StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset

Li, M.; Liu, H.; 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.; Liao, S.; Chen, A.; Xu, X.; Zhang, Y.; Li, Y.

doi:10.1101/2023.02.28.530414

bioRxiv · 10.1101/2023.02.28.530414

StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset

Li, M.·Liu, H.·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.·Liao, S.·Chen, A.·Xu, X.·Zhang, Y.·Li, Y.

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Abstract

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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Li, M., Liu, H., 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., Liao, S., Chen, A., Xu, X., Zhang, Y., Li, Y.. 2023-03-01. StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset. https://doi.org/10.1101/2023.02.28.530414

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