GEO series

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

GSE291037 Mus musculus (mouse) Single-cell RNA-seq 14 samples Submitted 2025/09/19 Platform GPL34328

At a glance

Single-cell RNA-seq · mouse · 14 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 14 samples: 14 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)

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 GSE291037 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 GSE291037_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1231436 and SRA study SRP567934. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

14 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 14 sample titles: 14 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

GSM8827884 P28_Fetal_WT
GSM8827885 P28_Fetal_ME
GSM8827886 P28_Postnatal_WT
GSM8827887 P28_Postnatal-ME
GSM8827888 P28_WT
GSM8827889 P28_Fetal-ME
GSM8827890 P28_Fetal-ME_Mll3KO
GSM8827891 P0_WT
GSM8827892 P0_Fetal-ME
GSM8827893 P0_Fetal-ME_Mll3KO
GSM8827894 E18_WT
GSM8827895 E18_ME
GSM8827896 E14_WT
GSM8827897 E14_ME

Datasets on a similar topic

GSE291040 Heritable fetal protection against MLL::ENL-driven leukemogenesis depends on MLL3 [Bulk RNA-seq] 24 samples shares mll3, mll, leukemogenesis, protection, enl-driven, heritable, against
GSE291041 Heritable fetal protection against MLL::ENL-driven leukemogenesis depends on MLL3 43 samples shares mll3, mll, leukemogenesis, protection, enl-driven, heritable, against
GSE301678 MLL3 and MLL4 redundantly license HSC self-renewal and myeloid identity while opposing B-cell priming, independently of COMPASS enzymatic functions [scRNA] 4 samples shares mll3, mll
GSE307073 Pan-Cancer MLL3 Mutational Landscape and Its Association with Immunotherapy 12 samples shares mll3, mll
GSE240512 Profiling of MCM2 and phosphorylated MCM2 in MLL3/4 double mutant and control ESCs [ChIP-seq] 12 samples shares mll3, mll
GSE301675 MLL3 and MLL4 redundantly license HSC self-renewal and myeloid identity while opposing B-cell priming, independently of COMPASS enzymatic functions [scATAC] 4 samples shares mll3, mll
GSE292237 H2AK119ub1-MLL2 counteraction underlies heritable H3K27me3 formation in oocytes 43 samples shares mll, heritable
GSE282851 Functional mapping of epigenomic regulators uncovers coordinated tumor suppression by the HBO1 and MLL1 complexes [scRNA-seq] 10 samples shares mll, scrna-seq

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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.