OSCR

Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients.

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] § Methods › Cytotoxicity and inflammatory gene set analysis ↔ Notebooks ipynb/Figures.ipynb, lines 340–358 · score 0.92 · IL1B, score genes, CCL2, CCL5, CTSW, CXCL10
  2. [2] § Results › Perioperative corticosteroid treatment was associated with variations in the intratumoral distribution of NK cell subsets in gliomas ↔ Notebooks ipynb/Figures.ipynb, lines 138–153 · score 0.86 · CD3E, CD3D, CD3G, IL2RB, FCGR3A, TRGC1
  3. [3] § Methods › scRNA-seq data processing ↔ Notebooks ipynb/Figures.ipynb, lines 66–83 · score 0.76 · Highly variable genes, seurat_v3, ARPACK, Raw, PCA, Scanpy
  4. [4] § Methods › Functional enrichment analysis ↔ Notebooks ipynb/Figures.ipynb, lines 241–329 · score 0.64 · Pathway enrichment, databases, GO, KEGG, Ontology, enriched
  5. [5] § Results › Perioperative corticosteroid treatment was associated with variations in the intratumoral distribution of NK cell subsets in gliomas ↔ Notebooks ipynb/Supplementary Figures.ipynb, lines 58–72 · score 0.63 · CD3D, CD3G, FCGR3A, NCAM1, genes
  6. [6] § Results › Corticosteroid-associated transcriptional changes were detectable in all sub-clusters of glioma-infiltrating NK cells ↔ Notebooks ipynb/Figures.ipynb, lines 155–180 · score 0.52 · CX3CR1, NKG7, CD160, GZMB, CCL4, XCL1

