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.

中文翻译：

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高通量、基于显微镜的分选可剖析细胞异质性。


显微镜是表征复杂细胞表型的强大工具，但大规模地将这些表型与基因型或 RNA 表达联系起来仍然具有挑战性。在这里，我们介绍视觉细胞分选，这是一种根据视觉表型物理分离数十万个活细胞的方法。自动成像和表型分析指导 Dendra2 的选择性照明，Dendra2 是一种在活细胞中表达的光转换荧光蛋白；然后使用荧光激活细胞分选来分离这些光激活细胞。首先，我们使用视觉细胞分选以汇集的形式评估数百个核定位序列变体，识别可改善核定位的变体并能够注释数千种人类蛋白质中的核定位序列。其次，我们回收紫杉醇治疗后保留正常核形态的细胞，然后获得它们的单细胞转录组，以确定与癌症中紫杉醇耐药性相关的途径。与其他方法不同，视觉细胞分选依赖于廉价的试剂和市售硬件。因此，它可以很容易地用于揭示视觉细胞表型和内部状态（包括基因型和基因表达程序）之间的关系。