GEO series

MeRIP-seq analysis of replicative senescent MEFs and WI-38 cells

GSE264231 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/04/18 Platform GPL24676Platform GPL24247

At a glance

RNA-seq (gene expression) · human · 16 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: 16 samples: 8 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.

Summary (written by the submitters)

Cellular senescence is the most intuitive manifestation of individual senescence, and m6A is one of the most common modifications in mRNA, playing an important role in regulating gene expression, splicing, RNA stability, and mRNA degradation. Previous research has suggested that m6A levels play an important role in the occurrence and development of aging and aging-related diseases.

Read the full summary ↓

However, there are few reports on the role of m6A in cellular senescence. Here, we conducted combined MeRIP-seq and RNA-Seq analyses on non-senescent and senescent MEFs and WI-38 cells. The results revealed substantial disparities in the distribution of m6A sites across the transcriptome between senescent and non-senescent cells.

This dataset

Open GSE264231 in finder → Find more 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 GSE264231_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 PRJNA1101511 and SRA study SRP502409. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in duplicate

MEF, replicative senescence, RNA-seq, ×2 MEF, nomal, RNA-seq, ×2 WI-38, replicative senescence, RNA-seq, ×2 WI-38, nomal, RNA-seq, ×2 MEF, replicative senescence, MeRIP, ×2 MEF, nomal, MeRIP, ×2 WI-38, replicative senescence, MeRIP, ×2 WI-38, nomal, MeRIP, ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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

GSM8214778 MEF, replicative senescence, RNA-seq, Rep1 MEF, replicative senesc…
GSM8214779 MEF, replicative senescence, RNA-seq,Rep2 MEF, replicative senesc…
GSM8214780 MEF, nomal, RNA-seq, Rep1 MEF, nomal, RNA-seq,
GSM8214781 MEF, nomal, RNA-seq, Rep2 MEF, nomal, RNA-seq,
GSM8214782 WI-38, replicative senescence, RNA-seq, Rep1 WI-38, replicative sene…
GSM8214783 WI-38, replicative senescence, RNA-seq, Rep2 WI-38, replicative sene…
GSM8214784 WI-38, nomal, RNA-seq, Rep1 WI-38, nomal, RNA-seq,
GSM8214785 WI-38, nomal, RNA-seq, Rep2 WI-38, nomal, RNA-seq,
GSM8214786 MEF, replicative senescence, MeRIP, Rep1 MEF, replicative senesc…
GSM8214787 MEF, replicative senescence, MeRIP,Rep2 MEF, replicative senesc…
GSM8214788 MEF, nomal, MeRIP, Rep1 MEF, nomal, MeRIP,
GSM8214789 MEF, nomal, MeRIP, Rep2 MEF, nomal, MeRIP,
GSM8214790 WI-38, replicative senescence, MeRIP, Rep1 WI-38, replicative sene…
GSM8214791 WI-38, replicative senescence, MeRIP, Rep2 WI-38, replicative sene…
GSM8214792 WI-38, nomal, MeRIP, Rep1 WI-38, nomal, MeRIP,
GSM8214793 WI-38, nomal, MeRIP, Rep2 WI-38, nomal, MeRIP,

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GSE235246 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma impair CD8+ T cell activation and responsiveness to immunotherapy in mice 38 samples shares senescent
GSE315101 Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs oligodendrocyte functions and alters neuronal activity [DLK1_snRNA-seq] 27 samples shares senescent
GSE288534 Targeting Senescent Hepatocytes for Treatment of Metabolic Dysfunction-associated Steatotic Liver Disease and Multi-organ Dysfunction 24 samples shares senescent
GSE235233 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma suppress CD8+ T cell activation and inhibit response to immune checkpoint therapy [Cultured_Human_Mouse] 16 samples shares senescent

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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.