Vesalius: high-resolution in silico anatomization of Spatial Transcriptomic data using Image Analysis

Characterization of tissue architecture is important to understand cell function and mechanisms in vivo. Spatial transcriptomics (ST), by recovering the transcriptome of a cell or tissue section while maintaining the two-dimensional location of the cell or tissue, can provide detailed insights into the cellular function in association with tissue architecture. Still, however, algorithmic development for ST has focused on identifying cells or spots with the same cell types located nearby and, therefore, cannot detect a tissue structure often composed of multiple cell types. Here, we present Vesalius to decipher tissue anatomy from ST data by converting transcriptomic information into a color code for image segmentation. Vesalius successfully detected tissue architecture in mouse embryo and brain from high resolution ST data by incorporating image processing algorithms. In contrast, previous spatial clustering approaches developed for low resolution ST data were unable to recover these structures. Intriguingly, Vesalius identifies genes linked to the morphology of tissue structures. In short, Vesalius is a tool to perform high-resolution in silico anatomization and molecular characterization from ST data. ### Competing Interest Statement The authors have declared no competing interest.