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Mechano-oncogenic cytoskeletal remodeling drives leukemic transformation with mitochondrial vesicle-mediated STING activation

GSE253848 Mus musculus (mouse) RNA-seq (gene expression) 54 samples Submitted 2025/01/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 54 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
54 samples: 54 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.
Paper
Mechano-oncogenic cytoskeletal remodeling drives leukemic transformation with mitochondrial vesicle-mediated STING activationCell stem cell · 2025
Summary (written by the submitters)
Mitochondria are integrated within the cytoskeleton for structural integrity and functional regulation, yet the pathological exploitation of these interactions in cell fate decisions remains largely unexplored. Here, we identify a cytoskeleton-mitochondria remodeling mechanism underlying leukemic transformation by the core-binding factor subunit beta and smooth muscle myosin heavy-chain fusion (CBFβ-SMMHC).
Read the full summary ↓
This chimera reconstructs a cytosolic filamentous cytoskeleton, inducing NMIIA phosphorylation and INF2-dependent filamentous actin (F-actin) assembly, which enhance cellular stiffness and tension, leading to calcium-mediated mitochondrial constriction, termed cytoskeletal co-option of mitochondrial constriction (CCMC). CCMC can also be triggered through diverse approaches independent of CBFβ-SMMHC, reconstructing a similar cytoskeleton and recapitulating acute myeloid leukemia (AML) with consistent immunophenotypes and inflammatory signatures. Notably, CCMC generates TOM20−PDH+mtDNA+ mitochondrial-derived vesicles that activate cGAS-STING signaling, with Sting knockout abrogating CCMC-induced leukemogenesis. Targeted inhibition of CCMC or STING suppresses AML propagation while sparing normal hematopoiesis. These findings establish CCMC as an intrinsic mechano-oncogenic process linking genetic mutations with cytoskeletal remodeling to oncogenic transformation, highlighting its promise as a therapeutic target.
Published in
Mechano-oncogenic cytoskeletal remodeling drives leukemic transformation with mitochondrial vesicle-mediated STING activation
Song Z, Cui Y, Xin L et al. · Cell stem cell 2025 · PMID 39986274 · doi:10.1016/j.stem.2025.01.013
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 GSE253848_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 PRJNA1067558 and SRA study SRP485211. Searching any of these in the dataset finder brings you back here.

Study design
54 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 54 sample titles: 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.