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0This technical report introduces Cellist, a multi-modal computational method designed to improve cell segmentation in high-resolution spatial transcriptomics. By integrating visual information from staining images with molecular expression data, the tool addresses the common challenge of transcript diffusion, which often leads to blurred boundaries and inaccurate cell identification. Research demonstrates that Cellist consistently outperforms existing methods across diverse platforms, such as Stereo-seq and 10x Xenium, by maintaining superior transcriptomic integrity and biological specificity. Its high computational efficiency and scalability make it suitable for analyzing large datasets with millions of spatial spots. The authors successfully applied the tool to lung cancer samples, uncovering the spatial organization of tumor clones and identifying specific immune cell subtypes linked to immunotherapy responses. Ultimately, the study highlights how accurate cell-level analysis can reveal intricate tissue architectures and complex intercellular interactions within the tumor microenvironment.
References:
Sun D, Zhang L, Han T, et al. Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics[J]. Nature Genetics, 2026: 1-12.
