 Spatial transcriptomics technologies are rapidly advancing and providing valuable insight into tissue structure and function. These techniques enable researchers to obtain gene expression information from intact tissue sections in their original physiological context, allowing for a better understanding of cellular interactions with the microenvironment. Additionally, computational strategists such as R and Python packages are used to analyze the data, enabling researchers to derive important bioinformation and overcome technical limitations. Future developments will likely involve the use of more advanced imaging techniques and the integration of spatial transcriptomic data with other types of data, such as proteins and metabolites. This article was offered by Jundu, Yuchen Yang, Fijian, and others.