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Single cell multiomics unravel the transcription networks controlling the different EMT tumor states [bulk RNA-seq]

GSE288196 Mus musculus (mouse) Single-cell RNA-seq 22 samples Submitted 2026/06/08 Platform GPL24247
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: 11 conditions, mostly in duplicateGroups 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)
Epithelial-to-mesenchymal transition (EMT) is a process by which cells lose their epithelial characteristics and acquire mesenchymal traits. In cancer, EMT is associated with tumor initiation, progression, invasion, metastasis and resistance to therapy. Recent studies demonstrated that EMT is not a binary switch, but presents intermediate states associated with different tumor functions.
Read the full summary ↓
The gene regulatory networks (GRNs) controlling the different EMT states remain elusive. Here, using multi-OMIC approaches combining single cell RNA-seq and single cell ATAC-seq, we define the transcriptomic and chromatin landscape associated with the distinct EMT states in mouse skin squamous cell carcinoma (SCC) exhibiting EMT. Using CRISPR/Cas9-mediated loss of function studies combined with functional characterization in vitro and in vivo, we unravel the cellular and molecular mechanisms regulated by Pitx1, Klf5, Nfatc1 and Creb3l1, transcription factors (TFs) controlling specific EMT states. Altogether, our results identify the transcriptional and chromatin landscape of the distinct EMT tumor states and uncover novel key TFs controlling these states and their transition, providing potential novel targets for anti-cancer therapy.
This dataset
Open GSE288196 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 GSE288196_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 PRJNA1216550 and SRA study SRP560353. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 conditions, mostly in duplicate
Control.d0.Ep+.sample ×2 Control.d8.Ep+.sample ×2 Control.d8.Ep-.sample ×2 Pitx1KO.d8.Ep+.sample ×2 Pitx1KO.d8.Ep-.sample ×2 Klf5KO.d8.Ep+.sample ×2 Klf5KO.d8.Ep-.sample ×2 Nfatc1KO.d8.Ep+.sample ×2 +3 more

Supports a between-group comparison across 22 samples.

How we read this: 11 replicated groups read from 22 sample titles; they account for 22 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.