Abstract:

Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR/Cas9 screening with microRaft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft, followed by gRNA Identification). By isolating microRafts that contain genetic clones harboring individual guide RNAs, we identify RNA binding proteins (RBPs) that influence the formation of stress granules, punctate protein-RNA assemblies, that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens.