Abstract Owing to recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for the interpretation of large tissues at the single-cell level. To generate accurate single-cell spatial gene expression profiles from high-resolution spatial omics data and associated images, a powerful one-stop toolbox is required. Here, we present StereoCell, an image-facilitated cell segmentation framework for high-resolution and large field-of-view spatial transcriptomics. StereoCell provides a comprehensive and systematic platform for the generation of high-confidence single-cell spatial data, which includes image stitching, registration, nuclei segmentation, and molecule labeling. During image stitching and molecule labeling, StereoCell delivers better-performing algorithms to reduce stitching error and time, in addition to improving the signal-to-noise ratio of single-cell gene expression data, in comparison with existing methods. Additionally, StereoCell has been shown to obtain highly accurate single-cell spatial data using mouse brain tissue, which facilitated clustering and annotation.

StereoCell enables highly accurate single-cell segmentation for spatial transcriptomics