ST2HE: A Cross-Platform Framework for Virtual Histology and Annotation of High-Resolution Spatial Transcriptomics Data

Abstract

High-resolution spatial transcriptomics (HR-ST) technologies offer unprecedented insights into tissue architecture but lack standardized frameworks for histological annotation. We present ST2HE, a cross-platform generative framework that synthesizes virtual hematoxylin and eosin (H&E) images directly from HR-ST data. ST2HE integrates nuclei morphology and spatial transcript coordinates using a one-step diffusion model, enabling histologically faithful image generation across diverse tissue types and HR-ST platforms. Conditional and tissue-independent variants support both known and novel tissue contexts. Evaluations on breast cancer, non-small cell lung cancer, and Kaposi's sarcoma demonstrate ST2HE's ability to preserve morphological features and support downstream annotations of tissue histology and phenotype classification. Ablation studies reveal that larger context windows, balanced loss functions, and multi-colored transcript visualization enhance image fidelity. ST2HE bridges molecular and histological domains, enabling interpretable, scalable annotation of HR-ST data and advancing computational pathology.