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Peptide-targeted lipid nanoparticles orchestrating IFN-γ and cisplatin crosstalk eliminate ovarian cancer [RNA-seq]

GSE339275 Mus musculus (mouse) Single-cell RNA-seq 20 samples Submitted 2026/07/28 Platform GPL28457

At a glance

Single-cell RNA-seq · mouse · 20 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: 20 samples: 5 × Control vs 5 × Model vs 5 × LNPIFN-Y vs 5 × ptLNPIFN-YGroups 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)

Ovarian cancer remains refractory to current immunotherapies, largely due to poor tumor targeting, immunosuppressive feedback, and the absence of durable memory responses. Here, we develop a peptide-targeted lipid nanoparticle (ptLNP) platform modified with an FSHR-affinity peptide (Pep LLV) that enables ovarian-directed co-delivery of interferon-gamma (IFN‑γ) mRNA and cisplatin (CDDP).

Read the full summary ↓

In an orthotopic mouse model, ptLNP-mediated IFN‑γ delivery suppresses tumor progression but upregulates intraovarian PD‑1 expression, creating an adaptive resistance loop that is relieved by co-delivered anti‑PD‑1 antibody. Single-cell RNA sequencing reveals that CDDP eliminates divergent tumor differentiation trajectories and enforces G2/M arrest, which prevents mitotic dilution of the IFN‑γ effector IRF1 and sustains pro-apoptotic signaling. This mechanism is validated by IRF1 gain- and loss-of-function experiments, demonstrating that IRF1 is both sufficient and necessary for the synergistic effect. In T-cell-deficient xenograft models, the IFN‑γ–CDDP combination achieves complete tumor regression, indicating direct cytostatic activity independent of adaptive immunity. Post-treatment IL‑12 boosting further establishes long-term CD4⁺ and CD8⁺ memory T cell responses, conferring protection against tumor re-challenge. Collectively, this study presents a modular platform that orchestrates cytokine therapy, checkpoint blockade, chemotherapy, and memory immunity, providing a mechanistic framework for combinatorial immunotherapy against refractory ovarian cancer.

This dataset

Open GSE339275 in finder → Find more mouse 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 GSE339275_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 PRJNA1498084 and SRA study SRP719702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

5 × Control vs 5 × Model vs 5 × LNPIFN-Y vs 5 × ptLNPIFN-Y

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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

GSM9892251 Control1 Control
GSM9892252 Control2 Control
GSM9892253 Control3 Control
GSM9892254 Control4 Control
GSM9892255 Control5 Control
GSM9892256 Model1 Model
GSM9892257 Model2 Model
GSM9892258 Model3 Model
GSM9892259 Model4 Model
GSM9892260 Model5 Model
GSM9892261 LNPIFN-Y1 LNPIFN-Y
GSM9892262 LNPIFN-Y2 LNPIFN-Y
GSM9892263 LNPIFN-Y3 LNPIFN-Y
GSM9892264 LNPIFN-Y4 LNPIFN-Y
GSM9892265 LNPIFN-Y5 LNPIFN-Y
GSM9892266 ptLNPIFN-Y1 ptLNPIFN-Y
GSM9892267 ptLNPIFN-Y2 ptLNPIFN-Y
GSM9892268 ptLNPIFN-Y3 ptLNPIFN-Y
GSM9892269 ptLNPIFN-Y4 ptLNPIFN-Y
GSM9892270 ptLNPIFN-Y5 ptLNPIFN-Y

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