:20)
  137. Tumors.merged <- FindClusters(Tumors.merged, resolution = 1)
  138. Tumors.merged <- RunUMAP(Tumors.merged, reduction = "rpca", dims = 1:20, n.neighbors = 100)
  140. DimPlot(Tumors.merged, reduction = "umap", group.by = "tissue", label = TRUE, label.box = TRUE, raster = FALSE, pt.size = 0.5, alpha = 0.1, cols = c("#F8766D", "#00BF7D")) +
  141. ggtitle("") +
  142. theme_bw(base_size = 15)
  144. ```
  146. Feature Plots for OPC-like and glioma markers
  147. ```{r}
  148. library(viridis)
  151. FeaturePlot(Tumors.merged, features = c("Top2a"), reduction = "umap", label = FALSE, order = TRUE, pt.size = 0.5) &
  152. scale_colour_viridis(option = "magma")
  154. FeaturePlot(Tumors.merged, features = c("Myc"), reduction = "umap", label = FALSE, order = TRUE, pt.size = 0.5) &
  155. scale_colour_viridis(option = "magma")
  157. #FeaturePlot(Tumors.merged, features = c("Ppp1r14b"), reduction = "umap", label = FALSE, order = TRUE, pt.size = 1.0) &
  158. scale_colour_viridis(option = "magma")
  160. FeaturePlot(Tumors.merged, features = c("Sox2"), reduction = "umap", label = FALSE, order = TRUE, pt.size = 1.0) &
  161. scale_colour_viridis(option = "magma")
  163. ```
  165. Find Markers
  166. ```{r}
  167. Tumors.merged[["joined"]] <- JoinLayers(Tumors.merged[["RNA"]])
  168. DefaultAssay(Tumors.merged) <- "joined"
  169. markers_df <- FindAllMarkers(Tumors.merged,
  170. logfc.threshold = 0.1,
  171. min.pct = 0.01,
  172. only.pos = FALSE)
  173. ```
  177. Cell Annotation by Azimuth
  178. ```{r}
  179. library(future)
  180. library(Azimuth)
  181. plan("multicore", workers = 4)
  182. options(future.globals.maxSize = 8000 * 1024^4)
  183. Tumors.merged <- RunAzimuth(Tumors.merged, reference = "mousecortexref", )
  185. #Idents(Tumors.Tumors.merged) <- "predicted.subclass"
  186. DimPlot(Tumors.merged, group.by = "predicted.class", label = TRUE, label.box = TRUE) &
  187. ggtitle("Tumors.merged Neuron Annotation")
  189. DimPlot(Tumors.merged, group.by = "predicted.subclass", label = TRUE, label.box = FALSE) &
  190. ggtitle("Tumors.merged CNS Annotation")
  192. ```
  194. Module scoring
  195. ```{r}
  196. gene_sets <- read.csv("C:/Users/denni/OneDrive/Desktop/Frontiers Resubmission/bioinformatics work/Genesets/all_genesets.csv", na.strings = "none")
  198. for (x in 1:length(colnames(gene_sets))) {
  199. Tumors.merged <- AddModuleScore(Tumors.merged, assay = "joined", features = list(gene_sets[gene_sets[,x] != "", x]),name = colnames(gene_sets)[x])
  200. }
  202. FeaturePlot(Tumors.merged, features = c("OPC1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  203. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  205. FeaturePlot(Tumors.merged, features = c("OL1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  206. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  208. FeaturePlot(Tumors.merged, features = c("ASTROCYTE1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  209. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  211. FeaturePlot(Tumors.merged, features = c("MICROGLIA1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  212. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  214. FeaturePlot(Tumors.merged, features = c("ENDOTHELIAL1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  215. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  217. FeaturePlot(Tumors.merged, features = c("NEURON1"), reduction = "umap", split.by = "tissue", label = TRUE, order = TRUE, pt.size = 1.0)&
  218. scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdYlBu")))
  220. ```
  224. Normal brain dataset
  225. ```{r}
  226. res <- 0.5
