Defining transcriptomic niches in human fibrotic lung with multi-sample spatial transcriptomics analysis using the MAPLE algorithm

Abstract

Idiopathic pulmonary fibrosis (IPF) is an age-related lung disease marked by patchy parenchymal fibrosis that disrupts gas exchange and leads to respiratory failure. Single-cell RNA-seq studies have revealed cellular heterogeneity in IPF, but the spatial contexts underlying alveolar epithelial cell loss, honeycomb cyst formation, and fibroblastic foci remain unclear, motivating comparisons based on multi-sample spatial transcriptomics (ST) across clinical and anatomical conditions. Here, we present a comprehensive pipeline for analyzing multi-sample lung ST data that accounts for experimental design, featuring the MAPLE algorithm to jointly identify cell-spot subpopulations and discover spatial biomarkers, with validation using 10X Xenium ST data. Our analysis reveals that IPF progression involves epithelial-to-mesenchymal state transitions and region-specific fibrotic programs, characterized by extracellular matrix remodeling in the upper lobes and B-cell-enriched immune niches in the lower lobes. We identify fibrotic regions with senescent cell (SnC) enrichment and demonstrate their spatial colocalization with aberrant basaloid niches.