In new study led by @ckikawa.bsky.social, we provide near real-time data on human neutralizing antibody landscape to influenza by measuring ~26,000 titers to >100 recent viral strains Data can inform vaccine selection & evolutionary/epidemiological modeling biorxiv.org
www.biorxiv.org
As background, seasonal influenza evolves to erode antibody immunity. Viruses w more antibody escape spread in human population & people more likely to be infected by strains their antibodies neutralize less well. Vaccine updated bi-annually to keep pace w viral evolution. But because it takes time to perform experiments, measurement of how current strains are neutralized by human serum antibodies can lag timeline for vaccine strain selection. Our goal was to use new approach to characterize human antibody landscape at scale in near real-time.
To do this, we used sequencing-based neutralization assays that measure many neutralization curves simultaneously (journals.asm.org & elifesciences.org) Approach enabled one grad student (@ckikawa.bsky.social) to measure ~26,000 neutralization curves in ~5 months.
In spring of 2025, we designed library of naturally occurring human seasonal influenza strains that represented diversity of available sequences at that time; this library continues to cover most sequenced diversity of H3N2 and H1N1 hemagglutinin today.
We then measured how 188 human sera recently collected at four different sites neutralized all 140 influenza strains in library. Titers are summarized below; can be examined interactively at jbloomlab.github.io & jbloomlab.github.io
Working w @huddlej.bsky.social & Trevor Bedford, we mapped neutralization titers on interactive Nextstrain trees to visualize neutralization across subclades and natural mutations. See: nextstrain.org nextstrain.org
nextstrain.org
Above visualizations just scratch surface of data: there is tremendous heterogeneity across sera from different individuals not easily summarized by median/mean. Indeed, we previously found this heterogeneity may be important for influenza evolution: elifesciences.org
Many more analyses are possible w these data. But we have made all data & code available now at github.com Reason is to provide near real-time titer data that can be leveraged by scientific community for real-time decisions like vaccine strain selection.
github.com
Thanks to @ckikawa.bsky.social & @huddlej.bsky.social for leading study, also: Andrea Loes, Sam Turner, Jover Lee, Ian Barr, Ben Cowling, Jan Englund, Alex Greninger, Ruth Harvey, H Hasegawa, Faith Ho, K Lacombe, Nancy Leung, Nicola Lewis, Heidi Peck, Shinji Watanabe, Derek Smith, Trevor Bedford Finally, we plan to repeat this effort in ~6 months prior to next vaccine strain selection. If you have sera cohorts that you think would be well suited for this type of study and are potentially interesting in collaborating, please feel free to reach out.