Fig. 1: MESA overview.

a, Multiomics neighborhood spatial analysis integrates spatial-omics data (for example, CODEX, MIBI, IMC and MERFISH) with single-cell datasets (for example, scRNA-seq and scATAC-seq) using algorithms like MaxFuse²². The framework is designed to be versatile, allowing the utilization of various other data integration methods to accommodate different analytical needs. This integration enriches spatial-omics data and creates an in silico multiomics spatial profiling for downstream analysis. Neighborhood identification follows, with multiomics information aggregated from each cell’s k-nearest neighbor (k-NN) into Neighborhood Feature Vector (NFV), capturing the local cellular environment. The k-NN is determined based on spatial distance. Different types of NFVs are computed, including cellular composition, local average protein expression and local average RNA expression (by in silico matching of scRNA-seq data). These NFVs serve as the basis for clustering to identify distinct, conserved cellular neighborhoods. MESA conducts DE analysis and GSEA to gain functional insights into the identified cellular neighborhoods. b, Ecology-inspired spatial analyses use the MDI to quantify variations in diversity across spatial scales. It works by dividing a tissue sample into patches of varying sizes, evaluating diversity within each patch, and subsequently calculating a mean diversity score corresponding to each scale. The MDI measures the rate of change in diversity across scales: low MDI values indicate consistent cellular diversity across scales, and higher values signal more pronounced diversity shifts, which may imply a disproportionate distribution of certain cell types within the tissue. To represent this, MESA generates a diversity heatmap by computing the entropy of local patches. Based on the diversity heatmap, the GDI evaluates spatial adjacency of patches sharing similar diversity levels, whereas the LDI identifies diversity hot spots (regions characterized by high diversity) and cold spots (regions characterized by low diversity). MESA analyzes hot spots and cold spots by examining cell-type prevalence and cohabitation within these regions, with the potential to reveal patterns that might not be evident when considering the entire tissue. The DPI measures the spatial proximity and size relationships between hot spots and cold spots, with higher values indicating larger and more proximate diversity spots. Created in BioRender.