Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of current ST technologies restrict their widespread use in clinical and research settings, making obtaining high-resolution transcriptomic profiles across large tissue areas challenging. Predicting ST from H\&E-stained histology images has emerged as a promising alternative to address these limitations but remains challenging due to the heterogeneous relationship between histomorphology and gene expression, which is affected by substantial variability across patients and tissue sections. In response, we propose PH2ST, a prompt-guided hypergraph learning framework, which leverages limited ST signals to guide multi-scale histological representation learning for accurate and robust spatial gene expression prediction. Extensive evaluations on two public ST datasets and multiple prompt sampling strategies simulating real-world scenarios demonstrate that PH2ST not only outperforms existing state-of-the-art methods, but also shows strong potential for practical applications such as imputing missing spots, ST super-resolution, and local-to-global prediction, highlighting its value for scalable and cost-effective spatial gene expression mapping in biomedical contexts.
View on arXiv@article{niu2025_2503.16816,
title={ PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction },
author={ Yi Niu and Jiashuai Liu and Yingkang Zhan and Jiangbo Shi and Di Zhang and Marika Reinius and Ines Machado and Mireia Crispin-Ortuzar and Jialun Wu and Chen Li and Zeyu Gao },
journal={arXiv preprint arXiv:2503.16816},
year={ 2025 }
}