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Airway Hillocks are Injury-Resistant Reservoirs of Plastic Stem Cells

GSE207479 Mus musculus (mouse) RNA-seq (gene expression) 186 samples Submitted 2024/03/05 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 186 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
186 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.
Paper
Airway hillocks are injury-resistant reservoirs of unique plastic stem cellsNature · 2024
Summary (written by the submitters)
Airway hillocks are newly described discrete epithelial structures of unknown function that are distinct from the classic pseudostratified epithelium lining the airway lumen. We show that hillocks are islands of stratified epithelia harboring migratory stem cells that are capable of extensive clonal expansion. Hillocks resist a broad range of injuries, preserving a basal stem cell population that ultimately resurfaces the airway lining.
Read the full summary ↓
The fully resurfaced hillock epithelium then exhibits plasticity and converts into a normal pseudostratified epithelium indistinguishable from its healthy, uninjured counterpart.
Published in
Airway hillocks are injury-resistant reservoirs of unique plastic stem cells
Lin B, Shah VS, Chernoff C et al. · Nature 2024 · PMID 38693267 · doi:10.1038/s41586-024-07377-1
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 GSE207479_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA855891 and SRA study SRP384921. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 186 more — browse all 186 samples with per-sample file links →

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