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Omental support of ovarian tumor cell growth is unabated in the absence of mature adipocytes

GSE281706 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2026/06/13 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
24 samples: 24 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.
Summary (written by the submitters)
The omentum, a specialized adipose tissue arising off the stomach, is a key premetastatic niche for ovarian cancer metastasis and growth within the peritoneum. Why the omentum promotes optimal ovarian cancer growth remains unclear, but the prevailing concept is that omental adipocytes and the accompanying metabolic exchange of FABP4-binding fatty acids are critical.
Read the full summary ↓
Here, we generated mice lacking mature adipocytes in all peritoneum tissues, including the omentum. ID8p53–/–Brca2–/– tumors retained a propensity to seed at regions typically associated with adipose depots, even without mature adipocytes. However, the lack of mature adipocytes did not suppress tumor expansion in the peritoneum, whereas removing the adipocyte-free omentum did. Single-cell RNA sequencing analysis revealed that endothelial FABP4 was especially high in the omentum, potentially accounting for the role of FABP4 in tumor growth rather than adipocyte FABP4, as previously thought. Thus, whether and how tumor cell-adjacent adipocytes impact tumor progression needs re-evaluation
This dataset
Open GSE281706 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 GSE281706_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 PRJNA1185261 and SRA study SRP544768. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
Datasets on a similar topic

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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.