How does the same infection, say the flu or a cold, give some people mild symptoms and leave others suffering? Why do people develop autoimmune diseases? And will we ever be able to predict when and how severely illness might emerge in an individual?

Researchers have formed a global partnership called the Human Immunome Project (HIP) to answer these questions and more. The project aims to generate the largest immunological dataset ever created to map human immune variation across the globe. This data will then be used to develop artificial intelligence models to understand the variability of the human immune system and its responses and link the immune system to physiology in health and disease.

John Tsang, PhD, MMath, Anthony N. Brady Professor of Immunobiology at Yale School of Medicine. “It’s a project where the world's leading systems immunologists, clinical scientists, AI experts, and human biologists are advancing and standardizing how immune data is collected, shared, and utilized for the benefit of all.”

Yale's Center for Systems and Engineering Immunology (CSEI) is one of 10 founding institutions in HIP's global network, which spans five continents. Tsang, who founded CSEI and serves as HIP's co-chief science officer, has pioneered systems human immunology and predictive metrics of human immune health and responses for the past 15 years.

Our immune system functions as a “universal stress responder” with immune cells constantly patrolling the body for any deviation from our normal state. When a tissue or organ shows signs of trouble, it sends an alarm that draws in immune cells. Sometimes this manifests as inflammation that makes us feel sick, but often the immune system works silently in the background, detecting and eliminating threats before they ever cause symptoms.

This ubiquitous and constant surveillance means the immune system intersects with nearly every disease process. But understanding what drives the immune system to respond differently in different people is essential for predicting and treating illness.

“To understand health and disease in a very broad way, you must actually know what the immune system is doing and what the state of the immune system looks like within a person,” Tsang says. But he notes, this is challenging as the immune system is made up of trillions of cells and molecules and is dynamic, continuously adapting based on lifetime exposures.

Tsang is a systems immunologist, computational biologist, and engineer. He is the Yale lead for and serves on the steering committee of the Chan Zuckerberg Biohub New York, which aims to harness and bioengineer immune cells for the early detection, prevention, and treatment of disease. And he and his lab have already made progress toward the goal of using the immune system itself as a window into current and future illness.

Tsang has developed an assessment that not only counts and catalogues about a million parameters including cells and immune molecules in a blood sample. It gives an extremely detailed snapshot of a person’s immune system at a particular moment in time. And by comparing one person’s information to that of many other people, both healthy and sick, he can estimate the health of one’s immune system.

Tsang and his colleagues chronicled their approach to quantify human immune health in a 2024 Nature Medicine paper¹. And it was recently described in a MIT Technology Review story², wherein the writer underwent Tsang’s blood assessment and obtained a score on the current state of his immune health.

To advance this work toward using this type of biological information as a predictor, researchers need more data, both over time and across multitudes of different people. Until now, no one has attempted to map immune variability across different geographic locations, ancestry, and life stages.

But that’s what HIP aims to do by establishing standardized human study and data generation protocols across its global network, enrolling participants of all ages and following them for up to five years. The group will start by using the approaches that underlie Tsang’s test with the ultimate goal of evaluating hundreds of thousands of individuals cross more than 100 sites around the world.

“This would be the most comprehensive and representative immunological dataset ever assembled," Tsang says. "Open to researchers worldwide, it would power quantitative and predictive models of the immune system."

These predictive models will be trained on the data collected from blood and select tissue samples. The group’s ultimate goal is to create models that predict immune response outcomes and health trajectory in an individual, and identify new targets for treatment.

“Such information will transform our ability to generate immune interventions to optimize health outcomes, with applications in all areas of health including vaccine development, infectious diseases, autoimmunity, pandemic preparedness, cancer, and neurodegeneration,” HIP says on its website.

Current precision medicine approaches are heavily focused on genetics, says Tsang, noting that this is incomplete. "Genetics provides the blueprint and tells you what's possible, but it actually doesn't tell you information about what's going to happen or what's going on right now," he says.

For many conditions including immune-related ones, genetics often explains only a small fraction of the differences between people. The immune system, by contrast, acts as a dynamic sensor, integrating information from throughout the body as it adapts to lifetime exposures.

Therefore, rather than relying solely on static genetic blueprints, HIP aims to build a dynamic map of immune systems across diverse populations over time, and it will treat the immune system as the “anchor” for precision medicine. The global nature of the project will capture immune variability across different ancestries, geographies, and environmental exposures.

Just as crucial, all data will be openly accessible to researchers worldwide, ensuring benefits flow back to contributing communities to empower scientists universally.

"We would like to empower what we call global immune-based precision medicine," says Tsang. “I envision a future where immune monitoring becomes as routine as checking your heart health.”

Reference:

1. https://medicine.yale.edu/news-article/can-ai-help-predict-alzheimers-cancer-study-targets-telltale-immune-changes/

2. https://www.technologyreview.com/2025/10/09/1125376/how-healthy-am-i-my-immunome-knows-the-score/

(Newswise/HG)