GEO series

Macrophage-mediated myelin recycling fuels brain cancer malignancy (scRNA-Seq)

GSE266145 Mus musculus (mouse) Single-cell RNA-seq 22 samples Submitted 2024/06/30 Platform GPL24247Platform GPL21626

At a glance

Single-cell RNA-seq · mouse · 22 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)
Samples: 22 samples: 22 samples, each with a distinct title — no replicated groupsGroups 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

Open GSE266145 in finder → Find more mouse 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 GSE266145_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 PRJNA1105998 and SRA study SRP504748. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39137777) ↗

Study design

22 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 22 sample titles: 22 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

GSM8241457 MG, Primary PDG-p53
GSM8241458 MDMs, Primary PDG-p53
GSM8241459 MG, Recurrent PDG-p53
GSM8241460 MDMs, Recurrent PDG-p53
GSM8241461 MDMs, Primary PDG-Ink4a
GSM8241462 MG, Primary PDG-Ink4a
GSM8241463 MDMs, Recurrent PDG-Ink4a
GSM8241464 MG, Recurrent PDG-Ink4a
GSM8241465 MG, Non-tumor bearing
GSM8241466 CD45-/CD45+, PDG-Ink4a Primary, b
GSM8241467 CD45-/CD45+, PDG-p53 Primary, a
GSM8241468 CD45-/CD45+, PDG-Ink4a Primary, c
GSM8241469 CD45-/CD45+, PDG-p53 Recurrent, a
GSM8241470 CD45-/CD45+, PDG-Ink4a Recurrent, b
GSM8241471 CD45-/CD45+, PDG-Ink4a Recurrent, c
GSM8241472 CD45-/CD45+, PDG-p53 Primary, b
GSM8241473 CD45-/CD45+, PDG-p53 Primary, c
GSM8241474 CD45-/CD45+, PDG-p53 Primary, d
GSM8241475 CD45-/CD45+, PDG-Ink4a Primary, d
GSM8241476 CD45-/CD45+, PDG-Ink4a Recurrent, d
GSM8241477 CD45-/CD45+, PDG-p53 Recurrent, b
GSM8241478 CD45-/CD45+, PDG-p53 Recurrent, c

Datasets on a similar topic

GSE266143 Macrophage-mediated myelin recycling fuels brain cancer malignancy (RNA-Seq in vitro) 36 samples shares macrophage-mediated, malignancy, recycling, myelin, fuels, brain
GSE266149 Macrophage-mediated myelin recycling fuels brain cancer malignancy 110 samples shares macrophage-mediated, malignancy, recycling, myelin, fuels, brain
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GSE311488 Chemotherapeutic Induction of Single-Stranded DNA Accumulation Sensitizes Triple-Negative Breast Cancer to Immunotherapy [scRNA-seq] 32 samples shares scrna-seq
GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares scrna-seq
GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq
GSE294999 Obesity-driven inflammation enables triple-negative breast cancer progression through immunosuppression [scRNA-Seq] 16 samples shares scrna-seq

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