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bioRxiv · 10.1101/2025.07.29.667357

Microfluidic Platform for Automatic Quantification of Malaria Parasite Invasion Under Physiological Flow Conditions

Abstract

Understanding the impact of forces generated by blood flow on biological processes in the circulatory system, such as the invasion of human red blood cells by malaria parasites, is currently limited by the lack of experimental systems that integrate them. Recent systematic quantification of the growth of Plasmodium falciparum, the species that causes the majority of malaria mortality, under a range of shaking conditions has shown that parasite invasion of erythrocytes is affected by the shear stress to which the interacting P. falciparum merozoites and their target red blood cells are exposed. Blood flow could similarly impact shear stress and therefore invasion in vivo, but there is currently no method to test the impact of flow-induced forces on parasite invasion. We have developed a microfluidic device with four channels, each with dimensions similar to those of a post-capillary venule, but with different flow velocities. Highly synchronised P. falciparum parasites are injected into the device, and parasite egress and invasion rates are quantified using newly developed custom video analysis, which fully automates cell type identification and trajectory tracking. The device was tested with both wild-type P. falciparum lines and lines in which genes encoding proteins involved in parasite invasion had been deleted. Deletion of Erythrocyte Binding Antigen 175 (PfEBA175) has a significant impact on invasion under flow, but not in static culture. These findings establish for the first time that flow conditions can critically affect parasite invasion in a genotype-dependent manner. The method can be applied to other biological processes affected by fluid motion, such as cell adhesion, migration, and mechanotransduction.

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BibTeXRIS

Kals, E., Kals, M., Introini, V., Vodenicharski, B., Kotar, J., Rayner, J. C., Cicuta, P.. 2025-07-29. Microfluidic Platform for Automatic Quantification of Malaria Parasite Invasion Under Physiological Flow Conditions. https://doi.org/10.1101/2025.07.29.667357

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