The next generation of antiparasitics will likely be discovered through high-content and high-throughput screening using automated microscopy. As the amount of antiparasitic drug screening data swells at a rapid pace, researchers need to keep up by also developing analytical tools that can quickly and reliably analyze these data. A variety of proprietary and open-source software exists for these approaches, but none that are oriented towards images of worms (whether parasitic or free-living). We developed wrmXpress to fill this software gap.
wrmXpress has been used in screens to identify compounds with microfilaricidal and macrofilaricidal effects, characterize structure-activity relationships of drug classes against schistosomes, and assess the effects of cholinergic compounds on worm feeding. It has already been used to analyze dozens of terabytes worth of data.
The next generation of wrmXpress will include a graphical user interface for more widespread and inclusive use, include modules for new phenotypic endpoints, and incorporate deep learning into image analysis. Our collaboration with the Zamanian Lab seeks to push the field forward and provide a flexible, intuitive software that is used by diverse labs and companies around the world.
Track wrmXpress updates
GitHub - zamanianlab/wrmXpress
This package contains a variety of Python and CellProfiler pipelines used for the analysis of worm imaging data. Some of these are specific to Zamanian lab experimental pipelines, but many of the modules should be robust to a diversity of species and experimental procedures.