Mosaic integration of spatial multiomic data based on hierarchical graph contrastive learning with SpatialMOSI.
TL;DR
SpatialMOSI provides a critical computational foundation for constructing integrative 3D molecular atlases from complex multimodal spatial data sets and demonstrates SpatialMOSI's versatility across multiple biological systems, accurately identifying spatially conserved domains, imputing missing omic layers, and reconstructing embryonic developmental trajectories.
Abstract
Spatial omic technologies have revolutionized tissue analysis by enabling multimodal molecular coprofiling within their native tissue context. Integrating multislice spatial multiomic data offers unprecedented opportunities to reconstruct three-dimensional (3D) tissue landscapes from multimodal molecular perspectives. However, current spatial omic integration methods remain narrowly focused on either vertical (cross-omic) or horizontal (cross-slice) integration, leaving a critical gap for a unified framework that simultaneously addresses both dimensions. Here we present SpatialMOSI, a unified framework for mosaic integration that concurrently resolves cross-modality and cross-section variations. At its core, SpatialMOSI employs a hierarchical graph contrastive learning (HiGCL) strategy that coordinates three integrative objectives: cross-omic alignment and fusion, cross-slice batch correction, and spatial microenvironment preservation. This approach operates on modality-specific latent representations while maintaining feature fidelity through decoding reconstruction. We demonstrate SpatialMOSI's versatility across multiple biological systems, accurately identifying spatially conserved domains, imputing missing omic layers, revealing B cell dynamics in germinal centers, delineating tumor-immune interactions, and reconstructing embryonic developmental trajectories. SpatialMOSI provides a critical computational foundation for constructing integrative 3D molecular atlases from complex multimodal spatial data sets.