The rapid development of spatial transcriptomics (ST) technologies has greatly advanced the understanding of gene expression, tissue architecture, cellular composition, and disease mechanisms within their spatial context. However, with the growing number and diversity of commercial platforms and computational analysis tools, selecting appropriate ST platforms and analytical methods has become increasingly challenging. Existing tools are often scattered across different platforms, typically designed for specific resolutions or technologies, and lack systematic cross-platform performance evaluation. As a result, researchers face substantial difficulty in choosing suitable analytical approaches tailored to their data characteristics and biological questions.
To address these challenges, the researchers systematically summarized 77 spatial transcriptomics platforms and 594 ST analysis tools, and developed SpatialToolDB (https://www.spatialtooldb.yelab.site/), a continuously updated database that systematically curates and classifies approximately 600 ST analytical tools. These tools span the entire analytical workflow, including image processing and registration, spatial domain identification, gene imputation, and inference of cell–cell interactions. In addition, SpatialToolDB integrates major ST data repositories, providing access to processed datasets and benchmarking resources, thereby establishing a centralized platform for tool and data exploration.
Furthermore, the researchers summarize key challenges and future directions in the field, including efficiency limitations of certain ST platforms, the lack of standardized integration frameworks for heterogeneous datasets, insufficient experimental validation of predicted interaction and persistent barriers to computational accessibility. By systematically integrating the existing tool ecosystem and providing an interactive resource portal, this review and SpatialToolDB offer a data-driven foundation to support informed selection of spatial transcriptomics platforms and analytical methods across diverse biological and translational research contexts.
Science Bulletin
Literature review