Paper

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The authors' code

Jupyter notebook · 380 lines · 12 KB · no license · 5 matches

  1. # %% [markdown]
  2. # # <font color = '#FF003D'> **Glioblastoma scRNA-seq project**
  4. # %%
  5. import os
  6. import math
  7. import warnings
  9. import numpy as np #version 1.26.4
  10. import scipy #version 1.15.2
  11. import pandas as pd #version 2.2.3
  12. import anndata as ad #version 0.11.3
  13. import scanpy as sc #version 1.11.0
  14. import scanpy.external as sce
  15. from cycler import cycler
  16. import openpyxl #version 3.1.5
  19. import seaborn as sns #version 0.13.2
  20. import matplotlib #version 3.7.4
  21. import matplotlib.pyplot as plt
  23. import rpy2 #version 3.5.11
  25. sc.settings.verbosity = 4
  26. pd.set_option("display.max_columns", None)
  27. warnings.filterwarnings('ignore')
  28. sc.set_figure_params(dpi = 80, dpi_save = 1000, facecolor = 'white')
  30. # %% [markdown]
  31. #
  33. # %% [markdown]
  34. # # **Reclustering of NK cells from total**
  36. # %%
  37. outfilename = os.path.join(data_folder, "Totale_WTandHealthy.h5ad")
  38. adata = sc.read_h5ad(outfilename)
  40. adata.obs['Treatment'] = adata.obs['sample'].map({
  41. "S21225_Lib_BS_03":"Dex-treated",
  42. "S21227_Lib_BS_06":"Dex-treated",
  43. "S21228_Lib_BS_07":"Untreated",
  44. "S21230_Lib_BS_09":"Untreated",
  45. "S21231_Lib_BS_S10":"Healthy",
  46. "S21232_Lib_BS_T10":"Untreated",
  47. "S21233_Lib_BS_S11" : "Healthy"})
  49. # %%
  50. recNK = adata[adata.obs['res_1.5'].isin(['12'])].copy()
  52. obs = ['res_0.5', 'res_0.8', 'res_1.0', 'res_1.5', 'res_2.0']
  53. var = ['highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm', 'highly_variable_nbatches']
  55. for obs in obs:
  56. del recNK.obs[obs]
  58. for var in var:
  59. del recNK.var[var]
  61. del recNK.uns
  62. del recNK.obsm
  63. del recNK.varm
  64. del recNK.obsp
  66. # %%
  67. sc.pp.highly_variable_genes(recNK,
  68. n_top_genes = 4000,
  69. flavor= 'seurat_v3',
  70. layer = "raw_counts",
  71. subset = False)
  73. sc.tl.pca(recNK,
  74. svd_solver = 'arpack',
  75. use_highly_variable = True,
  76. n_comps = 50)
  78. sc.pl.pca_variance_ratio(recNK,
  79. log = True)
  81. sce.pp.harmony_integrate(recNK,
  82. 'sample',
  83. max_iter_harmony = 100)
  85. # %%
  86. sc.pp.neighbors(recNK,
  87. n_neighbors = 15,
  88. n_pcs = 30,
  89. use_rep = "X_pca_harmony",
  90. key_added = "Harmony")
  92. sc.tl.umap(recNK,
  93. neighbors_key = "Harmony")
  95. clustering_labels = []
  96. for res in [0.5, 0.8, 1.0]:
  97. clustering_labels.append("res_{}".format(res))
  98. if "res_{}".format(res) in recNK.obs:
  99. print("res_{}".format(res) + " already exists... going on with next resolution.")
  100. continue
  101. sc.tl.leiden(recNK,
  102. resolution = res,
  103. key_added = "res_{}".format(res),
  104. neighbors_key = "Harmony")
  106. sc.pl.umap(recNK,
  107. color = clustering_labels,
  108. legend_loc = 'on data',
  109. legend_fontsize = 10,
  110. legend_fontoutline = 3,
  111. frameon = True,
  112. ncols = 5)
  114. # %%
  115. outfilename = os.path.join(data_folder, "reclustering_NKcells.h5ad")
