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Investigating the effects of IFN-gamma on attached and detached ovarian cancer cells

GSE337368 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2026/07/04 Platform GPL34290
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: 8 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.
Summary (written by the submitters)
The interplay between intrinsic and niche-driven mechanisms that enables DTCs to survive and home to distant organs remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) coupled with velocity analyses in ascites and metastasis-bearing omenta uncovered an emergent and distinct interferon-gamma centric transcriptional trajectory, enriched among early seeding clones.
Read the full summary ↓
Knockout of IFN-gamma receptor 1 (Ifngr1) in tumor cells significantly reduced metastatic burden and extended survival, underscoring the importance of tumor cell intrinsic IFN-gamma signaling in ovarian cancer metastasis. We identified that the tumor intrinsic IFN-gamma response and ascites-derived tumor-associated macrophages (TAMs) protect cancer cells from anoikis-mediated death. To investigate the mechanism underlying increased cancer cell survival under IFN-gamma treatment and ULA conditions, we performed bulk RNA-sequencing. We discovered increased expression of the anti-apoptotic gene Parp14 in cancer cells treated with IFN-gamma, specifically under ultra low attachment conditions.
This dataset
Open GSE337368 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 GSE337368_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 PRJNA1484962 and SRA study SRP714673. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
Reg_C2I ×3 Reg_C2P ×3 Reg_NI ×3 Reg_NP ×3 ULA_C2I ×3 ULA_C2P ×3 ULA_NI ×3 ULA_NP ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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
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.