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

Anthony Ajua12, Thomas Engleitner1, Meral Esen12, Michael Theisen34, Saadou Issifou2 and Benjamin Mordmüller12*

Author Affiliations

1 Institute of Tropical Medicine, University of Tübingen, Wilhelmstraße 27, Tübingen D-72074, Germany

2 Centre de Recherche Médicale de Lambaréné (CERMEL), Lambaréné, BP 118, Gabon

3 Center for Medical Parasitology at Department of International Health, Immunology and Microbiology, University of Copenhagen, Bartholinsgade 2, Copenhagen K, 1356, Denmark

4 Department of Clinical Biochemistry and Immunology, Statens Serum Institut, Artillerivej 5, Copenhagen S, 2300, Denmark

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Malaria Journal 2012, 11:367  doi:10.1186/1475-2875-11-367

Published: 6 November 2012



Antibodies play a central role in naturally acquired immunity against Plasmodium falciparum. Current assays to detect anti-plasmodial antibodies against native antigens within their cellular context are prone to bias and cannot be automated, although they provide important information about natural exposure and vaccine immunogenicity. A novel, cytometry-based workflow for quantitative detection of anti-plasmodial antibodies in human serum is presented.


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.

Malaria; Flow cytometry-based IFA; Algorithmic data analysis; Anti-malarial antibodies; Human serum