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

IF 36.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Nature Methods Pub Date : 2024-09-18 DOI:10.1038/s41592-024-02410-7
Zefang Tang, Shuchen Luo, Hu Zeng, Jiahao Huang, Xin Sui, Morgan Wu, Xiao Wang
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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. CAST is a deep learning-based method that enables across-sample searching and matching based on spatial molecular features and reconstructing spatially resolved single-cell multi-omic profiles, as well as supports downstream differential analysis.

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在单细胞分辨率的空间全息样本中进行搜索和匹配
空间 omics 技术利用空间信息表征组织分子特性,但整合和比较不同技术和模式的空间数据具有挑战性。目前还缺乏一种比较分析工具,能够搜索、匹配和直观显示多个样本空间分子特征的相似性和差异性。为了解决这个问题,我们引入了 CAST(空间 omics 跨样本配准),这是一种基于深度图神经网络的方法,能在单细胞水平上进行空间对空间的搜索和匹配。CAST 基于空间分子特征的内在相似性对组织进行配准,并重建空间分辨的单细胞多原子图谱。CAST 还允许进行空间分辨差异分析(Δ分析),以精确定位和可视化与疾病相关的分子通路和细胞间相互作用,以及单细胞相对翻译效率分析,以揭示不同细胞类型和区域之间翻译控制的差异。CAST 是一个综合框架,可用于跨技术、跨模式和跨样本条件的无缝单细胞空间数据搜索和匹配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nature Methods
Nature Methods 生物-生化研究方法
CiteScore
58.70
自引率
1.70%
发文量
326
审稿时长
1 months
期刊介绍: Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
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