GEO series

Transcriptomic landscape of human primary stromal cells (fibroblasts, endothelial cells and mesenchymal stem cells) upon therapy-induced senescence (genotoxic chemotherapy）

GSE216842 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/12/31 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 24 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: 24 samples: 8 conditions, mostly in triplicateGroups 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)

As a prominent hallmark of senescent cells, the senescence-associated secretory phenotype (SASP) accumulates over the course of chronological aging and in diverse age-related diseases, and is a crucial driver of chronic inflammation and aging-associated phenotypes. In humans, senescent cells accumulate in multiple tissues at different rates, from 2- to 20-fold when comparing young (<35 years) to old (>65 years) healthy donors.

Read the full summary ↓

For decades of years, the identification, characterization and pharmacological elimination of senescent cells have gained attention in the research field of aging and age-related disorders. However, the non-specificity of current senescence markers and the existence of different senescence programs strongly limits these tasks. Despite recent advances in high-throughput technologies, there is still a lack of in-depth comparison of the senescence-associated expression profile among distinct cell lineages in the tissue microenvironment. Here, we chose to profile the genome-wide expression of several primary normal human stromal cell lines including HBF1203, Huc068, HUVEC and IMR90 upon therapy-induced senescence (TIS), with the aim to determine molecular regulators of senescence phenotypes and define the potential contribution of senescent cells to human age-related pathologies. These data may provide a baseline to further explore how senescent cells arising in the local microenvironment can promote tissue dysfunction and organ degeneration in the course of an individual aging.

This dataset

Open GSE216842 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 GSE216842_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 PRJNA895476 and SRA study SRP405135. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in triplicate

HBF_C ×3 HBF_B ×3 huc_C ×3 huc_B ×3 HUVE_C ×3 HUVE_B ×3 IMR_C ×3 IMR_B ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

GSM6696638 HBF_C01 HBF_C
GSM6696639 HBF_C02 HBF_C
GSM6696640 HBF_C03 HBF_C
GSM6696641 HBF_B01 HBF_B
GSM6696642 HBF_B02 HBF_B
GSM6696643 HBF_B03 HBF_B
GSM6696644 huc_C01 huc_C
GSM6696645 huc_C02 huc_C
GSM6696646 huc_C03 huc_C
GSM6696647 huc_B01 huc_B
GSM6696648 huc_B02 huc_B
GSM6696649 huc_B03 huc_B
GSM6696650 HUVE_C01 HUVE_C
GSM6696651 HUVE_C02 HUVE_C
GSM6696652 HUVE_C03 HUVE_C
GSM6696653 HUVE_B01 HUVE_B
GSM6696654 HUVE_B02 HUVE_B
GSM6696655 HUVE_B03 HUVE_B
GSM6696656 IMR_C01 IMR_C
GSM6696657 IMR_C02 IMR_C
GSM6696658 IMR_C03 IMR_C
GSM6696659 IMR_B01 IMR_B
GSM6696660 IMR_B02 IMR_B
GSM6696661 IMR_B03 IMR_B

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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.