Single-cell multi-omics in IPF and COPD: from atlas to impact.

Abstract

Single-cell and spatial multi-omics technologies are transforming our understanding of the normal lung cellular repertoire as well the molecular and cellular networks of chronic lung diseases. In this review we focus on idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). We briefly review technologies and discuss technical and analytical challenges, including strategies to optimise batch correction, dataset integration, and sample-aware differential gene expression; we describe global lung reference atlases, enabled by large-scale data-sharing, providing accessible frameworks for cross-study validation, lineage and trajectory inference, and inter-disease comparisons; and we outline the major findings in COPD and IPF. In IPF, mechanistic insights include: (i) loss of the normal alveolar niche (ii) aberrant basaloid epithelial cells linked to failed regeneration, expansion of (iii) profibrotic SPP1+ macrophages and (iv) disease-associated CTHRC1⁺ and HAS1^hi fibroblast subtypes, that localise to fibrotic niches, and (v) extensive vascular and immune remodelling. In COPD these approaches demonstrate: (i) loss of aerocytes; (ii) expansion of MMP-expressing macrophages, (iii) activated neutrophil influx and (iv) B-cell lymphoid follicle activation and endothelial inflammation (iv) some overlapping features with IPF. We highlight gaps and future opportunities, including under-representation of early disease stages, incomplete resolution of neutrophils, tissue-resident immune compartments, and cross-organ integration. Looking forward, integration with clinical phenotypes and imaging is accelerating translation towards precision medicine, enabling biomarker discovery, cellular endotyping, and therapeutic prioritisation. Advances in multimodal profiling, computational pipelines, next generation technologies, open science initiatives and artificial intelligence will propel the field toward predictive, patient-specific insight and mechanistically informed, stratified trial design.