GEO series

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

GSE303831 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/12/05 Platform GPL24676

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 GSE303831 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 GSE303831_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 PRJNA1297737 and SRA study SRP604056. 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

GSM9136684 GBM12_PBS 1 GBM12_PBS
GSM9136685 GBM12_PBS 2 GBM12_PBS
GSM9136686 GBM12_PBS 3 GBM12_PBS
GSM9136687 GBM12_PBS 4 GBM12_PBS
GSM9136688 GBM12_OncoD 1 GBM12_OncoD
GSM9136689 GBM12_OncoD 2 GBM12_OncoD
GSM9136690 GBM12_OncoD 3 GBM12_OncoD
GSM9136691 GBM12_OncoD 4 GBM12_OncoD
GSM9136692 GBM12_OncoD-0J1 1 GBM12_OncoD-0J1
GSM9136693 GBM12_OncoD-0J1 2 GBM12_OncoD-0J1
GSM9136694 GBM12_OncoD-0J1 3 GBM12_OncoD-0J1
GSM9136695 GBM12_OncoD-0J1 4 GBM12_OncoD-0J1
GSM9136696 GBM28_PBS 1 GBM28_PBS
GSM9136697 GBM28_PBS 2 GBM28_PBS
GSM9136698 GBM28_PBS 3 GBM28_PBS
GSM9136699 GBM28_PBS 4 GBM28_PBS
GSM9136700 GBM28_OncoD 1 GBM28_OncoD
GSM9136701 GBM28_OncoD 2 GBM28_OncoD
GSM9136702 GBM28_OncoD 3 GBM28_OncoD
GSM9136703 GBM28_OncoD 4 GBM28_OncoD
GSM9136704 GBM28_OncoD-0J1 1 GBM28_OncoD-0J1
GSM9136705 GBM28_OncoD-0J1 2 GBM28_OncoD-0J1
GSM9136706 GBM28_OncoD-0J1 3 GBM28_OncoD-0J1
GSM9136707 GBM28_OncoD-0J1 4 GBM28_OncoD-0J1

Datasets on a similar topic

GSE303830 oHSV-mediated JAG1 blockade induces glioma senescence-associated secretory phenotype to increase macrophage activation and sensitize to cetuximab-mediated senolysis I 8 samples shares jag1, senescence-associated, cetuximab-mediated, ohsv-mediated, macrophage, activation, secretory, phenotype
GSE295260 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation 40 samples shares senescence-associated, activation, secretory, phenotype
GSE295258 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation 54 samples shares senescence-associated, activation, secretory, phenotype
GSE295261 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation [CUT&Tag] 24 samples shares senescence-associated, activation, secretory, phenotype
GSE336040 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation [CUT&Run] 17 samples shares senescence-associated, activation, secretory, phenotype
GSE276620 Oncogene induced senescence and senescence-associated secretory phenotype (SASP) determines the efficacy of palbociclib in PIK3CA mutated colorectal cancer 26 samples shares senescence-associated, secretory, phenotype
GSE282384 Substrate Stiffness Dictates Unique Doxorubicin-induced Senescence-associated Secretory Phenotypes and Transcriptomic Signatures in Human Pulmonary Fibroblasts 12 samples shares senescence-associated, secretory, phenotype
GSE247045 Mitochondrial injury induced by a Salmonella genotoxin triggers the proinflammatory senescence-associated secretory phenotype 9 samples shares senescence-associated, secretory, phenotype

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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.