GEO series

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

GSE324005 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/03/05 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 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.
Paper: Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignalbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Motivation: Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring.

Read the full summary ↓

We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. Results: This new functionality includes the compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems.

Published in

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

Mitchell JT, Stapleton O, Krishnan K et al. · bioRxiv : the preprint server for biology 2025 · PMID 40654902 · doi:10.1101/2025.05.02.651747

This dataset

Open GSE324005 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 GSE324005_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 PRJNA1432950 and SRA study SRP681312. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40654902) ↗

Study design

6 conditions, mostly in duplicate

tumor, untreated, mRNA, ×2 tumor, untreated, TCR, ×2 tumor, untreated, BCR, ×2 tumor, entinostat, mRNA, ×2 tumor, entinostat, TCR, ×2 tumor, entinostat, BCR, ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 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

GSM9566595 tumor, untreated, mRNA, rep1 tumor, untreated, mRNA,
GSM9566596 tumor, untreated, TCR, rep1 tumor, untreated, TCR,
GSM9566597 tumor, untreated, BCR, rep1 tumor, untreated, BCR,
GSM9566598 tumor, untreated, mRNA, rep2 tumor, untreated, mRNA,
GSM9566599 tumor, untreated, TCR, rep2 tumor, untreated, TCR,
GSM9566600 tumor, untreated, BCR, rep2 tumor, untreated, BCR,
GSM9566601 tumor, entinostat, mRNA, rep1 tumor, entinostat, mRNA,
GSM9566602 tumor, entinostat, TCR, rep1 tumor, entinostat, TCR,
GSM9566603 tumor, entinostat, BCR, rep1 tumor, entinostat, BCR,
GSM9566604 tumor, entinostat, mRNA, rep2 tumor, entinostat, mRNA,
GSM9566605 tumor, entinostat, TCR, rep2 tumor, entinostat, TCR,
GSM9566606 tumor, entinostat, BCR, rep2 tumor, entinostat, BCR,

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