Unsupervised discovery of toxoplasma gondii motility phenotypes

摘要

Toxoplasma gondii is a parasitic protozoan that causes disseminated 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 strategies. 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.