  227. ndims <- 20
  228. vars.reg <- c('nCount_RNA')
  230. nBrain_subset %<>%
  231. NormalizeData(normalization.method = "LogNormalize", scale.factor = 1000) %>%
  232. FindVariableFeatures(selection.method = "vst", nfeatures = 3000) %>%
  233. ScaleData(vars.to.regress = "nCount_RNA") %>%
  234. RunPCA(npcs = 50) %>%
  235. FindNeighbors(dims = 1:ndims) %>%
  236. FindClusters(resolution = res) %>%
  237. RunUMAP(dims = 1:ndims, reduction = "pca", n.neighbors = 200)
  239. gene_sets <- read.csv("C:/Users/denni/OneDrive/Desktop/Frontiers Resubmission/bioinformatics work/Genesets/all_genesets.csv", na.strings = "none")
  241. for (x in 1:length(colnames(gene_sets))) {
  242. nBrain_subset <- AddModuleScore(nBrain_subset, assay = "RNA", features = list(gene_sets[gene_sets[,x] != "", x]),name = colnames(gene_sets)[x])
  243. }
  244. library(viridis)
  245. DimPlot(nBrain_subset, reduction = "umap", label = TRUE, label.box = FALSE, raster = FALSE) +
  246. ggtitle("Normal Brain") +
  247. theme_bw(base_size = 10)
  249. ```
  251. Label metadata
  252. ```{r}
  253. [email hidden]$celltype <- "Neuron"
  255. [email hidden][[email hidden]$seurat_clusters == 1, ]$celltype <- "Oligo"
  256. [email hidden][[email hidden]$seurat_clusters == 2, ]$celltype <- "Astro"
  257. [email hidden][[email hidden]$seurat_clusters == 9, ]$celltype <- "Microglia"
  258. [email hidden][[email hidden]$seurat_clusters == 13, ]$celltype <- "OPC"
  259. [email hidden][[email hidden]$seurat_clusters == 15, ]$celltype <- "Endo"
  260. [email hidden][[email hidden]$seurat_clusters == 17, ]$celltype <- "Other"
  261. ```
  265. ```{r}
  266. library(pals)
  268. Glut_neurons <- rownames([email hidden][[email hidden]$predicted.subclass %in% c("L2/3 IT", "L5 ET", "L5 IT", "L5/6 NP", "L6 CT", "L6 IT", "L6 IT Car3","L6b"), ])
  270. GABA_neurons <- rownames([email hidden][[email hidden]$predicted.subclass %in% c("Lamp5", "Meis2", "Pvalb", "Sncg", "Sst", "Sst Chodl", "Vip"), ])
  272. [email hidden]$celltype <- [email hidden]$predicted.subclass
  274. [email hidden][Glut_neurons, ]$celltype <- "Glutamatergic Neuron"
  275. [email hidden][GABA_neurons, ]$celltype <- "GABAergic Neuron"
  277. [email hidden][rownames(filter([email hidden], predicted.subclass.score <= 0.75 & mapping.score <= 0.75 & OPC1 >= median([email hidden]$OPC1))), ]$celltype <- "OPC-like"
  279. DimPlot(nBrain_subset, reduction = "umap", group.by = "celltype", label = FALSE, label.box = FALSE, raster = FALSE) +
  280. ggtitle("Normal Brain") +
  281. theme_bw(base_size = 10) +
  282. scale_colour_manual(values = glasbey())
  283. ```
  285. ```{r}
  286. gene_sigs <- colnames([email hidden])[17:38]
  288. Glut_neurons <- rownames([email hidden][[email hidden]$predicted.subclass %in% c("L2/3 IT", "L5 ET", "L5 IT", "L5/6 NP", "L6 CT", "L6 IT", "L6 IT Car3","L6b"), ])
  290. GABA_neurons <- rownames([email hidden][[email hidden]$predicted.subclass %in% c("Lamp5", "Meis2", "Pvalb", "Sncg", "Sst", "Sst Chodl", "Vip"), ])
  292. [email hidden]$celltype <- [email hidden]$predicted.subclass
  294. [email hidden][Glut_neurons, ]$celltype <- "Glutamatergic Neuron"
  295. [email hidden][GABA_neurons, ]$celltype <- "GABAergic Neuron"
  297. [email hidden][rownames(filter([email hidden], predicted.subclass.score <= 0.75 & mapping.score <= 0.75 & OPC1 >= quantile([email hidden]$OPC1)[2])), ]$celltype <- "OPC-like"
  300. DimPlot(Tumors.merged, reduction = "umap", group.by = "celltype", label = TRUE, label.box = FALSE, raster = FALSE, pt.size = 1) +
  301. ggtitle("Tumors merged") +
  302. theme_bw(base_size = 10) +
  303. scale_colour_manual(values = glasbey())
  305. ```

