Machine Learning identifies remodeling patterns in human lung extracellular matrix

Monica J. Emerson, Oliver Willacy, Chris D. Madsen, Raphael Reuten, Christian B. Brøchner, Thomas K. Lund, Anders B. Dahl, Thomas H. L. Jensen*, Janine T. Erler*, Alejandro E. Mayorca-Guiliani*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Organ function depends on the three-dimensional integrity of the extracellular matrix (ECM). The structure resulting from the location and association of ECM components is a central regulator of cell behavior, but a dearth of matrix-specific analysis keeps it unresolved. Here, we deploy a high-resolution, 3D ECM mapping method and design a machine-learning powered pipeline to detect and characterize ECM architecture during health and disease. We deploy these tools in the human lung, an organ heavily dependent on ECM structure that can host diseases with different histopathologies. We analyzed segments from healthy, emphysema, usual interstitial pneumonia, sarcoidosis, and COVID-19 patients, and produced a remodeling signature per disease and a health/disease probability map from which we inferred the architecture of healthy and diseased ECM. Our methods demonstrate that exaggerated matrix deposition, or fibrosis, is not a single phenomenon, but a series of disease-specific alterations.
Original languageEnglish
JournalActa Biomaterialia
ISSN1742-7061
DOIs
Publication statusAccepted/In press - 2025

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