Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the...

Area of Science:

• Biomedical Engineering
• Molecular Biology
• Computational Biology

Background:

• Spatial transcriptomics and proteomics offer insights into tissue organization but are often disparate.
• Current same-slide multi-omics methods face limitations in multiplexing, resolution, signal retention, and integration.
• There is a need for advanced techniques to bridge spatial proteomics and transcriptomics for deeper biological understanding.

Purpose of the Study:

• To introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a novel workflow for integrated spatial multi-omics.
• To develop Spectral Graph Cross-Correlation (SGCC) for analyzing proteomic and transcriptomic data to identify spatially coordinated cellular states.
• To apply IN-DEPTH and SGCC to diffuse large B-cell lymphoma (DLBCL) to uncover microenvironmental mechanisms.

Main Methods:

• Developed IN-DEPTH, a streamlined workflow using spatial proteomics imaging to guide transcriptomic capture on the same slide.
• Implemented Spectral Graph Cross-Correlations (SGCC), a framework for integrating proteomic and transcriptomic data.
• Applied the workflow to study EBV-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL) samples.

Main Results:

• IN-DEPTH preserves RNA signal during integrated proteomic and transcriptomic analysis.
• SGCC enabled the resolution of spatially coordinated functional states in interacting cell populations.
• Analysis of DLBCL revealed coordinated tumor-macrophage-CD4 T-cell remodeling, C1Q macrophage enrichment, CD4 T-cell dysfunction, and a potential IL-27-STAT3 signaling axis.

Conclusions:

• IN-DEPTH provides a scalable approach for high-resolution spatial multi-omics.
• The integrated analysis uncovered key microenvironmental mechanisms in DLBCL.
• This methodology facilitates the development of robust spatial multi-modal AI models for clinical insights.