High-throughput, microscope-based sorting to dissect cellular heterogeneity
High-throughput, microscope-based sorting to dissect cellular heterogeneity
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DOI：
10.15252/msb.20209442
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发表时间：
2020-06-01
影响因子：
9.9
通讯作者：
Fowler, Douglas M.
中科院分区：
文献类型：
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作者：
Hasle, Nicholas;Cooke, Anthony;Fowler, Douglas M.
显微镜是描述复杂细胞表型的有力工具，但将这些表型与大规模的基因或RNA表达联系起来仍然具有挑战性。在这里，我们介绍了视觉细胞分类，这是一种根据视觉表型物理分离数十万活细胞的方法。自动化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可光转化的荧光蛋白；然后使用荧光激活的细胞分选分离这些光激活的细胞。首先，我们使用视觉细胞分类以池的形式评估数百个核定位序列变体，识别可以改善核定位的变体，并能够注释数千个人类蛋白质中的核定位序列。其次，我们恢复在紫杉醇治疗后保持正常核形态的细胞，然后获得它们的单细胞转录本，以确定与癌症紫杉醇耐药相关的途径。与其他方法不同，视觉细胞分选依赖于廉价的试剂和商业上可用的硬件。因此，它可以很容易地应用于揭示视觉细胞表型和内部状态之间的关系，包括基因类型和基因表达程序。
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.