04: Accounting for Cellular Mixture in Spatially Aware Cell-Cell Interaction Analysis

Thomas M. Menino Convention & Exhibition Center 

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while preserving tissue architecture, providing a powerful framework for studying cell–cell interactions (CCIs) in situ. However, many existing CCI methods inadequately account for mixed cellular composition within spatial measurements and often adapt single-cell frameworks without fully leveraging spatial information, and systematic benchmarking across platforms remains limited. We developed SpaCCI, a spatially aware CCI framework that models each spatial unit as a mixture of cell types by integrating ligand–receptor expression with location-specific cell-type abundance and neighborhood information to capture local and global interaction patterns. To contextualize SpaCCI, we benchmarked nine spatial CCI methods using realistic simulations and nine real datasets spanning Visium, Stereo-seq, and Xenium. Evaluations of prediction accuracy, spatial coherence, biological relevance, and computational efficiency revealed substantial performance variability across resolutions, tissues, and technologies, highlighting trade-offs and providing practical guidance for spatial CCI method selection and development.

Spatial Transcriptomics

Cell-cell Interaction

Ligand–receptor analysis 

Biometrics Section