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Transcriptomic profiling of chlorogenic acid and taurine treatment in human skin cells provides insights into cellular senescence mechanisms

GSE302932 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/03/23 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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
Transcriptomic profiling of chlorogenic acid and taurine treatment in human skin cells provides insights into cellular senescence mechanismsFrontiers in molecular biosciences · 2026
Summary (written by the submitters)
Chlorogenic acid (CGA) and taurine are well-known antioxidant compounds that have been demonstrated to reduce cellular senescence in the skin. Given their potential synergistic effects, co-treatment with CGA and taurine may enhance their individual efficacy. However, the biological mechanisms underlying their skin-protective effects remain underexplored.
Read the full summary ↓
This study aimed to identify genes responsive to CGA and taurine and elucidate their anti-senescence-associated pathways. To this end, we conducted transcriptome-wide RNA sequencing to profile gene expression changes of human epidermal keratinocytes, melanocytes, and fibroblasts by treatment of CGA, taurine, and their combination. The differential expression analysis identified 197 differentially expressed genes (DEGs) in response to the compounds, of which 62 were prioritized as aging-related DEGs based on their involvement in skin aging pathways from functional enrichment analysis and supporting evidence from public databases and prior studies. By identifying key genes and pathways that might contribute to the cellular longevity in human skin, this study provides molecular insights for developing anti-aging strategies with potential applications in dermatology.
Published in
Transcriptomic profiling of chlorogenic acid and taurine treatment in human skin cells provides insights into cellular senescence mechanisms
Kim B, Shin JG, Hong IS et al. · Frontiers in molecular biosciences 2026 · PMID 41938013 · doi:10.3389/fmolb.2026.1748185
This dataset
Open GSE302932 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 GSE302932_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: 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 PRJNA1292857 and SRA study SRP601643. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41938013) ↗
Study design
36 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 36 sample titles: 36 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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