High-speed fluorescence image-enabled cell sorting.
Daniel SchraivogelTerra M KuhnBenedikt RauscherMarta Rodríguez-MartínezMalte PaulsenKeegan OwsleyAaron MiddlebrookChristian TischerBeáta RamaszDiana Ordoñez-RuedaMartina DeesSara Cuylen-HaeringEric DieboldLars M SteinmetzPublished in: Science (New York, N.Y.) (2022)
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.
Keyphrases
- genome wide
- high speed
- cell cycle
- deep learning
- nuclear factor
- single cell
- cell therapy
- dna methylation
- atomic force microscopy
- toll like receptor
- single molecule
- gene expression
- high throughput
- cell proliferation
- stem cells
- clinical trial
- high resolution
- machine learning
- immune response
- mass spectrometry
- electronic health record
- double blind
- lps induced