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Hair follicle aging is driven by transepidermal stem cell elimination via COL17A1 proteolysis

GSE72863 Mus musculus (mouse) Expression microarray 12 samples Submitted 2015/12/01 Platform GPL10787
At a glance

Expression microarray · mouse · 12 samples

What it is
Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
Organism
Mus musculus (mouse)
Samples
12 samples: 12 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 expression table (series matrix) and the array files, from NCBI.
Summary (written by the submitters)
Hair loss is one of the typical aging phenotypes in mammals, yet the underlying mechanism(s) is unclear. Here we report that hair follicle stem cell (HFSC) aging causes the stepwise miniaturization of hair follicles and eventual hair loss both in wild-type mice and in humans. In vivo fate analysis of HFSCs revealed that the DNA damage response in HFSCs causes proteolysis of Type XVII Collagen (COL17A1/BP180) to trigger “HFSC aging”, characterized by their loss of stemness signature and epidermal commitment.
Read the full summary ↓
Those aged HFSCs are cyclically eliminated from the skin through their terminal epidermal differentiation, thereby causing hair follicle miniaturization. That process can be recapitulated by Col17a1 deficiency and prevented by forced maintenance of COL17A1 in HFSCs, demonstrating that stem cell homeostasis is the keystone against ultimate execution of the tissue/organ aging program.
This dataset
Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE72863_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.

Also filed as BioProject PRJNA295214. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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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