ABSTRACT: BACKGROUND: 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 the quantitative detection of anti-plasmodial antibodies in human serum is presented. METHODS: 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 semiimmune adults and pre-school children residing in a malaria endemic area. RESULTS: Fixation, permeabilization, and staining of infected RBCs were adapted for best operation in flow cytometry. As asexual vaccine candidates are designed to induce antibody patterns similar to semi-immune adults, serial dilutions of sera from heavily exposed individuals were compared to naive 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 preschool children vaccinated with 100 mug 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). CONCLUSIONS: 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.