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Single-cell multiomic profiling of lung immune cells identifies novel asthma risk genes and cell-type specific functions

GSE319044 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2026/02/12 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
32 samples: 32 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.
Summary (written by the submitters)
Genetic variants associated with asthma are mostly in non-coding regions, so it is challenging to identify their target genes and the cellular contexts. Functional profiling of blood immune cell types has helped link asthma risk variants to functions. However, lung immune cells differ from those circulating in blood, and high-resolution functional genomic data of lung immune cells are lacking.
Read the full summary ↓
Here we profiled single-cell multi-omics (RNA-seq and ATAC-seq) on lymphocytes of lung and spleen tissues. Cross-tissue comparison identified distinct transcriptomes for each immune cell type, but subtle differences in chromatin accessibility. We then assessed open chromatin regions of lung vs. blood, using a public dataset, for their enrichment of asthma risk. Strikingly, lung T cells show unique contributions to heritability of adult-onset (AOA) and childhood-onset asthma (COA), beyond blood T cells. We further identified 43 cis-regulatory elements (CREs) likely contributing to asthma risk. With enhancer-gene maps built from our single-cell data, we identified target genes for these CREs, including CCR4 and LRRC32 that show cell-type specific regulatory activities. These findings demonstrate the utility of single-cell multiomics to identify asthma risk genes and understand their cell-type specific functions.
This dataset
Open GSE319044 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 GSE319044_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 PRJNA1422011 and SRA study SRP676116. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 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 32 sample titles: 32 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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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.