Subcellular spatially resolved gene neighborhood networks in single cells.
Subcellular spatially resolved gene neighborhood networks in single cells.
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DOI：
10.1016/j.crmeth.2023.100476
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发表时间：
2023-05-22
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影响因子：
--
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中科院分区：
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基于图像的空间组学方法如荧光原位杂交（FISH）以单分子分辨率生成单细胞的分子谱。目前的空间转录组学方法集中在单个基因的分布。然而，RNA转录物的空间邻近性在细胞功能中可以发挥重要作用。我们展示了一个空间分辨的基因邻域网络（spaGNN）管道的亚细胞基因邻近关系的分析。在spaGNN中，亚细胞空间转录组学数据的基于机器学习的聚类产生多重转录本特征的亚细胞密度类。最近邻分析在不同的亚细胞区域产生异质基因邻近图。我们使用成纤维细胞和U2-OS细胞的多重错误鲁棒FISH数据和间充质干细胞（MSC）的连续FISH数据来说明spaGNN的细胞类型区分能力，揭示组织来源特异性MSC转录组学和空间分布特征。总的来说，spaGNN方法扩展了可用于细胞类型分类任务的空间特征。spaGNN检测基因邻近关系的层次聚类算法识别亚细胞斑块中的基因邻居最近邻分析揭示异质性局部基因邻近基因邻近关系在细胞分类中优于基因表达空间分辨转录组学技术，如多重错误鲁棒荧光原位杂交（FISH）和顺序FISH，产生高水平的三维数据集，其反映组织内细胞的组织以及细胞内分子的组织。我们试图开发一种分析方法，用于从基于图像的空间组学数据推断亚细胞分子相互作用网络。当RNA分子在空间上位于最近的邻域距离内时，该算法量化RNA分子的物理接近性。空间分辨的基因邻域用于生成在单个细胞的不同部分不同的网络。解码亚细胞基因邻域网络使下游任务（如细胞分类）优于每个细胞的整体基因表达，从而扩展了亚细胞空间特征在组学分析中的应用。Fang等开发了spaGNN管道，从亚细胞空间转录组学数据中提取基因邻近关系。该算法在构建基因邻域网络的同时检测基因在亚细胞区室中的邻近性。基因空间邻近关系在细胞分类中优于基因表达。
Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks. spaGNN detects the hierarchy of gene proximity relationships Clustering algorithm identifies gene neighbors in subcellular patches Nearest neighborhood analysis reveals heterogeneous local gene proximity Gene proximity relationship outperforms gene expression in cell classification Spatially resolved transcriptomic technologies such as multiplexed error-robust fluorescence in situ hybridization (FISH) and sequential FISH generate high-dimensional datasets that reflect the organization of cells within tissues but also the organization of molecules within cells. We sought to develop an analysis method for inferring subcellular molecular interaction networks from image-based spatial omics data. This algorithm quantifies the physical proximity of RNA molecules when they are spatially located within the nearest neighborhood distances. Spatially resolved gene neighborhoods are used to generate networks that are distinct at different parts of a single cell. Decoding subcellular gene neighborhood networks enables downstream tasks such as cell classification better than overall gene expression per cell, thus expanding the use of subcellular spatial features for omics analysis. Fang et al. developed the spaGNN pipeline to extract gene proximity relationships from subcellular spatial transcriptomics data. The algorithm detects the proximity of genes in subcellular compartments while constructing gene neighborhood networks. The gene spatial proximity relationships outperform gene expression in cell classification.
影响因子：
4.3
作者：
Puniyani K;Xing EP
通讯作者：
Xing EP