Integrating diverse statistical methods to analyse stage-discriminatory cell interactions in colorectal neoplasia

Spatial biology has the potential to unlock information about the disrupted cellular ecosystems that define human disease. Quantitative analysis of spatially-resolved cell interactions allows mapping of tissue self-organisation and assessment of why cells interact differently in physiological and pathological contexts. However, the complexity of mammalian tissues, that occur across a spectrum of length scales, presents significant challenges for spatial analysis, increasing the gap between our capacity to generate and biologically interpret these datasets. Here, we have adapted a range of mathematical tools to develop a suite of spatial descriptors, and deployed them to determine how cell interactions change as colorectal cancer progresses from benign precursors. We demonstrate that combining mathematical analyses permits insightful examination of tissue organisational structures and identifies variable cell-interaction pathways that underpin disease progression. Mathematical tool triangulation can cross-corroborate spatial biology findings, facilitating development of analysis pipelines that are robust to individual method limitations.