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Distinct modes of cell competition shape mammalian tissue morphogenesis

GSE128241 Mus musculus (mouse) Single-cell RNA-seq 975 samples Submitted 2019/05/31 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 975 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
975 samples
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)
Cell competition (CC)—the sensing and elimination of less fit “loser” cells by neighbouring “winner” cells—was first described in Drosophila. Although proposed as a selection mechanism to optimize tissue and organ development, its evolutionary generality remains unclear. Here, by employing live-imaging, lineage-tracing, single cell transcriptomics and genetics, we unearth two intriguing CC mechanisms that sequentially shape and maintain stratified tissue architecture during mouse skin development.
Read the full summary ↓
Early in embryonic epidermis, winner progenitors within the single-layered epithelium kill and clear neighbouring losers by engulfment. Upon stratification and skin barrier formation, the basal layer instead expels losers through a homeostatic upward flux of differentiating progeny. This CC switch is physiologically relevant, as when it is perturbed, so too is barrier formation. Our findings establish CC as a selective force to optimize function of a vertebrate tissue, but also illuminate how a tissue dynamically adjusts its CC strategies to preserve fitness as it encounters increased architectural complexity during morphogenesis.
This dataset
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.

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 GSE128241_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA526869 and SRA study SRP188334. Searching any of these in the dataset finder brings you back here.

Samples in this study

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