Key points are not available for this paper at this time.
Abstract Recent advances in spatial transcriptomics (ST) have brought unparalleled opportunities to unravel cellular diversity and cell-cell interactions within their spatial context. High-resolution ST techniques, including barcoding-based and imaging-based platforms, have achieved remarkable sub-cellular resolution. However, the precise segmentation of cells remains a significant challenge and hampers effective single-cell spatial analysis. Existing methods targeting specific techniques lack compatibility across different high-resolution ST platforms, and many of them cannot scale well to ST data with large fields of view. Here, we introduce Cellist, a novel multi-modal cell-segmentation method that synergistically combines image and expression information, enabling comprehensive investigations at the cell level. Employing Cellist on mouse brain Stereo-seq data, we showcased its superiority in yielding heightened within-cell homogeneity over existing methods. Furthermore, Cellist facilitated accurate spatial domain identification and cell-type annotation. Importantly, Cellist is suitable for various ST techniques including Seq-Scope, seqFISH+, STARmap, and 10x Xenium, exhibiting exceptional accuracy across diverse high-resolution ST platforms and biological systems, all while maintaining high computational efficiency. Finally, we applied Cellist to post-neoadjuvant immunotherapy non-small cell lung cancer (NSCLC) samples. Our analyses revealed the spatial heterogeneity of tumor clones and identified therapy response-related myeloid subtypes and structures. These findings highlight the immense potential of Cellist in enhancing the power of high-resolution ST techniques for characterizing the spatial organization of cells and unraveling intricate tissue architectures. Cellist is publicly available at https://github.com/wanglabtongji/Cellist.
Wang et al. (Thu,) studied this question.