Integrative single cell analysis to decode transcriptomic heterogeneity in pulmonary fibrosis across diverse pathologies
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Studies using single-cell transcriptional profiling of human samples have provided invaluable mechanistic insights into the development of various acute and chronic fibrotic lung states. However, the lack of data harmonization has impeded the comprehensive mapping of lung fibrosis, particularly for rarer etiologies with limited tissue availability. In this study, we performed new transcriptional analysis of human lungs with fibrotic disease states and integrated our data with approximately 1.3 million human cells from 15 studies, utilizing novel methods to address technical and batch effects. This integration allowed us to uncover common molecular signatures across different disease states including COVID-19 ARDS, idiopathic pulmonary fibrosis, chronic obstructive pulmonary disease, and interstitial lung disease, while identifying disease-specific transcriptomic profiles. Furthermore, our approach enabled the identification of a core gene signature for KRT17+KRT5- cells, a distinct subset implicated in pulmonary fibrosis across various etiologies which we validated using image-based spatial transcriptomics analyses. Additionally, our approach enabled us to deconvolute myeloid and stromal cell lineages in lung allografts from recipients with chronic lung allograft dysfunction, defining pathogenic transcriptional signatures and elucidating the intercellular crosstalk between donor- and recipient-derived cells in the allograft. Our approach provides a method to overcome the technical challenges in integrating single cell RNA-sequencing data from uncommon fibrotic lung diseases with extant datasets to define distinct and common molecular features and potential therapeutic targets.
