Cell-ECM Graphs: A Graph-Based Method for Joint Analysis of Cells and the Extracellular Matrix
Ghafoor, M.; Parkinson, J. E.; Sutherland, T. E.; Rattray, M.
Show abstract
Spatial proteomics technologies enable in situ characterization of both cells and the extracellular matrix (ECM), yet methods to jointly analyse their interactions remain limited. Here, we present a computational framework for constructing Cell-ECM graphs from spatial proteomics data, representing cells and ECM clusters as vertices and encoding cell-cell, ECM-ECM, and cell-ECM interactions as edges within a unified graph. This framework enables the application of established graph analytical methods to matrix biology, including node classification, unsupervised niche discovery, interaction analysis and whole-graph classification with explainable graph neural networks. Using both synthetic and real data, we show that Cell-ECM graphs capture alterations in ECM and cell-ECM interactions that are not resolved by traditional cell-only graphs. To promote accessibility and reproducibility, we provide an open-source Python package implementing the method, enabling its broad application to spatial proteomics studies of the ECM.
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