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bioRxiv · 10.1101/2023.12.25.573324

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Zhang, B.·Li, M.·Kang, Q.·Deng, Z.·Qin, H.·Su, K.·Feng, X.·Chen, L.·Liu, H.·Fang, S.·Zhang, Y.·Li, Y.·Brix, S.·Xu, X.
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Abstract

Stereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we update our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify that STCellbin can be applied on the mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets and outperforms other competitive methods. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology. Availability & ImplementationThe source code of STCellbin is available at https://github.com/STOmics/STCellbin.

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Zhang, B., Li, M., Kang, Q., Deng, Z., Qin, H., Su, K., Feng, X., Chen, L., Liu, H., Fang, S., Zhang, Y., Li, Y., Brix, S., Xu, X.. 2023-12-26. Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. https://doi.org/10.1101/2023.12.25.573324

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