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Oncolytic virus therapy mobilizes tumor-resident CD4+ bystander T cells to restore systemic anti-microbial immunity [ATAC-seq]

GSE302620 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/08/06 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 16 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
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
Oncolytic virotherapy mobilizes tumor-resident, granzyme B-producing bystander CD4(+) T cells to inhibit systemic microbial infectionMolecular therapy. Oncology · 2026
Summary (written by the submitters)
ATAC-seq library was prepared using Hyperactive ATAC-seq Library Prep Kit for Illumina (Vazyme, TD711) to study SM CD4+ T cells in lung and spleen when undergoing OV-BYTE treatment. Here, we further demonstrate that OV-BYTE delivered dual defense against tumorigenesis and pathogen infections in pre-clinical models. This study gives a epigenetic profiles of SM CD4+ T cells in lung and spleen with administration of OV-BYTE treatment.
Published in
Oncolytic virotherapy mobilizes tumor-resident, granzyme B-producing bystander CD4(+) T cells to inhibit systemic microbial infection
Yue S, He J, Ye S et al. · Molecular therapy. Oncology 2026 · PMID 42058600 · doi:10.1016/j.omton.2026.201187
This dataset
Open GSE302620 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE302620_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1291592 and SRA study SRP601062. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42058600) ↗
Study design
8 conditions, mostly in duplicate
Ly108+CD39- SM cells, PBS group, tumor, ×2 Ly108+CD39- SM cells, NDV-WT group, tumor, ×2 Ly108+CD39- SM cells, NDV-GP group, tumor, ×2 Ly108-CD39+ SM cells, NDV-GP group, tumor, ×2 Ly108+CD39- SM cells, PBS group, spleen, ×2 Ly108+CD39- SM cells, NDV-WT group, spleen, ×2 Ly108+CD39- SM cells, NDV-GP group, spleen, ×2 Ly108-CD39+ SM cells, NDV-GP group, spleen, ×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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