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The ac4C acetyltransferase Nat10 promotes RNA stability to support hematopoietic stem cell fate [RNA-seq-mouse]

GSE276138 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/03/21 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
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)
The control of gene expression program is believed to underlie hematopoietic stem cell (HSC) homeostasis. However, a comprehensive molecular understanding of the RNA level regulation is currently lacking. Here we provide evidence that the HSC-associated transcripts have longer 3’UTRs and more enriched AU-rich elements (AREs), which were recognized by Nat10.
Read the full summary ↓
Nat10 is highly expressed in HSC and its deficiency disrupts HSC self-renewal and long-term maintenance. In contrast to current model that invoke ac4C modification to mediate downstream effects, we find Nat10 recruits ribosomes to mRNA 3’UTRs and protects them from degradation by ZFP36-CNOT RNA decay complex. Nat10 depletion leads to reduction of mRNA hail-life, and both wild-type and catalytically inactive Nat10 promote the stability of mRNA with AREs. We present a model that Nat10 regulates mRNA stability as an ARE-binding protein rather than ac4C writer in HSC, a mechanism is required for hematopoietic homeostasis, highlighting the importance of transcript-specific characteristics determining the contribution of Nat10 in HSC fate.
This dataset
Open GSE276138 in finder → Find more mouse 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 GSE276138_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 PRJNA1154973 and SRA study SRP529844. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
32D cell Nat10-/- 0hr ×2 32D cell Nat10-/- 1hr ×2 32D cell Nat10-/- 3hr ×2 32D cell Nat10-/- 5hr ×2 32D cell WT 0hr ×2 32D cell WT 1hr ×2 32D cell WT 3hr ×2 32D cell WT 5hr ×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
Datasets on a similar topic

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