  116. print("Saving h5ad data to file {}".format(outfilename))
  117. recNK.write(outfilename)
  118. print("Done!")
  120. # %% [markdown]
  121. # # **Figure 1**
  123. # %%
  124. outfilename = os.path.join(data_folder, "reclustering_NKcells.h5ad")
  125. recNK = sc.read_h5ad(outfilename)
  127. # %%
  128. sc.pl.umap(recNK,
  129. color = 'res_0.8',
  130. na_in_legend = False,
  131. legend_loc = 'on data',
  132. legend_fontsize = 10,
  133. legend_fontoutline = 3,
  134. frameon = True,
  135. title = "",
  136. save = 'Figure 1A.png')
  138. # %%
  139. sc.tl.dendrogram(recNK,
  140. 'res_0.8',
  141. use_rep = "X_pca_harmony")
  143. Annotation = ["CD3D", "CD3G", 'CD3E', 'TRGC1', 'TRGC2',
  144. 'NCAM1', 'CD44', 'XCL1', 'XCL2', 'GZMK', 'IL2RB', 'IRF8', 'KLRC1', 'KLRC2', 'NCR1', 'KIR2DL4', 'ZNF683', 'EOMES', 'CD2', 'PRDM1',
  145. 'FCGR3A', 'GNLY', 'PRF1', 'GZMA', 'GZMB', 'GZMM', 'NCR3']
  147. sc.pl.dotplot(recNK,
  148. Annotation,
  149. 'res_0.8',
  150. standard_scale = 'var',
  151. swap_axes = False,
  152. dendrogram = True,
  153. save = "Figure 1B.png")
  155. # %%
  156. recNK = recNK[~recNK.obs['res_0.8'].isin(['4'])].copy()
  158. signatures = {
  159. 'NK1' : ['CD160', 'CTSD', 'CCL4', 'ADGRG1', 'CD38', 'CD247', 'CHST2', 'CX3CR1', 'KLRB1', 'LAIR2', 'IGFBP7', 'AKR1C3', 'FGFBP2', 'MYOM2', 'CLIC3', 'GZMB', 'PRF1', 'FCER1G', 'NKG7', 'SPON2'],
  160. 'NK2' : ['LTB', 'FOS', 'IL2RB', 'IFITM3', 'COTL1', 'IL7R', 'PIK3R1', 'AREG', 'ZFP36L2', 'DUSP2', 'CD44', 'SELL', 'GPR183', 'CMC1', 'KLRC1', 'TCF7', 'TPT1', 'XCL2', 'XCL1', 'GZMK']
  161. }
  163. for signature_name, genes in signatures.items():
  164. sc.tl.score_genes(recNK,
  165. genes,
  166. ctrl_size = 25,
  167. gene_pool = None,
  168. random_state = 0,
  169. score_name = f"{signature_name}_Score",
  170. copy = False)
  172. scores = ["NK1_Score", "NK2_Score"]
  174. for score in scores:
  175. sc.pl.umap(recNK,
  176. color = score,
  177. frameon = True,
  178. vmin = 0,
  179. color_map = 'inferno',
  180. save = f"Figure 1C - {score}.png")
  182. # %%
  183. recNK = recNK[~recNK.obs['res_0.8'].isin(['4'])].copy()
  185. sc.tl.embedding_density(recNK,
  186. basis = 'umap',
  187. groupby = 'Treatment')
  189. sc.pl.embedding_density(recNK,
  190. basis = 'umap',
  191. key = 'umap_density_Treatment',
  192. color_map = 'Reds',
  193. title = '',
  194. bg_dotsize = 0,
  195. fg_dotsize = 500
  196. save = 'Figure 1D (up).png')
  198. # %% [markdown]
  199. # # **Figure 2**
  201. # %%
  202. outfilename = os.path.join(data_folder, "reclustering_NKcells.h5ad")
  203. recNK = sc.read_h5ad(outfilename)
  205. # %%
  206. sub2 = recNK[recNK.obs['res_0.8'].isin(['2'])].copy()
  208. sc.tl.rank_genes_groups(sub2,
  209. 'Treatment',
  210. method = "wilcoxon",
  211. key_added = 'rank_genes',
  212. groups = ['Dex-treated'],
  213. reference = 'Untreated',
  214. use_raw = False,
  215. pts = True)
  217. DEGs = sc.get.rank_genes_groups_df(sub2,
  218. group = None,
  219. key = 'rank_genes')
  221. DEGs.to_excel('DEGs c2 - Dex-treated (up) vs Untreated (down).xlsx',
  222. index = True)
  224. # %%
  225. sub2_Universe = pd.DataFrame(sub2.var_names,
  226. columns = ["gene_ids"])
  228. sub2_Universe.to_excel("figures/sub2_Universe.xlsx",
