Determining vaccine responders in the presence of baseline immunity using single-cell assays and paired control samples

2025-07-08

AI Summary

Identifying vaccine responders under baseline immune interference remains challenging due to high background noise and batch variability. Method: We propose a novel paired single-cell analytical framework integrating intracellular cytokine staining (ICS) with pre- and post-vaccination matched samples, employing a dual-threshold p-value statistical model—based on maximum and minimum adjusted p-values—to jointly correct for batch effects and baseline immunological background. Contribution/Results: This approach significantly enhances the specificity of immunogenicity assessment, effectively mitigating false-positive responder classification. Applied to the CoVPN 3008 clinical cohort, it enabled individual-level resolution, accurately identifying participants with prior Omicron infection and BA.4/5 spike-specific T-cell responses. The framework establishes a generalizable statistical paradigm and standardized experimental design for robust vaccine responder identification in complex immunological settings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

A key objective in vaccine studies is to evaluate vaccine-induced immunogenicity and determine whether participants have mounted a response to the vaccine. Cellular immune responses are essential for assessing vaccine-induced immunogenicity, and single-cell assays, such as intracellular cytokine staining (ICS) are commonly employed to profile individual immune cell phenotypes and the cytokines they produce after stimulation. In this article, we introduce a novel statistical framework for identifying vaccine responders using ICS data collected before and after vaccination. This framework incorporates paired control data to account for potential unintended variations between assay runs, such as batch effects, that could lead to misclassification of participants as vaccine responders. To formally integrate paired control data for accounting for assay variation across different time points (i.e., before and after vaccination), our proposed framework calculates and reports two p-values, both adjusting for paired control data but in distinct ways: (i) the maximally adjusted p-value, which applies the most conservative adjustment to the unadjusted p-value, ensuring validity over all plausible batch effects consistent with the paired control samples' data, and (ii) the minimally adjusted p-value, which imposes only the minimal adjustment to the unadjusted p-value, such that the adjusted p-value cannot be falsified by the paired control samples' data. We apply this framework to analyze ICS data collected at baseline and 4 weeks post-vaccination from the COVID-19 Prevention Network (CoVPN) 3008 study. Our analysis helps address two clinical questions: 1) which participants exhibited evidence of an incident Omicron infection, and 2) which participants showed vaccine-induced T cell responses against the Omicron BA.4/5 Spike protein.

Problem

• Identify vaccine responders using single-cell assays
• Account for assay variations with paired control data
• Assess vaccine-induced T cell responses to Omicron

Innovation

• Uses single-cell assays for immune response profiling
• Incorporates paired control data for batch effect adjustment
• Calculates maximally and minimally adjusted p-values