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April 12th, 2025
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Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children\\\'s Environmental Health, Xinhua Hospital, Shangh
bioinformatics
biorxiv

SpatioCell: A Deep Learning Algorithm for High-resolution Single-cell Mapping through Deep Integration of Histology Image and Sequencing Data

Hou, N.Open in Google Scholar•Wu, Z.Open in Google Scholar•Yu, Y.Open in Google Scholar•Zhang, Y.Open in Google Scholar•Wu, J.Open in Google Scholar•Zhong, Z.Open in Google Scholar•Xu, F.Open in Google Scholar•Wang, Z.Open in Google Scholar•Yang, C.Open in Google Scholar•Tan, W.Open in Google Scholaret al.

Spatial transcriptomics (ST) enables gene expression analysis within spatial context of tissues. Since most data are limited to multicellular resolution, current computational methods can only estimate cell proportions. To address this gap, we introduce SpatioCell, a framework that integrates imaging data-derived morphological features with transcriptomic measurements for precise single-cell annotation. SpatioCell combines automated adaptive prompting with an optimized vision model for accurate cell segmentation and morphological analysis. Dynamic programming then optimizes cell-type assignments by integrating H&E-derived morphology and transcriptomic constraints, improving resolution within and beyond ST spots. Extensive benchmarking on histopathological, simulated, and real ST data from five distinct cancer types shows that SpatioCell outperforms state-of-the-art methods in both nuclear segmentation and cell annotation accuracy. Notably, SpatioCell reveals tumor microenvironment details, such as tumor boundaries, blood vessel structures, and immune infiltration, that were missed by other methods, while also showing strong potential in correcting deconvolution errors to further increase annotation accuracy. By redefining single-cell spatial mapping with unprecedented accuracy and resolution, SpatioCell enables precise analysis of tissue heterogeneity, offering new insights into cancer and tissue biology.

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