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RNA sensing induced by chromosome missegregation augments anti-tumor immunity V

GSE281351 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/11/12 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 16 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
Homo sapiens (human)
Samples
16 samples: 8 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
RNA sensing induced by chromosome missegregation augments anti-tumor immunityMolecular cell · 2025
Summary (written by the submitters)
We demonstrate that dsRNA is significantly accumulated in cancer cells following pharmacologic induction of micronuclei, stimulating MAVS-mediated dsRNA sensing in conjunction with the cGAS/STING pathway. Activation of cytosolic dsRNA sensing co-operates with dsDNA sensing to up-regulate immune cell migration and antigen-presenting machinery.
Read the full summary ↓
Tracing of dsRNA-sequence identity revealed that dsRNA-forming transcripts are predominantly generated from non-exonic regions, particularly in locations proximal to genes exhibiting high chromatin accessibility. Activation of this pathway by pulsed Monopolar Spindle 1 (MPS1) kinase inhibitor therapy, which potently induces micronuclei formation, thus induces cytoplasmic dsRNA-sensing and promotes anti-tumor immunity mediated by type I interferon signaling and cytotoxic lymphocyte activation in vivo. Taken together, these findings reveal a novel aspect of the dsRNA-sensing pathway in enhancing the anti-tumor efficacy of drugs that promote genomic instability and micronuclei formation, facilitating cancer immunogenicity.
Published in
RNA sensing induced by chromosome missegregation augments anti-tumor immunity
Sasaki N, Homme M, Murayama T et al. · Molecular cell 2025 · PMID 39706184 · doi:10.1016/j.molcel.2024.11.025
This dataset
Open GSE281351 in finder → Find more human 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 GSE281351_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 PRJNA1183427 and SRA study SRP543873. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39706184) ↗
Study design
8 conditions, mostly in duplicate
H1944.SCR.DMSO ×2 H1944.SCR.MPS1i ×2 H1944.STINGsg.DMSO ×2 H1944.STINGsg.MPS1i ×2 H1944.MAVSsg.DMSO ×2 H1944.MAVSsg.MPS1i ×2 H1944.DKO.DMSO ×2 H1944.DKO.MPS1i ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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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