From cells to niches: Rewiring cardiovascular disease through spatial immunity

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet their underlying immune mechanisms are still incompletely understood. A central limitation of current frameworks is that they largely conceptualize immune responses at the level of individual cell types, without fully accounting for how spatial context shapes cellular behavior. Over the past decade, bulk and single-cell transcriptomic approaches have substantially expanded our understanding of immune heterogeneity in cardiovascular tissues, revealing diverse macrophage, lymphocyte, and stromal populations implicated in disease progression. However, these approaches inherently disrupt tissue architecture and therefore fail to capture how immune cells operate within structured microenvironments. Recent advances in spatial transcriptomics, multiplexed imaging, and single-cell multi-omics now enable the interrogation of gene expression within intact tissues, providing direct insight into the spatial organization of immune responses. These technologies reveal that cardiovascular inflammation and repair are not diffusely distributed processes but are instead organized into discrete, spatially constrained niches. Within these niches, cellular identity, anatomical positioning, and local signaling gradients converge to regulate key processes including inflammation, fibrosis, and vascular remodeling. Evidence from myocardial infarction, atherosclerosis, and vascular injury demonstrates that microenvironments such as inflammatory foci, fibrotic border zones, and lipid-rich plaque regions orchestrate dynamic interactions among macrophages, T cells, fibroblasts, and endothelial cells. Here, we synthesize recent advances in spatial immunomics to propose a conceptual shift from cell-centric to niche-centric models of cardiovascular disease, framing tissues as structured immune ecosystems. We discuss computational strategies that integrate single-cell sequencing, spatial transcriptomics, and imaging data to reconstruct cellular neighborhoods and infer intercellular communication networks, while highlighting current limitations in resolving causality from spatial correlations. Finally, we explore the translational potential of spatial immunomics, including the identification of spatially defined biomarkers, the development of niche-targeted therapeutic strategies, and the construction of predictive models linking tissue architecture to clinical outcomes.