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oHSV-mediated JAG1 blockade induces glioma senescence-associated secretory phenotype to increase macrophage activation and sensitize to cetuximab-mediated senolysis

GSE304030 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/12/11 Platform GPL25526
At a glance

Single-cell RNA-seq · human · 24 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)
Samples
24 samples: 6 conditions, mostly with about 4 replicates eachGroups 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)
Oncolytic HSV-1 (oHSV) treatment induces Notch signaling and myelosuppression in the tumor microenvironment (TME) of preclinical models. In the clinic, upregulation of JAG1 gene expression was observed in recurrent high-grade glioma patients treated with CAN-3110. Since JAG1 is expressed on both glioma cells and tumor-associated macrophages (TAMs), and its expression correlated with a worse prognosis, we engineered a JAG1-antagonizing oHSV (OncoD-0J1) and interrogated its impact on the tumor and myeloid TME.
Read the full summary ↓
OncoD-0J1 antagonized JAG1-mediated Notch signaling and had a significant therapeutic advantage in vivo, which relied on Notch signature in tumor cells. Kinome profiling revealed that OncoD-0J1 treatment suppressed CDK1, resulting in a G2/M cell cycle checkpoint as seen by cell cycle analysis. Cell cycle arrest led to senescence and correlated with increased reactive oxygen species, p62 accumulation, autophagosome accumulation, and senescence-associated beta-galactosidase activity. This resulted in increased production of inflammatory chemokines and DAMPs such as IL-1β and extracellular ATP. RNA sequencing of murine macrophages co-cultured with infected human tumor cells showed enrichment of chemotactic and pro-inflammatory pathways as well as increased Fc receptor activation following OncoD-0J1 infection. Single-cell RNA sequencing and flow cytometric analysis of F4/80+ cells isolated from infected tumors showed a shift from tumor-supporting TAMs to inflammatory macrophages upon OncoD-0J1 treatment. Senescent cells showed heightened EGFR activation as a mechanism to escape death, which created a unique vulnerability for cetuximab as a senolytic agent. Combination therapy reduced EGFR signaling and induced macrophage-mediated antibody-dependent cellular cytotoxicity, thereby increasing the anti-tumor therapeutic response of OncoD-0J1 as a monotherapy.
This dataset
Open GSE304030 in finder → Find more 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 GSE304030_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 PRJNA1298796 and SRA study SRP604611. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 4 replicates each
GBM12_PBS ×4 GBM12_OncoD ×4 GBM12_OncoD-0J1 ×4 GBM28_PBS ×4 GBM28_OncoD ×4 GBM28_OncoD-0J1 ×4

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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
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