New genetic reference map accelerates human stem cell research

A team led by bioengineers at the University of California San Diego has developed a genome-scale reference map that details how individual genes control the functions and identities of human stem cells. This open-access resource could help researchers build virtual cell models for complex diseases, as well as design patient-specific treatments for these diseases.

The study was published in Nature Biotechnology.

The result is a kind of reference atlas; it's a way to look up what perturbing almost any gene does to a stem cell's behavior, measured here as the impact on its whole transcriptome."

Prashant Mali, study senior author, professor in the Shu Chien-Gene Lay Department of Bioengineering, UC San Diego Jacobs School of Engineering

This is the first genome-scale map of gene function in human induced pluripotent stem cells, which are adult cells that have been reprogrammed back into an embryonic-like state and can turn into any type of cell in the body, such as muscle, heart, skin or bone. Such a reference map is needed because the vast majority of what human genes actually do inside these cells remains a mystery.

To build this comprehensive reference map, the team used CRISPR technology to systematically switch off 11,692 expressed genes one by one, then measured the effect on cellular transcriptomes across more than 2.5 million single cells.

By compiling these data, the researchers grouped related genes and cellular components together based on shared molecular traits and functions. This allowed them to isolate previously hidden metabolic and self-renewal genes. The 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 team envisions that this open-access map could serve as a resource for streamlining and accelerating biomedical research.

"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."

"We are grateful for the support of the NIH, especially the Bridge2AI program and NHGRI," Mali said. "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."