Digital twins forecast individual immune trajectories

Abstract

The human immune system differs markedly between individuals and changes in response to infection, vaccination and treatment. AI virtual cells are being built to simulate cellular responses to perturbation. Yet these models, like most immune-response predictors, describe each person by a single pre-challenge measurement and forecast change from patterns pooled across people. Digital twins, updated with every new observation of an individual, could forecast how that person's immune state changes. Here we present ImmuneTwins, which builds these twins from single-cell profiles by personalizing a shared virtual-cell model with each individual's observations. In two external populations, ImmuneTwins reduced immune-age error by 32% and 42% relative to the best existing method. During repeated influenza vaccination, its twins predicted the direction of immune-cell change 35% more accurately than the best published method, and their error fell by 27% as observations accumulated. The twins revealed that immune cells and plasma proteins recover about half their deviation from a persistent personal reference within 90 days, whereas response size varies between challenges. They further localized B-cell remodelling in exceptional longevity and anticipated patient-specific immune trajectories during myeloma treatment. These results establish virtual-cell digital twins for forecasting individual immune change and identifying each person's characteristic immune features.