Single-Cell Spatial Transcriptomics Prediction from Histology with Multi-Scale Experts

Spatial transcriptomics (ST) offers complementary insights to underlying tissue morphology, yet the acquisition cost limits adoption in large cohorts. Virtual ST has demonstrated its cost-effective potential by predicting ST from H&E morphology, with recent methods extending prediction to single-cell resolution. At the cell level, sparse and heterogeneous gene expression creates a trade-off between preserving localized signals and capturing spatially coherent expression patterns. In this work, we introduce TIGER, a single-cell virtual ST framework that leverages pathology foundation model token embeddings at the spatial scale of individual cells. TIGER integrates local morphologic evidence with tissue-level context through two complementary branches: a focal branch that independently captures fine-grained morphomolecular signals and a context branch that integrates local cellular and global tissue context. A gene- and cell-specific routing mechanism adaptively combines the two. We evaluate TIGER on 43 H&E-paired Xenium tissue sections across diverse cancer types, comprising over 10 million cells. TIGER improves correlation by over 30% and significantly outperforms five state-of-the- art virtual ST baselines across a comprehensive set of conventional and sparsity-aware metrics. Furthermore, TIGER shows stronger generalization performance when evaluated on two external breast and lung cohorts (36 Xenium samples). The application of TIGER-predicted ST on downstream tasks, such as cell phenotyping and treatment response prediction, shows improved performance, underlining the biological and clinical utility of single-cell virtual ST. Code and preprocessed data are available: https://anonymous-hf.com/a/xgn0n1j4ylek/.