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Cellular heterogeneity in hypertrophic burn scars in response to carbon dioxide laser therapy

GSE320017 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2026/02/25 Platform GPL24676Platform GPL30173
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. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
16 samples: 16 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 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
Cellular heterogeneity in hypertrophic burn scars in response to carbon dioxide laser therapyCommunications medicine · 2026
Summary (written by the submitters)
Background: Ablative fractional carbon dioxide (AFCO2) laser therapy is used for treating pathological scarring. However, mechanisms underlying reduction in hypertrophic scarring are poorly understood. Methods: We investigated cellular mechanisms of AFCO2 laser therapy by performing single-cell RNA sequencing (scRNA-seq) on skin biopsies from burn survivors with hypertrophic scars before and after three sessions of AFCO2 therapy.
Read the full summary ↓
Scar reduction was assessed subjectively and objectively. Results: Those with a good response (GR) to laser therapy have scars less than 6 years from injury, whereas poor responders (PR) have scars over 6 years since injury. ScRNA-seq analysis of skin biopsies reveals that genes enriched in GR are associated with extracellular matrix and structure organisation (COL14A1, POSTN, SPARC); whereas genes enriched in PR are related to enhanced immune inflammatory responses (CXCL14, JUN, TNC). Notably, expression of the pro-fibrotic gene Engrailed-1 (EN1) remains elevated in PR scars compared to GR. The ECM-regulatory gene TIMP-1 (Tissue Inhibitor of Metalloproteinases 1) is also significantly upregulated in PR scars following treatment relative to GR scars, although at the protein level TIMP1 levels decrease in PR but increase in GR after therapy. The regenerative-associated gene, TRPS1 (Transcriptional Repressor GATA Binding 1) expression demonstrates opposing regulation post treatment, with upregulation observed in GR scars but downregulation in PR scars, underscoring distinct transcriptional trajectories associated with differential therapeutic outcomes. Finally, distinct intercellular communication networks and differentiation trajectories are observed after AFCO2, with regenerative mesenchymal fibroblasts predominating in GR but inflammatory fibroblasts associated with PR. Conclusions: We conclude AFCO2 laser therapy is more effective if done early after injury and distinct fibroblast recruitment is associated with a good response, specifically regenerative fibroblasts. Plain English summary: Ablative fractional carbon dioxide (AFCO2) laser therapy is used for reducing scarring. However, results are varied and the factors associated with a good response are poorly understood. We assessed scar reduction after AFCO2 therapy in patients with excessive scarring, taking skin biopsies to determine the cell types present in the treated areas and which genes they expressed. We showed that the therapy was more effective in patients who were less than 6 years from injury, those with a good response had scars containing regenerative fibroblasts and gene expression that was distinct from those with a poor response. We conclude that AFCO2 therapy is most effective if used early after injury and that a more regenerative, less inflammatory cell infiltration is associated with scar reduction.
Published in
Cellular heterogeneity in hypertrophic burn scars in response to carbon dioxide laser therapy
Chen YY, Mahony C, Turner J et al. · Communications medicine 2026 · PMID 41963536 · doi:10.1038/s43856-026-01521-w
This dataset
Open GSE320017 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 GSE320017_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 PRJNA1425982 and SRA study SRP678270. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41963536) ↗
Study design
16 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 16 sample titles: 16 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
Datasets on a similar topic

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