PT  - JOURNAL ARTICLE
AU  - Nicholas Hasle
AU  - Anthony Cooke
AU  - Sanjay Srivatsan
AU  - Heather Huang
AU  - Jason J. Stephany
AU  - Zachary Krieger
AU  - Dana Jackson
AU  - Weiliang Tang
AU  - Sriram Pendyala
AU  - Raymond J. Monnat, Jr.
AU  - Cole Trapnell
AU  - Emily M. Hatch
AU  - Douglas M. Fowler
TI  - Visual Cell Sorting: A High-throughput, Microscope-based Method to Dissect Cellular Heterogeneity
AID  - 10.1101/856476
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 856476
4099  - http://biorxiv.org/content/early/2019/11/26/856476.short
4100  - http://biorxiv.org/content/early/2019/11/26/856476.full
AB  - 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. Visual Cell Sorting uses automated imaging and phenotypic analysis to direct 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 the effect of 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 use Visual Cell Sorting to recover cells that retain normal nuclear morphologies after paclitaxel treatment, then derive their single cell transcriptomes to identify multiple pathways associated with paclitaxel resistance in human 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.