Spatially-resolved gene expression patterns of fibrosing ILDs [RNA-Seq]

Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP). We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved areas in IPF and CHP were remarkably distinct from normal controls, and displayed potential therapeutic targets. Hallmarks LMEs of each condition retained a distinct gene signature, but these could not be reproduced in matched lung tissue samples. Based on these gene expression signatures and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Lastly, we characterized a gene expression pattern associated with poor outcome . Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs. This new spatial transcriptomics approach has the potential to reclassify unclassifiable cases, to qualify the transcriptional relevance of smaller biopsies for clinical use, and to predict outcome at the time of diagnosis. Of the 12 cases of fibrosing ILD analyzed with Nanostring DSP, seven fresh-frozen, whole lung tissue samples (2 IPF, 2 CHP and 3 NSIP) were obtained at the time of intial SLB. Differential gene expression analysis was performed on bulk RNA-seq of the seven samples to compare the sensitivty between bulk and Nanostring DSP in identifying differentially expressed genes. Comparative gene expression analysis of RNA-seq from three different types of fibrosing ILDS (IPF, CHP and NSIP)