Disparate interferon signaling and shared aberrant basaloid cells in single-cell profiling of idiopathic pulmonary fibrosis and systemic sclerosis-associated interstitial lung disease

Idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated interstitial lung disease (SSc-ILD) differ in the predominant demographics and identified genetic risk alleles of affected patients, however both diseases frequently progress to respiratory failure and death. Contrasting advanced SSc-ILD to IPF provides insight to the role dysregulated immunity may play in pulmonary fibrosis. To analyze cell-type specific transcriptome commonalities and differences between IPF and SSc-ILD, we compared single-cell RNA-sequencing (scRNA-seq) of 21 explanted lung tissue specimens from patients with advanced IPF, SSc-ILD, and organ donor controls. Comparison of IPF and SSc-ILD tissue identified divergent patterns of interferon signaling, with interferon-gamma signaling upregulated in the SPP1hi and FABP4hi macrophages, cytotoxic T cells, and natural kill cells of IPF, while type I interferon signaling and production was upregulated in the corresponding SSc-ILD populations. Plasmacytoid dendritic cells were found in diseased lungs only, and exhibited upregulated cellular stress pathways in SSc-ILD compared to IPF. Alveolar type I cells were dramatically decreased in both IPF and SSc-ILD, with a distinct transcriptome signature separating these cells by disease. KRT5-/KRT17+ aberrant basaloid cells exhibiting markers of cellular senescence and epithelial-mesenchymal transition were identified in SSc-ILD for the first time. In summary, our study utilizes the enriched capabilities of scRNA-seq to identify key divergent cell types and pathways between IPF and SSc-ILD, providing new insights into the shared and distinct mechanisms between idiopathic and autoimmune interstitial lung diseases. To reduce batch effects in analyzing multiple samples, all control, IPF, and SSc-ILD samples were combined into 3 objects by disease status using Seurat’s IntegrateData function, followed by integration of these 3 objects. Following clustering and visualization with uniform manifold approximation and projection (UMAP), cell populations were classified using multiple gene markers in the transcriptome. Doublet cells were manually identified and subsequently removed. To better define individual cell types, the myeloid, lymphoid, epithelial, and stromal populations were separately reclustered. Differential gene expression analysis for IPF versus SSc-ILD cells for each cluster was performed using the Wilcoxon rank-sum statistical test.