StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset
Li, M.; Liu, H.; Li, M.; Fang, S.; Kang, Q.; Zhang, J.; Teng, F.; Wang, D.; Cen, W.; Li, Z.; Feng, N.; Guo, J.; He, Q.; Wang, L.; Zheng, T.; Li, S.; Bai, Y.; Xie, M.; Bai, Y.; Liao, S.; Chen, A.; Xu, X.; Zhang, Y.; Li, Y.
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BackgroundOwing to recent advances in resolution and field-of-view, spatially resolved transcriptomics sequencing, such as Stereo-seq, has emerged as a cutting-edge technology 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, a powerful computational tool is required. FindingsWe present CellBin, an image-facilitated one-stop pipeline for high-resolution and large field-of-view spatial transcriptomic data of Stereo-seq. CellBin provides a comprehensive and systematic platform for generating high-confidence single-cell spatial gene expression profiles, which specifically includes image stitching, image registration, tissue segmentation, nuclei segmentation and molecule labeling. CellBin is user-friendly and does not require a specific level of omics and image analysis expertise. ConclusionsDuring image stitching and molecule labeling, CellBin 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, CellBin has been shown to obtain highly accurate single-cell spatial data using mouse brain tissue, which facilitated clustering and annotation.
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