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Macrophage-mediated myelin recycling fuels brain cancer malignancy (VISIUM)

GSE266147 Mus musculus (mouse) Spatial transcriptomics 13 samples Submitted 2024/06/30 Platform GPL24247
At a glance

Spatial transcriptomics · mouse · 13 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Mus musculus (mouse)
Samples
13 samples: 5 conditions, mostly in triplicateGroups 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
Macrophage-mediated myelin recycling fuels brain cancer malignancyCell · 2024
Summary (written by the submitters)
Tumors growing in metabolically-challenged environments, such as glioblastoma in the brain, are particularly reliant on cross-talk with their tumor microenvironment (TME) to satisfy their high energetic needs. However, the intricacies of this metabolic interplay and the consequences on immune cell subset diversity and function remain largely unexplored.
Read the full summary ↓
We interrogated the heterogeneity of the glioblastoma TME using single cell multi-omics analyses in preclinical glioblastoma mouse models and patient samples, and identified metabolically-rewired tumor-associated macrophage (TAM) subpopulations that fuel glioblastoma malignancy. These TAM subsets, termed lipid-laden macrophages (LLMs) to reflect their increased lipid metabolism activity and cholesterol storage, are epigenetically rewired, display immunosuppressive features and are enriched in the aggressive mesenchymal glioblastoma subtype. In response to TME-derived cues triggering liver-X-receptor (LXR) expression, macrophages increase engulfment of cholesterol-rich myelin debris and acquire an LLM phenotype. Subsequently, LLMs directly transfer myelin-derived lipids to cancer cells in an LXR/Abca1-dependent manner, thereby fueling the heightened metabolic demands of mesenchymal glioblastoma. Furthermore, LLM content predicts clinical outcomes and immune checkpoint blockade response in glioblastoma patients and other cancer types. Our work provides an in-depth understanding of the immune-metabolic interplay during glioblastoma progression in a subtype- and microanatomical niche-dependent manner, thereby laying a framework for the discovery of targetable metabolic vulnerabilities in glioblastoma.
Published in
Macrophage-mediated myelin recycling fuels brain cancer malignancy
Kloosterman DJ, Erbani J, Boon M et al. · Cell 2024 · PMID 39137777 · doi:10.1016/j.cell.2024.07.030
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 GSE266147_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1106001 and SRA study SRP504759. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in triplicate
PDG-Inka Recurrent, ×3 PDG-p53 Primary, ×3 PDG-p53 Recurrent, ×3 PDG-Inka Primary, ×2 PDG-Ink4a Primary, ×2

Supports a between-group comparison across 13 samples.

How we read this: 5 replicated groups read from 13 sample titles; they account for 13 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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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.