GEO series

Splicing regulatory dynamics for precision analysis and treatment of heterogenous leukemias

GSE292324 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2025/07/09 Platform GPL20301Platform GPL34382

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples: 12 samples: 5 conditions, mostly in duplicateGroups 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: Splicing regulatory dynamics for precision analysis and treatment of heterogeneous leukemiasScience translational medicine · 2025

Summary (written by the submitters)

The role of splicing dysregulation in cancer is underscored by splicing factor mutations, however, its impact in the absence of such rare mutations remains poorly understood. Prompted by the finding that splicing uniquely resolved genetic subtypes of cancer, we developed an unsupervised computational workflow called OncoSplice to comprehensively define tumor molecular landscapes.

Read the full summary ↓

In adult and pediatric Acute Myeloid Leukemia (AML), OncoSplice identified the spectrum of driver genetics from splicing profiles alone, defined over a dozen new molecular subtypes recurrent across AML cohorts and discovered a dominant splicing subtype that partially phenocopies U2AF1-mutant splicing. Although pediatric leukemias lack splicing factor mutations, this U2AF1-like subtype similarly spanned pediatric and adult AML genetics and consistently predicted poor prognosis. Using long-read single-cell RNA-Seq, we confirmed discovered U2AF1-like splicing was shared across cell states, co-opts a healthy circadian gene program, was stable through relapse and induced a leukemia stem cell (LSC) program. Pharmacological inhibition of the OncoSplice implicated U2AF1-like splicing regulator, PRMT5, rescued leukemia mis-splicing and inhibited leukemic cell growth. Finally, genetic deletion of IRAK4, a common target of U2AF1-like and PRMT5 treatment, blocked leukemia development in xenograft models and induced differentiation. This work suggests broad splicing dysregulation, in the absence of select mutations, is a therapeutic target in heterogenous leukemias.

Published in

Splicing regulatory dynamics for precision analysis and treatment of heterogeneous leukemias

Venkatasubramanian M, Schwartz L, Ramachandra N et al. · Science translational medicine 2025 · PMID 40333990 · doi:10.1126/scitranslmed.adr1471

This dataset

Open GSE292324 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia landscape →

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 GSE292324_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 PRJNA1237905 and SRA study SRP571423. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40333990) ↗

Study design

5 conditions, mostly in duplicate

AML12 LR scRNA-Seq technical ×4 MDSL-CTL-Sample ×2 MDSL-PRT543-Sample ×2 AML14 LR scRNA-Seq technical ×2 AML7 LR scRNA-Seq technical ×2

Supports a between-group comparison across 12 samples.

How we read this: 5 replicated groups read from 12 sample titles; they account for 12 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

GSM8856324 MDSL-CTL-Sample-1 MDSL-CTL-Sample
GSM8856325 MDSL-CTL-Sample-2 MDSL-CTL-Sample
GSM8856326 MDSL-PRT543-Sample-4 MDSL-PRT543-Sample
GSM8856327 MDSL-PRT543-Sample-5 MDSL-PRT543-Sample
GSM8857480 AML12 LR scRNA-Seq technical rep1 AML12 LR scRNA-Seq tech…
GSM8857481 AML12 LR scRNA-Seq technical rep2 AML12 LR scRNA-Seq tech…
GSM8857482 AML12 LR scRNA-Seq technical rep3 AML12 LR scRNA-Seq tech…
GSM8857483 AML12 LR scRNA-Seq technical rep4 AML12 LR scRNA-Seq tech…
GSM8857484 AML14 LR scRNA-Seq technical rep1 AML14 LR scRNA-Seq tech…
GSM8857485 AML14 LR scRNA-Seq technical rep2 AML14 LR scRNA-Seq tech…
GSM8857486 AML7 LR scRNA-Seq technical rep1 AML7 LR scRNA-Seq techn…
GSM8857487 AML7 LR scRNA-Seq technical rep2 AML7 LR scRNA-Seq techn…

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