Where Latent Meets Spatial: Cross-Modal Learning Between scRNA-seq and Proteomics

Abstract

Understanding the spatial and molecular heterogeneity of tissues is integral to advancing precision medicine. Here, we present an unsupervised integration framework that bridges single-cell (SC) RNA sequencing and spatial proteomics (SP) CODEX data, focusing on liver hepatocellular carcinoma (HCC). By combining these two weakly linked modalities, we surpass the resolution limits of conventional spot-based spatial transcriptomics (ST) approaches and gain a more nuanced view of cellular organization in tumor tissues. Our pipeline builds on the MaxFuse algorithm, enhanced with biologically grounded receptor-ligand (R-L) interactions derived from SC data using CellPhoneDB. This modification improves cross-modal alignment: F-1 score increased from 0.66 to 0.69, and Adjusted Rand Index from 0.78 to 0.83. Importantly, the inferred SC pseudo-Visium spots constructed demonstrate robust Spearman correlation with their corresponding Visium ST data, validating the fidelity of our approach. Moreover, by mapping RNA readouts onto microenvironments identified via SP and the SPACE-GM method, we reveal distinct spatially organized niches with contrasting enrichment patterns--such as immune-rich regions with inflammatory signaling, stromal areas with hypoxia and mTORC1 activity, and epithelial zones showing metabolic reprogramming--supporting the accuracy of our integration. Beyond these insights, the SC-to-SP mapping provides superior spatial granularity relative to traditional spot-level deconvolution methods like Tangram, thereby enabling finer delineation of molecular heterogeneity. Moving forward, we aim to refine hyperparameter settings, incorporate gene expression-adjusted R-L interaction effects, and extend this strategy to diverse tissue types. By accurately resolving subcellular interactions and microenvironmental structure, our computational pipeline holds promise for guiding target identification and novel therapeutic strategies in translational cancer research.