A new blood-testing method developed at Fred Hutch Cancer Center is transforming how scientists predict which flu strains will circulate next season, allowing the World Health Organization to make more informed vaccine recommendations that protect people across both hemispheres. Instead of testing one blood sample against a single flu strain, researchers can now test one sample against over 100 strains at once, producing the largest-ever dataset of flu-fighting antibodies to inform global vaccine composition .

Why Does the WHO Need to Predict Flu Strains Months in Advance?

The seasonal influenza virus constantly evolves to evade immunity built up from past infections and vaccinations, which is why annual flu shots need to be updated each year. Vaccine manufacturers require several months to prepare doses for the upcoming winter flu season, forcing them to predict which strains will be most dominant eight to 12 months in advance . This means decisions made in February affect the vaccines people receive the following fall, and decisions in September shape what the Southern Hemisphere receives during its flu season.

The World Health Organization convenes two meetings each year to integrate genetic and blood test data from around the world and determine whether circulating strains have changed enough to warrant updating the vaccine . The challenge has always been that traditional testing methods are slow and limited, forcing researchers to make educated guesses about which strains matter most.

How Does This New Blood-Testing Method Work?

The breakthrough came from Andrea Loes, a staff scientist and lab manager in the Bloom Lab at Fred Hutch, who developed a way to rapidly measure how antibodies in human blood fight many different flu strains in a single experiment. Here's how the process works:

• Barcode tagging: Researchers add a unique nucleotide barcode to the hemagglutinin (HA) gene of each flu strain they want to track, essentially creating a nametag for each virus variant.
• Library creation: The barcoded HA genes are stitched into the backbone of a lab-adapted flu strain, then all strains are pooled into a single library that can be tested against a blood sample simultaneously.
• Infection and measurement: The library is applied to blood cells, and after 16 hours, researchers extract the viral RNA produced. Strains that successfully infect cells produce more RNA with their specific barcode, revealing which viruses the antibodies couldn't block.
• Reference standardization: A fixed amount of reference RNA unrelated to the viruses is added to account for technical variations in extraction and measurement .

This approach is fundamentally different from traditional methods, which test one blood sample against one viral strain at a time. If researchers choose the wrong strains to analyze, the vaccine misses the mutations that help the virus hide from the immune system. By testing all strains at once, the new method eliminates this guessing game.

What Impact Did This Research Have on Real-World Vaccines?

Caroline Kikawa, a graduate student in the University of Washington Medical Scientist Training Program working with evolutionary biologist Jesse Bloom at Fred Hutch, took on the ambitious task of applying this method at scale for the WHO. She produced more than 25,000 measurements comprising the largest-ever single-study dataset of influenza-fighting antibodies, which was published in the journal Virus Evolution . Remarkably, she completed almost all the lab work herself in under six months.

"That was the first time that we did this at scale for this purpose. It was really stressful because I didn't have any of the data yet," Kikawa said during her initial presentation to WHO scientists.

Caroline Kikawa, Graduate Student, University of Washington Medical Scientist Training Program

Her data directly informed the WHO's vaccine composition recommendations made in fall 2025, which shaped the flu vaccines that people from Cape Town to Sydney to Buenos Aires received in 2026 as autumn began in the Southern Hemisphere . Earlier this year, she provided updated data that helped inform composition recommendations for the flu vaccine that Seattle and the rest of the Northern Hemisphere will receive in the upcoming fall season. Kikawa's work was recognized with a Beyond The Journal award for exemplary data sharing, acknowledging her commitment to making results immediately available to the scientific community.

Why Is Human Blood Data Better Than Animal Testing?

Traditionally, researchers have tested flu strains against the immune systems of ferrets, which share enough biology and lung structure with humans to serve as a reasonable stand-in. However, ferrets are typically exposed to only one specific flu strain, unlike humans who have a long history of multiple exposures to multiple strains plus annual flu shots that boost immunity . This means ferret antibodies don't accurately reflect the complex, layered immunity that real people develop over their lifetimes.

"You can take a virus that infects humans, you can put it into ferrets, and there's no changes that need to happen for the virus to successfully infect that ferret," explained John Huddleston, a staff scientist specializing in using genetics to forecast how the flu virus is likely to evolve from season to season.

John Huddleston, PhD, Staff Scientist, Laboratory of Trevor Bedford, Fred Hutch

Testing human blood makes far more sense biologically, but the challenge has always been scale. There are too many different strains of flu and not enough blood samples to test one strain against one sample the way it's done with ferrets. The Bloom Lab's innovation solved this problem by allowing researchers to test one blood sample against dozens or even hundreds of strains simultaneously, making efficient use of limited human samples while generating data that directly reflects how real human immune systems respond to circulating viruses.

What Does This Mean for Future Flu Seasons?

This breakthrough represents a significant shift in how global health authorities approach vaccine development. Rather than relying on predictions and ferret studies, the WHO now has access to real-time data about how human antibodies are actually performing against current and emerging flu strains. This allows for more precise vaccine composition decisions that better match the strains people will actually encounter.

The method also provides what researchers call a "near real-time picture" of how well the human immune system is coping with current strains of seasonal influenza . As the virus continues to evolve and new variants emerge, this approach allows scientists to quickly assess whether existing vaccines will remain effective or whether updates are needed. For millions of people receiving annual flu shots, this means vaccines that are more likely to provide protection against the strains they'll actually face during the upcoming flu season.