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Identifying ST6GAL1 as a novel factor for human dermal fibroblast photoaging

GSE329475 Homo sapiens (human) RNA-seq (gene expression) 6 samples Submitted 2026/09/02 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 6 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
6 samples: 3 × HDF cell, NC vs 3 × HDF cell, UVAGroups 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)
Due to increasing global UV exposure from environmental changes and lifestyle factors, understanding the molecular mechanisms of photoaging is critical in dermatological research. This study addresses the underexplored role of glycosylation in dermal fibroblast photoaging by establishing an in vitro model using human dermal fibroblasts (HDF) exposed to UVA.
Read the full summary ↓
Transcriptomic profiling revealed significant enrichment of gene sets associated with glycosylation and carbohydrate derivative synthesis. We employed lectin arrays and binding assays to identify candidate oligosaccharide ligands responsive to UVA exposure, revealing a marked upregulation of cell-surface sialylation levels during photoaging. Western blot analysis further indicated specific overexpression of the sialyltransferase ST6GAL1 in photoaged HDFs. Intervention with the sialyltransferase inhibitor 3FAx-Neu5Ac or ST6GAL1 gene silencing significantly alleviated the skin photoaging markers including p16, MMPs and SA-β-gal staining, while ST6GAL1 overexpression replicated these photoaging characteristics. Mechanistically, pathway analysis indicated that ST6GAL1 mediates photoaging via the regulation of the RAS-ERK-p16 signaling axis, while treatment with the RAS inhibitor reversed the phenotypic changes induced by ST6GAL1 knockdown. In a reconstructed human skin equivalent model of UVA photoaging, immunostaining confirmed the presence of ST6GAL1 alongside RAS and p16 induction. This study uniquely identifies ST6GAL1 as a specific biomarker for fibroblast photoaging, offering a novel therapeutic target for the diagnosis and intervention of photoaging.
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 GSE329475_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 PRJNA1459316 and SRA study SRP695952. Searching any of these in the dataset finder brings you back here.

Study design
3 × HDF cell, NC vs 3 × HDF cell, UVA

Supports a between-group comparison across 6 samples.

How we read this: 2 replicated groups read from 6 sample titles; they account for 6 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
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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.