空间成像技术的最新进展彻底改变了单细胞水平上高分辨率多通道图像、基因表达和空间位置的获取。我们的研究引入了 xSiGra，一种可解释的基于图形的人工智能模型，旨在通过利用空间成像技术的多模态特征来阐明已识别的空间细胞类型的可解释特征。通过构建以免疫组织学图像和基因表达作为节点属性的空间细胞图，xSiGra 采用混合图转换器模型来描绘空间细胞类型。此外，xSiGra 集成了梯度加权类激活映射组件的新变体，以揭示可解释的特征，包括各种细胞类型的关键基因和细胞，从而促进从空间数据中获得更深入的生物学见解。通过对现有方法进行严格的基准测试，xSiGra 在不同的空间成像数据集上展示了卓越的性能。 xSiGra 在肺肿瘤切片上的应用揭示了细胞的重要性评分，说明细胞活性不仅由其自身决定，而且还受到邻近细胞的影响。此外，利用已识别的可解释基因，xSiGra 揭示了内皮细胞亚群与肿瘤细胞的相互作用，表明其在复杂细胞相互作用中的异质性潜在机制。© 作者 2024。由牛津大学出版社出版。

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.© The Author(s) 2024. Published by Oxford University Press.