← BioTransfer GEO Dataset Finder
GEO series

A single cell atlas of human glioma under therapy

GSE174554 Mus musculus (mouse) Single-cell RNA-seq 113 samples Submitted 2022/07/05 Platform GPL24676Platform GPL21697Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 113 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Topic
GlioblastomaThis series is part of our curated briefing on it.
Samples
113 samples
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.
Summary (written by the submitters)
Most clinical trials for glioblastoma (GBM) enroll patients at recurrence. However, most pre-clinical animal models are of primary disease. Most studies have used tissues from primary tumors. Knowledge of GBM cellular composition at recurrence is limited. We profiled 80 human IDH-wild-type GBM specimens via single-nucleus RNA sequencing, 40 primary tumors and 40 patient-matched recurrent tumors.
Read the full summary ↓
Select cohorts were also profiled via single-cell assay for transposase-accessible chromatin, single-cell digital-spatial, and single-cell spatial-transcriptomic assays. All patients were treated only with standard-of-care therapy: temozolomide, radiation and surgical resection. The cohort had a 1.2 male/female ratio, ages 35–76. We found a significant increase at recurrence in cells with the Verhaak mesenchymal phenotype and fewer proneural cells. Although there was an increase in the percentage of cycling cells overall, the fraction of cycling proneural cells decreased while the fraction of cycling mesenchymal cells increased. These changes were concomitant with a significant increase in the percentage of tumor-infiltrating monocytic-lineage cells derived from the periphery. Taken together, these findings support a proneural-to-mesenchymal shift at recurrence due to an increase in the birth rate of mesenchymal cells, supported by an expansion of myeloid-derived cells from peripheral blood. Although the percentages of glioblastoma-infiltrating T-cells are generally low (~1% on average in our data), we found a statistically significant increase in T-cell abundance at recurrence. We identified a cohort of T-cell outliers that demonstrate 2-to-8-fold increases in T-cells over average levels. We validated that T-cells in these tumors have extravasated beyond the perivascular space and into the cellular tumor via immunohistochemistry and spatial profiling. We present our ongoing analysis of the paracrine signals, upstream transcription-factor expression, and cis-regulatory grammars utilized in immune-outlier GBMs. We describe how these programs change under therapy. These studies shed light on the effect of standard therapy in shaping GBM composition at recurrence.
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 GSE174554_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 PRJNA730532. Searching any of these in the dataset finder brings you back here.

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 113 more — browse all 113 samples with per-sample file links →

More Glioblastoma datasets

Search all RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.