Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell–cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell–cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

中文翻译：

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scHolography：单细胞空间邻域重建和分析的计算方法


空间转录组学改变了我们研究组织复杂性的能力。然而，以单细胞分辨率准确解剖组织组织仍然具有挑战性。在这里，我们介绍 scHolography，这是一种基于机器学习的方法，旨在利用空间和单细胞 RNA 测序数据重建单细胞空间邻域并促进 3D 组织可视化。全息摄影采用高维转录组到空间的投影，推断细胞之间的空间关系，定义空间邻域并增强细胞间通讯的分析。当应用于人类和小鼠数据集时，scHolography 可以对空间细胞邻域、细胞间相互作用和肿瘤免疫微环境进行定量评估。 scHolography 共同提供了一个强大的计算框架，用于阐明 3D 组织组织并分析细胞水平的空间动力学。