Traditional transcriptomics studies rely on the average expression across all cells, making it challenging to gain insights into the differences between individual cells and their relative positions within the tissue. In contrast, single-cell RNA sequencing (scRNA-seq) offers a breakthrough approach for understanding cellular heterogeneity by analyzing gene expression at the level of individual cells. However, scRNA-seq does not take into account the spatial location of cells (Aldridge and Teichmann, 2020). Indeed, studying the spatial information of gene expression is crucial to reveal tissue complexity and intercellular interactions. For example, in embryonic development, where cells perform specific developmental tasks based on their spatial location (Cui et al., 2025; Li et al., 2025b). In tumors, the spatial arrangement of cell populations influences proliferation, metastasis, and immune evasion (Binnewies et al., 2018).

To address these problems, spatial transcriptomics (ST) has emerged. ST allows researchers to move beyond traditional bulk and scRNA-seq, enabling the analysis of gene expression within the actual spatial context of tissues. It helps identify spatial heterogeneity in tissues, decode the networks of cellular communication within microenvironments, and track the dynamic changes that occur during development and disease. These insights not only enhance our understanding of biology and disease but also provide a foundation for developing clinical applications, such as more precise disease subtyping, better prognostic tools, and optimized treatment strategies.

In recent years, ST technology has demonstrated great potential in the field of basic research, with the number of related publications increasing rapidly, and both platforms and data analysis algorithms being continuously updated. Several insightful reviews have chronicled the remarkable journey of ST from its inception in the 1980s to its current state, summarizing its transformative impact on life science research (Bressan et al., 2023; Moses and Pachter, 2022; Vandereyken et al., 2023). However, the field still faces challenges such as the complexity of technology and data analysis, particularly due to the lack of harmonized standards. Effectively translating ST technologies into clinical practice has become a key issue that needs to be addressed. This review explores advancements in ST technology and data analysis methods. It addresses key issues such as technology selection, algorithm optimization, and smooth integration into clinical practice. The goal is to offer theoretical insights that can guide best practices in these areas.