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RNA-seq of sorted murine long-term hematopoietic stem cells segmented on CUX1 levels with and without CUX1 knockdown

GSE274033 Mus musculus (mouse) RNA-seq (gene expression) 35 samples Submitted 2025/08/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 35 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
35 samples: 35 samples, each with a distinct title — no replicated groupsGroups 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)
Hematopoietic stem cells (HSCs) possess properties of both self-renewal and multipotent differentiation; however, the regulation of these opposing properties remains elusive. Using a novel reporter mouse, we demonstrate the dose-dependent role of the transcription factor, CUX1, in HSC fate. As CUX1 levels naturally rise, HSCs gradually lose repopulating capacity.
Read the full summary ↓
CUX1 knockdown reverses this process and reverts HSCs to an earlier cell state—restoring the transcriptome, chromatin accessibility, repopulating capacity, and self-renewal ability of HSCs with normally low CUX1 levels. In the absence of CUX1, endogenous retroelements (EREs) are aberrantly expressed, leading to a cell-intrinsic inflammatory response, which contributes to CUX1-dependent HSC activity. Likewise, loss of the transcription factor IRF1 attenuates CUX1-mediated HSC reprogramming. This mechanism could be relevant to leukemogenesis as acute myeloid leukemias with CUX1 deletions upregulate EREs and the interferon response. Our findings attribute CUX1 and innate immune activation to the complex molecular underpinnings of HSC fate decisions.
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 GSE274033_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 PRJNA1144496 and SRA study SRP524690. Searching any of these in the dataset finder brings you back here.

Study design
35 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 35 sample titles: 35 distinct titles with little repetition. 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
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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.