Automated microscopy for high-throughput malaria research.

Project: Research council

Project Details


Many smaller, simpler microscopes can often see more than one large, expensive machine. We propose to innovate a research-quality, fully automated microscope design that can be tailored to a particular experiment and easily replicated to perform many experiments in parallel. With computer vision to control and analyse these experiments, the bottlenecks of equipment and staff time are removed, and it becomes possible to keep pace with new genetic technologies - even for previously time-consuming studies, for example measuring the invasion of red blood cells by plasmodium parasites. We will develop the microscopy and computer vision technologies, demonstrate their efficacy in our malaria lab and those of our collaborators, and release open-source designs that allow others to replicate our progress. The ability to screen hundreds of different mutant strains efficiently will lead to a deeper understanding of many diseases, ultimately creating new drug discovery targets and potentially leading to new vaccines for conditions like malaria.

Gene editing is in the midst of a revolution thanks to CRISPR-Cas9 protocols, and cell phenotyping needs to keep pace. Specifically in malaria, our collaborators are now scaling up knockouts in P. falciparum using CRISPR, and expect to make 200 knockout lines this year, and 1000 in the next five years . While strain generation is scaling so dramatically , phenotyping is not, i.e. we cannot determine the function of these genes - we need robust and cheap scalable phenotyping assays, which involve live cell imaging, and specifically of host/pathogen invasions. It is not conceivable to perform these assays through current methods and technologies. New, much more automated and affordable approaches to imaging have to be developed and deployed. This would then allow us to systematically screen GM lines for several phenotypes, including merozoite number, cytokinesis, egress and invasion. We address here the case of malaria, but point out that very similar challenges and objectives can be identified in many other infectious diseases.
Effective start/end date1/07/1830/06/21

Collaborative partners


  • Engineering and Physical Sciences Research Council


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