Clinical diagnostics has progressed from conventional approaches (e.g., auscultation, palpation, serological testing, and microbial culture) to advanced molecular and imaging technologies. Modern molecular diagnostics, which analyze alterations in DNAs, RNAs, and proteins, have addressed the limitations of traditional histomorphological evaluation in terms of diagnostic accuracy, early detection, pathological classification, and therapeutic guidance. The rapid advancements in clinical diagnostics have deepened our understanding of diseases while accelerating precise diagnosis, personalized treatment strategies, and prognosis assessment.

In the early 19th century, optical microscopy established the foundation for clinical diagnosis. Since histomorphological changes are often driven by gene expression, the molecular-level evaluation of clinical diagnosis was desperately needed. By the 1950s, techniques such as immunohistochemistry (IHC) and immunofluorescence (IF) emerged, enabling protein detection and molecular-level characterization of clinical tissue samples. The rise of molecular biology in the late 1970s laid the groundwork for advancements in molecular pathology, which allowed for the detection of disease-related gene mutations, gene rearrangements, and gene expression changes, leading to more precise diagnoses.

During the 1990s, with the rapid development of genomics technologies, gene chips, fluorescence in situ hybridization (FISH), and high-throughput DNA sequencing were integrated into clinical diagnosis. After 2000, next-generation sequencing (NGS) revolutionized molecular pathology, enabling whole-genome and whole-transcriptome analysis of cancer and genetic diseases. For example, The Cancer Genome Atlas (TCGA) project provided critical data for cancer mechanism research and precision therapy through multi-omics analysis. Post-2010, liquid biopsy technology eliminated the need for tissue sections in tumor detection, facilitating early tumor screening and monitoring of tumor drug resistance. Entering the 2020s, the integration of digital pathology and artificial intelligence (AI) reformed the efficiency and accuracy of the pathological image analysis, driving next-generation molecular pathology toward precision and personalized diagnosis and treatment.

In recent years, emerging spatial omics technologies have greatly contributed to the investigations on molecular pathology of diseases due to their high-throughput and high-resolution mapping capacity of spatial expression profiles in heterogeneous tissues like tumors. The spatial context of gene expression profile presents important information about cellular expression dynamics, cell location pattern, interaction between certain cell types with surrounding cells, and extracellular matrix, which provides an unprecedented deep insight into the molecular mechanism of diseases.

As powerful tools for delineating spatial features of cell types and functions, spatial omics technologies provide critical insights across genomics, epigenomics, transcriptomics, proteomics, and metabolomics dimensions. Spatial genomics and epigenomics technologies can elucidate the spatial distribution patterns of genomic variations, chromatin accessibility, and epigenetic modifications, which have been applied to region-specific chromatin remodeling analysis for deciphering chemotherapy resistance mechanisms (Cheng et al., 2025a), spatial regulation of immune microenvironments characterization (Chen et al., 2025), and spatiotemporal regulatory networks mapping during brain development (Zhang et al., 2024), providing additional insights for clinical medical research.

Spatial transcriptomics employs high-throughput sequencing or iterative imaging to spatially analyze RNAs across tissue sections, revealing in situ gene expression differences among cell populations. Spatial proteomics uses mass spectrometry or antibody-based methods to spatially localize proteins, providing detailed protein expression data across cells and regions. Multiplex immunofluorescence technologies have been widely applied in disease pathogenesis research due to their superior sample stability, capacity to directly assess functional protein states, and broad applicability in clinical tumor profiling. Spatial metabolomics uses mass spectrometry imaging (MSI) to visualize the spatial distribution of metabolites including lipids, choline compounds, and secondary metabolites. The spatial metabolomics technology is routinely employed for metabolic heterogeneity analysis in tissue microenvironments, metabolic network mapping in neurological systems, metabolic remodeling characterization in pathological tissues, drug target identification, and early diagnostic biomarker screening.

Thus, spatial omics holds great promise for application in modern molecular diagnostics, contributing to precision medicine, enabling personalized treatment strategies, and improving clinical outcomes. Below, we systematically review the classification and features of spatial omics techniques and their application in clinical medicine and molecular diagnostics. Further, the current challenges and future perspectives of spatial omics technologies in clinical applications are discussed.