  229. index = True)
  231. # %%
  232. %load_ext rpy2.ipython
  234. # %%
  235. %%R
  236. library(dbplyr) #version 2.3.4
  237. library(biomaRt)
  238. library(clusterProfiler)
  239. library(org.Hs.eg.db)
  241. # %%
  242. %%R
  243. DEGs = read.xlsx("figures/DEGs c2 - Dex-treated (up) vs Untreated (down).xlsx")
  244. universe = read.xlsx("figures/sub2_Universe.xlsx",
  245. colNames = F)
  247. # Settings
  248. thr_deg = 0.05
  249. thr_pvalue = 0.05
  250. thr_qvalue = 0.05
  252. # Convert hugo symbols to gene ids (nomi a numeri)
  253. mart = useEnsembl(biomart = "ensembl",
  254. dataset = "hsapiens_gene_ensembl")
  256. G_list = getBM(filters = "hgnc_symbol",
  257. attributes = c("hgnc_symbol", "entrezgene_id"),
  258. values = as.character(universe[,1]),
  259. mart = mart,
  260. useCache = FALSE)
  262. genes_id_mapping = G_list
  264. invalid_hgnc_symbol = sort(unique(genes_id_mapping[which(duplicated(genes_id_mapping$hgnc_symbol)), "hgnc_symbol"]))
  265. invalid_entrezgene_id = sort(unique(genes_id_mapping[which(duplicated(genes_id_mapping$entrezgene_id)), "entrezgene_id"]))
  266. invalid = which(genes_id_mapping$hgnc_symbol %in% invalid_hgnc_symbol | genes_id_mapping$entrezgene_id %in% invalid_entrezgene_id | is.na(genes_id_mapping$hgnc_symbol) | is.na(genes_id_mapping$entrezgene_id))
  268. if (length(invalid) > 0) {
  269. genes_id_mapping = genes_id_mapping[-invalid,]
  270. }
  271. rownames(genes_id_mapping) = 1:nrow(genes_id_mapping)
  272. background_set = as.character(sort(unique(genes_id_mapping$entrezgene_id)))
  274. # Functional analysis
  275. data = DEGs[which(DEGs$genes %in% genes_id_mapping$hgnc_symbol),]
  276. all_pathway_enrichment = NULL
  278. for (dg in sort(unique(data$Cluster))) {
  279. curr_res = data[which(data$Cluster == dg),,drop = FALSE]
  280. enrichment_set = as.character(sort(unique(genes_id_mapping$entrezgene_id[which(genes_id_mapping$hgnc_symbol %in% curr_res$genes)])))
  281. # perform pathway enrichment analysis
  282. pathway_enrichment = NULL
  283. pathway_enrichment_GO = enrichGO(gene = enrichment_set, OrgDb = org.Hs.eg.db, keyType = "ENTREZID", ont = "ALL", pvalueCutoff = thr_pvalue, pAdjustMethod = "fdr", universe = background_set, qvalueCutoff = thr_qvalue, minGSSize = 10, maxGSSize = 500, readable = FALSE, pool = TRUE)
  284. if (nrow(pathway_enrichment_GO) > 0) {
  285. pathway_enrichment_GO = as.data.frame(pathway_enrichment_GO)
  286. pathway_enrichment_GO$Database = paste0("GO",pathway_enrichment_GO$ONTOLOGY)
  287. pathway_enrichment_GO$ONTOLOGY = NULL
  288. pathway_enrichment = rbind(pathway_enrichment, pathway_enrichment_GO)
  289. }
  290. pathway_enrichment_kegg = enrichKEGG(gene = enrichment_set, organism = "hsa", keyType = "kegg", pvalueCutoff = thr_pvalue, pAdjustMethod = "fdr", universe = background_set, minGSSize = 10, maxGSSize = 500, qvalueCutoff = thr_qvalue, use_internal_data = FALSE)
  291. if (nrow(pathway_enrichment_kegg) > 0) {
  292. pathway_enrichment_kegg = as.data.frame(pathway_enrichment_kegg)
  293. pathway_enrichment_kegg$Database = "KEGG"
  294. pathway_enrichment = rbind(pathway_enrichment,pathway_enrichment_kegg)
  295. }
