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TOWARDS THE GENERATION OF A LASER CAPTURE MICRODISSECTION-BASED HUMAN ANAGEN HAIR FOLLICLE TRANSCRIPTOME ATLAS

GSE272333 Homo sapiens (human) RNA-seq (gene expression) 110 samples Submitted 2025/11/12 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 110 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
Homo sapiens (human)
Samples
110 samples: 42 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.
Paper
Toward the generation of a laser-capture microdissection-based human hair follicle transcriptome atlasThe Journal of investigative dermatology · 2026
Summary (written by the submitters)
While this is available for murine HFs, systematic characterization of the transcriptome of specific human hair follicle (HF) compartments remains a major unmet challenge in human hair research. Here, we have aimed to initiate the generation of such an atlas to reveal HF compartment-specific transcriptome. We performed laser capture microdissection (LCM) on 8 selected compartments, i.e.
Read the full summary ↓
bulge outer root sheath (ORS), suprabulbar ORS, inner root sheath (IRS), precortical hair matrix (HM), germinative HM, dermal papilla (DP), the lower dermal cup (DC), and the connective tissue sheath (CTS), of human anagen VI scalp HFs, and analysed these by RNA-seq. Principal component analysis (PCA) was used to validate the distinctness of the microdissected compartments. Biological replicates were clearly separated by HF compartments and a high degree of similarity of the distinct transcriptional profiles of each examined compartment was seen. Afterwards, we matched well-known signature genes to the correct HF compartments. For example, VCAN (versican) and ALPL (alkaline phosphatase) were correctly expressed in the DP, the lower DC, and suprabulbar CTS, while TCHH (trichohyalin) was solely found in the proximal IRS, and KRT85 (Keratin 85) and KRT31 (Keratin 31) were almost exclusively expressed in the precortical HM. These transcriptomic data were used to generate an easily accessible database for identifying and defining compartment-specific gene expression profiles, which can be ultimately made publicly accessible (“hair follicle MONasterium InteraCtive atlas – hairMONic”). This human HF transcriptomic map and the associated interactive database will support the identification of HF compartment-specific signature genes, and facilitates the development of compartment-specific, therapeutic interventions for the management of human HF disorders.
Published in
Toward the generation of a laser-capture microdissection-based human hair follicle transcriptome atlas
Fehrholz M, Epping L, Piccini I et al. · The Journal of investigative dermatology 2026 · PMID 41275906 · doi:10.1016/j.jid.2025.11.006
This dataset
Open GSE272333 in finder → Find more human 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 GSE272333_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: 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 PRJNA1136475 and SRA study SRP520231. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41275906) ↗
Study design
42 conditions, mostly in duplicate
Donor F IRS ×3 Donor B HM2 ×2 Donor B DC ×2 Donor B DP ×2 Donor B gHM ×2 Donor B IRS ×2 Donor B sbORS ×2 Donor B bORS ×2 +59 more

Supports a between-group comparison across 85 samples.

How we read this: 42 replicated groups read from 110 sample titles; they account for 85 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

+ 70 more — browse all 110 samples with per-sample file links →

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.