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Heritable fetal protection against MLL::ENL-driven leukemogenesis depends on MLL3 [Bulk RNA-seq]

GSE291040 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/09/19 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 5 conditions, mostly with about 4 replicates eachGroups 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)
MLL rearrangements (MLLr) are the most common cause of congenital and infant leukemias. MLLr arise prior to birth and require very few cooperating mutations for transformation, yet congenital leukemias are 10-fold less common than infant leukemias and >100-fold less common than childhood leukemias overall. This raises the question of whether mechanisms exist to suppress leukemic transformation during fetal life.
Read the full summary ↓
Here, we show that in mice, fetal MLL::ENL exposure creates a heritable, leukemia-resistant state. MLL::ENL imposes a negative selective pressure on fetal hematopoietic progenitors that persists after birth to suppress transformation. These changes do not occur when MLL::ENL is induced shortly after birth, and transformation proceeds efficiently in that context. The fetal barrier to transformation is enforced by MLL3. It can be bypassed by cooperating mutations or inactivation of MLL3. Heritable, fetal protection against leukemic transformation may explain the low incidence of congenital leukemias in humans despite prenatal MLL rearrangement.
This dataset
Open GSE291040 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 GSE291040_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 PRJNA1231447 and SRA study SRP567827. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly with about 4 replicates each
P0 WT LSK ×4 P0 MLL-ENL LSK (E10.5 ME) ×4 P28 WT LSK ×4 P28 MLL-ENL LSK (E10.5 ME) ×4 P28 MLL-ENL LSK (P0 ME) ×4 378R- P28 ME RLT Vav Txp LSK (E10.5 ME) ×1 378RR- P28 ME RLT Vav Txp LSK (E10.5 ME) ×1 379nt- P28 ME RLT Vav Txp LSK (E10.5 ME) ×1 +1 more

Supports a between-group comparison across 20 samples.

How we read this: 5 replicated groups read from 24 sample titles; they account for 20 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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