On Aug. 28, 2026, scientists from Gladstone Institutes, UC San Francisco, and Stanford University, in collaboration with Biohub, have unveiled a massive, high-resolution functional map of human immune cells that promises to transform our understanding of how genetics control health and disease.

Published in the journal Cell, the study represents a landmark achievement in immunology and genomics. By systematically stress-testing genes across the genome in 22 million human immune cells, scientists moved beyond mere DNA sequencing to decode the dynamic circuits that govern how these genes actually work in the context of health and disease. This leap from observation to intervention offers a powerful new framework for designing cancer immunotherapies and treating autoimmune conditions, among other things.

“To understand the significance of this study, you have to look at the last three decades of biology,” says Alex Marson, MD, PhD, director of the Gladstone-UCSF Institute of Genomic Immunology and a senior author of the study. “First came the Human Genome Project, which gave us the blueprint of our genes. Then, projects like the Human Cell Atlas showed us how different cells read that blueprint. Now, we’re in a grand third wave: discovering what happens to cells when you make targeted changes within the genome. We finally have a way to decode the link between genetic sequence and cell state.”

The resulting dataset also stands as the largest contribution yet to the Billion Cells Project, a Biohub-led effort to generate a massive, open-source dataset of one billion single cells—data that can be used to train advanced AI models that can predict how cells behave, speed scientific discovery, and uncover new ways to treat disease. The project is part of Biohub’s global Virtual Biology Initiative, which seeks to create the open-data foundation for AI-accelerated biology.

The study used a cutting-edge technology called Perturb-seq, which allowed the team to “turn off” nearly 12,800 different genes one by one in human T cells—the critical cells that orchestrate how the body fights disease. Rather than relying on experimental cell lines that have long been the standard in laboratory research, the team instead performed massive screens on actual human immune cells, or so-called “primary” cells, from blood donors. Through this, they observed how genes function in their natural state, providing a much clearer roadmap for treating autoimmune diseases and designing better cancer therapies.

Among key findings, the study reveals the intricacies of how genes work together to influence immune function—and how these “circuits” operate very differently depending on circumstances such as whether cells are resting or fighting an infection.

This context-specific data is also essential for the future of “virtual biology,” where AI models are used to predict cell behavior. Notably, it serves as proof that such models must be trained on a diverse set of cell states and health scenarios to make accurate, reliable predictions.