Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Spatial transcriptomics is a rapidly evolving technology that enables the capture of gene expression patterns in tissue samples while preserving positional information. It has wide-ranging applications in biological research and bioinformatics, allowing researchers to investigate and track spatial variations in gene expression across different tissues, conditions, and diseases. With spatial transcriptomics data analysis gaining traction, the number of publicly available datasets is rising. However, spatial transcriptomics remains a highly specialized experimental technique, with significant technical and financial constraints. To facilitate access to spatial data, we have recently developed DeepSpaceDB, a comprehensive and dynamic database for spatial transcriptomics data exploration. This article presents detailed workflows outlining the components of the database and its navigation with the help of a few examples. First, the analysis of a mouse brain sample is demonstrated, exploring quality indicators, spatially variable genes and pathways, and gene expression variations between the hippocampus and hypothalamus. Next, the identification and annotation of differentially expressed genes associated with immune activity is further explored by comparing metastatic regions of colorectal origin with distant areas of healthy tissue in murine livers. DeepSpaceDB, with its advanced tools and interactive features, serves as a valuable resource for spatial transcriptomics research, enabling deeper exploration of tissue organization and disease biology.