Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Abstract

Advances in spatial transcriptomics (ST) have fundamentally transformed our understanding of tissue biology by enabling gene expression profiling within intact spatial contexts and uncovering tissue organization and microenvironmental interactions. However, current high-resolution ST platforms remain constrained by high costs, limited tissue coverage, and technical artifacts, often yielding noisy, sparse, and incomplete data that compromise analytical accuracy, biological interpretation, and clinical utility. To address these challenges, we introduce Pixel2Gene, a deep learning framework that integrates co-registered histology images with ST data to enable histology-guided reconstruction and prediction of spatial gene expression. Pixel2Gene enhances existing expression measurements by denoising low-confidence data and reconstructing coherent expression patterns, while also predicting gene expression in unmeasured tissue regions and new samples lacking direct transcriptomic profiling. We systematically evaluated Pixel2Gene across multiple high-resolution ST platforms, including Visium HD, Xenium, and CosMx, spanning diverse tissue types and disease contexts using downsampling simulations and cross-platform comparisons in clinical samples. Across all settings, Pixel2Gene consistently improved data consistency, mitigated dropout effects, restored biologically meaningful spatial structure, and enabled accurate downstream analyses. By leveraging the scalability and ubiquity of routine histology, Pixel2Gene facilitates comprehensive, cost-effective ST profiling at whole-tissue scale, supporting large cohort studies, translational research, and next-generation biomarker discovery.