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Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation [RNA-seq]

GSE263759 Mus musculus (mouse) Single-cell RNA-seq 64 samples Submitted 2025/07/20 Platform GPL21103
At a glance

Single-cell RNA-seq · mouse · 64 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
64 samples: 64 samples, each with a distinct title — no replicated groupsGroups 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
Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulationCell systems · 2025
Summary (written by the submitters)
Macrophages are innate immune cells involved in host defense. Dissecting the regulatory landscape that enables their swift and specific response to pathogens, we performed time-series analysis of gene expression and chromatin accessibility in murine macrophages exposed to various immune stimuli, and we functionally evaluated gene knockouts at scale using a combined CROP-seq and CITE-seq assay.
Read the full summary ↓
We identified new roles of transcription regulators such as Spi1/PU.1 and JAK-STAT pathway members in immune cell homeostasis and response to pathogens. Macrophage activity was modulated by splicing proteins SFPQ and SF3B1, histone acetyltransferase EP300, cohesin subunit SMC1A, and mediator complex proteins MED8 and MED14. We further observed crosstalk among immune signaling pathways and identified molecular drivers of pathogen-induced dynamics. In summary, this study establishes a time-resolved regulatory map of pathogen response in macrophages, and it describes a broadly applicable method for dissecting immune-regulatory programs through integrative time-series analysis and high-content CRISPR screening.
Published in
Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation
Traxler P, Reichl S, Folkman L et al. · Cell systems 2025 · PMID 40782800 · doi:10.1016/j.cels.2025.101346
This dataset
Open GSE263759 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 GSE263759_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 PRJNA1099192 and SRA study SRP501190. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40782800) ↗
Study design
64 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 64 sample titles: 64 distinct titles with little repetition. 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

+ 24 more — browse all 64 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.