Multi-omic profiling of lung inflammation in early life cystic fibrosis

Background: Inflammation is key driver of lung disease in cystic fibrosis (CF), yet no anti-inflammatory therapies exist. A better understanding of the pathophysiology of CF inflammation would help identify novel therapeutic targets. Aim: To apply single cell technologies to bronchoalveolar lavage (BAL) collected from children with CF to understand inflammation in CF. Methods: BAL from children with CF was profiled using a 20-plex spectral flow cytometry panel and CITE-seq (Chromium 10X platform and Biolegend Total-Seq A Universal Cocktail). Results: BAL samples from 8 children aged between 5-6years were analysed. CITE-seq transcriptome data was annotated using the Azimuth lung reference and identified 12 immune cell subsets and 3 epithelial cell populations. Additional sub-clustering of alveolar macrophages, which make up approximately 72% of BAL cells, revealed clusters associated with maturation status (immature/monocyte derived macrophages), response to infection (LPS and IFN-mediated viral responses) and vesicle trafficking. Cell proportions identified using flow cytometry and CITE-seq were significantly positively correlated for all cell types. Analysis of samples based on disease severity (bronchiectasis vs. no bronchiectasis) and CFTR modulator treatment, identified significant differential gene expression for multiple cell types. Conclusion: Multi-omic profiling of BAL from children with CF provides a comprehensive assessment of immune cell populations and their functional subsets. In particular, this offered novel insights into alveolar macrophages. Further investigation of differential gene expression based on disease severity and modulator treatment will highlight new therapeutic approaches.