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

Understanding the impact that the forces generated by blood flow have on biological processes that take place in the circulatory system, such as malaria parasite replication, is currently limited by the lack of experimental systems to integrate them. Malaria kills over 600,000 people annually, with the blood stage responsible for all the clinical symptoms. Recent quantification of growth, of \textit{Plasmodium falciparum}, the deadliest malaria strain of malaria, under conditions of fluid motion generated by orbital shaking has shown that the importance of certain ligands used during invasions differs under conditions of high wall shear stress, but there is currently no method avalable to systematicly test how vaying flow conditions impacts growth and invasion. We have developed a microfluidic device with four channels, with dimensions similar to a post-capillary venule, each with different flow velocities. Highly synchronised drug-treated parasites are injected to ensure a high rate of egress and invasion in a short window. Invasion in these channels is quantified using software we have developed, which fully automates cell type identification and trajectory tracking. Testing was done with two wild-type lines, 3D7 and NF54, and two lines missing proteins involved in attachment early in invasion. Over the flow velocities, significant differences in invasion rates were only seen in the line missing attachment protein Erythrocyte Binding Antigen 175 (PfEBA175). These findings unveil the critical influence of flow conditions on parasite invasion and demonstrate a method that has application to address a range of questions on the effect of physical flow forces on malaria invasion as well as in studying broader biological processes affected by fluid motion, including cell adhesion, migration, and mechanotransduction.

This repository contains two min folders.

• analysis contains all the code used to analyse the datasets. The most important files are Cell.py and MalariaAnalysis.py which contains the functions and classes used for segmenting the cells, and generating trajectories, and classifying cells.  
• data contains output from each successful experiment, including a dataframe with information about cell populations over time and a set of plots showing some basic statistics for that given experiment repeat. The plots contain data averaged over 1s, 30s and 60s (indicated in parentheses).