Understanding complex diseases such as cancer requires insight into both the intrinsic properties of individual cells and their interactions within the tumor microenvironment. Integrating single-cell RNA sequencing (scRNA-seq), which captures transcriptional states, with spatial proteomics, which provides spatially resolved protein expression data, offers a powerful opportunity to unravel disease mechanisms. In this protocol, we describe a framework that unifies scRNA-seq and spatial proteomics into a joint graph-based representation. This approach enables accurate prediction of disease pathologies and reveals critical cell-cell interactions that drive disease progression.
Tsang et al. (Thu,) studied this question.