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Fc-optimized CD40 Agonistic Antibody Induces Tertiary Lymphoid Structures and Systemic Antitumor Immunity in Metastatic Cancer Patients

GSE298036 Mus musculus (mouse) Single-cell RNA-seq 33 samples Submitted 2025/05/28 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 33 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
Mus musculus (mouse)
Samples
33 samples: 33 samples, each with a distinct title — no replicated groupsGroups 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
Fc-optimized CD40 agonistic antibody elicits tertiary lymphoid structure formation and systemic antitumor immunity in metastatic cancerCancer cell · 2025
Summary (written by the submitters)
These studies were performed to understand the transcriptional profile of immune cells following treatment with 2141-V11, the main comparison was injected tumors versus non-injected ones. In summary: hCD40/hFcγR mice were inoculated bilaterally with E0771 tumors (5*105). IT 2141-V11 or isotype-matched control Abs were given into one tumor (the largest of the two) on days 14 and 17. Both tumors were excised on day 18 for scRNAseq and TCR-seq of CD45+ cells.
Published in
Fc-optimized CD40 agonistic antibody elicits tertiary lymphoid structure formation and systemic antitumor immunity in metastatic cancer
Osorio JC, Knorr DA, Weitzenfeld P et al. · Cancer cell 2025 · PMID 40816292 · doi:10.1016/j.ccell.2025.07.013
This dataset
Open GSE298036 in finder → Find more mouse 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 GSE298036_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 PRJNA1267139 and SRA study SRP587349. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40816292) ↗
Study design
33 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 33 sample titles: 33 distinct titles with little repetition. 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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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.