A cells transcriptional programme is not fully defined by gene expression alone, but by the tissue context in which that programme is enacted. Single-cell RNA sequencing resolves molecular identity after dissociation, whereas spatial transcriptomics preserves tissue architecture but remains constrained by assay-specific sparsity and gene coverage. Here we present OmniCell, a tissue-contextual transcriptomic foundation model pretrained on 67 million dissociated and spatially resolved profiles. By integrating gene identity, expression magnitude and tissue context, OmniCell links transcriptional programmes to the cellular neighbourhoods and anatomical contexts in which they operate. OmniCell organised transcriptomes across molecular, cellular and tissue scales. It recovered cell-type-specific programmes and tissue-aligned gene modules, preserved robust cell-state structure across batches, species and rare populations, and improved the reconstruction of spatial cell identity, anatomical domains and cell-type composition. In human liver cancer Stereo-seq data, OmniCell resolved a tumour-margin transition zone characterised by immune infiltration, acute-phase inflammation, coagulation/complement activity and metallothionein-linked metal-ion detoxification. Contextual gene-embedding similarity analysis showed that gene relationships differed across tumour core, transition-zone and paratumour/adjacent non-malignant niches, indicating that OmniCell captures tissue-dependent gene function rather than expression similarity alone. In mouse brain development and macaque cortex, spatial virtual perturbations mapped regulatory genes onto stage- and region-specific anatomical programmes. Together, these results establish tissue context as a primary axis of transcriptomic representation and provide a framework for studying how cellular programmes acquire context-dependent biological meaning in intact tissues.
细胞的转录程序并非仅由基因表达单独决定，而是由该程序执行的组织环境所定义。单细胞RNA测序在解离后解析分子身份，而空间转录组学虽能保留组织结构，但仍受限于特定检测方法的稀疏性和基因覆盖范围。本文提出OmniCell——一个基于6700万解离及空间分辨谱预训练的组织环境转录组基础模型。通过整合基因身份、表达量级和组织环境，OmniCell将转录程序与其运作的细胞邻域及解剖环境相关联。该模型在分子、细胞和组织尺度上组织转录组，成功恢复了细胞类型特异性程序和组织对齐的基因模块，在不同批次、物种和稀有群体中保持了稳健的细胞状态结构，并提升了空间细胞身份、解剖区域和细胞类型组成的重建精度。在人类肝癌Stereo-seq数据中，OmniCell解析出以免疫浸润、急性期炎症、凝血/补体活性及金属硫蛋白相关金属离子解毒为特征的肿瘤边缘过渡区。上下文基因嵌入相似性分析显示，基因关系在肿瘤核心区、过渡区和癌旁/邻近非恶性生态位中存在差异，表明OmniCell捕获的是组织依赖性基因功能而非单纯的表达相似性。在小鼠脑发育和猕猴皮层研究中，空间虚拟扰动将调控基因映射至阶段性和区域特异性解剖程序。这些结果共同确立了组织环境作为转录组表征的核心维度，并为研究细胞程序如何在完整组织中获得环境依赖性生物学意义提供了框架。
Pang, J. et al. · CC-BY 4.0