Cells Keep Diverse Company in Diseased Tissues

Area of Science:

• Spatial biology
• Computational biology
• Ecology

Background:

• Spatial profiling technologies have advanced disease understanding but underexplored cellular diversity's role.
• Existing methods lack comprehensive analysis of tissue-level cellular heterogeneity and its spatial organization.

Purpose of the Study:

• Introduce multiomics and ecological spatial analysis (MESA) to quantify tissue cellular diversity.
• Integrate spatial and single-cell expression data to reveal tissue organization and disease correlates.
• Establish cellular diversity as a critical factor in disease progression.

Main Methods:

• Developed MESA, an ecology-inspired framework for spatial and single-cell expression data integration.
• Quantified tissue diversity across multiple scales, identifying cellular neighborhoods and diversity hotspots.
• Applied MESA to human tonsil, murine lupus spleen, colorectal cancer, and hepatocellular carcinoma datasets.

Main Results:

• MESA identified novel germinal center organization in human tonsil tissue.
• Observed increasing cellular diversity with disease progression in a murine lupus model.
• MESA's diversity metrics outperformed established subtypes in predicting colorectal cancer patient survival.
• Multi-omic integration in hepatocellular carcinoma revealed more immune cell ligand-receptor interactions than single-modality analysis.

Conclusions:

• MESA provides an orthogonal metric of spatial organization beyond conventional compartments.
• Tissue cellular diversity is a critical correlate of disease progression.
• Multi-omic integration within spatial biology is crucial for comprehensive analysis and discovery.