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Bcl11a orchestrates multilineage integrity in aging hematopoiesis by regulating multipotent progenitor fate decisions

GSE317231 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2026/07/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 10 conditions, mostly in triplicateGroups 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)
Aging hematopoiesis is characterized by myeloid skewing and impaired lymphoid output, a phenomenon traditionally attributed to intrinsic deterioration of hematopoietic stem cells (HSCs). Here, we demonstrate that aging-associated hematopoietic dysfunction is critically shaped at the level of multipotent progenitors (MPPs). We show that aging drives coordinated functional alterations across the MPP compartment, with expansion and reinforced myeloid output of MPP3 accompanied by functional attrition of lymphoid-primed MPP4, collectively exacerbating lineage imbalance.
Read the full summary ↓
We identify Bcl11a as a dose-dependent regulator of MPP fate that preserves progenitor quiescence and lineage plasticity. Mechanistically, Bcl11a restrains premature myeloid activation by maintaining MPP3 quiescence through repression of the tyrosine kinase Fer, while simultaneously sustaining latent lymphoid potential via activation of the Irf8–Ebf1 transcriptional axis. Importantly, enhancing Bcl11a expression in aged mice restores balanced myeloid and lymphoid output, reprograms aging-associated transcriptional signatures, and rejuvenates multilineage hematopoietic function. Together, these findings establish Bcl11a as a central regulator of progenitor resilience during aging and identify MPPs as critical control points for preserving immune competence across the lifespan.
This dataset
Open GSE317231 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 GSE317231_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 PRJNA1406321 and SRA study SRP665401. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 conditions, mostly in triplicate
3m_KOiMOS_MPP3_T1 ×3 3m_KOiMOS_MPP3_T2 ×3 3m_WTiMOS_MPP3_T1 ×3 3m_WTiMOS_MPP3_T2 ×3 3m_WTiMOS_MPP4_T1 ×3 3m_WTiMOS_MPP4_T2 ×3 24m_WTiMOS_MPP3_T1 ×3 24m_WTiMOS_MPP3_T2 ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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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