Abstract
Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.