A flow cytometry-based workflow for detection and quantification of anti-plasmodial antibodies in vaccinated and naturally exposed individuals

Fixed red blood cells (RBCs), infected with late stages of P. falciparum were utilized to detect malaria-specific antibodies by flow cytometry with subsequent automated data analysis. Available methods for data-driven analysis of cytometry data were assessed and a new overlap subtraction algorithm (OSA) based on open source software was developed. The complete workflow was evaluated using sera from two GMZ2 malaria vaccine trials in semi-immune adults and pre-school children residing in a malaria endemic area.

Fixation, permeabilization, and staining of infected RBCs were adapted for best operation in flow cytometry. As asexual blood-stage vaccine candidates are designed to induce antibody patterns similar to those in semi-immune adults, serial dilutions of sera from heavily exposed individuals were compared to naïve controls to determine optimal antibody dilutions. To eliminate investigator effects introduced by manual gating, a non-biased algorithm (OSA) for data-driven gating was developed. OSA-derived results correlated well with those obtained by manual gating (r between 0.79 and 0.99) and outperformed other model-driven gating methods. Bland-Altman plots confirmed the agreement of manual gating and OSA-derived results. A 1.33-fold increase (p=0.003) in the number of positive cells after vaccination in a subgroup of pre-school children vaccinated with 100 μg GMZ2 was present and in vaccinated adults from the same region we measured a baseline-corrected 1.23-fold, vaccine-induced increase in mean fluorescence intensity of positive cells (p=0.03).

The current workflow advances detection and quantification of anti-plasmodial antibodies through improvement of a bias-prone, low-throughput to an unbiased, semi-automated, scalable method. In conclusion, this work presents a novel method for immunofluorescence assays in malaria research.