Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics.

ABSTRACT: Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III su

SUBMITTER: Carlson RJ 

PROVIDER: S-EPMC11014611 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature