A visual-omics foundation model to bridge histopathology with spatial transcriptomics.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Chen W, Zhang P, Tran TN, Xiao Y, Li S, Shah VV, Cheng H, Brannan KW, Youker K, Lai L, Fang L, Yang Y, Le NT, Abe JI, Chen SH, Ma Q, Chen K, Song Q, Cooke JP, Wang G. (2025). A visual-omics foundation model to bridge histopathology with spatial transcriptomics. Nature methods. PMID 40442373 · PMC12240810 · DOI 10.1038/s41592-025-02707-1

证据类型：PRIMARY_RESEARCH

主题：rna-seq、spatial-omics

被引次数（采集时）：38

原始记录：[Europe PMC](https://europepmc.org/article/MED/40442373)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building on OmiCLIP, our Loki platform offers five key functions: tissue alignment, annotation via bulk RNA sequencing or marker genes, cell-type decomposition, image-transcriptomics retrieval and spatial transcriptomics gene expression prediction from hematoxylin and eosin-stained images. Compared with 22 state-of-the-art models on 5 simulations, and 19 public and 4 in-house experimental datasets, Loki demonstrated consistent accuracy and robustness.

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