Spatial transcriptomics in human biomedical research and clinical application

Abstract Over the past 15 years, single-cell RNA sequencing (scRNA-seq) technology, in combination with other omics, has revealed the mechanisms of human development, tumors, and complex diseases at the genome, transcriptome, and proteome levels. However, this approach fails to directly reflect relevant spatial information, such as cell location and interactions. This limitation has been addressed with the advancement of the combination of high-resolution scRNA-seq and spatial transcriptomics (ST), which enables the identification of cell composition, intercellular and intermolecular interaction, and unravels the mechanisms of disease phenotypes. This review explores two types of ST - imaging-based ST (iST) and sequencing-based ST (sST) - and demonstrates how ST analysis can follow disease pathogenesis in a spatiotemporal manner, searching for disease-specific biomarkers. ST technology is an effective tool for resolving major biomedical and clinical problems, including tumor research, brain science, embryonic development, organ atlas construction and other pathological analysis. Looking towards the future, despite its limitations, ST has the potential to address these problems in conjunction with “dynamics, multi-omics, and resolution”. Ultimately, the development of ST technology, improvement of algorithms, utilization of deep learning, and refinement of the analysis process and interpretation will determine the key to transforming ST from bench to bedside.