IPF

IPF profiles cellular transcriptional programs in Idiopathic Pulmonary Fibrosis using single-cell RNA sequencing to characterize lung cell populations and disease-associated states.

Key Features:

• Single-cell dataset: Profiles 312,928 cells from 32 IPF-affected lungs, 29 healthy control lungs, and 18 chronic obstructive pulmonary disease (COPD) lungs for comparative analysis.
• Parenchymal compartment analysis: Targets the three major lung parenchymal compartments—epithelium, endothelium, and stroma—for compartment-specific characterization.
• Aberrant basaloid cells: Identifies a novel epithelial population enriched in IPF that co-expresses basal epithelial, mesenchymal, senescence, and developmental transcription factor markers and localizes at the edge of myofibroblast foci.
• Vascular endothelial cell redistribution: Reveals an expanded vascular endothelial population in IPF lungs that is transcriptomically identical to endothelial cells of the bronchial circulation.
• Stromal cell analysis: Identifies fibroblasts and myofibroblasts and applies manifold-based algorithms such as diffusion maps and diffusion pseudotime to trace origins of activated myofibroblasts.
• Validation: Corroborates cell population findings using immunohistochemistry and independent datasets.
• Lung disease single-cell atlas: Provides the largest lung disease single-cell atlas reported to date.

Scientific Applications:

• Aberrant transcriptional program cataloguing: Provides a comprehensive catalogue of disease-associated transcriptional programs in IPF at single-cell resolution.
• Single-cell disease framework: Establishes a framework for analyzing complex interstitial lung diseases using single-cell RNA sequencing.
• Pathogenesis and comparative studies: Supports research into IPF pathogenesis and comparative analyses across IPF, COPD, and healthy lungs.

Methodology:

Analyses are performed on single-cell RNA-seq data using transcriptomic profiling and manifold-based algorithms, specifically diffusion maps and diffusion pseudotime.