英国伦敦大学学院Kenneth D. Harris和瑞典斯德哥尔摩大学Mats Nilsson团队合作揭示了概率性细胞分型可在原位精确映射密切相关的细胞类型。该项研究成果在线发表在2019年11月18日的《自然—方法学》上。

研究人员介绍了通过原位测序（pciSeq）进行的概率细胞分型。该方法利用以前的单细胞RNA测序（scRNA-seq）分类来使用多重原位RNA检测来识别细胞类型。他们通过映射小鼠海马区CA1的抑制性神经元来应用这种方法，可从以前的大量工作中找到其层状组织来获得其真实状况。他们的方法在与基本事实相匹配的空间排列中识别了这些神经元类别，并在与它们的已知组织相匹配的模式中进一步识别了多类同皮质锥体细胞。这种方法将允许识别大脑和其他组织中紧密相关的细胞类型的空间组织。

据了解，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cRNA-seq揭示了全基因组表达模式，该模式定义了其许多紧密相关的神经元类型，但无法揭示其空间排列。

附：英文原文

Title: Probabilistic cell typing enables fine mapping of closely related cell types in situ

Author: Xiaoyan Qian, Kenneth D. Harris, Thomas Hauling, Dimitris Nicoloutsopoulos, Ana B. Muoz-Manchado, Nathan Skene, Jens Hjerling-Leffler, Mats Nilsson

Issue&Volume: 2019-11-18

Abstract: 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.

DOI: 10.1038/s41592-019-0631-4

Source: https://www.nature.com/articles/s41592-019-0631-4