Spatiotemporal Dynamics of Central Nervous System Diseases: Advancing Translational Neuropathology via Single-Cell and Spatial Multiomics

Central nervous system (CNS) diseases, a leading cause of global disability and mortality, encompass a wide range of brain disorders such as stroke, Alzheimer's disease, Parkinson's disease, and so on. These diseases are characterized by dynamic cellular heterogeneity and disrupted intercellular crosstalk, yet their molecular drivers remain incompletely resolved. Single-cell RNA sequencing (scRNA-seq) dissects transcriptional diversity at cellular resolution, while spatial transcriptomics (ST) maps niche-specific interactions within tissue architecture—complementary approaches that have revealed disease-associated subpopulations, neural–glial communication, and microenvironmental remodeling. However, standalone omics layers inadequately capture the genetic, epigenetic, and functional cascades underlying CNS pathologies. Here, we highlight the transformative potential of integrating scRNA-seq and ST with multiomic profiling to delineate spatially orchestrated molecular networks. Such multiomic convergence enables systematic deconstruction of molecular mechanisms and intercellular communication across disease progression. By correlating these signatures with clinical phenotypes, this strategy accelerates biomarker discovery, patient stratification, and therapeutic target identification. We further discuss challenges in data harmonization, subcellular spatial resolution, and computational scalability that must be addressed to realize personalized CNS medicine. This synthesis advocates for interdisciplinary frameworks to translate multiomic insights into mechanistically grounded diagnostics and therapies, ultimately bridging the gap between molecular discovery and precision clinical intervention.