Pooled protein tagging,cellular imaging,and in situ sequencing for monitoring drug action in real time

The levels and subcellular localizations of proteins regulate critical aspects of many cellular processes and can become targets of therapeutic intervention. However, high-throughput methods for the discovery of proteins that change localization either by shuttling between compartments, by binding larger complexes, or by localizing to distinct membraneless organelles are not available. Here we describe a scalable strategy to characterize effects on protein localizations and levels in response to different perturbations. We use CRISPR-Cas9-based intron tagging to generate cell pools expressing hundreds of GFP-fusion proteins from their endogenous promoters and monitor localization changes by time-lapse microscopy followed by clone identification using in situ sequencing. We show that this strategy can characterize cellular responses to drug treatment and thus identify nonclassical effects such as modulation of protein–protein interactions, condensate formation, and chemical degradation.

Currently available mass-spectrometry methods (Rix and Superti-Furga 2009; Martinez Molina et al. 2013; Savitski et al. 2014; Huber et al. 2015; Drewes and Knapp 2018) for monitoring the effects of cellular perturbations on proteomes cannot be scaled efficiently to monitor time-dependent effects in high throughput. A different approach to study drug action is live-cell imaging of protein dynamics in cells expressing a protein of interest fused to a fluorescent tag. Traditionally, such reporter cells are generated either by overexpression to nonphysiologic levels, by oligonucleotide-directed homologous recombination in yeast, or by using CRISPR-Cas9 and homology-directed repair (HDR) to endogenously tag proteins in human cells (Ghaemmaghami et al. 2003; Huh et al. 2003; Chong et al. 2015; Leonetti et al. 2016). In addition to those targeted approaches, “gene trapping” or “CD-tagging” strategies, which rely on the random, viral integration of fluorescent tags as synthetic exons, have been used for analyzing dynamic changes in response to drugs (Jarvik et al. 1996; Morin et al. 2001; Cohen et al. 2008; Kang et al. 2016), but they are limited by integration site biases and require the isolation and characterization of clones before using them in an arrayed format. Recently, a strategy combining genome engineering and gene trapping using homology-independent CRISPR-Cas9 editing to place a fluorescent tag as a synthetic exon into introns of individual target genes has been described (Serebrenik et al. 2019). The strategy relies on a generic sgRNA excising a fluorescent tag flanked by splice acceptor and donor sites from a generic donor plasmid, which is coexpressed with a gene-specific intron-targeting sgRNA specifying the integration site. Here we show the scalability of that strategy to enable pooled protein tagging of more than 900 metabolic enzymes and epigenetic modifiers. Exposing the GFP-tagged cells to compounds allows us to monitor drug effects on the localization and levels of hundreds of proteins in real time in a pooled format, followed by identification of responding clones by in situ sequencing of the expressed intron-targeting sgRNA that corresponds to the tagged protein (Fig. 1A).Open in a separate windowFigure 1.Pooled GFP intron-tagging of metabolic enzymes. (A) Schematic outline of the approach. (B) Identification of targetable introns within metabolic genes. (C) FACS sorting of clones with successful GFP-tagging by signal enrichment over background mCherry intensity used as control for autofluorescence. (D) Representative image of sorted GFP-tagged cell pool. Scale bar, 25 µm. (E) Comparison of RNA-seq expression in HAP1 cells between genes for which GFP-tagged cells could be isolated and genes that were targeted in the sgRNA library but did not result in successful clone isolation.