We introduce cytoNet, a method to characterize multicellular topology from microscopy images. 7Accessible over the web, cytoNet quantifies the spatial relationships in cell communities using 8 principles of graph theory, and evaluates the effect of cell-cell interactions on individual cell 9 phenotypes. We demonstrate cytoNet's capabilities in two applications relevant to regenerative 10 medicine: quantifying the morphological response of endothelial cells to neurotrophic factors present 11 in the brain after injury, and characterizing cell cycle dynamics of differentiating neural progenitor cells. 12The framework introduced here can be used to study complex cell communities in a quantitative 13 manner, leading to a deeper understanding of environmental effects on cellular behavior. 14 A cell's place in its environment influences a large part of its behavior. Advances in the field of phenotypic 15 screening have yielded automated image analysis software that provide detailed phenotypic information 16 at the single-cell level (such as morphology, stain texture and stain intensity) from microscopy images in 17 a high-throughput manner 1,2 . However, current image analysis pipelines often do not account for spatial 18 and density-dependent effects on cell phenotype. Various types of cell-cell interactions including 19 juxtacrine and paracrine signaling are an integral part of biological processes that affect the behavior of 20 individual cells. The recent emergence of technologies for multiparametric mapping of protein and RNA 21 expression in individual cells while preserving the spatial structure of the tissue 3 has further highlighted 22 the need to study single-cell behavior in the context of cell communities. 23

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