A new era of discovery for immunology: Expert Q& A

by Sophia Ramirez, Yale University

edited by Lisa Lock, reviewed by Andrew Zinin

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When two people become ill at the same time, they often experience different symptoms. For instance, during the COVID-19 pandemic, some people had symptoms akin to a mild flu, while others were critically ill. Similarly, people with chronic diseases can have very different disease courses and responses to treatment.

These different reactions are influenced by people's immune systems, but the underlying mechanisms at the cellular and molecular levels are often unknown. Immunologists study the biology of these differences, and their work is increasingly aided by two advances: technology that allows them to measure many mechanisms in a cell at once and shared access to growing collections of data.

Ruth Montgomery, Ph.D., is a professor of medicine (rheumatology) at Yale School of Medicine. In a Comment recently published in Nature Immunology, she and her colleagues described the technological developments and infrastructure investments that have changed the landscape of human immunology research. We're now in a time when researchers can use massive amounts of data to "predict disease risk, promote early diagnosis, and design interventions that promote health," they write.

We talked to Montgomery about this new era of immunology and what it means for understanding disease and treating people.

What technological advance has enabled this new era of immunology?

It's called high-throughput, high-dimensional profiling, and it's a way to take a snapshot of all the cellular processes happening in a moment. It's a collection of techniques, including single-cell RNA sequencing and mass cytometry. We can measure 22,000 genes in one experiment and hundreds and hundreds of proteins in one tiny sample. We get vastly more information per drop of sample than was technically possible in the past.

What can you learn from these massive quantities of data?

These data help us identify possible signatures—genetic or cellular markers that could identify people who are more vulnerable or resistant to certain diseases.

Immunologists used to have only one target, say a single gene, that might explain the severity of disease or an inability to respond to a medication, for example. Now, instead of focusing on one thing, we can take a much broader look. The high-throughput profiling technology allows us to look at so many things at once.

How do researchers effectively interpret all of these data?

These high-throughput tools create a giant amount of data, which you need new tools to interpret and understand. We need multiple teams to analyze all this cellular data.

Before we even get the cellular data, we need people with the clinical skills to understand patient populations, enroll patients and collect samples. The clinicians help us understand what patients might be dealing with. Then we need the lab side to make the best use of limited samples. Finally, we integrate and analyze these data with our computational colleagues. They are able to see patterns, visualize the data and create models.

Human biology is incredibly complex. The immune response could be affected by upstream regulatory molecules or downstream effector molecules. We want to look at all of that at once, not one thing at a time. Doing our investigations in human subjects allows us to bring that complexity into the studies. When we're trying to understand human diseases, we need to look at the complexity.

What sort of diseases can immunologists study this way?

People can have different responses to infections and vaccines, and as immunologists, it's our job to understand why. This analysis could be used to study nearly every disease.

For instance, our recent work has been on COVID, as it was for a lot of immunologists during that incredibly high-pressure time when we were all desperate for answers. Now, we're working on sickle cell disease as well as the effects of aging on the immune system. I have worked on asthma in the past. It's been a joy for me during my time at Yale to collaborate widely with people in different departments who focus on different diseases.

Can these massive amounts of data be shared between researchers?

Absolutely. These immunology data are stored and shared by two main groups: the Cooperative Centers on Human Immunology and the Human Immunology Project Consortium. These groups are maintained by the National Institutes of Health (NIH). They are trying to profile both healthy and affected populations to understand what makes people more vulnerable to diseases like autoimmune diseases, West Nile virus and Lyme disease.

As a researcher, when you're collecting these multidimensional data, you focus on and publish results relevant to your study. But you still have thousands of other genes identified and collected. All of those other data are now shared in these NIH databases for other scientists to use. The NIH supports our development of these data resources, and they host the data-sharing mechanisms. This is a tremendous resource for researchers.

Can you give an example of how researchers might use these data in a large immunology study?

A great example of this is the Yale School of Medicine work during COVID. We were members of a large consortium study across 15 sites that enrolled more than 1,000 patients. The people we enrolled were inpatients with COVID, so they were often very sick. The clinician teams first sorted people based on severity, including people who recovered pretty quickly and went home and people who succumbed to their disease.

We visited with these patients 10 times. The first six visits were during the first 28 days of hospitalization, and the last four were over a year of follow-up. At each of these visits, we collected 10 milliliters of blood, which we put through 14 or 15 different processes. We also collected airway cells from patients who were intubated and a nasal swab, which allowed us to sequence the virus.

From these samples, one group in our consortium used mass spectroscopy for metabolomics to identify small molecules in the circulation. That group of researchers studied all those small metabolites in circulation that contribute to disease, though we don't always know how. In this study, we identified some single-carbon metabolites that looked important but did not reach the high threshold for significance.

In another part of this study, we sequenced the patients' DNA and did a genome-wide association study here at the Yale Center for Genome Analysis. We identified DNA differences for the enzymes that generated those relevant metabolites, but those findings also did not quite reach the threshold of significance. However, when we combined the metabolite finding with the enzyme finding, we had a profile that identified a severe patient group.

If we were just a metabolomics lab or had just sequenced their genes, we would not have had such a beautiful paper. But combining those two helped us fully understand how this metabolite is relevant in explaining parts of severe COVID disease.

Along with measurement and signature identification, could this approach incorporate gene editing?

Yes, and that's pretty much where we're at now. If you identify a signature that indicates a change you'd like to make for a patient population, then you could develop a therapy to modify those particular genes. There are many opportunities here for improving patient outcomes, like identifying people at higher risk or those who will respond to medications.

Publication details

Ruth R. Montgomery et al, From data to discovery: a unified framework for human immunology, Nature Immunology (2026). DOI: 10.1038/s41590-026-02624-1

Journal information: Nature Immunology

Key medical concepts

MetabolomicsGenome-Wide Association Study

Clinical categories

Allergy and immunologyCommon illnesses & Prevention Provided by Yale University Who's behind this story?

Lisa Lock

BA art history, MA material culture. Former museum editor, paramedic, and transplant coordinator. Editing for Science X since 2021. Full profile →

Andrew Zinin

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

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