DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.

Spatial transcriptomics (ST) technologies provide genome-wide transcriptomic profiles in tissue context but lack direct protein-level measurements, which are critical for interpreting cellular function and microenvironmental organization. To bridge this gap, we develop DGAT (Dual-Graph Attention Network), a deep learning framework that imputes spatial protein expression from ST data by learning RNA-protein relationships from spatial transcriptomic and proteomic datasets. The model constructs heterogeneous graphs integrating transcriptomic, proteomic, and spatial information, encoded using graph attention networks. Task-specific decoders reconstruct mRNA and predict protein abundance from a shared latent representation. Benchmarking across public and in-house datasets demonstrates that DGAT outperforms existing methods in protein imputation accuracy. Applied to ST datasets lacking protein measurements, the framework reveals spatially distinct cell states, immune phenotypes, and tissue architectures not evident from transcriptomics alone. Here, we show that this framework accurately reconstructs spatial protein landscapes, reveals biologically meaningful tissue organization, and enables protein-level interpretation from transcriptomics-only spatial data.