Analyzing tens of thousands of neutralization curves to understand viral evolution

Jesse Bloom

Fred Hutch Cancer Center / HHMI

Neutralization curves measure viral infectivity as function of antibody concentration

We developed way to measure these curves in much higher throughput

We barcode virions and read out infectivity by sequencing

At a high level, we convert counts to infectivity then fit curve

Component 1: Python package that fits neutralization curves

Component 2: pipeline that processes data to fit tens of thousands of curves

https://github.com/jbloomlab/seqneut-pipeline

Input 1: list of viral barcodes

Input 2: sample and sequencing data for each well of 96-well plate

Input 3: overall configuration file

https://github.com/jbloomlab/flu-seqneut-2025/blob/main/config.yml

Output: all curves for each sera

Output: numerical titers for all sera

Component 3: visualization of results

Summary

We now have a method by which one graduate student can measure ~25,000 neutralization titers in a few months

However, analyzing the data is harder than just looking at a hemagglutination-inhibition assay plate

The computational methods described here make the analysis easy enough that many labs will start using this approach