Chenlan Sun, Zhengxia Wang, Qingchen Zhang, Xiaodong Bai, Chong Yin, Zhipei Sang, Hongsheng Xie, Yuxing Li
We present ProST, an image prompt-guided multimodal representation learning framework for spatial domain identification on ST data. ProST is designed to effectively exploit morphology-aware information from histological images while reducing the impact of irrelevant visual noise, thereby generating robust and spatially coherent embeddings. We further optimized histological image feature extraction to improve the use of morphological information. Evaluations on multiple ST datasets demonstrate that ProST consistently outperforms existing methods in spatial domain identification, visualization, spatial trajectory inference, and gene expression imputation. Its high accuracy and strong generalization make ProST a powerful tool for resolving fine-grained tissue structures and revealing underlying biological complexity.
MOTIVATION: Spatial transcriptomics (ST) enables gene expression profiling while preserving the spatial organization of tissues, providing a powerful tool for dissecting tissue architecture and cellular heterogeneity. However, existing methods tend to prioritize improvements in clustering performance and overlook the fundamental objective of spatial domain identification, which is the recovery of spatial regions with coherent biological structures. As a result, the generated visualizations often fail to accurately capture fine-grained tissue organization. In addition, current approaches do not fully exploit the rich morphological and microenvironmental information embedded in histological images, which limits the representation learning capability in spatial domain identification.
RESULTS: We present ProST, an image prompt-guided multimodal representation learning framework for spatial domain identification on ST data. ProST is designed to effectively exploit morphology-aware information from histological images while reducing the impact of irrelevant visual noise, thereby generating robust and spatially coherent embeddings. We further optimized histological image feature extraction to improve the use of morphological information. Evaluations on multiple ST datasets demonstrate that ProST consistently outperforms existing methods in spatial domain identification, visualization, spatial trajectory inference, and gene expression imputation. Its high accuracy and strong generalization make ProST a powerful tool for resolving fine-grained tissue structures and revealing underlying biological complexity.
AVAILABILITY AND IMPLEMENTATION: ProST is implemented in Python and is freely available at https://github.com/Snake-Bio/ProST. The source code used in this study has been archived on Zenodo at DOI: 10.5281/zenodo.22144978. All datasets used in this study are publicly available at https://zenodo.org/records/22685502.