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

TL;DR

This paper presents a new statistical method for identifying vaccine responders using single-cell assay data, accounting for assay variability with paired control samples to improve classification accuracy.

Contribution

The authors introduce a novel framework that incorporates paired control data and calculates two types of p-values to accurately identify vaccine responders from ICS data.

Findings

Effective identification of vaccine responders using the proposed framework.

Application to COVID-19 vaccine data revealed immune responses against Omicron.

Framework accounts for batch effects, reducing misclassification.

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.