Abstract
Limitations of assay variability, labor costs, and availability of cells can affect the conduct of large population-based studies. The ability to determine optimal conditions for laboratory assessment of immune outcomes, including measurement of cytokines, can reduce the number of peripheral blood mononuclear cells (PBMCs) needed, reduce the labor costs involved, and the variability in secreted cytokine response by pooling cytokines from the same cell culture supernatant. Previously, we used response surface methodology to predict optimal conditions for vaccinia virus-stimulated cytokine responses in recipients of smallpox vaccine. Here, we apply the same approach for a measles vaccine study. PBMCs were collected from vaccinated subjects, and seven cytokines (IFN-γ, IL-2, TNF-α, IL-10, IFN-α, IFN-λ1, and IL-6) involved in measles virus-specific cytokine immune responses were examined. PBMCs were stimulated with differing multiplicity of infection (MOI) and days in culture (incubation time). Response surface methodology was used to select the optimal MOI and incubation time for each secreted cytokine. Our results demonstrate that each cytokine's optimal conditions (MOI and incubation time) differ for each virus (measles vs. vaccinia) and each cytokine's optimal conditions for each virus can be predicted using response surface methodology. These conditions allow for cytokines with overlapping optimal conditions to be pooled from the same supernatant in culture to reduce the number of PBMCs used, the costs involved, and assay variability. Therefore, response surface methodology is an effective technique that can be used to optimize antigen-specific secreted cytokines prior to population-based studies.