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Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

GSE266577 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2024/10/08 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 48 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
Homo sapiens (human)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
48 samples: 48 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
Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancerCancer cell · 2024
Summary (written by the submitters)
To characterize the spatial tumor microenvironment (TME) of high-grade serous ovarian cancer (HGSC) at the single cell level, we performed single-cell RNA sequencing (scRNA-seq) on 48 tumor or ascites samples from 29 HGSC patients. This data set includes paired scRNA-seq data from chemo-naive and post-neoadjuvant chemotherapy (IDS) samples of 22 HGSC patients.
Published in
Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancer
Launonen IM, Niemiec I, Hincapié-Otero M et al. · Cancer cell 2024 · PMID 39658541 · doi:10.1016/j.ccell.2024.11.005
This dataset
Open GSE266577 in finder → Find more human RNA-seq datasets → Ovarian cancer landscape →
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 GSE266577_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 PRJNA1107618. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39658541) ↗
Study design
48 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 1 samples read as cases, 27 as controls.

How we read this: Read from 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

More Ovarian cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.