A team led by bioengineers at the University of California San Diego announced on July 13 the development of a genome-scale reference map that details how individual genes control the functions and identities of human stem cells. The open-access resource is expected to help researchers build virtual cell models for complex diseases and design patient-specific treatments.
The study, published in Nature Biotechnology, presents what researchers describe as the first genome-scale map of gene function in human induced pluripotent stem cells. These are adult cells reprogrammed into an embryonic-like state capable of turning into any type of cell in the body. The majority of human gene functions inside these cells remain unknown, prompting the need for such a reference map.
To create this comprehensive resource, the team used CRISPR technology to systematically switch off 11,692 expressed genes one by one and measured their effects on cellular transcriptomes across more than 2.5 million single cells. By compiling these data, researchers grouped related genes and cellular components based on shared molecular traits and functions. This process allowed them to isolate previously hidden metabolic and self-renewal genes.
The new map enabled researchers to uncover previously unrecognized cell regulators and confirm their roles experimentally. For example, they identified a specific gene called DBR1 as the main regulator for RNA editing—specifically, the conversion of adenosine to inosine.
"The map we generated works as a hypothesis engine - it's a starting point for what a given gene does and which genes might be worth pursuing as targets to drive differentiation into cell states of interest," said study co-first author Yesh Doctor, a bioengineering PhD student in Mali's lab. "Scientists can use it to look up the functions of genes and build hypotheses on them instead of having to run the experiments themselves." An additional statement from Mali read, "We are grateful for the support of the NIH, especially the Bridge2AI program and NHGRI. These comprehensive, genome-scale screens enable generation of reference maps that are not just invaluable for basic science discovery, but also an important resource for powering future computational and AI tools for genotype-phenotype prediction, one of the central pursuits in genetics research."
