StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.28.530414v1?rss=1

Authors: 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.

Abstract: With recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for interpreting large tissues at the spatial single-cell level. To handle the high-resolution spatial omics dataset with associated images and generate spatial single-cell level gene expression, a powerful one-stop toolbox is required. Here, we propose StereoCell, an image-facilitated cell segmentation framework for high-resolution and large field-of-view spatial omics. StereoCell offers a comprehensive and systematic solution to generating high-confidence spatial single-cell data, including image stitching, registration, nuclei segmentation, and molecule labeling. In image stitching and molecule labeling, StereoCell delivers the best-performing algorithms to reduce stitching error and improve the signal-to-noise ratio of single-cell gene expression compared to existing methods. Meanwhile, as demonstrated using mouse brain, StereoCell has been shown to obtain high-accuracy spatial single-cell data, which facilitates clustering and annotation.

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