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Transcriptomic profiling of human senescent cells upon medicinal intervention with ginkgo boliba extract or rattan tea extract

GSE156448 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/31 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 3 × CTRL vs 3 × BLEO vs 3 × BLEO/GBE vs 3 × BLEO/RTEGroups 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)
Aging and age-related pathologies can be delayed by specifically targeting the senescence-associated secretory phenotype (SASP), a hallmark feature of senescent cells. Achieving the goal using natural or synthetic agents would have a tremendous impact on the quality of lifespan and burden of age-related chronic diseases.
Read the full summary ↓
We report the potential of ginkgo boliba extract (GBE), a commonly used dietary supplement and traditional herbal medicine for hundreds of years in China as an antioxidant and freeradical scavenger, or rattan tea extract (RTE), a medicinal agent used for many years for the treatment of inflammation, fatty liver, tumor, diabetes and hyperlipidemia, in targeting senescent cells via suppression of the SASP. This study demonstrates the efficacy of GBE and RTE as medicinal products in controlling the influence of senescent cells and provides a strong rationale for their future use in aging control and geriatric medicine.
This dataset
Open GSE156448 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 GSE156448_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 PRJNA658000 and SRA study SRP278052. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × CTRL vs 3 × BLEO vs 3 × BLEO/GBE vs 3 × BLEO/RTE

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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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.