新加坡国立大学Jinmiao Chen研究小组利用SpatialGlue从空间多组学中解密空间域。2024年6月21日，《自然—方法学》杂志在线发表了这项成果。

研究人员介绍了一种具有双重关注机制的图神经网络模型——SpatialGlue，该模型通过对空间位置和组学测量进行组内整合，然后进行跨组学整合，从而解密空间域。研究人员利用不同技术，包括空间表观基因组-转录组和转录组-蛋白质组模式，在不同组织类型获得的数据上演示了SpatialGlue。与其他方法相比，SpatialGlue捕捉到了更多解剖细节，更准确地解析了大脑皮层等空间域。

该方法还识别出了细胞类型，如位于三个不同区域的脾脏巨噬细胞亚群，而这些在原始数据注释中是没有的。SpatialGlue可以很好地扩展数据规模，并可用于整合三种模式。该空间多组学分析工具结合了互补性组学模式的信息，从而获得细胞和组织特性的整体视图。

研究人员表示，现在，空间组学技术的进步允许从同一个组织切片获取多种类型的数据。为了充分发挥这些数据的潜力，人们需要有空间信息的数据整合方法。

附：英文原文

Title: Deciphering spatial domains from spatial multi-omics with SpatialGlue

Author: Long, Yahui, Ang, Kok Siong, Sethi, Raman, Liao, Sha, Heng, Yang, van Olst, Lynn, Ye, Shuchen, Zhong, Chengwei, Xu, Hang, Zhang, Di, Kwok, Immanuel, Husna, Nazihah, Jian, Min, Ng, Lai Guan, Chen, Ao, Gascoigne, Nicholas R. J., Gate, David, Fan, Rong, Xu, Xun, Chen, Jinmiao

Issue&Volume: 2024-06-21

Abstract: Advances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrated SpatialGlue on data acquired from different tissue types using different technologies, including spatial epigenome–transcriptome and transcriptome–proteome modalities. Compared to other methods, SpatialGlue captured more anatomical details and more accurately resolved spatial domains such as the cortex layers of the brain. Our method also identified cell types like spleen macrophage subsets located at three different zones that were not available in the original data annotations. SpatialGlue scales well with data size and can be used to integrate three modalities. Our spatial multi-omics analysis tool combines the information from complementary omics modalities to obtain a holistic view of cellular and tissue properties.

DOI: 10.1038/s41592-024-02316-4

Source: https://www.nature.com/articles/s41592-024-02316-4