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Cell state dynamics during early stages of serous endometrial carcinoma

GSE269332 Mus musculus (mouse) Single-cell RNA-seq 30 samples Submitted 2025/08/08 Platform GPL19057Platform GPL30172
At a glance

Single-cell RNA-seq · mouse · 30 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
30 samples: 30 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
Cell state dynamics during early stages of serous endometrial carcinomaThe Journal of pathology · 2025
Summary (written by the submitters)
Serous endometrial carcinoma (SEC) is one of the most aggressive and lethal types of uterine cancer, responsible for about 40% of all endometrial cancer-related deaths. Due to the rapid progression of SEC, early detection of this disease is of utmost importance. However, molecular and cellular dynamics during the pre-dysplastic stage of this disease remain largely unknown.
Read the full summary ↓
Here, we provide a comprehensive census of cell types and their states for normal, pre-dysplastic, and dysplastic endometrium in a mouse model of SEC. This model is associated with inactivation of tumor suppressor genes Trp53 and Rb1, whose pathways are altered frequently in SEC. We report that pre-dysplastic changes are characterized by expanded and increasingly diverse immature luminal epithelial cell populations. Decrease of differentiated cell states is accompanied by a previously unreported reduction in number and strength of predicted interactions between epithelial and stromal endometrial cells. Such reduction is transient and is followed by formation of a new set of intercellular interactions marking further cancer progression. By using a multi-level approach combining single-cell and spatial transcriptomics paired with screening for clinically relevant genes in human endometrial carcinoma, we identified a panel of genes with potential use as early diagnostic, prognostic, and therapeutic targets. In summary, our results suggest an important role of luminal epithelial cell state in SEC pathogenesis, uncover transient reduction in cell-cell interactions prior the onset of cancer-associated dysplastic changes, and validate our mouse SEC model as a promising comparative platform in preclinical settings.
Published in
Cell state dynamics during early stages of serous endometrial carcinoma
Flesken-Nikitin A, Pirtz MG, Ashe CS et al. · The Journal of pathology 2025 · PMID 40923619 · doi:10.1002/path.6470
This dataset
Open GSE269332 in finder → Find more mouse 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 GSE269332_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 PRJNA1121235 and SRA study SRP512411. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40923619) ↗
Study design
30 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 30 sample titles: 30 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
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