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Functional Dynamics of Dendritic Cells in Response to Cutibacterium acnes Strains Associated with Healthy and Acne-prone Skin

GSE255918 Homo sapiens (human) RNA-seq (gene expression) 68 samples Submitted 2025/02/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 68 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
68 samples: 24 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
Functional Dynamics of Dendritic Cells in Response to Cutibacteriumacnes Strains Associated with Healthy and Acne-Prone SkinThe Journal of investigative dermatology · 2025
Summary (written by the submitters)
The pathogenesis of acne vulgaris is multifactorial and is influenced by the presence of Cutibacterium acnes. This study investigated how different strains of C. acnes, associated with either healthy skin (CH) or acne-prone skin (CA), selectively modulate the function of three dendritic cell (DC) subtypes: Langerhans cell-derived (LCDC), monocyte-derived (moDCs), and myeloid (mDCs).
Read the full summary ↓
Our findings revealed that all DC subtypes secreted varying levels of TGF-β, IL-1β, IL-6, IL-12p70, and IL-23 in response to C. acnes stimulation. Notably, moDCs exhibited the highest cytokine secretion in response to CA-ribotypes, indicating their potent immune responsiveness. Conversely, LCDCs were the least responsive, regardless of the ribotypes. Principal component (PCA) and gene ontology (GO) analysis of bulk RNA sequencing data of DCs exposed to C. acnes identified extensive transcriptional changes, particularly in immune response pathways. moDCs showed the most pronounced transcriptional changes, aligning with their high cytokine secretion levels. Additionally, we identified specific genes upregulated by CA-ribotypes, with notable differences among the DC types, highlighting the unique contributions of each DC subtype to the skin's immune defense mechanisms against C. acnes. Overall, our study underscores the complexity of DC responses in the skin's immune environment and provides valuable insights into their roles in acne.
Published in
Functional Dynamics of Dendritic Cells in Response to Cutibacteriumacnes Strains Associated with Healthy and Acne-Prone Skin
Del Vecchio G, Nguyen NPN, Ouyang K et al. · The Journal of investigative dermatology 2025 · PMID 40020992 · doi:10.1016/j.jid.2025.02.132
This dataset
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.

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 GSE255918_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1077240 and SRA study SRP489975. Searching any of these in the dataset finder brings you back here.

Study design
24 conditions, mostly in duplicate
LCDC cell type, cells alone, ×4 moDC cell type, cells alone, ×4 LCDC cell type, 43PA1, ×3 mDC cell type, cells alone, ×3 LCDC cell type, 110PA ×2 LCDC cell type, 110PA3, ×2 LCDC cell type, 110PA4, ×2 LCDC cell type, 42PA3, ×2 +30 more

Supports a between-group comparison across 54 samples.

How we read this: 24 replicated groups read from 68 sample titles; they account for 54 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

+ 28 more — browse all 68 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.