  296. if (!is.null(pathway_enrichment) && nrow(pathway_enrichment) > 0) {
  297. pathway_enrichment = data.frame(pathway_enrichment, check.rows = FALSE, stringsAsFactors = FALSE)
  298. pathway_enrichment = unique(pathway_enrichment[order(pathway_enrichment$Description),])
  299. genes_hugo = pathway_enrichment$geneID
  300. for (i in 1:length(genes_hugo)) {
  301. curr_hugo = NULL
  302. for (j in strsplit(genes_hugo[i], split = "\\/")[[1]]) {
  303. curr_hugo = c(curr_hugo, genes_id_mapping$hgnc_symbol[which(genes_id_mapping$entrezgene_id == j)])
  304. }
  305. genes_hugo[i] = paste0(sort(unique(curr_hugo)), collapse = "/")
  306. }
  307. pathway_enrichment$geneName = genes_hugo
  308. pathway_enrichment = pathway_enrichment[,c("ID", "Description", "geneName", "pvalue", "p.adjust", "qvalue", "GeneRatio", "BgRatio", "geneID", "Count", "Database")]
  309. pathway_enrichment = unique(pathway_enrichment)
  310. rownames(pathway_enrichment) = 1:nrow(pathway_enrichment)
  311. # save results
  312. if (!is.null(pathway_enrichment) && nrow(pathway_enrichment) > 0) {
  313. pathway_enrichment = pathway_enrichment[,c("ID", "Description", "geneName", "pvalue", "p.adjust", "qvalue", "GeneRatio", "BgRatio", "Count", "Database")]
  314. pathway_enrichment$Configuration = "Bright c2"
  315. pathway_enrichment$cluster = dg
  316. all_pathway_enrichment = rbind(all_pathway_enrichment, pathway_enrichment)
  317. }
  318. }
  319. }
  321. pathway_enrichment = all_pathway_enrichment
  322. rownames(pathway_enrichment) = 1:nrow(pathway_enrichment)
  323. Bright_c2_pathways = pathway_enrichment
  325. write.xlsx(Bright_c2_pathways,
  326. "figures/Bright_c2_pathways.xlsx",
  327. sheetName = "Bright c2",
  328. append = FALSE,
  329. rowNames = FALSE)
  331. # %% [markdown]
  332. # # **Figure 3**
  334. # %%
  335. outfilename = os.path.join(data_folder, "reclustering_NKcells.h5ad")
  336. recNK = sc.read_h5ad(outfilename)
  338. recNK = recNK[~recNK.obs['res_0.8'].isin(['4'])].copy()
  340. # %%
  341. #Figures 3A and 3B
  342. signatures = {
  343. Cytotoxicity : ['GZMA', 'GZMB', 'GZMH', 'GZMM', 'GZMK', 'GNLY', 'PRF1', 'CTSW'],
  344. Inflammatory : ['CCL2', 'CCL3', 'CCL4', 'CCL5', 'CXCL10', 'CXCL9', 'IL1B', 'IL6', 'IL7', 'IL15', 'IL18']
  345. }
  347. for signature_name, genes in signatures.items():
  348. sc.tl.score_genes(recNK,
  349. genes,
  350. ctrl_size = 25,
  351. gene_pool = None,
  352. random_state = 0,
  353. score_name = f"{signature_name}_Score",
  354. copy = False)
  356. df = recNK[['res_0.8', 'Treatment', "Cytotoxicity_Score", "Inflammatory_Score"]]
  358. df.to_excel('figures/NK cells – Scores.xlsx', index = True)
  360. # %%
  361. #Figures 3C and 3D
  362. recNK = recNK[~recNK.obs['res_0.8'].isin(['4'])].copy()
  364. cluster_list = ['0', '1', '2', '3', '5']
  366. for cl in cluster_list:
  367. clust = recNK[recNK.obs['res_0.8'] == cl].copy()
  368. gene_ids = clust.var.index.values
  369. clusters = clust.obs['Treatment']
  370. obs = clust[:, gene_ids].X.toarray()
  372. obs = pd.DataFrame(obs,
  373. columns = gene_ids,
  374. index = clusters)
  376. outfile = f'figures/Matrix expression x Treatment - {clust}.xlsx'
  377. obs.T.to_excel(outfile, index = True)
  379. # %%

