A multi-dimensional, time-lapse, high content screening platform applied to schistosomiasis drug discovery

Approximately 10% of the world's population is at risk of schistosomiasis, a disease of poverty caused by the Schistosoma parasite. To facilitate drug discovery for this complex flatworm, we developed an automated high-content screen to quantify the multidimensional responses of Schistosoma mansoni post-infective larvae (somules) to chemical insult. We describe an integrated platform to process worms at scale, collect time-lapsed, bright-field images, segment highly variable and touching worms, and then store, visualize, and query dynamic phenotypes. To demonstrate the methodology, we treated somules with seven drugs that generated diverse responses and evaluated 45 static and kinetic response descriptors relative to concentration and time. For compound screening, we used the Mahalanobis distance to compare multidimensional phenotypic effects induced by 1323 approved drugs. Overall, we characterize both known anti-schistosomals and identify new bioactives. Apart from facilitating drug discovery, the multidimensional quantification provided by this platform will allow mapping of chemistry to phenotype. Steven Chen et al. develop an automated, time-lapsed, high-content screen to quantify the responses of Schistosoma mansoni larvae to chemical insult. They apply their method to evaluate 45 static and kinetic response endpoints for seven drugs and screen 1323 approved drugs. Their work identifies anti-schistosomal compounds and underscores the value of quantifying motion in phenotypic drug discovery.