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Prostaglandin E2-EP4 signaling shapes immunosuppressive tumor microenvironment in human tumors by suppressing bioenergetics and ribosome biogenesis in infiltrating immune cells [RNA-seq]

GSE242272 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2024/09/28 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 18 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: 18 samples: 6 conditions, mostly in triplicateGroups 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)

Prostaglandins (PGs) are inflammatory mediators. Aspirin-like non-steroidal anti-inflammatory drugs (NSAIDs) inhibit cyclooxygenase (COX), an initiating enzyme of PG biosynthesis1. Daily use of aspirin-like drugs lowers the risk of cancer death not only prophylactically but therapeutically suggesting critical roles of PGs in cancer. However, general mechanisms by which PGs contribute to progression of a wide variety of human cancers remain elusive.

Read the full summary ↓

Here, using scRNAseq analysis comparing immune cells infiltrating human tumors and a syngeneic mouse tumor, we have dissected this issue. We found that in human tumors, COX is dominantly expressed in infiltrating myeloid cells, and, among PG receptors, PGE receptor EP4 and to a less extent EP2 are expressed in both T cells and myeloid cells. DEG analysis between PTGER4hi and PTGER4lo CD8+ T cells indicates downregulation of IL-2-STAT5 signaling, oxidative phosphorylation, glycolysis and Myc targets in EP4hi CD8+ T cells, as revealed by suppressed expression of IL-2 receptor subunit gene and genes of OXPHOS, glycolytic enzymes and ribosomal proteins (RPs). Similar downregulation of OXPHOS, glycolytic enzymes and RP genes is found in PTGER4hi myeloid cells. Notably, such downregulation of OXPHOS and RP genes was found in immune cells infiltrating LLC1 mouse tumor, and reversed by treatment of tumor-bearing mice with EP2 and EP4 antagonists. In vitro in CD8+ T cells, EP4 is induced upon TCR activation and PGE2 acts on EP4, downregulates Il2ra expression and suppresses expression of Myc and PGC-1, thereby impairing both mitochondria and glycolysis as well as ribosome biogenesis in these cells. Similarly, EP4 is induced upon activation in macrophages and PGE2 downregulates Myc and OXPHOS pathwaysin these cells irrespective of other stimuli. These results demonstrate that PGE2 shapes immunosuppressive tumor microenvironment by hampering bioenergetic metabolism and ribosome biogenesis in infiltrating immune cells via EP4 receptor.

This dataset

Open GSE242272 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 GSE242272_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 PRJNA1012903 and SRA study SRP458457. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

CD8_24h_Vehicle ×3 CD8_24h_PGE2 ×3 CD8_48h_Vehicle ×3 CD8_48h_PGE2 ×3 CD8_60h_Vehicle ×3 CD8_60h_PGE2 ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM7757588 CD8_24h_Vehicle1 CD8_24h_Vehicle
GSM7757589 CD8_24h_Vehicle2 CD8_24h_Vehicle
GSM7757590 CD8_24h_Vehicle3 CD8_24h_Vehicle
GSM7757591 CD8_24h_PGE2_1 CD8_24h_PGE2
GSM7757592 CD8_24h_PGE2_2 CD8_24h_PGE2
GSM7757593 CD8_24h_PGE2_3 CD8_24h_PGE2
GSM7757594 CD8_48h_Vehicle1 CD8_48h_Vehicle
GSM7757595 CD8_48h_Vehicle2 CD8_48h_Vehicle
GSM7757596 CD8_48h_Vehicle3 CD8_48h_Vehicle
GSM7757597 CD8_48h_PGE2_1 CD8_48h_PGE2
GSM7757598 CD8_48h_PGE2_2 CD8_48h_PGE2
GSM7757599 CD8_48h_PGE2_3 CD8_48h_PGE2
GSM7757600 CD8_60h_Vehicle1 CD8_60h_Vehicle
GSM7757601 CD8_60h_Vehicle2 CD8_60h_Vehicle
GSM7757602 CD8_60h_Vehicle3 CD8_60h_Vehicle
GSM7757603 CD8_60h_PGE2_1 CD8_60h_PGE2
GSM7757604 CD8_60h_PGE2_2 CD8_60h_PGE2
GSM7757605 CD8_60h_PGE2_3 CD8_60h_PGE2

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