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Targeted partial reprogramming of age-associated cell states improves markers of health in mouse models of aging

GSE201710 Mus musculus (mouse) RNA-seq (gene expression) 49 samples Submitted 2024/11/12 Platform GPL30173Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 49 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
Mus musculus (mouse)
Samples
49 samples: 16 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.
Paper
Targeted partial reprogramming of age-associated cell states improves markers of health in mouse models of agingScience translational medicine · 2024
Summary (written by the submitters)
The study aims to explore the effects of CDKN2A-OSK expression across various tissues and human primary cells using next-generation sequencing. Mouse tissues and human cultured cells were collected at specific time points, followed by RNA extraction and next-generation sequencing. The sequencing data for mouse RNA were aligned to the mm9 genome, while the human RNA data were aligned to the Hg38 genome using standard RNA alignment and analysis protocols.
Published in
Targeted partial reprogramming of age-associated cell states improves markers of health in mouse models of aging
Sahu SK, Reddy P, Lu J et al. · Science translational medicine 2024 · PMID 39259812 · doi:10.1126/scitranslmed.adg1777
This dataset
Open GSE201710 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 GSE201710_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 PRJNA832732 and SRA study SRP372522. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39259812) ↗
Study design
16 conditions, mostly in duplicate
OSK Liver ×5 OSK Skin ×5 OSK Spleen ×5 Control Liver ×4 Control Skin ×4 Control Spleen ×4 OSK Bone marrow ×3 Control Bone marrow ×3 +8 more

Supports a between-group comparison across 49 samples.

How we read this: 16 replicated groups read from 49 sample titles; they account for 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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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.