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Loss of PTDSS1 in tumor cells improves immunogenicity and response to anti-PD-1 therapy [scRNAseq]

GSE292614 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2025/09/10 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
24 samples: 8 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)
PTDSS1 (Phosphatidylserine synthase 1) encodes an enzyme that facilitates production of phosphatidylserine (PS), which mediates a global immunosuppressive signal. Here, based on in vivo CRISPR screen, we identified PTDSS1 as a target to improve anti-PD-1 therapy. Depletion of PTDSS1 in tumor cells increased expression of IFNγ-regulated genes, including B2m, Cxcl9, Cxcl10, and Stat1, even in the absence of IFNγ stimulation in vitro.
Read the full summary ↓
Loss of PTDSS1 in tumor cells also led to increased expression of MHC-I, which was associated with enhanced cytotoxicity of CD8+ T cells and increased frequency of an iNOS+ myeloid subset. A gene signature derived from the iNOS+ myeloid cell subset correlated with clinical benefit in patients treated with anti-PD-1 therapy. Moreover, genetic and pharmacological inhibition of PTDSS1 in different tumor models improved anti-PD-1 therapy. Together, our results provide insights on a new therapeutic strategy for overcoming immunosuppression by inhibiting PTDSS1 and provide rationale for development of a combination immunotherapy strategy comprised of PTDSS1 inhibition plus PD-1 blockade.
This dataset
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.

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 GSE292614_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1240302 and SRA study SRP572242. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly with about 4 replicates each
Ptdss1 KD tumor, PBS treated, ×4 Ptdss1 KD tumor, anti-PD-1 treated, ×4 WT tumor, PBS treated, ×4 WT tumor, anti-PD-1 treated, ×4 batch1, Ptdss1 KD tumor, PBS treated, ×2 batch1, Ptdss1 KD tumor, anti-PD-1 treated, ×2 batch1, WT tumor, PBS treated, ×2 batch1, WT tumor, anti-PD-1 treated, ×2

Supports a between-group comparison across 24 samples.

How we read this: 8 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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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.