GEO series

Single-cell transcriptomic profiling of frontal fibrosing alopecia reveals preserved cellular architecture with stress-driven epithelial reprogramming and non-cytotoxic inflammation

GSE314972 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2026/06/29 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 8 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 8 samples: 4 × Control scalp scRNA-seq – CON vs 4 × FFA scalp scRNA-seq – FFAGroups 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)

Background: Frontal fibrosing alopecia (FFA) is a primary cicatricial alopecia characterised by progressive follicular fibrosis and irreversible hair loss. Despite increasing clinical recognition, the cellular mechanisms sustaining disease persistence remain incompletely defined. Objectives: To define cell-type–specific transcriptional states and inter-compartmental signalling networks in lesional scalp from patients with FFA using single-cell RNA sequencing.

Read the full summary ↓

Methods: Single-cell RNA sequencing was performed on lesional scalp biopsies from patients with FFA (n = 4) and matched control samples (n = 4). Cells were annotated into major lineages and refined subpopulations, and functional programmes and ligand–receptor-mediated communication were analysed. Results: Analysis of 38,984 high-quality cells revealed preservation of overall cellular composition and lineage architecture in FFA compared with controls. Despite this stability, marked disease-associated transcriptional reprogramming was observed across epithelial, immune and stromal compartments. Keratinocytes retained compartment-specific identity but exhibited prominent activation of stress-response and inflammatory pathways, particularly within upper follicle and bulge/outer root sheath populations. Dermal fibroblasts aligned along a continuous inflammation–fibrosis axis, with relative expansion of inflammatory and ACTA2⁺ states and strengthened coupling between inflammatory and fibrotic programmes. Immune profiling demonstrated skewing of CD4⁺ T cells towards Th1-, Th2- and Th22-associated signatures, together with macrophage reprogramming towards pro-fibrotic, tissue-remodelling states. Ligand–receptor analysis identified TGFB-centred signalling as the dominant inter-compartmental communication axis linking epithelial stress to immune modulation and fibroblast activation. Conclusions: FFA exhibits preserved cellular architecture but extensive, compartment-specific transcriptional reprogramming. Chronic epithelial stress and TGFB-driven inter-compartmental signalling, rather than overt cytotoxic inflammation, emerge as central disease mechanisms.

This dataset

Open GSE314972 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 GSE314972_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 PRJNA1393362 and SRA study SRP657968. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × Control scalp scRNA-seq – CON vs 4 × FFA scalp scRNA-seq – FFA

Supports a between-group comparison across 8 samples.

How we read this: 2 replicated groups read from 8 sample titles; they account for 8 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

GSM9419468 Control scalp scRNA-seq – CON256 Control scalp scRNA-seq…
GSM9419469 Control scalp scRNA-seq – CON265 Control scalp scRNA-seq…
GSM9419470 Control scalp scRNA-seq – CON266 Control scalp scRNA-seq…
GSM9419471 Control scalp scRNA-seq – CON267 Control scalp scRNA-seq…
GSM9419472 FFA scalp scRNA-seq – FFA005 FFA scalp scRNA-seq – F…
GSM9419473 FFA scalp scRNA-seq – FFA007 FFA scalp scRNA-seq – F…
GSM9419474 FFA scalp scRNA-seq – FFA010 FFA scalp scRNA-seq – F…
GSM9419475 FFA scalp scRNA-seq – FFA011 FFA scalp scRNA-seq – F…

Datasets on a similar topic

GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares architecture, single-cell
GSE241825 Mesenchymal stromal cell reprogramming and aberrant inflammatory signalling in myelofibrosis: insights from single-cell RNA sequencing 21 samples shares reprogramming, single-cell
GSE276193 Integrative Single-Cell Analysis Reveals Iron Overload-Induced Senescence and Metabolic Reprogramming in Ovarian Endometriosis-Associated Infertility 18 samples shares reprogramming, single-cell
GSE211376 Harmonized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastoma 11 samples shares architecture, single-cell
GSE217707 Single-cell sequencing analysis reveals the tissue architecture and empowers clinical trial in advanced conjunctival melanoma 10 samples shares architecture, single-cell
GSE283621 Single-cell multi-omics analysis identifies metabolism-linked epigenetic reprogramming as a driver of therapy-resistant medulloblastoma 79 samples shares reprogramming, single-cell
GSE337829 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [ChIP-Seq] 32 samples shares architecture, single-cell
GSE283619 Single-cell multi-omics analysis identifies metabolism-linked epigenetic reprogramming as a driver of therapy-resistant medulloblastoma [Multiome] 30 samples shares reprogramming, single-cell

Search all human RNA-seq datasets in GEO →

Share this dataset

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.