Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics

Recent advances in spatial transcriptomics (ST) have brought unprecedented insights into cellular diversity and cell–cell interactions within their spatial context. High-resolution ST techniques, including barcoding-based and imaging-based platforms, have achieved remarkable subcellular resolution. However, precise cell segmentation remains a major challenge, hampering effective single-cell spatial analysis. Existing methods are often platform specific and lack scalability for datasets with large fields of view. Here we introduce Cellist, a new, multi-modal, cell-segmentation method that combines image and expression information, enabling comprehensive cell-level analyses. Applied to mouse brain Stereo-seq data, Cellist improves within-cell transcriptomic coherence compared to existing approaches. It further enhances spatial domain identification and cell-type annotation. Importantly, Cellist is compatible with various ST techniques including Seq-Scope, seqFISH+, STARmap and 10x Xenium, exhibiting robust performance and high computational efficiency across diverse ST platforms and biological systems. Finally, application to post-neoadjuvant immunotherapy, nonsmall cell lung-cancer samples reveals the spatial heterogeneity of tumor clones and identifies therapy response-related myeloid subtypes and structures. These findings highlight the potential of Cellist in enhancing the power of high-resolution ST techniques for characterizing intricate tissue architectures. Cellist is publicly available at https://github.com/wanglabtongji/Cellist . Cellist is a computationally efficient cell-segmentation method combining imaging and expression data from spatial transcriptomics across different technologies. (a) Number of cells (left) and gene coverage (right) generated by different segmentation methods in each tile from the Seq-Scope mouse liver dataset. Exact sample sizes are provided in the Source Data. (b) Evaluation of intracellular expression homog... [7123 chars]