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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
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Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.
Bohan Li1, Feng Bao2,3,4, Yimin Hou1
1School of Artificial Intelligence, Beihang University, Beijing, China.
Nature Communications
|August 2, 2024
Summary
soScope enhances spatial omics data quality and resolution for various molecular profiles. This unified framework improves tissue architecture identification and corrects biases across diverse spatial technologies.
Area of Science:
- Spatial omics
- Computational biology
- Bioinformatics
Background:
- Spatial omics technologies profile molecular categories beyond transcriptomics.
- Limited spatial resolution hinders detailed tissue architecture characterization.
- Existing computational methods lack adaptability for diverse spatial omics data.
Purpose of the Study:
- Introduce soScope, a unified generative framework.
- Enhance data quality and spatial resolution for diverse spatial omics data.
- Address limitations of current computational methods.
Main Methods:
- soScope aggregates multimodal tissue information (omics, spatial relations, images).
- Jointly infers omics profiles at enhanced resolutions using omics-specific modeling and distribution priors.
- Evaluated on Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq platforms.
Main Results:
- Improved identification of biologically meaningful intestine and kidney architectures.
- Revealed fine embryonic heart structures not resolvable at original resolution.
- Corrected sample and technical biases from sequencing and processing.
- Extended to spatial multiomics (e.g., CITE-seq, ATAC-seq) for simultaneous enhancement.
Conclusions:
- soScope is a versatile tool for improving spatial omics data utilization.
- Enhances resolution and quality across diverse spatial omics technologies.
- Facilitates deeper understanding of tissue architecture and biological processes.
