基因表達圖譜繪製
作者：
小柯機器人發布時間：2019/11/21 13:04:43
德國亥姆霍茲協會馬克斯·德爾布呂克分子醫學中心Nikolaus Rajewsky和以色列希伯來大學Nir Friedman研究組合作繪製基因表達圖譜。2019年11月20日，國際學術期刊《自然》在線發表了這一成果。
研究人員通過搜索測序細胞的空間排列來重建空間位置。在這之前他們對其知之甚少。在這些細胞中，附近細胞的轉錄譜通常（但不總是）比更遠的細胞更相似。他們將該任務表述為用於概率嵌入的廣義最優運輸問題，並推導了一種有效的迭代算法來解決該問題。研究人員重建了哺乳動物肝臟和腸上皮，果蠅和斑馬魚胚胎，哺乳動物小腦和整個腎臟的切片中基因的空間表達，並使用重建的組織來鑑定具有空間信息的基因。因此，他們確定了動物組織中基因的空間表達的組織原理，可以利用該原理來推斷單個細胞的空間位置的有意義的概率。該框架（novoSpaRc）可以合併以前的空間信息，並且可以與任何單細胞技術兼容。可以使用他們的方法測試構成基因表達圖譜基礎的其他原則。
據了解，單個細胞中的多重RNA測序正在改變基礎和臨床生命科學。但是，通常首先必須分離組織，因此有關空間關係和細胞之間通訊的關鍵信息會丟失。現有的重建組織的方法通過使用標記基因的表達空間模式（通常不存在），將空間位置分配給每個細胞，而與其他細胞無關。
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