Last Updated: 28/08/2024
Image-based quantification of Plasmodium sporozoites in a single oocyst using deep learning-based segmentation and 3D reconstruction
Objectives
To perform more robust quantification, this proposal sets to develop methods to accurately quantify sporozoites in single oocysts using super-resolution imaging and deep-learning based image processing pipelines.
Malaria remains an important public health issue. The oocyst stage is the most under-studied stage of the malaria parasite due to technical difficulties in isolating and staining these parasites and the lack of robust in vitro cultures. This is the expansion phase of the parasite in the mosquito host yet we have only a rudimentary understanding of the magnitude of this expansion: To date, only two studies have attempted to quantify the number of sporozoites in single oocysts, likely due to the time-consuming nature of manual counting. The methodology will be validated by manual quantification. Furthermore, this method will be used to investigate mosquito species-specific differences in the magnitude of oocyst maturation using different parasite isolates in Asian and African mosquito vectors. This method can be further developed to classify oocyst microstructures and enable in-depth studies of stage specific gene and protein expression over the entire developmental cycle of the oocyst in combination with in situ -omics methodologies. It is also anticipated that an accurate assessment of the magnitude of oocyst maturation will improve our understanding of the quantitative dynamics of parasite transmission from mosquito to mammalian host.
Dec 2023 — Oct 2025
$73,688
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