High‐throughput, microscope‐based sorting to dissect cellular heterogeneity

作者：Nicholas Hasle, Anthony Cooke, Sanjay Srivatsan, Heather Z Huang, Jason J. Stephany, Zachary Krieger, Dana L. Jackson, Weiliang Tang, Sriram Pendyala, Raymond J. Monnat, Cole Trapnell, Emily M. Hatch, Douglas M. Fowler · 发表于：Molecular Systems Biology · 年份：2020 · DOI：10.15252/msb.20209442 · 被引用次数：87 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、3D Printing in Biomedical Research

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.