美国麻省理工学院Wang Xiao团队实现在单细胞分辨率下对空间组学样本进行搜索和匹配。2024年9月18日，《自然—方法学》杂志在线发表了这项成果。

研究人员表示，空间组学技术通过空间信息特征化组织的分子属性，但在不同技术和模式下整合和比较空间数据具有挑战性。缺乏一个能够搜索、匹配并可视化多样本中空间分子特征相似性和差异性的比较分析工具。

为了解决这个问题，研究人员介绍了CAST（空间组学跨样本对齐），一种基于深度图神经网络的方法，能够在单细胞水平上进行空间到空间的搜索和匹配。CAST基于空间分子特征的内在相似性对组织进行对齐，并重建空间分辨的单细胞多组学剖面。

CAST还允许进行空间分辨的差异分析，以确定和可视化与疾病相关的分子通路和细胞间相互作用，并进行单细胞相对翻译效率分析，揭示不同细胞类型和区域之间翻译控制的变化。CAST作为一个集成框架，用于在不同技术、模式和样本条件下实现无缝的单细胞空间数据搜索和匹配。

附：英文原文

Title: Search and match across spatial omics samples at single-cell resolution

Author: Tang, Zefang, Luo, Shuchen, Zeng, Hu, Huang, Jiahao, Sui, Xin, Wu, Morgan, Wang, Xiao

Issue&Volume: 2024-09-18

Abstract: Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (Analysis) to pinpoint and visualize disease-associated molecular pathways and cell–cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions.

DOI: 10.1038/s41592-024-02410-7

Source: https://www.nature.com/articles/s41592-024-02410-7