Unsupervised Discovery of Toxoplasma gondii Motility Phenotypes
This work addresses the need to understand T. gondii motility for therapeutic development in toxoplasmosis, but it is incremental as it applies existing unsupervised methods to a specific biological context.
The researchers tackled the problem of identifying Toxoplasma gondii motility phenotypes by developing an unsupervised computational pipeline to analyze parasite motion before and after Ca2+ addition, resulting in parameterized and grouped motility patterns based on spatiotemporal dynamics.
Toxoplasma gondii is a parasitic protozoan that causes dis- seminated toxoplasmosis, a disease that afflicts roughly a third of the worlds population. Its virulence is predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its mechanism of motility and in particular, its motility patterns in the context of its lytic cycle, are critical to the eventual development of therapeutic strate- gies. Here, we present an end-to-end computational pipeline for identifying T. gondii motility phenotypes in a completely unsupervised, data-driven way. We track the parasites before and after addition of extracellular Ca2+ to study its effects on the parasite motility patterns and use this information to parameterize the motion and group it according to similarity of spatiotemporal dynamics.