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Updated: May 22, 2026
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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
Published on: October 31, 2025
Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics
Dongqing Sun1,2,3, Lele Zhang4, Tong Han1,2
1Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai, China.
Nature Genetics
|May 20, 2026
Summary
Cellist, a novel multi-modal cell segmentation tool, enhances spatial transcriptomics analysis by integrating image and expression data. It offers improved cell segmentation and analysis across diverse platforms, advancing tissue architecture characterization.
Area of Science:
- Genomics
- Computational Biology
- Cell Biology
Background:
- Spatial transcriptomics (ST) offers insights into cellular diversity and interactions within tissues.
- High-resolution ST techniques achieve subcellular resolution but face challenges in precise cell segmentation.
- Existing segmentation methods are often platform-specific and lack scalability for large datasets.
Purpose of the Study:
- To introduce Cellist, a novel, multi-modal cell segmentation method for spatial transcriptomics.
- To enable comprehensive cell-level analyses by combining image and gene expression data.
- To develop a scalable and platform-agnostic solution for cell segmentation in ST.
Main Methods:
- Cellist integrates both image and gene expression data for cell segmentation.
- The method was applied to mouse brain Stereo-seq data for validation.
- Compatibility and performance were tested across various ST platforms (Seq-Scope, seqFISH+, STARmap, 10x Xenium).
Main Results:
- Cellist demonstrated improved within-cell transcriptomic coherence compared to existing methods.
- Enhanced spatial domain identification and cell-type annotation were achieved.
- Robust performance and high computational efficiency were observed across diverse ST platforms and biological systems, including nonsmall cell lung cancer samples.
Conclusions:
- Cellist enhances the power of high-resolution ST techniques for intricate tissue architecture characterization.
- The tool facilitates detailed analysis of tumor heterogeneity and therapy response.
- Cellist is a versatile and efficient solution for advancing single-cell spatial analysis.