Figures.ipynb at commit 9115b1f, no license · at the source

Overview

Authors: Simone Balin1, Paolo Marzano1, Alessandro Limonta1,2, Sara Terzoli3, Matteo Simonelli4,5, Lorenzo Bello6,7, Federico Pessina4,8, Benedetta Savino9,10, Joanna Mikulak1, Francesca Calcaterra1, Elena Fontana1, Massimo Locati9,10, Domenico Mavilio1,2, Silvia Della Bella1,2
ORCID iDs: Paolo Marzano, Alessandro Limonta, Benedetta Savino
  1. Unit of Clinical and Experimental Immunology, IRCCS Humanitas Research Hospital,Rozzano, Milan Italy
  2. Unit of Clinical and Experimental Immunology, Department of Medical Biotechnology and Translational Medicine, University of Milan,via Fratelli Cervi 93, Segrate, 20054 Milan Italy
  3. Fondazione Human Technopole,Milan, Italy
  4. Department of Biomedical Sciences, Humanitas University,Pieve Emanuele, Milan, Italy
  5. Department of Medical Oncology and Hematology, IRCCS Humanitas Research Hospital,Rozzano, Milan Italy
  6. Unit of Oncological Neurosurgery, IRCCS Istituto Ortopedico Galeazzi,Milan, Italy
  7. Department of Oncology and Hemato-Oncology, University of Milan,Milan, Italy
  8. Department of Neurosurgery, IRCCS Humanitas Research Hospital,Rozzano, Milan Italy
  9. Laboratory of Leukocyte Biology, Department of Medical Biotechnology and Translational Medicine, University of Milan,Segrate, Milan Italy
  10. Laboratory of Leukocyte Biology, IRCCS Humanitas Research Hospital,Rozzano, Milan Italy
Institutions: IRCCS Humanitas Research Hospital (Italy); University of Milan (Italy); Human Technopole (Italy); Humanitas University (Italy); IRCCS Ospedale Galeazzi - Sant'Ambrogio (Italy)
Journal: Scientific reports, volume 16, issue 1, article 26614
Dates: received 20 February 2026; accepted 6 May 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-52528-1 · PMID 42168437 · PMCID PMC13503855 · OpenAlex W7162025691
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Glioblastoma, NK cells, Perioperative corticosteroids, Single cell-RNA sequencing, Cancer, Immunology, Neuroscience, Oncology
MeSH: Adrenal Cortex Hormones*, Brain Neoplasms*, Dexamethasone*, Glioblastoma*, Killer Cells, Natural*, Lymphocytes, Tumor-Infiltrating*, Single-Cell Analysis*, Female, Humans, Male, Middle Aged (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: Università degli Studi di Milano; Ministero della Salute; Humanitas Research Hospital
Citations: not cited yet (Europe PMC); 41 references in the paper
Research resources: RRID:SCR_002798, RRID:SCR_002811, RRID:SCR_004129, imported in Python RRID:SCR_008394, RRID:SCR_014601, analyzed with Scanpy RRID:SCR_018139, RRID:SCR_018209, RRID:SCR_018931, RRID:SCR_022798, RRID:SCR_025630, KEGG RRID:SCR_027172

Abstract

Glioblastoma is the most common and aggressive malignant primary brain tumor in adults, characterized by poor prognosis and limited response to current therapeutic strategies. Corticosteroids, particularly dexamethasone, are widely used in clinical practice to control symptomatic peritumoral edema, yet they exert profound immunosuppressive effects whose impact on tumor-infiltrating NK cells remains poorly defined. To address this issue, we analyzed a publicly available single-cell RNA sequencing dataset of CD45⁺ cells isolated from five IDH-wildtype glioblastomas, including two patients undergoing perioperative dexamethasone treatment and three untreated patients, alongside two non-tumor brain samples. Our results suggested that perioperative dexamethasone exposure was associated with a reshaped intratumoral NK cell landscape, characterized by a relative enrichment of CD56bright subsets. Perioperative corticosteroid treatment was also associated with transcriptional features consistent with impaired NK cell effector programs, as evidenced by reduced cytotoxicity and inflammation scores, down-regulation of granzymes, perforin, pro-inflammatory cytokines, and activating receptors, and concomitant upregulation of inhibitory molecules. Gene set enrichment analysis further demonstrated strong downregulation of NK cell-mediated cytotoxicity and cell-killing pathways. Together with previous evidence of corticosteroid-associated impairment of glioma-infiltrating dendritic cells, these findings suggest that perioperative steroid therapy may affect both cytotoxic and antigen-presenting innate compartments. If confirmed in larger patient cohorts, these observations may support the importance of minimizing steroid exposure to optimize immunotherapeutic efficacy.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-52528-1.

