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Stem-cell states converge in multi-stage cutaneous squamous cell carcinoma development [scRNA-seq]

GSE261766 Mus musculus (mouse) Single-cell RNA-seq 28 samples Submitted 2024/03/22 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 28 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
28 samples: 28 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)
Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse normal and tumor samples, and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in pre-malignant tumor cells expressing markers of lineage plasticity and drug resistance.
Read the full summary ↓
Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity, while inhibition of differentiation repressed plasticity and potentiated responses to cell-cycle inhibitors. Manipulation of this cell state transition point may provide a rich source of potential combinatorial targets for cancer therapy. These scRNAseq data encompass normal skin, papilloma, and squamous cell cacinoma samples.
This dataset
Open GSE261766 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 GSE261766_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 PRJNA1088744 and SRA study SRP495757. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
28 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 10 samples read as cases, 4 as controls.

How we read this: Read from 28 sample titles: 28 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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