GEO series

Immune-dominated cellular heterogeneity and stromal plasticity in keloid infiltrating and hypercellular zones revealed by single-cell RNA sequencing

GSE335482 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2026/06/23 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: 8 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: Immune-dominated cellular heterogeneity and stromal plasticity in keloid infiltrating and hypercellular zones revealed by single-cell RNA sequencingFrontiers in immunology · 2026

Summary (written by the submitters)

Background Keloids are aggressive fibroproliferative disorders characterized by a sclerotic core and an actively invading margin, yet the cellular and molecular basis of this spatial heterogeneity remains poorly understood. The roles of immune-stromal crosstalk in driving peripheral invasion have not been systematically dissected. Methods Paired infiltrating and hypercellular zones were collected from anterior chest keloids of four patients.

Read the full summary ↓

Single‑cell RNA sequencing was performed on 128,678 cells after rigorous quality control. Unbiased clustering, differential gene expression analysis, pseudotime trajectory reconstruction, and cell-cell interaction profiling were applied to compare the landscapes of cell composition, function, and differentiation/development trajectories between the two zones. Results The hypercellular zone was dominated by extracellular matrix‑producing myofibroblasts, whereas the infiltrating zone was enriched in mononuclear phagocytes and endothelial cells, exhibiting a loose collagen architecture permissive for cellular invasion. Infiltrating zone fibroblasts adopted an immunomodulatory, inflammatory cancer‑associated fibroblast‑like phenotype, while macrophages displayed a type I interferon signature and immunoglobulin‑mediated activation, indicating a chronic inflammatory state with features reminiscent of certain autoimmune conditions. Langerhans cells followed a three‑stage developmental trajectory from a stress‑responsive to a terminal NF‑κB‑driven effector state, orchestrating neutrophil and Th17 cell recruitment via chemokine and cytokine networks, with significant enrichment of the IL‑17 signaling pathway. Endothelial cells at the margin underwent endothelial‑to‑mesenchymal transition, and Schwann cells exhibited phenotypic plasticity, mirroring aggressive tissue remodeling. Conclusions In the context of this spatially resolved analysis, this study redefines the keloid margin as an immunology‑dominated niche characterized by profound cellular and spatial heterogeneity that is associated with features that could drive peripheral invasion. These findings suggest a shift of the paradigm of keloid pathogenesis from a fibroblast‑centric model to an immune‑driven integrated landscape in this cohort and provide a theoretical foundation for keloid precision therapies and zone‑specific biomarkers for treatment response and disease monitoring.

Published in

Immune-dominated cellular heterogeneity and stromal plasticity in keloid infiltrating and hypercellular zones revealed by single-cell RNA sequencing

Cao Y, Duan L, Lin D et al. · Frontiers in immunology 2026 · PMID 42433363 · doi:10.3389/fimmu.2026.1873878

This dataset

Open GSE335482 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 GSE335482_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 PRJNA1478627 and SRA study SRP709731. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42433363) ↗

Study design

8 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 8 sample titles: 8 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

GSM9814425 Patient 1 Hypercellular zone
GSM9814426 Patient 1 Infiltrating zone
GSM9814427 Patient 2 Hypercellular zone
GSM9814428 Patient 2 Infiltrating zone
GSM9814429 Patient 3 Hypercellular zone
GSM9814430 Patient 3 Infiltrating zone
GSM9814431 Patient 4Hypercellular zone
GSM9814432 Patient 4 Infiltrating zone

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