Neutralization titers reveal the structure of polyclonal antibody responses
Henry Alston, Thierry Mora, Aleksandra M. Walczak
TLDR
A new method uses neutralization titers to quantitatively predict polyclonal antibody response structure, avoiding complex experimental measurements.
Key contributions
- Proposes using neutralization titer statistics to predict polyclonal antibody response composition.
- Identifies two response types: collective (many antibodies) or dominated by few strong binders.
- Shows Gumbel distribution accurately describes titer distributions in influenza cohorts prior to immune challenge.
- Introduces an equilibrium binding model that quantitatively captures titer data and response structure.
Why it matters
Understanding polyclonal antibody responses is crucial for assessing immunity robustness. This work offers a novel, experimental-free approach to quantify these responses, providing insights into how antibodies contribute to protection. This could accelerate vaccine development and immunity assessment for various pathogens.
Original Abstract
The composition of a polyclonal antibody response is hard to measure experimentally but contains vital information about the robustness of immunity. Here, we argue that the statistics of neutralization titers alone can be used to make quantitative predictions about the composition of the response, circumventing challenges arising through sequencing and monoclonal antibody expression. We show that the response against influenza within a cohort can be either driven by a collective phenomenon where many antibodies contribute to neutralization, or dominated by just a few strong binders, leading to a broad distribution of titers across individuals described by a Gumbel distribution from extreme value theory. Comparing titers across cohorts, we find that Gumbel statistics {accurately describe} individuals prior to an immune challenge. We propose an equilibrium binding model that quantitatively captures titer data and illustrates the structure of the polyclonal response. Our approach extends generically to immune responses to other pathogens.
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