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Single-cell analysis reveals Mycobacterium tuberculosis ESX-1–mediated accumulation of permissive macrophages in infected mouse lungs

GSE263891 Mus musculus (mouse) Single-cell RNA-seq 6072 samples Submitted 2024/04/20 Platform GPL30172Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 6072 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
6072 samples: 6072 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
Single-cell analysis reveals Mycobacterium tuberculosis ESX-1-mediated accumulation of permissive macrophages in infected mouse lungsScience advances · 2025
Summary (written by the submitters)
Mycobacterium tuberculosis (MTB) infects and replicates in lung mononuclear phagocytes (MNPs) with astounding ability to evade elimination. A virulence determinant that contributes to MTB’s ability to survive within MNPs is ESX-1, a type VII secretion system. However, how MTB virulence factors influence mononuclear cell recruitment and/or differentiation remains unknown.
Read the full summary ↓
Here, using single-cell RNA sequencing, we studied the role of ESX-1 in MNP heterogenicity and response in mice and murine bone marrow-derived macrophages. We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces an anti-inflammatory signature that may lead to more permissive MNPs. Spatial transcriptomics revealed an upregulation of anti-inflammatory signals in MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 mediates the recruitment and differentiation of anti-inflammatory MNPs, which MTB can infect and manipulate for survival.
Published in
Single-cell analysis reveals Mycobacterium tuberculosis ESX-1-mediated accumulation of permissive macrophages in infected mouse lungs
Zheng W, Borja M, Dorman LC et al. · Science advances 2025 · PMID 39813329 · doi:10.1126/sciadv.adq8158
This dataset
Open GSE263891 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 GSE263891_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 PRJNA1093473 and SRA study SRP498709. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39813329) ↗
Study design
6072 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 the first 400 of 6072 sample titles: 400 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

+ 6032 more — browse all 6072 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.