RT Journal Article
SR Electronic
T1 A proximity-dependent biotinylation map of a human cell: an interactive web resource
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 796391
DO 10.1101/796391
A1 Christopher D. Go
A1 James D.R. Knight
A1 Archita Rajasekharan
A1 Bhavisha Rathod
A1 Geoffrey G. Hesketh
A1 Kento T. Abe
A1 Ji-Young Youn
A1 Payman Samavarchi-Tehrani
A1 Hui Zhang
A1 Lucie Y. Zhu
A1 Evelyn Popiel
A1 Jean-Philippe Lambert
A1 Étienne Coyaud
A1 Sally W.T. Cheung
A1 Dushyandi Rajendran
A1 Cassandra J. Wong
A1 Hana Antonicka
A1 Laurence Pelletier
A1 Alexander F. Palazzo
A1 Eric A. Shoubridge
A1 Brian Raught
A1 Anne-Claude Gingras
YR 2021
UL http://biorxiv.org/content/early/2021/03/17/796391.abstract
AB Compartmentalization is a defining characteristic of eukaryotic cells, partitioning cellular processes into discrete subcellular locations. High throughput microscopy1 and biochemical fractionation coupled with mass spectrometry2–6 have helped to define the proteomes of a variety of organelles and macromolecular structures. However, many other intracellular compartments have remained refractory to such approaches, due for example to difficulty in purifying non-membrane bound structures. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach for defining the composition of cellular compartments in living cells7–10. Here we present a BioID-based map of a human cell based on 192 markers from 32 different subcellular compartments, comprising 35,902 high confidence proximity interactions, and defining the intracellular locations of 4,145 unique proteins in HEK 293 cells. Our localization predictions meet or exceed previous-approaches, with higher specificity, and enabled the discovery of proteins at the mitochondrial outer membrane-endoplasmic reticulum (ER) interface that are critical for mitochondrial homeostasis. Based on this dataset, we have established 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 better understand BioID datasets.Competing Interest StatementThe authors have declared no competing interest.