How Cells Shape Each Other: Statistical Models for Tissue Systems Biology

Thomas M. Menino Convention & Exhibition Center 

Advances in genomic profiling in situ now allow the measurement of molecular activity across intact tissues with exceptional breadth and resolution. These data, obtained from spatial sequencing, high-plex imaging, or omics-enhanced digital pathology, reveal how cellular states are shaped by their local environments. I will present work addressing key challenges in spatial biology: quantifying cell–cell interactions, uncovering spatially coordinated molecular gradients, and achieving power-preserving data reduction that enables analysis at unprecedented scale. Building on these developments, I will show how causal inference can be applied to spatial omics to perform in silico perturbations of tissue neighborhoods, predicting how the molecular landscape would change if a particular cell type were added or removed. Together, these developments bring us closer to predictive tissue biology, where computational models can anticipate how cellular interventions reshape tissue function.