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

Repository

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pmarzano97/GBM

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 9115b1f683420a7d311c25dbea6fc7c88f744ea8, 2 December 2025
Languages: Jupyter (3)
Size: 7 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (3 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), rpy2 (3 files), Scanpy (3 files), SciPy (3 files), seaborn (3 files), clusterProfiler (1 file), scVelo (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
4 files
At the source: github.com/pmarzano97/GBM.git

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

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Data

Datasets cited

Data availability

scRNA data were downloaded from the Zenodo Repository (RRID:SCR\_004129) where raw data are available upon request. All the codes used for data processing and analysis are available in a public GitHub repository (https://github.com/pmarzano97/GBM.git).

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

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Version 2, 28 September 2026

  • Funding: added Università degli Studi di Milano; Ministero della Salute; Humanitas Research Hospital

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 8 keywords, 11 MeSH terms, 40 references, 11 RRIDs.

Cite

This paper

Balin, S., Marzano, P., Limonta, A., Terzoli, S., Simonelli, M., Bello, L., Pessina, F., Savino, B., Mikulak, J., Calcaterra, F., Fontana, E., Locati, M., Mavilio, D., & Della Bella, S. (2026). Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients. Scientific reports, 16(1), 26614. https://doi.org/10.1038/s41598-026-52528-1

BibTeX

@article{balin2026single,
author = {Balin, Simone and Marzano, Paolo and Limonta, Alessandro and Terzoli, Sara and Simonelli, Matteo and Bello, Lorenzo and Pessina, Federico and Savino, Benedetta and Mikulak, Joanna and Calcaterra, Francesca and Fontana, Elena and Locati, Massimo and Mavilio, Domenico and Della Bella, Silvia},
title = {{Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {26614},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-52528-1},
url = {https://doi.org/10.1038/s41598-026-52528-1},
pmid = {42168437},
pmcid = {PMC13503855}
}

RIS

TY - JOUR
AU - Balin, Simone
AU - Marzano, Paolo
AU - Limonta, Alessandro
AU - Terzoli, Sara
AU - Simonelli, Matteo
AU - Bello, Lorenzo
AU - Pessina, Federico
AU - Savino, Benedetta
AU - Mikulak, Joanna
AU - Calcaterra, Francesca
AU - Fontana, Elena
AU - Locati, Massimo
AU - Mavilio, Domenico
AU - Della Bella, Silvia
TI - Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/21
VL - 16
IS - 1
SP - 26614
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-52528-1
UR - https://doi.org/10.1038/s41598-026-52528-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41598-026-52528-1",
"type": "article-journal",
"title": "Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients",
"container-title": "Scientific reports",
"author": [
{
"family": "Balin",
"given": "Simone"
},
{
"family": "Marzano",
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},
{
"family": "Limonta",
"given": "Alessandro"
},
{
"family": "Terzoli",
"given": "Sara"
},
{
"family": "Simonelli",
"given": "Matteo"
},
{
"family": "Bello",
"given": "Lorenzo"
},
{
"family": "Pessina",
"given": "Federico"
},
{
"family": "Savino",
"given": "Benedetta"
},
{
"family": "Mikulak",
"given": "Joanna"
},
{
"family": "Calcaterra",
"given": "Francesca"
},
{
"family": "Fontana",
"given": "Elena"
},
{
"family": "Locati",
"given": "Massimo"
},
{
"family": "Mavilio",
"given": "Domenico"
},
{
"family": "Della Bella",
"given": "Silvia"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "26614",
"DOI": "10.1038/s41598-026-52528-1",
"PMID": "42168437",
"PMCID": "PMC13503855",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-52528-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
21
]
]
}
}

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