细胞已经进化出它们的通讯方法来感知它们的微环境并发送生物信号。除了使用配体和受体进行通信外，细胞还使用包括间隙连接在内的多种通道与其近邻进行通信。然而，当前的方法无法有效捕获各种微环境的影响。在这里，我们提出了一种研究空间转录组学 (ST) 数据中细胞邻居依赖性基因表达 (CellNeighborEX) 的新方法。为了根据微环境对细胞进行分类，CellNeighborEX 使用直接细胞定位或根据 ST 技术混合多个细胞的转录组。对于每种细胞类型，CellNeighborEX 都会识别与合作细胞类型相关的不同基因集，从而提供进一步的见解。我们发现，在小鼠胚胎、脑和肝癌等不同组织中，细胞根据其邻近细胞类型表达不同的基因。这些基因与发育或转移等关键生物过程相关。我们进一步验证了基因表达是由邻近伙伴通过空间可视化诱导的。邻近依赖性基因表达表明，参与细胞间相互作用的新潜在基因超出了配体-受体共表达所能发现的范围。© 2023 作者。根据 CC BY 4.0 许可证条款发布。

Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.© 2023 The Authors. Published under the terms of the CC BY 4.0 license.