Image-guided Spatial Omics Enhancement reveals Hidden Spatial Microstructures
Jiahao Liu, Gongning Luo, Qiaoming Liu, Suyu Dong, Guohua Wang, Yuming ZhaoAbstract
Motivation
The rapid advancement of spatial omics is fundamentally hindered by the resolution gap between physical capture platforms and genuine biological microstructures, a challenge compounded by inherent data sparsity and noise. While current image-guided computational methods attempt to bridge this gap, they often lack the multi-modal flexibility, non-linear modeling, and scalability required for modern, whole-tissue datasets.
Results
To address this, we introduce Bell, a modality-agnostic deep learning framework that reconstructs high-fidelity spatial microstructures by dynamically fusing histological images, spatial coordinates, and low-resolution molecular measurements via an adaptive attention mechanism. The study also presents mmBell, an extension utilizing a unified encoder structure to achieve cross-modal integration for increasingly complex multi-omics data. Systematically validated across over 10 spatial platforms and 20 datasets, Bell and mmBell consistently outperform state-of-the-art methods in resolution enhancement and noise suppression. Ultimately, this framework provides a highly robust, scalable solution for deeply deciphering complex spatial tissue organization.
Availability and implementation
Bell is available from the GitHub repository.