Work

ST2HE: enhancing spatial transcriptomics interpretability via virtual staining for histological annotation

Year: 2026

Type: article

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 images directly from HR-ST data. ST2HE integrates nuclei morphology and spatial transcript coordinates using a one-step diffusion model, enabling histologically informative 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.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbag326

Authors: Zhentao Liu, Arun Das, Wen Meng, Yu-Chiao Chiu, Shou-Jiang Gao, Yufei Huang

Institutions: University of Pittsburgh, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,885

Funders: National Institutes of Health, National Cancer Institute