celltype_analysis.Rmd at commit d8ef78d, no license · at the source

Overview

Authors: Dennis Huang1,2, Angeliki Mela3, Hye-Jin Park2, Peter Canoll3, Patrizia Casaccia1,2
  1. Program in Molecular, Cellular and Developmental Biology at The Graduate Center of The City University of New, New York, NY, United States
  2. Neuroscience Initiative, Advance Science Research Center, Graduate Center of The City University of New York, New York, NY, United States
  3. Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, United States
Institutions: The Graduate Center, CUNY (United States); City University of New York (United States); Columbia University Irving Medical Center (United States)
Journal: Frontiers in cellular neuroscience, volume 20, article 1713437
Dates: received 26 September 2025; accepted 26 May 2026; published online 30 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncel.2026.1713437 · PMID 42539873 · PMCID PMC13424661 · OpenAlex W7166560337
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other condition (population), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, Connectivity
Keywords: brain tumor, glia, neuron, p53, PDGF, progenitor, transcription
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (R35 NS111604, R01 NS103473); NCI NIH HHS (P30 CA013696, U54 CA274504)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Gliomas are characterized by poor survival rate and limited options for treatment. Based on the transcriptional enrichment for oligodendrocyte progenitor cell (OPC) transcripts, the proneural glioma is thought to arise from transformation of OPCs. Here, we injected mutant BB-p53n OPCs (with Trp53 deletion and PDGF-BB overexpression) into recipient mice and performed single-nucleus RNA sequencing (snRNA-seq) of the injected cells and of brain tissue at early and late time points after injection, coincident with neuroimaging detection of tumoral masses. Analysis of tumor-bearing brain samples, identified a cluster that was not detected in the normal brain, but was enriched for OPC markers (Olig2), cell cycle genes (Myc) and glioma markers (Top2a, Sox2), which we named “OPC-like.” The clusters with “OPC-like” signature, were also the ones with greater genomic distribution of inferred copy number variations (inferCNVs) and high proliferative rate, and were therefore denoted as “tumors.” The inferCNV genomic load was higher in late-stage samples compared to early ones, indicative of progressive genomic instability. Immunohistochemical analysis validated the high proliferative rate and widespread expression of the “OPC-like” markers TOP2A and SOX2. Pseudotime analysis of cycling cells identified a trajectory of decreasing cell cycle checkpoint regulation and increasing synaptic signaling from early to late timepoints. Thus, the early timepoints were characterized by the emergence of highly proliferative cell clusters with a unique “OPC-like” transcriptional signature and inferCNVs, and the late timepoints were characterized by further genomic spreading of inferCNVs, loss of cell cycle checkpoints and transcriptional changes consistent with increased neuron-glioma interactions

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 4 matches between paragraphs and lines of code.

dennishuang02/bbp53n_glioma_submission

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d8ef78dbc932c254ffc2e88a133b7dcc5d7dc785, 7 May 2026
Languages: R (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (3 files), Seurat (3 files), tidyverse (3 files), clusterProfiler (1 file), edgeR (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/dennishuang02/BBp53n_glioma_submission/tree/main

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

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

Data

Datasets cited

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material. Data deposited in GEO n.GSE309333 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE309333). Code: https://github.com/dennishuang02/BBp53n_glioma_submission/tree/main.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 7 keywords, 2 funders, 52 references.

Cite

This paper

Huang, D., Mela, A., Park, H.-J., Canoll, P., & Casaccia, P. (2026). Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression. Frontiers in cellular neuroscience, 20, 1713437. https://doi.org/10.3389/fncel.2026.1713437

BibTeX

@article{huang2026single,
author = {Huang, Dennis and Mela, Angeliki and Park, Hye-Jin and Canoll, Peter and Casaccia, Patrizia},
title = {{Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression}},
journal = {Frontiers in cellular neuroscience},
year = {2026},
month = jun,
volume = {20},
pages = {1713437},
publisher = {Frontiers Media SA},
issn = {1662-5102},
doi = {10.3389/fncel.2026.1713437},
url = {https://doi.org/10.3389/fncel.2026.1713437},
pmid = {42539873},
pmcid = {PMC13424661}
}

RIS

TY - JOUR
AU - Huang, Dennis
AU - Mela, Angeliki
AU - Park, Hye-Jin
AU - Canoll, Peter
AU - Casaccia, Patrizia
TI - Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression
T2 - Frontiers in cellular neuroscience
J2 - Front Cell Neurosci
PY - 2026
DA - 2026/06/30
VL - 20
SP - 1713437
SN - 1662-5102
PB - Frontiers Media SA
DO - 10.3389/fncel.2026.1713437
UR - https://doi.org/10.3389/fncel.2026.1713437
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fncel.2026.1713437",
"type": "article-journal",
"title": "Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression",
"container-title": "Frontiers in cellular neuroscience",
"author": [
{
"family": "Huang",
"given": "Dennis"
},
{
"family": "Mela",
"given": "Angeliki"
},
{
"family": "Park",
"given": "Hye-Jin"
},
{
"family": "Canoll",
"given": "Peter"
},
{
"family": "Casaccia",
"given": "Patrizia"
}
],
"container-title-short": "Front Cell Neurosci",
"volume": "20",
"page": "1713437",
"DOI": "10.3389/fncel.2026.1713437",
"PMID": "42539873",
"PMCID": "PMC13424661",
"ISSN": "1662-5102",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fncel.2026.1713437",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}

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