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Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach.

Code ↔ Paper

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The 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Method details › Single-cell and single nuclei sequencing › Data processing and downstream analyses ↔ 01_validation_preprocessing.R, the whole file · a weak match · score 0.72 · Cell Ranger, SCTransform, Seurat, matrices, filtering, PCA
  2. [2] § Method details › Single-cell and single nuclei sequencing › Data processing and downstream analyses ↔ 03_annotation_and_signatures.R, the whole file · a weak match · score 0.71 · module scores, AddModuleScore, gene signatures, endothelial, validation

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 46 lines · 1.3 KB · no license · 1 match

  2. # 01_validation_preprocessing.R
  3. # Preprocess the validation snRNA-seq dataset generated in this study
  5. source("scripts/00_config.R")
  7. # Example: load Cell Ranger matrices
  8. sample_dirs <- list.dirs(file.path(data_dir, "validation_dataset"), recursive = FALSE)
  10. seurat_list <- list()
  12. for (s in sample_dirs) {
  13. sample_name <- basename(s)
  14. counts <- Read10X(file.path(s, "filtered_feature_bc_matrix"))
  16. obj <- CreateSeuratObject(
  17. counts = counts,
  18. project = sample_name,
  19. min.cells = 3,
  20. min.features = 200
  21. )
  23. obj$sample_id <- sample_name
  24. seurat_list[[sample_name]] <- obj
  25. }
  27. # Merge samples
  28. combined <- merge(seurat_list[[1]], y = seurat_list[-1])
  30. # QC metrics
  31. combined[["percent.mito"]] <- PercentageFeatureSet(combined, pattern = "^MT-")
  33. # Filtering example (adjust thresholds to match manuscript)
  34. combined <- subset(combined, subset =
  35. nFeature_RNA > 300 &
  36. nFeature_RNA < 8000 &
  37. percent.mito < 20)
  39. # Normalization and clustering
  40. combined <- SCTransform(combined, verbose = FALSE)
  41. combined <- RunPCA(combined)
  42. combined <- RunUMAP(combined, dims = 1:30)
  43. combined <- FindNeighbors(combined, dims = 1:30)
  44. combined <- FindClusters(combined, resolution = 0.5)
  46. saveRDS(combined, file.path(obj_dir, "validation_processed.rds"))

01_validation_preprocessing.R at commit 977d889, no license · at the source

Overview

Authors: Julia K. Sundheimer1,2,3, Julia Benzel1,2, Aniello Federico1,2, Stefanie Volz1,2, Maximilian Knoll4,5, Britta Statz1,2, Tuyu Zheng1,2, Szymon W. Kmiecik6, Jürgen Burhenne6, Gzona Bajraktari-Sylejmani6, Sophia Scheuermann1,7,8,9,10,11, Anke King1,2, Torsten Müller12, Jens-Martin Hübner1,2, Mathias Kalxdorf12, Heike Peterziel1,13, Ina Oehme1,13, Jeroen Krijgsveld12, Christian M. Seitz1,7,8, Marcel Kool1,14, Stefan M. Pfister1,2,7,15, Kristian W. Pajtler1,2,7,15, Kendra K. Maaß1,2,7
ORCID iDs: Szymon W. Kmiecik
15 affiliations
  1. Hopp Children’s Cancer Center Heidelberg (KiTZ),69120 Heidelberg, Germany
  2. Division of Pediatric Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ),Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
  3. Faculty of Biosciences, Heidelberg University,Im Neuenheimer Feld 234, 69120 Heidelberg, Germany
  4. Department of Radiation Oncology, Heidelberg University Hospital,Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
  5. Clinical Cooperation Unit Radiation Oncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ),Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
  6. Internal Medicine IX - Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University, Medical Faculty Heidelberg / Heidelberg University Hospital,Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
  7. Heidelberg Faculty of Medicine, Department of Pediatric Hematology, Oncology and Immunology, Heidelberg University Hospital,Im Neuenheimer Feld 430, 69120 Heidelberg, Germany
  8. Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ),Heidelberg, Germany
  9. Department of General Pediatrics, Hematology and Oncology, University Children’s Hospital Tuebingen,Tübingen, Germany
  10. University of Tuebingen, iFIT Cluster of ExCellence (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”,Tübingen, Germany
  11. German Cancer Research Consortium (DKTK), Partner Site Tuebingen, German Cancer Research Center (DKFZ),Heidelberg, Germany
  12. Division of Proteomics of Stem Cells and Cancer, German Cancer Research Center (DKFZ),Heidelberg, Germany
  13. Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK),69120 Heidelberg, Germany
  14. Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht (UMCU),Utrecht, The Netherlands
  15. National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital,69120 Heidelberg, Germany
Institutions: Hopp Children's Cancer Center Heidelberg (Germany); German Cancer Research Center (Germany); Heidelberg University (Germany); University Hospital Heidelberg (Germany); University Children's Hospital Tübingen (Germany); University of Tübingen (Germany); University Medical Center Utrecht (Netherlands); National Center for Tumor Diseases (Germany)
Journal: Scientific reports, volume 16, issue 1, article 12061
Dates: received 29 August 2025; accepted 31 March 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-47499-2 · PMID 41963440 · PMCID PMC13070042 · OpenAlex W7153159265
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, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Blood-brain barrier, Tight junctions, Efflux pumps, Transporter, Receptor, Ependymoma, Cancer, Neuroscience, Oncology
MeSH: Blood-Brain Barrier*, Brain Neoplasms*, Ependymoma*, Animals, ATP Binding Cassette Transporter, Subfamily B, Member 1, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Mice, Multiomics (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: Deutsches Krebsforschungszentrum (DKFZ) (1052)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

