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Airway immune signatures of protection and disease progression in recent human tuberculosis household contacts

GSE326212 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2026/05/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 17 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
Homo sapiens (human)
Samples
17 samples: 17 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
Airway immune signatures of protection and disease progression in recent human tuberculosis household contactsNature immunology · 2026
Summary (written by the submitters)
The local early immune factors dictating whether individuals who have been infected with Mycobacterium tuberculosis remain healthy or progress to active TB have not been defined. We interrogated the airway immune response at single cell resolution in bronchoalveolar lavage (BAL) from PET-CT characterised recent TB household contacts, who either controlled the infection or progressed to TB disease, as well as of active TB patients at diagnosis.
Read the full summary ↓
Single cell RNA-sequencing revealed type I IFN-dependent and -independent neutrophil signatures in BAL from active TB patients and TB progressors. We report an inverse relationship between airway neutrophils and T cells, with T cells showing signatures of exhaustion, cytotoxicity and cell death in progressors and active TB patients with a neutrophil dominated airway profile. Conversely, we identified T cell signatures of protection in non-progressor contacts dominated by genes related to regulation, quiescence and a stem-like profile. Both the signature of TB progression and the stem-like T cell signature of non-progressors from human airways were recapitulated in scRNA-seq data from non-human primate (NHP) granulomas, associated with disease or immune protection, respectively. Our findings from early human airway responses in TB contacts reveal genes, pathways and cell states that may dictate infection outcome and inform strategies for developing effective host-directed therapies and vaccines.
Published in
Airway immune signatures of protection and disease progression in recent human tuberculosis household contacts
Branchett WJ, Kim JW, Shields J et al. · Nature immunology 2026 · PMID 42343006 · doi:10.1038/s41590-026-02544-0
This dataset
Open GSE326212 in finder → Find more human 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 GSE326212_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 PRJNA1444144 and SRA study SRP687254. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42343006) ↗
Study design
17 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 17 sample titles: 17 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
Datasets on a similar topic

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