单细胞论文记录(part7)--DL and alignment of spatially resolved single-cell transcriptomes with Tangram

2024-06-18 01:47:55

学习笔记，仅供参考，有错必纠
阅读状态：略读
Journal：Nature Methods
Year：2021
Authors：Tommaso Biancalani， Gabriele Scalia，Aviv Regev

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

abstract

绘制器官的生物图谱需要我们在空间上研究整个单细胞转录组，并将这些细胞特征与解剖学尺度联系起来. Single-cell和RNA-seq (sc/snRNA-seq)可以全面地描述细胞，但会失去空间信息. 空间转录组学可以进行空间测量，但分辨率较低，灵敏度有限.Targeted in situ technologies(靶向原位技术)解决了这两个问题，但在基因通量方面是有限的. 为了克服这些限制，我们提出了Tangram，一种将sc/snRNA-seq数据与从同一区域收集的各种形式的空间数据相匹配的方法，包括MERFISH、STARmap、smFISH、Spatial Transcriptomics(Visium)和组织学图像. Tangram可以映射任何类型的sc/snRNA-seq数据，包括多模态数据，如来自SHARE-seq的数据，我们用它来揭示染色质可及性的空间模式. 我们通过重建视觉和躯体运动区的单细胞分辨率的全基因组解剖学综合空间图，在健康的小鼠脑组织上演示了T

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