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OpenCell: Endogenous tagging for the cartography of human cellular organization
Author(s) -
Nathan Cho,
Keith C. Cheveralls,
AndreasDavid Brunner,
Kibeom Kim,
André C. Michaelis,
Preethi Raghavan,
Hirofumi Kobayashi,
Laura Savy,
Jason Y. Li,
Hera Canaj,
James Y.S. Kim,
Edna M. Stewart,
Christian Gnann,
Frank McCarthy,
Joana P. Cabrera,
Rachel M. Brunetti,
Bryant B. Chhun,
Greg Dingle,
Marco Y. Hein,
Bo Huang,
Shalin B. Mehta,
Jonathan S. Weissman,
Rafael Gómez-Sjöberg,
Daniel N. Itzhak,
Loïc A. Royer,
Matthias Mann,
Manuel D. Leonetti
Publication year - 2022
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.abi6983
Subject(s) - computational biology , human proteome project , proteome , cluster analysis , spatial analysis , computer science , biology , proteomics , information retrieval , geography , artificial intelligence , bioinformatics , genetics , gene , remote sensing
Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

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