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Identification of CD74-positive antigen presenting glioma cells in primary human tumors and murine models of NF1 high-grade glioma

GSE317486 Mus musculus (mouse) Single-nucleus RNA-seq 10 samples Submitted 2026/02/28 Platform GPL24247
At a glance

Single-nucleus RNA-seq · mouse · 10 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
10 samples: 3 × NF1 HGG17 Allo Nf1 vs 3 × NF1 HGG17 Allo wtGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Identification of CD74-positive antigen presenting glioma cells in primary human tumors and murine models of NF1 high-grade gliomaMolecular cancer therapeutics · 2026
Summary (written by the submitters)
High-grade gliomas (HGG) arising in the context of Neurofibromatosis type 1 (NF1) present significant therapeutic challenges. To better understand the tumor microenvironment and immune interactions in these tumors, we developed and validated an immunocompetent murine allograft model. Using the NF1-HGG17 cell line, derived from sporadic gliomas in NPcis mice (Nf1/Tp53 mutant), we performed orthotopic allografting into the striatum of male B6 mice. Single-nucleus RNA sequencing (snRNA-seq) was employed to characterize the cellular composition of the tumor at 28 days post-injection.
Published in
Identification of CD74-positive antigen presenting glioma cells in primary human tumors and murine models of NF1 high-grade glioma
Brosius SN, Sussman JH, DiStefano IA et al. · Molecular cancer therapeutics 2026 · PMID 41926771 · doi:10.1158/1535-7163.MCT-25-1215
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE317486_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1413414 and SRA study SRP666028. Searching any of these in the dataset finder brings you back here.

Study design
3 × NF1 HGG17 Allo Nf1 vs 3 × NF1 HGG17 Allo wt

Supports a between-group comparison across 6 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 6 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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