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Systematic metabolome analysis of hematopoietic cells identified uridine as a vital anti-aging factor in hematopoiesis

GSE240731 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/06/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
12 samples
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)
In this study, we employed immunomagnetic enrichment and fluorescence-activated cell sorting (FACS) to isolate 15 hematopoietic cell types, over 9×108 hematopoietic cells from 200 young and 150 aged mice, ranging from HSPCs to mature blood cells. UPLC-MS/MS untargeted analysis identified about 2000 metabolites in each cell population, which contributed to define cell-specific signatures and depict aging landscape of blood cells.
Read the full summary ↓
We further validated our findings by experimentally screening metabolites delaying senescence and identified uridine as potential regulator to rejuvenate aged HSPCs. To enable public access to our dataset, we have constructed an open-source platform (MetaB, www.XXX.com) for easy data browsing and analysis. Collectively, our study developed a solid reference benchmark for metabolic studies in hematopoietic system and provided additional clarity to the mechanisms underlying hematopoietic aging.
This dataset
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.

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 GSE240731_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1005171 and SRA study SRP454881. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

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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.