GEO series

Chronic hypoxia remodels the tumor microenvironment to support glioma stem cell growth [scRNAseq+Multiome]

GSE210736 Homo sapiens (human) Single-cell RNA-seq 39 samples Submitted 2024/04/03 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 39 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 39 samples: 30 distinct titles, 9 of them repeatedGroups 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: Chronic hypoxia remodels the tumor microenvironment to support glioma stem cell growthActa neuropathologica communications · 2024

Summary (written by the submitters)

Cerebral organoids co-cultured with patient derived glioma stem cells (GLICOs) are an experimentally tractable research tool useful for investigating the role of the human brain tumor microenvironment in glioblastoma. Here we develop long-term GLICOs, a novel model in which COs are minimally seeded with GSCs and tumor development is monitored over extended durations (ltGLICOs).

Read the full summary ↓

Single-cell profiling of ltGLICOs revealed an unexpectedly long latency period prior to GSC expansion, and that normal organoid development was unimpaired by the presence of low numbers of GSCs. However, as organoids age they experience chronic hypoxia and oxidative stress which remodels the tumor microenvironment to promote GSC expansion. Receptor-ligand modelling identified astrocytes, which secreted various pro-tumorigenic ligands including FGF1, as the primary cell type for GSC crosstalk and single-cell multi-omic analysis revealed these astrocytes were under the control of ischemic regulatory networks. Functional validation confirmed hypoxia as a driver of pro-tumorigenic astrocytic ligand secretion and that GSC expansion was accelerated by pharmacological induction of oxidative stress. When controlled for genotype, the close association between glioma aggressiveness with patient age has very few proposed biological explanations. Our findings indicate that age-associated increases in cerebral vascular insufficiency and associated regional chronic cerebral hypoxia may contribute to this phenomenon.

Published in

Chronic hypoxia remodels the tumor microenvironment to support glioma stem cell growth

Nicholson JG, Cirigliano S, Singhania R et al. · Acta neuropathologica communications 2024 · PMID 38528608 · doi:10.1186/s40478-024-01755-6

This dataset

Open GSE210736 in finder → Find more human RNA-seq datasets →

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.

Series matrix ↓

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 GSE210736_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

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.

Raw reads on ENA ↗

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 PRJNA867344 and SRA study SRP390516. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38528608) ↗

Study design

30 distinct titles, 9 of them repeated

1M, CO, ×2 2M, CO, ×2 4M, CO, ×2 8M, CO, ×2 12M, CO, ×2 18M, CO, ×2 24M, CO, ×2 7M_CO_cntrl ×2 +1 more

How we read this: Read from 39 sample titles: 30 distinct titles with little repetition. 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

GSM6436150 1M, CO, 1
GSM6436151 1M, CO, 2
GSM6436152 2M, CO, 1
GSM6436153 2M, CO, 2
GSM6436154 4M, CO, 1
GSM6436155 4M, CO, 2
GSM6436156 8M, CO, 1
GSM6436157 8M, CO, 2
GSM6436158 12M, CO, 1
GSM6436159 12M, CO, 2
GSM6436160 18M, CO, 1
GSM6436161 18M, CO, 2
GSM6436162 24M, CO, 1
GSM6436163 24M, CO, 2
GSM6436166 5M, CO, mulitome, ATAC-seq
GSM6436167 8M, CO, multiome, ATAC-seq
GSM6436168 12M, CO, multiome, ATAC-seq
GSM6436169 5M, CO, mulitome, RNA-seq
GSM6436170 8M, CO, multiome, RNA-seq
GSM6436171 12M, CO, multiome, RNA-seq
GSM7869058 4M_CO_3
GSM7869059 8M_CO_3
GSM7869060 12M_CO_3
GSM7869061 7M_CO_cntrl_1
GSM7869062 7M_CO_cntrl_2
GSM7869063 7M_CO_HPX_1
GSM7869064 7M_CO_HPX_2
GSM7869065 5M_CO_2_GEX
GSM7869066 5M_CO_3_GEX
GSM7869067 9M_CO_2_GEX
GSM7869068 9M_CO_3_GEX
GSM7869069 12M_CO_2_GEX
GSM7869070 12M_CO_3_GEX
GSM7869071 5M_CO_2_ATAC
GSM7869072 5M_CO_3_ATAC
GSM7869073 9M_CO_2_ATAC
GSM7869074 9M_CO_3_ATAC
GSM7869075 12M_CO_2_ATAC
GSM7869076 12M_CO_3_ATAC

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