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Single-cell RNA sequencing clarifies dermal fibroblast subset representation in vitro and reveals variable persistence of keloid disease-associated features [bulk-RNAseq]

GSE303591 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/08/11 Platform GPL23227
At a glance

Single-cell RNA-seq · human · 16 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
16 samples: 4 × Keloid dermal cells, control without AA, vs 4 × Normal dermal cells, control without AA, vs 4 × Keloid dermal cells, with AA, vs 4 × Normal dermal cells, with AA,Groups 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)
In vitro models of scarring and fibrosis are essential to improve our understanding of disease mechanisms and ultimately develop much-needed effective therapeutic strategies. This is particularly true for keloids, the example of pathological scarring exploited in this study, as there is no animal model. Our emerging appreciation of fibroblast heterogeneity from relatively new single cell RNA sequencing (scRNA-seq) information leaves a knowledge gap about what is represented in typical fibroblast cultures.
Read the full summary ↓
Specifically, it is important to know whether quantitative differences in fibroblast subtypes observed in pathological tissues are represented and/or whether disease-associated molecular alterations of subtypes are maintained. This study performed scRNA-seq on patient-matched keloid and normal adjacent dermis immediately ex vivo, which was compared to sc- and bulk-RNA-seq on primary dermal fibroblast cultures from the same samples after 4+ passages. Freshly dissociated tissue showed anticipated differences in cell proportions in keloid versus normal skin; however, comparably for both tissue types, there was an assimilation of fibroblast subtypes after culture. Cultured cells clustered conspicuously from the original populations, with evidence of only minor heterogeneity persisting. Cells displayed, to varying degrees, elements of each of the original subset signatures; FAP+/SFRP2+ mesenchymal features were strongest. Pseudo-bulk analysis of ex vivo mesenchymal subpopulations showed cell-intrinsic keloid versus normal skin transcriptional differences consistent with current disease understanding; however, only a subset of these persisted in vitro. Cell-cell communication analysis provides potential strategies to maintain specific cell populations and their in vivo phenotypes. As an example, we report that culture with ascorbic acid (stimulating cell-derived extracellular matrix) enriched the mesenchymal signature. The data presented herein provide resources supporting greater understanding of, and strategies to refine, essential human fibroblast culture models.
This dataset
Open GSE303591 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 GSE303591_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 PRJNA1295892 and SRA study SRP603271. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × Keloid dermal cells, control without AA, vs 4 × Normal dermal cells, control without AA, vs 4 × Keloid dermal cells, with AA, vs 4 × Normal dermal cells, with AA,

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
Datasets on a similar topic

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