Spatial transcriptomics reveals cellular heterogeneity, intercellular communication, and tissue organization, but its cost and limited accessibility restrict clinical use. Here, we present VISTA, a model that integrates multi-scale histological features and spatial context to infer spatial gene expression from H&E-stained tissue images. Across leave-one-section-out cross-validation and independent validation, VISTA robustly predicted thousands of genes and outperformed state-of-the-art methods. Beyond expression reconstruction, VISTA enabled clinically relevant downstream analyses. In TCGA breast cancer samples, it identified survival-associated genes, stratified prognostic risk groups, and revealed adverse tumor-associated spatial subtypes. In our in-house intrahepatic cholangiocarcinoma cohort, it preserved tumor–normal organization and identified CLDN4 and CYP3A4 as complementary spatial biomarkers. In HER2+ breast cancer, it predicted pathological response to neoadjuvant trastuzumab-based therapy and linked response-associated regions to immune and cytokine-related programs. These results support virtual spatial transcriptomics from routine histopathology for oncology applications.
Shaoqing Jiao, Zhen Yuan, Dazhi Lu et al.· bioRxiv· 0 citations
Spatial omic technologies have revolutionized tissue analysis by enabling multimodal molecular coprofiling within their native tissue context. Integrating multislice spatial multiomic data offers unprecedented opportunities to reconstruct three-dimensional (3D) tissue landscapes from multimodal molecular perspectives. However, current spatial omic integration methods remain narrowly focused on either vertical (cross-omic) or horizontal (cross-slice) integration, leaving a critical gap for a unified framework that simultaneously addresses both dimensions. Here we present SpatialMOSI, a unified framework for mosaic integration that concurrently resolves cross-modality and cross-section variations. At its core, SpatialMOSI employs a hierarchical graph contrastive learning (HiGCL) strategy that coordinates three integrative objectives: cross-omic alignment and fusion, cross-slice batch correction, and spatial microenvironment preservation. This approach operates on modality-specific latent representations while maintaining feature fidelity through decoding reconstruction. We demonstrate SpatialMOSI's versatility across multiple biological systems, accurately identifying spatially conserved domains, imputing missing omic layers, revealing B cell dynamics in germinal centers, delineating tumor-immune interactions, and reconstructing embryonic developmental trajectories. SpatialMOSI provides a critical computational foundation for constructing integrative 3D molecular atlases from complex multimodal spatial data sets.
Peimeng Zhen, Han Shu, Bingtao Wang et al.· Genome Research· 0 citations
Case studies further validated predicted circRNA-drug associations, including cisplatin, enzalutamide, and sorafenib, against published experimental findings, demonstrating HMCDSP's capacity to reveal clinically relevant biomarkers and inform personalized therapeutic strategies.
Yongtian Wang, Wen-Kai Shen, Jiahao Li et al.· Interdisciplinary Sciences C...· 0 citations
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