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Updated: May 22, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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 Sun^1,2,3, Lele Zhang⁴, Tong Han^1,2

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

This summary is machine-generated.

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.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: May 22, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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.