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

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Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Xingyi Li^1,2,3, Jialuo Xu¹, Gaoyuan Du¹

¹School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|June 9, 2026

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This summary is machine-generated.

STransformer, a deep learning framework, analyzes diverse spatial biological data. It deciphers complex tissue heterogeneity and disease mechanisms across various tissues and modalities.

Area of Science:

Computational Biology
Bioinformatics
Deep Learning

Background:

Spatially resolved technologies allow simultaneous data acquisition within tissue slices, preserving spatial context.
Existing computational methods struggle to universally process diverse spatial multi-modal and multi-omics data.
Understanding tissue heterogeneity is crucial for deciphering disease pathogenesis.

Purpose of the Study:

To introduce STransformer, a unified deep learning framework for analyzing spatial multi-modal and multi-omics data.
To develop a flexible computational approach for dissecting complex tissue heterogeneity.

Main Methods:

Developed STransformer, a deep learning framework capturing short-range cellular interactions and tissue-wide patterns.
Applied STransformer to diverse spatial data, including multi-modal and multi-omics datasets.

Keywords:

graph neural networks spatial multi‐modal and multi‐omics tissue heterogeneity transformer

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Evaluated STransformer's performance across different species, tissue types, and data modalities.

Main Results:

STransformer successfully delineated anatomical structures in the human cortex and uncovered mechanisms in Alzheimer's disease.
Characterized spatiotemporal developmental trajectories in chicken cardiogenesis using spatial multi-modal data.
Deciphered immune microenvironments in human tonsil and inferred regulatory mechanisms in mouse embryonic brain using spatial multi-omics data.

Conclusions:

STransformer is a versatile and robust framework for analyzing complex spatial biological data.
The framework advances the understanding of tissue heterogeneity and disease pathogenesis.
Enables seamless processing of spatial multi-modal and multi-omics data for comprehensive biological insights.