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

ABSTRACT: 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. 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE319044 | GEO | 2026/02/12

REPOSITORIES: GEO

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Project description:Background: Asthma is common chronic inflammatory disease of the airways with a heterogenous clinical presentation. Individual differences in asthma susceptibility remain poorly understood, although genetics is thought to play a major role. Aim: To build a polygenic risk score (PRS) for asthma and determine whether predictive genetic variants can be epigenomically linked to specific pathophysiological mechanisms. Methods: PRSs were constructed using data from genome-wide association studies and performance was validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14,926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and exacerbations. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from 14 primary cell types, including lung epithelial cells and T lymphocytes was used for epigenomic PRS partitioning. Results: All PRSs successfully predicted risk to develop asthma and related outcomes, with the strongest predictive power (2.42 odds ratios per PRS standard deviation, area under the curve of 0.736) achieved for childhood-onset asthma. PRSs allowed for stratification of the Rotterdam Study cohort into groups at low or high risk to develop asthma. PRS partitioning using genome-wide epigenomic profiles identified 5 clusters of variants within gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. Conclusions: PRSs can predict whether individuals in a Dutch cohort developed asthma and asthma-related phenotypes, which is most effective for childhood-onset asthma. Importantly, we show that PRS partitioning based on epigenomics data dissects a genetic risk score into blocks of regulatory variants with differential predictive power, which likely represent distinct genetically driven disease pathways. These findings have potential implications for personalized risk mitigation and treatment strategies.

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

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