A significant obstacle in treating brain tumors is the limited drug penetration across the blood-brain barrier (BBB), characterized by an interplay of endothelial tight junctions and efflux pumps. Brain tumors can alter BBB characteristics; however, there is limited understanding in ependymoma (EPN), the third most common pediatric brain tumor. To this end, we characterized EPN tumor (n = 364) and healthy brain tissues (n = 225) at RNA level and identified a distinct EPN group-specific BBB transcriptional pattern. Analyses of public datasets from Aubin and Gojo as well as a validation single-cell dataset (n = 8) could further specify a novel BBB signature expressed in an endothelial subpopulation. Clinically relevant drugs (n = 3) that were effective against EPN in vitro were further evaluated for BBB penetration in our subtype-specific patient-derived xenograft (PDX) models. Idasanutlin showed overall low brain-to-plasma ratios, while the P-glycoprotein (PGP) substrates temsirolimus and etoposide accumulated slightly more in zinc finger translocation associated (ZFTA)-fusion positive EPN than in PFA tumors and adjacent brain. These differences align with modestly lower PGP levels in ZFTA PDX, although expression does not necessarily reflect transporter activity and was not consistently observed in patient tumors. Despite these differences, all tested drugs remained below their effective in vitro levels. In summary, multi-omics analyses of BBB characteristics improve the understanding of drug penetrance and may potentially guide treatment choices in the context of molecular EPN groups within upcoming clinical trials.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-47499-2.

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.

afederico-sci/scRNAseq_BBB_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 977d8892c54258c122619fa169161fd6da2fe9d1, 8 March 2026
Languages: R (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
7 files
At the source: github.com/afederico-sci/scRNAseq_BBB_analysis

Code availability

Code for single cell analysis is available here: https://github.com/afederico-sci/scRNAseq_BBB_analysis.

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

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;
  • 6 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 availability

Human RNA array data are publicly available (GEO: GSE64415 and GEO: GSE50161, GSE50385, GSE21687, GSE3526). All other data are available from the corresponding author upon request.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 23 authors, 9 keywords, 10 MeSH terms, 1 funder, 71 references.

Cite

This paper

Sundheimer, J. K., Benzel, J., Federico, A., Volz, S., Knoll, M., Statz, B., Zheng, T., Kmiecik, S. W., Burhenne, J., Bajraktari-Sylejmani, G., Scheuermann, S., King, A., Müller, T., Hübner, J.-M., Kalxdorf, M., Peterziel, H., Oehme, I., Krijgsveld, J., Seitz, C. M., . . . Maaß, K. K. (2026). Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach. Scientific reports, 16(1), 12061. https://doi.org/10.1038/s41598-026-47499-2

BibTeX

@article{sundheimer2026ependymoma,
author = {Sundheimer, Julia K. and Benzel, Julia and Federico, Aniello and Volz, Stefanie and Knoll, Maximilian and Statz, Britta and Zheng, Tuyu and Kmiecik, Szymon W. and Burhenne, Jürgen and Bajraktari-Sylejmani, Gzona and Scheuermann, Sophia and King, Anke and Müller, Torsten and Hübner, Jens-Martin and Kalxdorf, Mathias and Peterziel, Heike and Oehme, Ina and Krijgsveld, Jeroen and Seitz, Christian M. and Kool, Marcel and Pfister, Stefan M. and Pajtler, Kristian W. and Maaß, Kendra K.},
title = {{Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {12061},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-47499-2},
url = {https://doi.org/10.1038/s41598-026-47499-2},
pmid = {41963440},
pmcid = {PMC13070042}
}

RIS

TY - JOUR
AU - Sundheimer, Julia K.
AU - Benzel, Julia
AU - Federico, Aniello
AU - Volz, Stefanie
AU - Knoll, Maximilian
AU - Statz, Britta
AU - Zheng, Tuyu
AU - Kmiecik, Szymon W.
AU - Burhenne, Jürgen
AU - Bajraktari-Sylejmani, Gzona
AU - Scheuermann, Sophia
AU - King, Anke
AU - Müller, Torsten
AU - Hübner, Jens-Martin
AU - Kalxdorf, Mathias
AU - Peterziel, Heike
AU - Oehme, Ina
AU - Krijgsveld, Jeroen
AU - Seitz, Christian M.
AU - Kool, Marcel
AU - Pfister, Stefan M.
AU - Pajtler, Kristian W.
AU - Maaß, Kendra K.
TI - Ependymoma group-specific blood-brain barrier differences uncovered by a multi-omics approach
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/10
VL - 16
IS - 1
SP - 12061
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-47499-2
UR - https://doi.org/10.1038/s41598-026-47499-2
LA - en
ER -

CSL-JSON

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