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Phosphine-Nitrogen-Phosphine chelated CoCl2 (Co68) is a novel therapeutic agent for pancreatic cancer [RNA-seq]

GSE288554 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2025/02/09 Platform GPL18480
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 duplicateGroups 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)
Pancreatic cancer is projected to become the second leading cause of cancer-related deaths globally by 2030, yet effective therapeutic options remain limited. Within the pancreatic cancer tumor microenvironment (TME), tumor-associated macrophages (TAMs) secrete interleukin-1 beta (IL-1β), promoting cancer progression while suppressing type I interferons (IFN-I), which is critical for effective tumor treatment.
Read the full summary ↓
Utilizing the convolutional neural network (CNN)-based DLINP model developed in our laboratory, we identified Co68—an effective metal catalyst featuring a Phosphine-Nitrogen-Phosphine (PNP)-chelated CoCl₂ complex—as a promising candidate to modulate these immune pathways. In preclinical animal models, Co68 demonstrated superior antitumor efficacy compared to the STING agonist DMXAA and showed enhanced therapeutic effects when combined with PD-1 blockade. Single-cell RNA sequencing (scRNA-seq) revealed that Co68 reprograms TAMs to express interferon-stimulated genes (ISGs), attenuates pro-inflammatory cytokine secretion, and disrupts the IL-1β-PGE2 feedback loop, thereby facilitating the recruitment of NK and cytotoxic CD8+ T cells into the TME. Mechanistically, Co68 activates the IFN-I signaling pathway through the TLR4-TRIF-IFN-I axis and weakens inflammation via the TLR4-SYK-STAT1 pathway. Collectively, these findings highlight the therapeutic potential of Co68, derived from PNP-pincer chemistry, to reshape immune dynamics within the pancreatic cancer TME, positioning it as a promising candidate for innovative immunotherapy strategies.
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 GSE288554_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 PRJNA1218427 and SRA study SRP561093. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
RAW-Co68-1h ×2 RAW-Co68-2h ×2 RAW-Co68-3h ×2 RAW-LPS-1h ×2 RAW-LPS-2h ×2 RAW-LPS-3h ×2 RAW-LPS-Co68-1h ×1 RAW-LPS-Co68-2h ×1 +4 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 12 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.