SpatioEv: Spatial evolution of protein and morphological features reveals development dynamics of cells and spatial neighbourhoods

Understanding cellular function in tissues demands sophisticated tools to decode complex microenvironmental interactions. Current spatial analysis methods often lack the comprehensive framework needed to systematically analyse cell morphology, dynamics, interactions, and extracellular matrix (ECM) architecture. We introduce SpatioEv, a unified computational framework for highly multiplexed tissue imaging that addresses these critical gaps. SpatioEv integrates automated quality control, cell phenotyping, neighbourhood identification, multi-scale spatial characterization, niche boundary analysis, ECM fiber-cell interaction mapping, and spatiotemporal trajectory inference. This pipeline enables reproducible cell annotation, reveals novel ECM-cell interactions, characterizes tissue neighbourhood boundaries, and infers developmental progressions directly from spatial data. Using this, we can identify disease-specific spatial signatures distinguishing rheumatoid arthritis from osteoarthritis, characterize diverse tumour boundary phenotypes in cancer metastases in liver, and map evolutionary trajectories in pancreatic ductal adenocarcinoma (PDAC) at single-niche resolution. Our findings highlight the significance of spatial context in shaping cell behaviour and underscore its potential to uncover emergent tissue architecture and cellular dynamics. By addressing major analytical challenges, SpatioEv provides a scalable, adaptable platform for advancing spatial biology and translational research.