Probabilistic cell typing enables fine mapping of closely related cell types in situ
Probabilistic cell typing enables fine mapping of closely related cell types in situ
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DOI：
10.1038/s41592-019-0631-4
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发表时间：
2020-01-01
期刊：
影响因子：
48
通讯作者：
Nilsson, Mats
中科院分区：
文献类型：
--
作者：
Qian, Xiaoyan;Harris, Kenneth D.;Nilsson, Mats
了解组织的功能需要了解其组成细胞类型的空间组织。在大脑皮层中，单细胞RNA测序（scRNA-seq）揭示了全基因组表达模式，这些模式定义了许多密切相关的神经元类型，但无法揭示它们的空间排列。在这里，我们介绍了通过原位测序（pciSeq）进行概率细胞分型，这是一种利用先前scRNA-seq分类来使用多重原位RNA检测识别细胞类型的方法。我们应用这种方法映射的抑制性神经元的小鼠海马CA 1区，地面真理是从以前的广泛工作，确定其层状组织。我们的方法确定了这些神经元类的空间排列匹配地面真相，并进一步确定了多个类的等皮质锥体细胞的模式匹配其已知的组织。这种方法将允许识别空间组织密切相关的细胞类型在整个大脑和其他tissues. Probably细胞分型原位测序（pciSeq），利用以前的单细胞RNA测序分类和多重原位RNA检测空间映射细胞类型准确地在小鼠海马和isocortex。
Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.Probabilistic cell typing by in situ sequencing (pciSeq), leverages previous single-cell RNA sequencing classification and multiplexed in situ RNA detection to spatially map cell types accurately in the mouse hippocampus and isocortex.