Abstract
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
Highlights
We developed Visual Cell Sorting, a flexible and simple high-throughput method that uses commercial hardware to enable the investigation of cells according to visual phenotype
We demonstrate that Visual Cell Sorting enables visual phenotypic sorting into 4 bins, increases the throughput of cellular separation by 1,000-fold compared to other single-cell photoconversion-based technologies (Chien et al, 2015; Binan et al, 2016, 2019; Kuo et al, 2016), and permits pooled genetic screening and transcriptomic profiling
The imaging, analysis, and photoactivation steps are performed at each field of view, and unlike previous photoactivatable marker-based methods, these steps are automated, allowing hundreds of thousands of cells to be assessed per experiment
Summary
High content imaging (Boutros et al, 2015), in situ sequencing methods (Lee et al, 2014, 2015; Chen et al, 2015; Moffitt et al, 2016; Emanuel et al, 2017; Eng et al, 2019; Feldman et al, 2019; Wang et al, 2019), and other approaches (Chien et al, 2015; Binan et al, 2016, 2019; Kuo et al, 2016; David et al, 2017) have revolutionized the investigation of how genetic variants and gene expression programs dictate cellular morphology, organization, and behavior. The photoactivatable marker technology single-cell magneto-optical capture was used to isolate cells that successfully resolved ionizing radiationinduced DNA damage foci (Binan et al, 2019)
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