M. Ghafoor, J. E. Parkinson, T. Pham, S. Georgaka, M. J. Hayley, E. Jokl, K. P. Hanley, J. E. Allen, T. E. Sutherland, M. Rattray
Spatial proteomics technologies now profile cells and the extracellular matrix (ECM) together in situ. Yet analysis tools remain cell-centric, despite the ECM playing an essential role in health and disease. Here we present Mantpy, a framework that represents the ECM, and its interface with cells, as spatial graphs. Mantpy builds ECM graphs directly from matrix markers and links them with cell graphs for joint cell-ECM analysis, supporting graph statistics, explainable graph deep learning and visualisation. From a single ECM marker to multiplexed panels of ECM and cellular markers, Mantpy recovers layered tissue architecture in human intestine, resolves disease-associated matrix composition and organisation in infected mouse liver, and characterises cell-matrix associations in mouse lung. Released with ECM-inclusive datasets and interoperating with the scverse ecosystem, Mantpy extends the unit of spatial analysis beyond the cell, to the matrix that surrounds it.