德国亥姆霍兹协会马克斯·德尔布吕克分子医学中心Nikolaus Rajewsky和以色列希伯来大学Nir Friedman研究组合作绘制基因表达图谱。2019年11月20日，国际学术期刊《自然》在线发表了这一成果。

研究人员通过搜索测序细胞的空间排列来重建空间位置。在这之前他们对其知之甚少。在这些细胞中，附近细胞的转录谱通常（但不总是）比更远的细胞更相似。他们将该任务表述为用于概率嵌入的广义最优运输问题，并推导了一种有效的迭代算法来解决该问题。研究人员重建了哺乳动物肝脏和肠上皮，果蝇和斑马鱼胚胎，哺乳动物小脑和整个肾脏的切片中基因的空间表达，并使用重建的组织来鉴定具有空间信息的基因。因此，他们确定了动物组织中基因的空间表达的组织原理，可以利用该原理来推断单个细胞的空间位置的有意义的概率。该框架（novoSpaRc）可以合并以前的空间信息，并且可以与任何单细胞技术兼容。可以使用他们的方法测试构成基因表达图谱基础的其他原则。

据了解，单个细胞中的多重RNA测序正在改变基础和临床生命科学。但是，通常首先必须分离组织，因此有关空间关系和细胞之间通讯的关键信息会丢失。现有的重建组织的方法通过使用标记基因的表达空间模式（通常不存在），将空间位置分配给每个细胞，而与其他细胞无关。

附：英文原文

Title: Gene expression cartography

Author: Mor Nitzan, Nikos Karaiskos, Nir Friedman, Nikolaus Rajewsky

Issue&Volume: 2019-11-20

Abstract: Multiplexed RNA sequencing in individual cells is transforming basic and clinical life sciences14. Often, however, tissues must first be dissociated, and crucial information about spatial relationships and communication between cells is thus lost. Existing approaches to reconstruct tissues assign spatial positions to each cell, independently of other cells, by using spatial patterns of expression of marker genes5,6which often do not exist. Here we reconstruct spatial positions with little or no prior knowledge, by searching for spatial arrangements of sequenced cells in which nearby cells have transcriptional profiles that are often (but not always) more similar than cells that are farther apart. We formulate this task as a generalized optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to solve it. We reconstruct the spatial expression of genes in mammalian liver and intestinal epithelium, fly and zebrafish embryos, sections from the mammalian cerebellum and whole kidney, and use the reconstructed tissues to identify genes that are spatially informative. Thus, we identify an organization principle for the spatial expression of genes in animal tissues, which can be exploited to infer meaningful probabilities of spatial position for individual cells. Our framework (novoSpaRc) can incorporate prior spatial information and is compatible with any single-cell technology. Additional principles that underlie the cartography of gene expression can be tested using our approach. A new computational framework, novoSpaRc, leverages single-cell data to reconstruct spatial context for cells and spatial expression across tissues and organisms, on the basis of an organization principle for gene expression.

DOI: 10.1038/s41586-019-1773-3

Source:https://www.nature.com/articles/s41586-019-1773-3