小柯机器人

加拿大多伦多大学Anne-Claude Gingras小组绘制出人类细胞的临近依赖性生物素化图谱。该研究于2021年6月2日在线发表于国际一流学术期刊《自然》。

研究人员展示了在192个亚细胞标记的基础上基于BioID的人类细胞图谱，并定义了HEK293细胞中4,145种独特蛋白质的细胞内位置。这些定位预测超出了先前方法的特异性，并且能够在线粒体外膜和内质网之间的界面处发现对线粒体稳态至关重要的蛋白质。在此数据集的基础上，研究人员创建了humancellmap.org作为公共资源，为用户BioID数据的本地化分析提供在线工具，并演示了如何使用该资源更好地了解BioID结果。

据介绍，区室化是真核细胞的定义特征，并将不同的生化过程划分为不同的亚细胞位置。显微镜和生化分馏结合质谱已经定义了各种不同细胞器的蛋白质组，但许多细胞内隔室对这种方法仍然难以解决。BioID等邻近依赖性生物素化技术提供了一种替代方法来定义活细胞中细胞区室的组成。

附：英文原文

Title: A proximity-dependent biotinylation map of a human cell

Author: Christopher D. Go, James D. R. Knight, Archita Rajasekharan, Bhavisha Rathod, Geoffrey G. Hesketh, Kento T. Abe, Ji-Young Youn, Payman Samavarchi-Tehrani, Hui Zhang, Lucie Y. Zhu, Evelyn Popiel, Jean-Philippe Lambert, tienne Coyaud, Sally W. T. Cheung, Dushyandi Rajendran, Cassandra J. Wong, Hana Antonicka, Laurence Pelletier, Alexander F. Palazzo, Eric A. Shoubridge, Brian Raught, Anne-Claude Gingras

Issue&Volume: 2021-06-02

Abstract: Compartmentalization is a defining characteristic of eukaryotic cells, and partitions distinct biochemical processes into discrete subcellular locations. Microscopy1 and biochemical fractionation coupled with mass spectrometry2,3,4 have defined the proteomes of a variety of different organelles, but many intracellular compartments have remained refractory to such approaches. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach to define the composition of cellular compartments in living cells5,6,7. Here we present a BioID-based map of a human cell on the basis of 192 subcellular markers, and define the intracellular locations of 4,145 unique proteins in HEK293 cells. Our localization predictions exceed the specificity of previous approaches, and enabled the discovery of proteins at the interface between the mitochondrial outer membrane and the endoplasmic reticulum that are crucial for mitochondrial homeostasis. On the basis of this dataset, we created humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to understand BioID results better.

DOI: 10.1038/s41586-021-03592-2

Source: https://www.nature.com/articles/s41586-021-03592-2

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html