High-speed fluorescence image–enabled cell sorting
Author(s) -
Daniel Schraivogel,
Terra M. Kuhn,
Benedikt Rauscher,
Marta RodríguezMartínez,
Malte Paulsen,
Keegan Owsley,
Aaron Middlebrook,
Christian Tischer,
Beáta Ramasz,
Diana OrdoñezRueda,
Martina Dees,
Sara CuylenHaering,
Eric D. Diebold,
Lars M. Steinmetz
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.abj3013
Subject(s) - sorting , cell sorting , phenotype , mitosis , image (mathematics) , computational biology , biology , cell , computer science , computer vision , artificial intelligence , microbiology and biotechnology , genetics , gene , programming language
Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.
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