On Jul. 24, 2025, researchers at the Broad Institute and the National Emerging Infectious Diseases Laboratories (NEIDL) at Boston University have used an image-based screening method developed at the Broad to identify human genes that, when silenced, impair the Ebola virus’s ability to infect. The method, known as optical pooled screening (OPS), enabled the scientists to test, in about 40 million CRISPR-perturbed human cells, how silencing each gene in the human genome affects virus replication.

Although outbreaks of Ebola virus are rare, the disease is severe and often fatal, with few treatment options. Rather than targeting the virus itself, one promising therapeutic approach would be to interrupt proteins in the human host cell that the virus relies upon. However, finding those regulators of viral infection using existing methods has been difficult and is especially challenging for the most dangerous viruses like Ebola that require stringent high-containment biosafety protocols.

Using machine-learning-based analyses of images of perturbed cells, they identified multiple host proteins involved in various stages of Ebola infection that when suppressed crippled the ability of the virus to replicate. Those viral regulators could represent avenues to one day intervene therapeutically and reduce the severity of disease in people already infected with the virus. The approach could be used to explore the role of various proteins during infection with other pathogens, as a way to find new drugs for hard-to-treat infections.

The team used CRISPR to knock out each gene in the human genome, one at a time, in nearly 40 million human cells, and then infected each cell with Ebola virus. They next fixed those cells in place in laboratory dishes and inactivated them, so that the remaining processing could occur outside of the high-containment lab.

After taking images of the cells, they measured overall viral protein and RNA in each cell using the CellProfiler image analysis software, and to get even more information from the images, they turned to AI. With help from team members in the Eric and Wendy Schmidt Center at the Broad, led by study co-author and Broad core faculty member Caroline Uhler, they used a deep learning model to automatically determine the stage of Ebola infection for each single cell. The model was able to make subtle distinctions between stages of infection in a high-throughput way that wasn’t possible using prior methods.

By sequencing parts of the CRISPR guide RNA in all 40 million cells individually, the researchers determined which human gene had been silenced in each cell, indicating which host proteins (and potential viral regulators) were targeted. The analysis revealed hundreds of host proteins that, when silenced, altered overall infection level, including many required for viral entry into the cell.

Knocking out other genes enhanced the amount of virus within inclusion bodies, structures that form in the human cell to act as viral factories, and prevented the infection from progressing further. Some of these human genes, such as UQCRB, pointed to a previously unrecognized role for mitochondria in the Ebola virus infection process that could possibly be exploited therapeutically. Indeed, treating cells with a small molecule inhibitor of UQCRB reduced Ebola infection with no impact on the cell’s own health.

Other genes, when silenced, altered the balance between viral RNA and protein. For example, perturbing a gene called STRAP resulted in increased viral RNA relative to protein. The researchers are currently doing further studies in the lab to better understand the role of STRAP and other proteins in Ebola infection and whether they could be targeted therapeutically.

The study appears in Nature Microbiology. The study’s approach could also be used to examine other pathogens and emerging infectious diseases and look for new ways to treat them.