Cells Keep Diverse Company in Diseased Tissues

Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribution of cellular diversity remains underexplored. In this issue, Ding and colleagues introduce multiomics and ecological spatial analysis (MESA), an ecology-inspired framework that integrates spatial and single-cell expression data to quantify tissue diversity across multiple scales. MESA both identifies distinct cellular neighborhoods and computes a variety of diversity metrics alongside the identification of diversity "hotspots." Applied to human tonsil tissue, MESA revealed previously undetected germinal center organization, whereas in spleen tissue of a murine lupus model, MESA highlights increasing cellular diversity with disease progression. Importantly, diversity hotspots do not correspond to conventional compartments identified by existing methods, presenting an orthogonal metric of spatial organization. In colorectal cancer, MESA's diversity metrics outperformed established subtypes at predicting patient survival, whereas in hepatocellular carcinoma, multiomic integration identified significantly more ligand-receptor interactions between immune cells compared with single-modality analysis. This work establishes cellular diversity within tissues as a critical correlate of disease progression and underscores the value of multiomic integration in spatial biology. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.