Unsupervised Discovery of Toxoplasma gondii Motility Phenotypes

TL;DR

This paper introduces an unsupervised computational pipeline to identify and analyze motility phenotypes of Toxoplasma gondii, aiding understanding of its movement mechanisms for potential therapeutic development.

Contribution

It presents a novel, fully unsupervised method for classifying T. gondii motility patterns based on spatiotemporal dynamics, without prior labeling.

Findings

Identified distinct motility phenotypes in T. gondii

Demonstrated effects of extracellular Ca2+ on parasite movement

Provided a data-driven framework for studying parasite motility

Abstract

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.