Objective

CRISPR-based genetic screens are accelerating biological discovery, but current methods have inherent limitations. Widely used pooled screens work well for mechanisms that affect cell survival and proliferation, and can be extended by sortable marker proteins. However, they are restricted to measuring the distribution of guide RNAs before and after applying a selective challenge, and do not provide any detailed phenotypic information. Since the actual cellular responses are not measured, the interpretation and validation of screening hits is generally work-intensive and prone to false positive results. Arrayed CRISPR screens, in which only one gene is targeted at a time, allow for more comprehensive molecular readouts, but at much lower throughput. We have recently developed a third and complementary screening paradigm, single-cell CRISPR screens, based on the idea that gRNAs and their induced cellular responses are already compartmentalized within single cells. We combined pooled CRISPR screening with single-cell RNA sequencing into a broadly applicable workflow, directly linking guide RNA expression to transcriptome responses in thousands of individual cells (Datlinger et al. 2017 Nature Methods). Our method for CRISPR droplet sequencing (CROP-seq) enables pooled CRISPR screens for entire gene signatures that can be derived directly from the data. Due to its single-cell resolution, CROP-seq can localize the effect of perturbations in complex tissues and cellular differentiation hierarchies, and can work efficiently on scarce material. Furthermore, CROP-seq is compatible with virtually all current methods for single-cell RNA-seq and established strategies for pooled library cloning. Since the original publication, we continued to develop CROP-seq with a particular focus on in vivo screens in Cas9 mice. We are exploring combinations with alternative functions, such as CRISPR inhibition, activation and targeted epigenetic modifications. Given the increasing throughput of single-cell transcriptomics and the advent of single-cell multi-omics technology (reviewed in: Bock et al. 2016 Trends in Biotechnology), CROP-seq has the potential to provide comprehensive characterization of large CRISPR libraries and constitutes a powerful method for dissecting cellular regulation at scale.